Techniques – Page 3

22/03/2014

Our Iceberg Is Melting

[Dring Dring] The telephone soundbite announced the start of the coaching session.

<Bob> Good morning Leslie. How are you today?

<Leslie> I have been better.

<Bob> You seem upset. Do you want to talk about it?

<Leslie> Yes, please. The trigger for my unhappiness is that last week I received an email demanding that I justify the time I spend doing improvement work and a summons to a meeting to ‘discuss some issues that have been raised‘.

<Bob> OK. I take it that you do not know what or who has triggered this inquiry.

<Leslie> You are correct. My working hypothesis is that it is the end of the financial year and budget holders are looking for opportunities to do some pruning – to meet their cost improvement program targets!

<Bob> So what is the problem? You have shared the output of your work. You have demonstrated significant improvements in safety, flow, quality and productivity and you have described both them and the methodology clearly.

<Leslie> I know. That us why I was so upset to get this email. It is as if everything that we have achieved has been ignored. It is almost as if it is resented.

<Bob> Ah! You may well be correct. This is the nature of paradigm shifts. Those who have the greatest vested interest in the current paradigm get spooked when they feel it start to wobble. Each time you share the outcome of your improvement work you create emotional shock-waves. The effects are cumulative and eventually there will be is a ‘crisis of confidence’ in those who feel most challenged by the changes that you are demonstrating are possible. The whole process is well described in Thomas Kuhn’s The Structure of Scientific Revolutions. That is not a book for an impatient reader though – for those who prefer something lighter I recommend “Our Iceberg is Melting” by John Kotter.

<Leslie> Thanks Bob. I will get a copy of Kotter’s book – that sounds more my cup of tea. Will that tell me what to do?

<Bob> It is a parable – a fictional story of a colony of penguins who discover that their iceberg is melting and are suddenly faced with a new and urgent potential risk of not surviving the storms of the approaching winter. It is not a factual account of a real crisis or a step-by-step recipe book for solving all problems – it describes some effective engagement strategies in general terms.

<Leslie> I will still read it. What I need is something more specific to my actual context.

<Bob> This is an improvement-by-design challenge. The only difference from the challenges you have done already is that this time the outcome you are looking for is a smooth transition from the ‘old’ paradigm to the ‘new’ one. Kuhn showed that this transition will not start to happen until there is a new paradigm because individuals choose to take the step from the old to the new and they do not all do that at the same time. Your work is demonstrating that there is a new paradigm. Some will love that message, some will hate it. Rather like Marmite.

<Leslie> Yes, that make sense. But how do I deal with an unseen enemy who is stirring up trouble behind my back?

<Bob> Are you are referring to those who have ‘raised some issues‘?

<Leslie> Yes.

<Bob> They will be the ones who have most invested in the current status quo and they will not be in senior enough positions to challenge you directly so they are going around spooking the inner Chimps of those who can. This is expected behaviour when the relentlessly changing reality starts to wobble the concrete current paradigm.

<Leslie> Yes! That is exactly how it feels.

<Bob> The danger lurking here is that your inner Chimp is getting spooked too and is conjuring up Gremlins and Goblins from the Computer! Left to itself your inner Chimp will steer you straight into the Victim Vortex. So you need to take it for a long walk, let it scream and wave its hairy arms about, listen to it, and give it lots of bananas to calm it down. Then put your put your calmed-down Chimp into its cage and your ‘paradigm transition design’ into the Computer. Only then will you be ready for the ‘so-justify-yourself’ meeting. At the meeting your Chimp will be out of its cage like a shot and interpreting everything as a threat. It will disable you and go straight to the Computer for what to do – and it will read your design and follow the ‘wise’ instructions that you have put in there.

<Leslie> Wow! I see how you are using the Chimp Paradox metaphor to describe an incredibly complex emotional process in really simple language. My inner Chimp is feeling happier already!

<Bob> And remember that you are in all in the same race. Your collective goal is to cross the finish line as quickly as possible with the least chaos, pain and cost. You are not in a battle – that is lose-lose inner Chimp thinking. The only message that your interrogators must get from you is ‘Win-win is possible and here is how we can do it‘. That will be the best way to soothe their inner Chimps – the ones who fear that you are going to sink their boat by rocking it.

<Leslie> That is really helpful. Thank you again Bob. My inner Chimp is now snoring gently in its cage and while it is asleep I have some Improvement-by-Design work to do and then some Computer programming.

15/03/2014

Reducing Avoidable Harm

“Primum non nocere” is Latin for “First do no harm”.

It is a warning mantra that had been repeated by doctors for thousands of years and for good reason.

Doctors can be bad for your health.

I am not referring to the rare case where the doctor deliberately causes harm. Such people are criminals and deserve to be in prison.

I am referring to the much more frequent situation where the doctor has no intention to cause harm – but harm is the outcome anyway.

Very often the risk of harm is unavoidable. Healthcare is a high risk business. Seriously unwell patients can be very unstable and very unpredictable. Heroic efforts to do whatever can be done can result in unintended harm and we have to accept those risks. It is the nature of the work. Much of the judgement in healthcare is balancing benefit with risk on a patient by patient basis. It is not an exact science. It requires wisdom, judgement, training and experience. It feels more like an art than a science.

The focus of this essay is not the above. It is on unintentionally causing avoidable harm.

Or rather unintentionally not preventing avoidable harm which is not quite the same thing.

Safety means prevention of avoidable harm. A safe system is one that does that. There is no evidence of harm to collect. A safe system does not cause harm. Never events never happen.

Safe systems are designed to be safe. The root causes of harm are deliberately designed out one way or another. But it is not always easy because to do that we need to understand the cause-and-effect relationships that lead to unintended harm. Very often we do not.

In 1847 a doctor called Ignaz Semmelweis made a very important discovery. He discovered that if the doctors and medical students washed their hands in disinfectant when they entered the labour ward, then the number of mothers and babies who died from infection was reduced.

And the number dropped a lot.

It fell from an annual average of 10% to less than 2%! In really bad months the rate was 30%.

The chart below shows the actual data plotted as a time-series chart. The yellow flag in 1848 is just after Semmelweis enforced a standard practice of hand-washing.

Semmelweis did not know the mechanism though. This was not a carefully designed randomised controlled trial (RCT). He was desperate. And he was desperate because this horrendous waste of young lives was only happening on the doctors ward. On the nurses ward, which was just across the corridor, the maternal mortality was less than 2%.

The hospital authorities explained it away as ‘bad air’ from outside. That was the prevailing belief at the time. Unavoidable. A risk that had to be just accepted.

Semmeleis could not do a randomized controlled trial because they were not invented until a century later.

And Semmelweis suspected that the difference between the mortality on the nurses and the doctors wards was something to do with the Mortuary. Only the doctors performed the post-mortems and the practice of teaching anatomy to medical students using post-mortem dissection was an innovation pioneered in Vienna in 1823 (the first yellow flag on the chart above). But Semmelweis did not have this data in 1847. He collated it later and did not publish it until 1861.

What Semmelweis demonstrated was the unintended and avoidable deaths were caused by ignorance of the mechanism of how microorganisms cause disease. We know that now. He did not.

It would be another 20 years before Louis Pasteur demonstrated the mechanism using the famous experiment with the swan neck flask. Pasteur did not discover microorganisms; he proved that they did not appear spontaneously in decaying matter as was believed. He proved that by killing the bugs by boiling, the broth in the flask stayed fresh even though it was exposed to the air. That was a big shock but it was a simple and repeatable experiment. He had a mechanism. He was believed. Germ theory was born. A Scottish surgeon called Joseph Lister read of this discovery and surgical antisepsis was born.

Semmelweis suspected that some ‘agent’ may have been unwittingly transported from the dead bodies to the live mothers and babies on the hands of the doctors. It was a deeply shocking suggestion that the doctors were unwittingly killing their patients.

The other doctors did not take this suggestion well. Not well at all. They went into denial. They discounted the message and they discharged the messenger. Semmelweis never worked in Vienna again. He went back to Hungary and repeated the experiment. It worked.

Even today the message that healthcare practitioners can unwittingly bring avoidable harm to their patients is disturbing. We still seek solace in denial.

Hospital acquired infections (HAI) are a common cause of harm and many are avoidable using simple, cheap and effective measures such as hand-washing.

The harm does not come from what we do. It comes from what we do not do. It happens when we omit to follow the simple safety measures that have be proven to work. Scientifically. Statistically Significantly. Understood and avoidable errors of omission.

So how is this “statistically significant scientific proof” acquired?

By doing experiments. Just like the one Ignaz Semmelweis conducted. But the improvement he showed was so large that it did not need statistical analysis to validate it. And anyway such analysis tools were not available in 1847. If they had been he might have had more success influencing his peers. And if he had achieved that goal then thousands, if not millions, of deaths from hospital acquired infections may have been prevented. With the clarity of hindsight we now know this harm was avoidable.

No. The problem we have now is because the improvement that follows a single intervention is not very large. And when the causal mechanisms are multi-factorial we need more than one intervention to achieve the improvement we want. The big reduction in avoidable harm. How do we do that scientifically and safely?

About 20% of hospital acquired infections occur after surgical operations.

We have learned much since 1847 and we have designed much safer surgical systems and processes. Joseph Lister ushered in the era of safe surgery, much has happened since.

We routinely use carefully designed, ultra-clean operating theatres, sterilized surgical instruments, gloves and gowns, and aseptic techniques – all to reduce bacterial contamination from outside.

But surgical site infections (SSIs) are still common place. Studies show that 5% of patients on average will suffer this complication. Some procedures are much higher risk than others, despite the precautions we take. And many surgeons assume that this risk must just be accepted.

Others have tried to understand the mechanism of SSI and their research shows that the source of the infections is the patients themselves. We all carry a ‘bacterial flora’ and normally that is no problem. Our natural defense – our skin – is enough. But when that biological barrier is deliberately breached during a surgical operation then we have a problem. The bugs get in and cause mischief. They cause surgical site infections.

So we have done more research to test interventions to prevent this harm. Each intervention has been subject to well-designed, carefully-conducted, statistically-valid and very expensive randomized controlled trials. And the results are often equivocal. So we repeat the trials – bigger, better controlled trials. But the effects of the individual interventions are small and they easily get lost in the noise. So we pool the results of many RCTs in what is called a ‘meta-analysis’ and the answer from that is very often ‘not proven’ – either way. So individual surgeons are left to make the judgement call and not surprisingly there is wide variation in practice. So is this the best that medical science can do?

No. There is another way. What we can do is pool all the learning from all the trials and design a multi-facetted intervention. A bundle of care. And the idea of a bundle is that the separate small effects will add or even synergise to create one big effect. We are not so much interested in the mechanism as the outcome. Just like Ignaz Semmelweiss.

And we can now do something else. We can test our bundle of care using statistically robust tools that do not require a RCT. They are just as statistically valid as a RCT but a different design.

And the appropriate tool for this to measure the time interval between adverse the events – and then to plot this continuous metric as a time-series chart.

But we must be disciplined. First we must establish the baseline average interval and then we introduce our bundle and then we just keep measuring the intervals.

If our bundle works then the interval between the adverse events gets longer – and we can easily prove that using our time-series chart. The longer the interval the more ‘proof’ we have. In fact we can even predict how long we need to observe to prove that ‘no events’ is a statistically significant improvement. That is an elegant an efficient design.

Here is a real and recent example.

The time-series chart below shows the interval in days between surgical site infections following routine hernia surgery. These are not life threatening complications. They rarely require re-admission or re-operation. But they are disruptive for patients. They cause pain, require treatment with antibiotics, and the delay recovery and return to normal activities. So we would like to avoid them if possible.

The green and red lines show the baseline period. The green line says that the average interval between SSIs is 14 days. The red line says that an interval more than about 60 days would be surprisingly long: valid statistical evidence of an improvement. The end of the green and red lines indicates when the intervention was made: when the evidence-based designer care bundle was adopted together with the discipline of applying it to every patient. No judgement. No variation.

The chart tells the story. No complicated statistical analysis is required. It shows a statistically significant improvement. And the SSI rate fell by over 80%. That is a big improvement.

We still do not know how the care bundle works. We do not know which of the seven simultaneous simple and low-cost interventions we chose are the most important or even if they work independently or in synergy. Knowledge of the mechanism was not our goal.

Our goal was to improve outcomes for our patients – to reduce avoidable harm – and that has been achieved. The evidence is clear.

That is Improvement Science in action.

And to read the full account of this example of the Science of Improvement please go to:

http://www.journalofimprovementscience.org

It is essay number 18.

And avoid another error of omission. If you have read this far please share this message – it is important.

08/03/2014

The Battle of the Chimps

Improvement implies change.
Change implies action.
Action implies decision.

So how is the decision made?
With Urgency?
With Understanding?

Bitter experience teaches us that often there is an argument about what to do and when to do it. An argument between two factions. Both are motivated by a combination of anger and fear. One side is motivated more by anger than fear. They vote for action because of the urgency of the present problem. The other side is motivated more by fear than anger. They vote for inaction because of their fear of future failure.

The outcome is unhappiness for everyone.

If the ‘action’ party wins the vote and a failure results then there is blame and recrimination. If the ‘inaction’ party wins the vote and a failure results then there is blame and recrimination. If either party achieves a success then there is both gloating and resentment. Lose Lose.

The issue is not the decision and how it is achieved.The problem is the battle.

Dr Steve Peters is a psychiatrist with 30 years of clinical experience. He knows how to help people succeed in life through understanding how the caveman wetware between their ears actually works.

In the run up to the 2012 Olympic games he was the sports psychologist for the multiple-gold-medal winning UK Cycling Team. The World Champions. And what he taught them is described in his book – “The Chimp Paradox“.

Steve brilliantly boils the current scientific understanding of the complexity of the human mind down into a simple metaphor.

One that is accessible to everyone.

The metaphor goes like this:

There are actually two ‘beings’ inside our heads. The Chimp and the Human. The Chimp is the older, stronger, more emotional and more irrational part of our psyche. The Human is the newer, weaker, logical and rational part. Also inside there is the Computer. It is just a memory where both the Chimp and the Human store information for reference later. Beliefs, values, experience. Stuff like that. Stuff they use to help them make decisions.

And when some new information arrives through our senses – sight and sound for example – the Chimp gets first dibs and uses the Computer to look up what to do. Long before the Human has had time to analyse the new information logically and rationally. By the time the Human has even started on solving the problem the Chimp has come to a decision and signaled it to the Human and associated it with a strong emotion. Anger, Fear, Excitement and so on. The Chimp operates on basic drives like survival-of-the-self and survival-of-the-species. So if the Chimp gets spooked or seduced then it takes control – and it is the stronger so it always wins the internal argument.

But the human is responsible for the actions of the Chimp. As Steve Peters says ‘If your dog bites someone you cannot blame the dog – you are responsible for the dog‘. So it is with our inner Chimps. Very often we end up apologising for the bad behaviour of our inner Chimp.

Because our inner Chimp is the stronger we cannot ‘control’ it by force. We have to learn how to manage the animal. We need to learn how to soothe it and to nurture it. And we need to learn how to remove the Gremlins that it has programmed into the Computer. Our inner Chimp is not ‘bad’ or ‘mad’ it is just a Chimp and it is an essential part of us.

Real chimpanzees are social, tribal and territorial. They live in family groups and the strongest male is the boss. And it is now well known that a troop of chimpanzees in the wild can plan and wage battles to acquire territory from neighbouring troops. With casualties on both sides. And so it is with people when their inner Chimps are in control.

Which is most of the time.

Scenario:
A hospital is failing one of its performance targets – the 18 week referral-to-treatment one – and is being threatened with fines and potential loss of its autonomy. The fear at the top drives the threat downwards. Operational managers are forced into action and do so using strategies that have not worked in the past. But they do not have time to learn how to design and test new ones. They are bullied into Plan-Do mode. The hospital is also required to provide safe care and the Plan-Do knee-jerk triggers fear-of-failure in the minds of the clinicians who then angrily oppose the diktat or quietly sabotage it.

This lose-lose scenario is being played out in 100’s if not 1000’s of hospitals across the globe as we speak. The evidence is there for everyone to see.

The inner Chimps are in charge and the outcome is a turf war with casualties on all sides.

So how does The Chimp Paradox help dissolve this seemingly impossible challenge?

First it is necessary to appreciate that both sides are being controlled by their inner Chimps who are reacting from a position of irrational fear and anger. This means that everyone’s behaviour is irrational and their actions likely to be counter-productive.

What is needed is for everyone to be managing their inner Chimps so that the Humans are back in control of the decision making. That way we get wise decisions that lead to effective actions and win-win outcomes. Without chaos and casualties.

To do this we all need to learn how to manage our own inner Chimps … and that is what “The Chimp Paradox” is all about. That is what helped the UK cyclists to become gold medalists.

In the scenario painted above we might observe that the managers are more comfortable in the Pragmatist-Activist (PA) half of the learning cycle. The Plan-Do part of PDSA – to translate into the language of improvement. The clinicians appear more comfortable in the Reflector-Theorist (RT) half. The Study-Act part of PDSA. And that difference of preference is fueling the firestorm.

Improvement Science tells us that to achieve and sustain improvement we need all four parts of the learning cycle working smoothly and in sequence.

So what at first sight looks like it must be pitched battle which will result in two losers; in reality is could be a three-legged race that will result in everyone winning. But only if synergy between the PA and the RT halves can be achieved.

And that synergy is achieved by learning to respect, understand and manage our inner Chimps.

15/02/201423/10/2021

Navigating the Nerve Curve

The emotional roller-coaster ride that is associated with change, learning and improvement is called the Nerve Curve.

We are all very familiar with the first stages – of Shock, Denial, Anger, Bargaining, Depression and Despair. We are less familiar with the stages associated with the long climb out to Resolution: because most improvement initiatives fail for one reason of another.

The critical first step is to “Disprove Impossibility” and this is the first injection of innovation. Someone (the ‘innovator’) discovers that what was believed to be impossible is not. They only have to have one example too. One Black Swan.

The tougher task is to influence those languishing in the ‘Depths of Despair’ that there is hope and that there is a ‘how’. This is not easy because cynicism is toxic to innovation. So an experienced Improvement Science Practitioner (ISP) bypasses the cynics and engages with the depressed-but-still-healthy-sceptics.

The challenge now is how to get a shed load of them up the hill.

When we first learn to drive we start on the flat, not on hills, for a very good reason. Safety.

We need to learn to become confident with the controls first. The brake, the accelerator, the clutch and the steering wheel. This takes practice until it is comfortable, unconscious and almost second nature. We want to achieve a smooth transition from depression to delight, not chaotic kangaroo jumps!

Only when we can do that on the flat do we attempt a hill-start. And the key to a successful hill start is the sequence. Hand brake on for safety, out of gear, engine running, pointing at the goal. Then we depress the clutch and select a low gear – we do not want to stall. Speed is not the goal. Safety comes first. Then we rev the engine to give us the power we need to draw on. Then we ease the clutch until the force of the engine has overcome the force of gravity and we feel the car wanting to move forward. And only then do we ease the handbrake off, let the clutch out more and hit the gas to keep the engine revs in the green.

So when we are planning to navigate a group of healthy sceptics up the final climb of the Nerve Curve we need to plan and prepare carefully.

What is least likely to be successful?

Well, if all we have is our own set of wheels, a cheap and cheerful mini-motor, then it is not going to be a good idea to shackle a trailer to it; fill the trailer with sceptics and attempt a hill start. We will either stall completely or burn out our clutch. We may even be dragged backwards into the Cynic Infested Toxic Swamp.

So what if we hire a bus, load up our sceptical passengers, and have a go. We may be lucky – but if we have no practice doing hill starts with a full bus then we could be heading for disappointment; or disaster.

So what is a safer plan:
1) First we need to go up the mountain ourselves to demonstrate it is possible.
2) Then we take one or two of the least sceptical up in our car to show it is safe.
3) We then invite those sceptics with cars to learn how to do safe hill starts.
4) Finally we ask the ex-sceptics to teach the fresh-sceptics how to do it.

Brmmmm Brmmmm. Off we go.

18/01/2014

Sticks or Carrots?

[Beep Beep] Bob’s laptop signaled the arrival of Leslie to their regular Webex mentoring session. Bob picked up the phone and connected to the conference call.

<Bob> Hi Leslie, how are you today?

<Leslie> Great thanks Bob. I am sorry but that I do not have a red-hot burning issue to talk about today.

<Bob> OK – so your world is completely calm and orderly now. Excellent.

<Leslie> I wish! The reason is that I have been busy preparing for the monthly 1-2-1 with my boss.

<Bob> OK. So do you have a few minutes to talk about that?

<Leslie> What can I tell you about it?

<Bob> Can you just describe the purpose and the process for me?

<Leslie> OK. The purpose is improvement – for both the department and the individual. The process is that all departmental managers have an annual appraisal based on their monthly 1-2-1 chats and the performance scores for their departments are used to reward the top 15% and to ‘performance manage’ the bottom 15%.

<Bob> H’mmm. What is the commonest emotion that is associated with this process?

<Leslie> I would say somewhere between severe anxiety and abject terror. No one looks forward to it. The annual appraisal feels like a lottery where the odds are stacked against you.

<Bob> Can you explain that a bit more for me?

<Leslie> Well, the most fear comes from being in the bottom 15% – the fear of being ‘handed your hat’ so to speak. Fortunately that fear motivates us to try harder and that usually saves us from the chopper because our performance improves. The cost is the extra stress, working late and taking ‘stuff’ home.

<Bob> OK. And the anxiety?

<Leslie> Paradoxically that mostly comes from the top 15%. They are anxious to sustain their performance. Most do not and the Boss’s Golden Manager can crash spectacularly! We have seen it so often. It is almost as if being the Best carries a curse! So most of us try to stay in the middle of the pack where we do not stick out – a sort of safety in the herd strategy. It is illogical I know because there is always a ‘top’ 15% and a ‘bottom’ 15%.

<Bob> You mentioned before that it feels like a lottery. How come?

<Leslie> Yes – it feels like a lottery but I know it has a rational scientific basis. Someone once showed me the ‘statistically significant evidence’ that proves it works.

<Bob> That what works exactly?

<Leslie> That sticks are more effective than carrots!

<Bob> Really! And what does the performance run charts look like – over the long term – say monthly over 2-3 years?

<Leslie> That is a really good question. They are surprisingly stable – well completely stable in fact. The wobble up and down of course but there is no sign of improvement over the long term – no trend. If anything it is the other way.

<Bob> So what is the rationale for maintaining the stick-is-better-than-the-carrot policy?

<Leslie> Ah! The message we are getting is ‘as performance is not improving and sticks have been scientifically proven to be more effective than carrots then we will be using a bigger stick in future‘.

<Bob> Hence the atmosphere of fear and anxiety?

<Leslie> Exactly. But that is the way it must be I suppose.

<Bob> Actually it is not. This is an invalid design based on rubbish intuitive assumptions and statistical smoke-and-mirrors that creates unmeasurable emotional pain and destroys both people and organisations!

<Leslie> Wow! Bob! I have never heard you use language like that. You are usually so calm and reasonable. This must be really important!

<Bob> It is – and for that reason I need to shock you out of your apathy – and I can do that best by you proving it to yourself – scientifically – with a simple experiment. Are you up for that?

<Leslie> You betcha! This sounds like it is going to be interesting. I had better fasten my safety belt! The Nerve Curve awaits.

The Stick-or-Carrot Experiment

<Bob> Here we go. You will need five coins, some squared-paper and a pencil. Coloured ones are even better.

<Leslie> OK. Does it matter what sort of coins?

<Bob> No. Any will do. Imagine you have four managers called A,B,C and D respectively. Each month the performance of their department is measured as the number of organisational targets that they are above average on. Above average is like throwing a ‘head’, below average is like throwing a ‘tail’. There are five targets – hence the coins

<Leslie>OK. That makes sense – and it feels better to use the measured average – we have demonstrated that arbitrary performance targets are dangers – especially when imposed blindly across all departments.

<Bob> Indeed. So can you design a score sheet to track the data for the experiment.

<Leslie>Give me a minute. Will this suffice?

<Bob> Perfect! Now simulate a month by tossing all five coins – once for each manager – and record the outcome of each as H to T , then tot up the number of heads for each manager.

<Leslie> OK … here is what I got.

<Bob>Good. Now repeat this 11 more times to give you the results for a whole year. In the n(Heads) column colour the boxes that have scores of zero or one as red – these are the Losers. Then colour the boxes that have 4 or 5 as green – these are the Winners.

<Leslie>OK, that will take me a few minutes – do you want to get a coffee or something.

[Five minutes later]

Here you go. That gives 96 opportunities to win or lose and I counted 9 Losers and 9 Winners so just under 20% for each. The majority were in the unexceptional middle. The herd.

<Bob> Excellent. A useful way to visualise this is using a Tally chart. Just run down the column of n(Heads) and create the Tally chart as you go. This is one of the oldest forms of counting in existence. There are fossil records that show Tally charts being used thousands of years ago.

<Leslie> I think I understand what you mean. We do not wait until all the data is in then draw the chart, we update it as we go along – as the data comes in.

<Bob> Spot on!

<Leslie> Let me see. Wow! That is so cool! I can see the pattern appearing almost magically – and the more data I have the clearer the pattern is.

<Bob>Can you show me?

<Leslie> Here we go.

<Bob> Good. This is the expected picture. If you repeated this many times you would get the same general pattern with more 2 and 3 scores.

Now I want you to do an experiment.

Assume each manager that is classed as a Winner in one month is given a reward – a ‘pat on the back’ from their Boss. And each manager that is classed as a Loser is given a ‘written warning’. Now look for the effect that this has.

<Leslie> But we are using coins – which means the outcome is just a matter of chance! It is a lottery.

<Bob> I know that and you know that but let us assume that the Boss believes that the monthly feedback has an effect. The experiment we are doing is to compare the effect of the carrot with the stick. The Boss wants to know which results in more improvement and to know that with scientific and statistical confidence!

<Leslie> OK. So what I will do is look at the score the following month for each manager that was either a Winner or a Loser; work out the difference, and then calculate the average of those differences and compare them with each other. That feels suitably scientific!

<Bob> OK. What do you get.

<Leslie> Just a minute, I need to do this carefully. OK – here it is.

<Bob> Excellent. Just eye-balling the ‘Measured improvement after feedback’ columns I would say the Losers have improved and the Winners have deteriorated!

<Leslie> Yes! And the Losers have improved by 1.29 on average and the Winners have deteriorated by 1.78 – and that is a big difference for such small sample. I am sure that with enough data this would be a statistically significant difference! So it is true, sticks work better than carrots!

<Bob>Not so fast. What you are seeing is a completely expected behaviour called “Regression to the Mean“. Remember we know that the score for each manager each month is the result of a game of chance, a coin toss, a lottery. So no amount of stick or carrot feedback is going to influence that.

<Leslie>But the data is saying there is a difference! And that feels like the experience we have – and why fear stalks the management corridors. This is really confusing!

<Bob>Remember that confusion arises from invalid or conflicting unconscious assumptions. There is a flaw in the statistical design of this experiment. The ‘obvious’ conclusion is invalid because of this flaw. And do not be too hard on yourself. The flaw eluded mathematicians for centuries. But now you know there is one can you find it?

<Leslie>OMG! The use of the average to classify the managers into Winners or Losers is the flaw! That is just a lottery. Who the managers are is irrelevant. This is just a demonstration of how chance works.

But that means … OMG! If the conclusion is invalid then sticks are not better than carrots and we have been brain-washed for decades into accepting a performance management system that is invalid – and worse still is used to ‘scientifically’ justify systematic persecution! I can see now why you get so angry!

<Bob>Bravo Leslie. We need to check your understanding. Does that mean carrots are better than sticks?

<Leslie>No! The conclusion is invalid because the assumptions are invalid and the design is fatally flawed. It does not matter what the conclusion actually is.

<Bob>Excellent. So what conclusion can you draw?

<Leslie>That this short-term carrot-or-stick feedback design for achieving improvement in a stable system is both ineffective and emotionally damaging. In fact it could well be achieving precisely the opposite effect that it is intended to. It may be preventing improvement! But the story feels so plausible and the data appears to back it up. What is happening here is we are using statistical smoke-and-mirrors to justify what we have already decided – and only an true expert would spot the flaw! Once again our intuition has tricked us!

<Bob>Well done! And with this new insight – how would you do it differently? What would be a better design?

<Leslie>That is a very good question. I am going to have to think about that – before my 1-2-1 tomorrow. I wonder what might happen if I show this demonstration to my Boss? Thanks Bob, as always … lots of food for thought.

11/01/2014

What is my P.A.R.T?

Improvement implies change, but change does not imply improvement.

Change follows action. Action follows planning. Effective planning follows from an understanding of the system because it is required to make the wise decisions needed to achieve the purpose.

The purpose is the intended outcome.

Learning follows from observing the effect of change – whatever it is. Understanding follows from learning to predict the effect of both actions and in-actions.

All these pieces of the change jigsaw are different and they are inter-dependent. They fit together. They are a system.

And we can pick out four pieces: the Plan piece, the Action piece, the Observation piece and the Learning piece – and they seem to follow that sequence – it looks like a learning cycle.

This is not a new idea.

It is the same sequence as the Scientific Method: hypothesis, experiment, analysis, conclusion. The preferred tool of Academics – the Thinkers.

It is also the same sequence as the Shewhart Cycle: plan, do, check, act. The preferred tool of the Pragmatists – the Doers.

So where does all the change conflict come from? What is the reason for the perpetual debate between theorists and activists? The incessant game of “Yes … but!”

One possible cause was highlighted by David Kolb in his work on ‘experiential learning’ which showed that individuals demonstrate a learning style preference.

We tend to be thinkers or doers and only a small proportion us say that we are equally comfortable with both.

The effect of this natural preference is that real problems bounce back-and-forth between the Tribe of Thinkers and the Tribe of Doers. Together we are providing separate parts of the big picture – but as two tribes we appear to be unaware of the synergistic power of the two parts. We are blocked by a power struggle.

The Experiential Learning Model (ELM) was promoted and developed by Peter Honey and Alan Mumford (see learning styles) and their work forms the evidence behind the Learning Style Questionnaire that anyone can use to get their ‘score’ on the four dimensions:

Pragmatist – the designer and planner
Activist – the action person
Reflector – the observer and analyst
Theorist – the abstracter and hypothesis generator

The evidence from population studies showed that individuals have a preference for one of these styles, sometimes two, occasionally three and rarely all four.

That observation, together with the fact that learning from experience requires moving around the whole cycle, leads to an awareness that both individuals and groups can get ‘stuck’ in their learning preference comfort zone. If the learning wheel is unbalanced it will deliver a bumpy ride when it turns! So it may be more comfortable just to remain stationary and not to learn.

Which means not to change. Which means not to improve.

So if we are embarking on an improvement exercise – be it individual or collective – then we are committed to learning. So where do we start on the learning cycle?

The first step is action. To do something – and the easiest and safest thing to do is just look. Observe what is actually happening out there in the real world – outside the office – outside our comfort zone. We need to look outside our rhetorical inner world of assumptions, intuition and pre-judgements. The process starts with Study.

The next step is to reflect on what we see – we look in the mirror – and we compare what are actually seeing with what we expected to see. That is not as easy as it sounds – and a useful tool to help is to draw charts. To make it visual. All sorts of charts.

The result is often a shock. There is often a big gap between what we see and what we perceive; between what we expect and what we experience; between what we want and what we get; between our intent and our impact.

That emotional shock is actually what we need to power us through the next phase – the Realm of the Theorist – where we ask three simple questions:
Q1: What could be causing the reality that I am seeing?
Q2: How would I know which of the plausible causes is the actual cause?
Q3: What experiment can I do to answer my question and clarify my understanding of Reality?

This is the world of the Academic.

The third step is design an experiment to test our new hypothesis. The real world is messy and complicated and we need to be comfortable with ‘good enough’ and ‘reasonable uncertainty’. Design is about practicalities – making something that works well enough in practice – in the real world. Something that is fit-for-purpose. We are not expecting perfection; not looking for optimum; not striving for best – just significantly better than what we have now. And the more we can test our design before we implement it the better because we want to know what to expect before we make the change and we want to avoid unintended negative consequences – the NoNos. This is Plan.

Then we act … and the cycle of learning has come one revolution … but we are not back at the start – we have moved forward. Our understanding is already different from when were were at this stage before: it is deeper and wider. We are following the trajectory of a spiral – our capability for improvement is expanding over time.

So we need to balance our learning wheel before we start the journey or we will have a slow, bumpy and painful ride!

We need to study, then plan, then do, then study the impact.

One plausible approach is to stay inside our comfort zones, play to our strengths and to say “What we need is a team made of people with complementary strengths. We need a Department of Action for the Activists; a Department of Analysis for the Reflectors; a Department of Research for the Theorists and a Department of Planning for the Pragmatists.“

But that is what we have now and what is the impact? The Four Departments have become super-specialised and more polarised. There is little common ground or shared language. There is no common direction, no co-ordination, no oil on the axle of the wheel of change. We have ground to a halt. We have chaos. Each part is working but independently of the others in an unsynchronised mess.

We have cultural fibrillation. Change output has dropped to zero.

A better design is for everyone to focus first on balancing their own learning wheel by actively redirecting emotional energy from their comfort zone, their strength, into developing the next step in their learning cycle.

Pragmatists develop their capability for Action.
Activists develop their capability for Reflection.
Reflectors develop their capability for Hypothesis.
Theorists develop their capability for Design.

The first step in the improvement spiral is Action – so if you are committed to improvement then investing £10 and 20 minutes in the 80-question Learning Style Questionnaire will demonstrate your commitment to yourself. And that is where change always starts.

30/11/2013

Seeing Inside the Black Box

Improvement Science requires the effective, efficient and coordinated use of diagnosis, design and delivery tools.

Experience has also taught us that it is not just about the tools – each must be used as it was designed.

The craftsman knows his tools and knows what instrument to use, where and when the context dictates; and how to use it with skill.

Some tools are simple and effective – easy to understand and to use. The kitchen knife is a good example. It does not require an instruction manual to use it.

Other tools are more complex. Very often because they have a specific purpose. They are not generic. And they may not be intuitively obvious how to use them. Many labour-saving household appliances have specific purposes: the microwave oven, the dish-washer and so on – but they have complex controls and settings that we need to manipulate to direct the “domestic robot” to deliver what we actually want. Very often these controls are not intuitively obvious – we are dealing with a black box – and our understanding of what is happening inside is vague.

Very often we do not understand how the buttons and dials that we can see and touch – the inputs – actually influence the innards of the box to determine the outputs. We do not have a mental model of what is inside the Black Box. We do not know – we are ignorant.

In this situation we may resort to just blindly following the instructions; or blindly copying what someone else does; or blindly trying random combinations of inputs until we get close enough to what we want. No wiser at the end than we were at the start. The common thread here is “blind”. The box is black. We cannot see inside.

And the complex black box is deliberately made so – because the supplier of the super-tool does not want their “secret recipe” to be known to all – least of all their competitors.

This is a perfect recipe for confusion and for conflict. Lose-Lose-Lose.

Improvement Science is dedicated to eliminating confusion and conflict – so Black Box Tools are NOT on the menu.

Improvement Scientists need to understand how their tools work – and the best way to achieve that level of understanding is to design and build their own.

This may sound like re-inventing the wheel but it is not about building novel tools – it is about re-creating the tried and tested tools – for the purpose of understanding how they work. And understanding their strengths, their weaknesses, their opportunities and their risks or threats.

And doing that requires guidance from a mentor who has been through this same learning journey. Starting with simple, intuitive tools, and working step-by-step to design, build and understand the more complex ones.

So where do we start?

In the FISH course the first tool we learn to use is a Gantt Chart.

It was invented by Henry Laurence Gantt about 100 years ago and requires nothing more than pencil and paper. Coloured pencils and squared paper are even better.

This is an example of a Gantt Chart for a Day Surgery Unit.

At the top are the “tasks” – patients 1 and 2; and at the bottom are the “resources”.

Time runs left to right.

Each coloured bar appears twice: once on each chart.

The power of a Gantt Chart is that it presents a lot of information in a very compact and easy-to-interpret format. That is what Henry Gantt intended.

A Gantt Chart is like the surgeon’s scalpel. It is a simple, generic easy-to-create tool that has a wide range of uses. The skill is knowing where, when and how to use it: and just as importantly where-not, when-not and how-not.

The second tool that an Improvement Scientist learns to use is the Shewhart or time-series chart.

It was invented about 90 years ago.

This is a more complex tool and as such there is a BIG danger that it is used as a Black Box with no understanding of the innards. The SPC and Six-Sigma Zealots sell it as a Magic Box. It is not.

We could paste any old time-series data into a bit of SPC software; twiddle with the controls until we get the output we want; and copy the chart into our report. We could do that and hope that no-one will ask us to explain what we have done and how we have done it. Most do not because they do not want to appear ‘ignorant’. The elephant is in the room though. There is a conspiracy of silence.

The elephant-in-the-room is the risk we take when use Black Box tools – the risk of GIGO. Garbage In Garbage Out.

And unfortunately we have a tendency to blindly trust what comes out of the Black Box that a plausible Zealot tells us is “magic”. This is the Emporer’s New Clothes problem. Another conspiracy of silence follows.

The problem here is not the tool – it is the desperate person blindly wielding it. The Zealots know this and they warn the Desperados of the risk and offer their expensive Magician services. They are not interested in showing how the magic trick is done though! They prefer the Box to stay Black.

So to avoid this cat-and-mouse scenario and to understand both the simpler and the more complex tools, and to be able to use them effectively and safely, we need to be able to build one for ourselves.

And the know-how to do that is not obvious – if it were we would have already done it – so we need guidance.

And once we have built our first one – a rough-and-ready working prototype – then we can use the existing ones that have been polished with long use. And we can appreciate the wisdom that has gone into their design. The Black Box becomes Transparent.

So learning how the build the essential tools is the first part of the Improvement Science Practitioner (ISP) training – because without that knowledge it is difficult to progress very far. And without that understanding it is impossible to teach anyone anything other than to blindly follow a Black Box recipe.

Of course Magic Black Box Solutions Inc will not warm to this idea – they may not want to reveal what is inside their magic product. They are fearful that their customers may discover that it is much simpler than they are being told. And we can test that hypothesis by asking them to explain how it works in language that we can understand. If they cannot (or will not) then we may want to keep looking for someone who can and will.

23/11/2013

Space-and-Time

<Lesley>Hi Bob! How are you today?

<Bob>OK thanks Lesley. And you?

<Lesley>I am looking forward to our conversation. I have two questions this week.

<Bob>OK. What is the first one?

<Lesley>You have taught me that improvement-by-design starts with the “purpose” question and that makes sense to me. But when I ask that question in a session I get an “eh?” reaction and I get nowhere.

<Bob>Quod facere bonum opus et quomodo te cognovi unum?

<Lesley>Eh?

<Bob>I asked you a purpose question.

<Lesley>Did you? What language is that? Latin? I do not understand Latin.

<Bob>So although you recognize the language you do not understand what I asked, the words have no meaning. So you are unable to answer my question and your reaction is “eh?”. I suspect the same is happening with your audience. Who are they?

<Lesley>Front-line clinicians and managers who have come to me to ask how to solve their problems. There Niggles. They want a how-to-recipe and they want it yesterday!

<Bob>OK. Remember the Temperament Treacle conversation last week. What is the commonest Myers-Briggs Type preference in your audience?

<Lesley>It is xSTJ – tough minded Guardians. We did that exercise. It was good fun! Lots of OMG moments!

<Bob>OK – is your “purpose” question framed in a language that the xSTJ preference will understand naturally?

<Lesley>Ah! Probably not! The “purpose” question is future-focused, conceptual , strategic, value-loaded and subjective.

<Bob>Indeed – it is an iNtuitor question. xNTx or xNFx. Pose that question to a roomful of academics or executives and they will debate it ad infinitum.

<Lesley>More Latin – but that phrase I understand. You are right. And my own preference is xNTP so I need to translate my xNTP “purpose” question into their xSTJ language?

<Bob>Yes. And what language do they use?

<Lesley>The language of facts, figures, jobs-to-do, work-schedules, targets, budgets, rational, logical, problem-solving, tough-decisions, and action-plans. Objective, pragmatic, necessary stuff that keep the operational-wheels-turning.

<Bob>OK – so what would “purpose” look like in xSTJ language?

<Lesley>Um. Good question. Let me start at the beginning. They came to me in desperation because they are now scared enough to ask for help.

<Bob>Scared of what?

<Lesley>Unintentionally failing. They do not want to fail and they do not need beating with sticks. They are tough enough on themselves and each other.

<Bob>OK that is part of their purpose. The “Avoid” part. The bit they do not want. What do they want? What is the “Achieve” part? What is their “Nice If”?

<Lesley>To do a good job.

<Bob>Yes. And that is what I asked you – but in an unfamiliar language. Translated into English I asked “What is a good job and how do you know you are doing one?”

<Lesley>Ah ha! That is it! That is the question I need to ask. And that links in the first map – The 4N Chart®. And it links in measurement, time-series charts and BaseLine© too. Wow!

<Bob>OK. So what is your second question?

<Lesley>Oh yes! I keep getting asked “How do we work out how much extra capacity we need?” and I answer “I doubt that you need any more capacity.”

<Bob>And their response is?

<Lesley>Anger and frustration! They say “That is obvious rubbish! We have a constant stream of complaints from patients about waiting too long and we are all maxed out so of course we need more capacity! We just need to know the minimum we can get away with – the what, where and when so we can work out how much it will cost for the business case.”

<Bob>OK. So what do they mean by the word “capacity”. And what do you mean?

<Lesley>Capacity to do a good job?

<Bob>Very quick! Ho ho! That is a bit imprecise and subjective for a process designer though. The Laws of Physics need the terms “capacity”, “good” and “job” clearly defined – with units of measurement that are meaningful.

<Lesley>OK. Let us define “good” as “delivered on time” and “job” as “a patient with a health problem”.

<Bob>OK. So how do we define and measure capacity? What are the units of measurement?

<Lesley>Ah yes – I see what you mean. We touched on that in FISH but did not go into much depth.

<Bob>Now we dig deeper.

<Lesley>OK. FISH talks about three interdependent forms of capacity: flow-capacity, resource-capacity, and space-capacity.

<Bob>Yes. They are the space-and-time capacities. If we are too loose with our use of these and treat them as interchangeable then we will create the confusion and conflict that you have experienced. What are the units of measurement of each?

<Lesley>Um. Flow-capacity will be in the same units as flow, the same units as demand and activity – tasks per unit time.

<Bob>Yes. Good. And space-capacity?

<Lesley>That will be in the same units as work in progress or inventory – tasks.

<Bob>Good! And what about resource-capacity?

<Lesley>Um – Will that be resource-time – so time?

<Bob>Actually it is resource-time per unit time. So they have different units of measurement. It is invalid to mix them up any-old-way. It would be meaningless to add them for example.

<Lesley>OK. So I cannot see how to create a valid combination from these three! I cannot get the units of measurement to work.

<Bob>This is a critical insight. So what does that mean?

<Lesley>There is something missing?

<Bob>Yes. Excellent! Your homework this week is to work out what the missing pieces of the capacity-jigsaw are.

<Lesley>You are not going to tell me the answer?

<Bob>Nope. You are doing ISP training now. You already know enough to work it out.

<Lesley>OK. Now you have got me thinking. I like it. Until next week then.

<Bob>Have a good week.

19/10/2013

Improvement-by-Twitter

Sat 5th October

It started with a tweet.

08:17 [JG] The NHS is its people. If you lose them, you lose the NHS.

09:15 [DO] We are in a PEOPLE business – educating people and creating value.

Sun 6th October

08:32 [SD] Who isn’t in people business? It is only people who buy stuff. Plants, animals, rocks and machines don’t.

09:42 [DO] Very true – it is people who use a service and people who deliver a service and we ALL know what good service is.

09:47 [SD] So onus is on us to walk our own talk. If we don’t all improve our small bits of the NHS then who can do it for us?

Then we were off … the debate was on …

10:04 [DO] True – I can prove I am saving over £160 000.00 a year – roll on PBR !?

10:15 [SD] Bravo David. I recently changed my surgery process: productivity up by 35%. Cost? Zero. How? Process design methods.

11:54 [DO] Exactly – cost neutral because we were thinking differently – so how to persuade the rest?

12:10 [SD] First demonstrate it is possible then show those who want to learn how to do it themselves. http://www.saasoft.com/fish/course

We had hard evidence it was possible … and now MC joined the debate …

12:48 [MC] Simon why are there different FISH courses for safety, quality and efficiency? Shouldn’t good design do all of that?

12:52 [SD] Yes – goal of good design is all three. It just depends where you are starting from: Governance, Operations or Finance.

A number of parallel threads then took off and we all had lots of fun exploring each others knowledge and understanding.

17:28 MC registers on the FISH course.

And that gave me an idea. I emailed an offer – that he could have a complimentary pass for the whole FISH course in return for sharing what he learns as he learns it. He thought it over for a couple of days then said “OK”.

Weds 9th October

06:38 [MC] Over the last 4 years of so, I’ve been involved in incrementally improving systems in hospitals. Today I’m going to start an experiment.

06:40 [MC] I’m going to see if we can do less of the incremental change and more system redesign. To do this I’ve enrolled in FISH

Fri 11th October

06:47 [MC] So as part of my exploration into system design, I’ve done some studies in my clinic this week. Will share data shortly.

21:21 [MC] Here’s a chart showing cycle time of patients in my clinic. Median cycle time 14 mins, but much longer in 2 pic.twitter.com/wu5MsAKk80

21:22 [MC] Here’s the same clinic from patients’ point if view, wait time. Much longer than I thought or would like pic.twitter.com/iVLTDsbG8m

21:24 [MC] Two patients needed to discuss surgery or significant news, that takes time and can’t be rushed.

21:25 [MC] So, although I started on time, worked hard and finished on time. People were waited ages to see me. Template is wrong!

21:27 [MC] By the time I had seen the the 3rd patient, people were waiting 45 mins to see me. That’s poor.

21:28 [MC] The wait got progressively worse until the end of the clinic.

Sunday 13th October

16:02 [MC] As part of my homework on systems, I’ve put my clinic study data into a Gantt chart. Red = waiting, green = seeing me pic.twitter.com/iep2PDoruN

16:34 [SD] Hurrah! The visual power of the Gantt Chart. Worth adding the booked time too – there are Seven Sins of Scheduling to find.

16:36 [SD] Excellent – good idea to sort into booked time order – it makes the planned rate of demand easier to see.

16:42 [SD] Best chart is Work In Progress – count the number of patients at each time step and plot as a run chart.

17:23 [SD] Yes – just count how many lines you cross vertically at each time interval. It can be automated in Excel

17:38 [MC] Like this? pic.twitter.com/fTnTK7MdOp

This is the work-in-progress chart. The most useful process monitoring chart of all. It shows the changing size of the queue over time. Good flow design is associated with small, steady queues.

18:22 [SD] Perfect! You’re right not to plot as XmR – this is a cusum metric. Not a healthy WIP chart this!

There was more to follow but the “ah ha” moment had been seen and shared.

Weds 16th October

MC completes the Online FISH course and receives his well-earned Certificate of Achievement.

This was his with-the-benefit-of-hindsight conclusion:

I wish I had known some of this before. I will have totally different approach to improvement projects now. Key is to measure and model well before doing anything radical.

Improvement Science works.
Improvement-by-Design is a skill that can be learned quickly.
FISH is just a first step.

05/10/2013

The Power of the Converted Skeptic

One of the biggest challenges in Improvement Science is diffusion of an improvement outside the circle of control of the innovator.

It is difficult enough to make a significant improvement in one small area – it is an order of magnitude more difficult to spread the word and to influence others to adopt the new idea!

One strategy is to shame others into change by demonstrating that their attitude and behaviour are blocking the diffusion of innovation.

This strategy does not work. It generates more resistance and amplifies the differences of opinion.

Another approach is to bully others into change by discounting their opinion and just rolling out the “obvious solution” by top-down diktat.

This strategy does not work either. It generates resentment – even if the solution is fit-for-purpose – which it usually is not!

So what does work?

The key to it is to convert some skeptics because a converted skeptic is a powerful force for change.

But doesn’t that fly in the face of established change management theory?

Innovation diffuses from innovators to early-adopters, then to the silent majority, then to the laggards and maybe even dinosaurs … doesn’t it?

Yes – but that style of diffusion is incremental, slow and has a very high failure rate. What is very often required is something more radical, much faster and more reliable. For that it needs both push from the Confident Optimists and pull from some Converted Pessimists. The tipping point does not happen until the silent majority start to come off the fence in droves: and they do that when the noisy optimists and equally noisy pessimists start to agree.

The fence-sitters jump when the tug-o-war stalemate stops and the force for change becomes aligned in the direction of progress.

So how is a skeptic converted?

Simple. By another Converted Skeptic.

Here is a real example.

We are all skeptical about many things that we would actually like to improve.

Personal health for instance. Something like weight. Yawn! Not that Old Chestnut!

We are bombarded with shroud-waver stories that we are facing an epidemic of obesity, rapidly rising rates of diabetes, and all the nasty and life-shortening consequences of that. We are exhorted to eat “five portions of fruit and veg a day” … or else! We are told that we must all exercise our flab away. We are warned of the Evils of Cholesterol and told that overweight children are caused by bad parenting.

The more gullible and fearful are herded en-masse in the direction of the Get-Thin-Quick sharks who then have a veritable feeding frenzy. Their goal is their short-term financial health not the long-term health of their customers.

The more insightful, skeptical and frustrated seek solace in the chocolate Hob Nob jar.

For their part, the healthcare professionals are rewarded for providing ineffective healthcare by being paid-for-activity not for outcome. They dutifully measure the decline and hand out ineffective advice. Their goal is survival too.

The outcome is predictable and seemingly unavoidable.

So when a disruptive innovation comes along that challenges the current dogma and status quo, the healthy skeptics inevitably line up and proclaim that it will not work.

Not that it does not work. They do not know that because they never try it. They are skeptics. Someone else has to prove it to them.

And I am a healthy skeptic about many things.

I am skeptical about diets – the evidence suggests that their proclaimed benefit is difficult to achieve and even more difficult to sustain: and that is the hall-mark of either a poor design or a deliberate, profit-driven, yet legal scam.

So I decided to put an innovative approach to weight loss to the test. It is not a diet – it is a design to achieve and sustain a healthier weight to height ratio. And for it to work it must work for me because I am a diet skeptic.

The start of the story is HERE

I am now a Converted Healthier Skeptic.

I call the innovative design a “2 out of 7 Lo-CHO” policy and what that means is for two days a week I just cut out as much carbohydrate (CHO) as feasible. Stuff like bread, potatoes, rice, pasta and sugar. The rest of the time I do what I normally do. There is no need for me to exercise and no need for me to fill up on Five Fruit and Veg.

The chart above is the evidence of what happened. It shows a 7 kg reduction in weight over 140 days – and that is impressive given that it has required no extra exercise and no need to give up tasty treats completely and definitely no need to boost the bottom-line of a Get-Thin-Quick shark!

It also shows what to expect. The weight loss starts steeper then tails off as it approaches a new equilibrium weight. This is the classic picture of what happens to a “system” when one of its “operational policies” is wisely re-designed.

Patience, persistence and a time-series chart are all that is needed. It takes less than a minute per day to monitor the improvement.

Even I can afford to invest a minute per day.

The BaseLine© chart clearly shows that the day-to-day variation is quite high: and that is expected – it is inherent in the 2-out-of-7 Lo-CHO design. It is the not the short-term change that is the measure of success – it is the long-term improvement that is important.

It is important to measure daily – because it is the daily habit that keeps me mindful, aligned, and on-goal. It is not the measurement itself that is the most important thing – it is the conscious act of measuring and then plotting the dot in the context of the previous dots. The picture tells the story. No further “statistical” analysis is required.

The power of this chart is that it provides hard evidence that is very effective for nudging other skeptics like me into giving the innovative idea a try. I know because I have done that many times now. I have converted other skeptics. It is an innovation infection.

And the same principle appears to apply to other areas. What is critical to success is tangible and visible proof of progress. That is what skeptics need. Then a rational and logical method and explanation that respects their individual opinion and requirements. The design has to work for them. And it must make sense.

They will come out with a string of “Yes … buts” and that is OK because that is how skeptics work. Just answer their questions with evidence and explanations. It can get a bit wearing I admit but it is worth the effort.

An effective Improvement Scientist needs to be a healthy skeptic too – i.e. an open minded one.

31/08/2013

Race for the Line

It is surprising how competitive most people are. We are constantly comparing ourselves with others and using what we find to decide what to do next. Groan or Gloat. Chase or Cruise.

This is because we are social animals. Comparing with other is hard-wired into us. We have little choice.

But our natural competitive behaviour can become counter-productive when we learn that we can look better-by-comparison if we block or trip-up our competitors. In a vainglorious attempt to make ourselves look better-by-comparison we spike the wheels of our competitors’ chariots. We fight dirty.

It is not usually openly aggressive fighting. Most of our spiking is done passively. Often by deliberately not doing something. A deliberate act of omission. And if we are challenged we often justify our act of omission by claiming we were too busy.

This habitual passive-aggressive learned behaviour is not only toxic to improvement, it creates a toxic culture too. It is toxic to everything.

And it ensures that we stay stuck in The Miserable Job Swamp. It is a bad design.

So we need a better one.

One idea is to eliminate competition. This sounds plausible but it does not work. We are hard-wired to compete because it has proven to be a very effective long term survival strategy. The non-competitive have not survived. To be deliberately non-competitive will guarantee mediocrity and future failure.

A better design is to leverage our competitive nature and this is surprisingly easy to do.

We flip the “battle” into a “race”.

To do that we need:

1) A clear destination – a shared common purpose – that can be measured. We need to be able to plot our progress using objective evidence.

2) A proven, safe, effective and efficient route plan to get us to our destination.

3) A required arrival time that is realistic. Open-ended time-scales do not work.

4) Regular feedback to measure our individual progress and to compare ourselves with others. Selective feedback is ineffective. Secrecy or anonymous feedback is counter-productive at best and toxic at worst.

5) The ability to re-invest our savings on all three win-win-win dimensions: emotional, temporal and financial. This fuels the engine of improvement. Us.

The rest just happens – but not by magic – it happens because this is a better Improvement-by-Design.

13/07/2013

The Art of Juggling

Improvement Science is like three-ball juggling.

And there are different sets of three things that an Improvementologist needs to juggle:

the Quality-Flow-Cost set and
the Governance-Operations-Finance set and
the Customer-Staff-Organization set.

But the problem with juggling is that it looks very difficult to do – so almost impossible to learn – so we do not try. We give up before we start. And if we are foolhardy enough to try (by teaching ourselves using the suck-it-and-see or trial-and-error method) then we drop all the balls very quickly. We succeed in reinforcing our impossible-for-me belief with evidence. It is a self-fulfilling prophesy. Only the most tenacious, self-motivated and confident people succeed – which further reinforces the I-Can’t-Do belief of everyone else.

The problem here is that we are making an Error of Omission.

We are omitting to ask ourselves two basic questions “How does a juggler learn their art?” and “How long does it take?“

The answer is surprising.

It is possible for just about anyone to learn to juggle in about 10 minutes. Yes – TEN MINUTES.

Skeptical? Sure you are – if it was that easy we would all be jugglers. That is the “I Can’t Do” belief talking. Let us silence that confidence-sapping voice once and for all.

Here is how …

You do need to have at least one working arm and one working eyeball and something connecting them … and it is a bit easier with two working arms and two working eyeballs and something connecting them.

And you need something to juggle – fruit is quite good – oranges and apples are about the right size, shape, weight and consistency (and you can eat the evidence later too).

And you need something else.

You need someone to teach you.

And that someone must be able to juggle and more importantly they must be able to teach someone else how to juggle which is a completely different skill.

Those are the necessary-and-sufficient requirements to learn to juggle in 10 minutes.

The recent picture shows an apprentice Improvement Scientist at the “two orange” stage – just about ready to move to the “three orange” stage.

Exactly the same is true of learning the Improvement Science juggling trick.

The ability to improve Quality, Flow and Cost at the same time.

The ability to align Governance, Operations and Finance into a win-win-win synergistic system.

The ability to delight customers, motivate staff and support leaders at the same time.

And the trick to learning to juggle is called step-by-step unlearning. It is counter-intuitive.

To learn to juggle you just “unlearn” what is stopping you from juggling. You unlearn the unconscious assumptions and habits that are getting in the way.

And that is why you need a teacher who knows what needs to be unlearned and how to help you do it.

And for an apprentice Improvement Scientist the first step on the Unlearning Journey is FISH.

06/07/2013

Step 5 – Monitor

Improvement-by-Design is not the same as Improvement-by-Desire.

Improvement-by-Design has a clear destination and a design that we know can get us there because we have tested it before we implement it.

Improvement-by-Desire has a vague direction and no design – we do not know if the path we choose will take us in the direction we desire to go. We cannot see the twists and turns, the unknown decisions, the forks, the loops, and the dead-ends. We expect to discover those along the way. It is an exercise in hope.

So where pessimists and skeptics dominate the debate then Improvement-by-Design is a safer strategy.

Just over seven weeks ago I started an Improvement-by-Design project – a personal one. The destination was clear: to get my BMI (body mass index) into a “healthy” range by reducing weight by about 5 kg. The design was clear too – to reduce energy input rather than increase energy output. It is a tried-and-tested method – “avoid burning the toast”. The physical and physiological model predicted that the goal was achievable in 6 to 8 weeks.

So what has happened?

To answer that question requires two time-series charts. The input chart of calories ingested and the output chart of weight. This is Step 5 of the 6M Design® sequence.

Remember that there was another parameter in this personal Energy-Weight system: the daily energy expended.

But that is very difficult to measure accurately – so I could not do that.

What I could do was to estimate the actual energy expended from the model of the system using the measured effect of the change. But that is straying into the Department of Improvement Science Nerds. Let us stay in the real world a bit longer.

Here is the energy input chart …

It shows an average calorie intake of 1500 kcal – the estimated required value to achieve the weight loss given the assumptions of the physiological model. It also shows a wide day-to-day variation. It does not show any signal flags (red dots) so an inexperienced Improvementologist might conclude that this just random noise.

It is not. The data is not homogeneous. There is a signal in the system – a deliberate design change – and without that context it is impossible to correctly interpret the chart.

Remember Rule #1: Data without context is meaningless.

The deliberate process design change was to reduce calorie intake for just two days per week by omitting unnecessary Hi-Cal treats – like those nice-but-naughty Chocolate Hobnobs. But which two days varied – so there is no obvious repeating pattern in the chart. And the intake on all days varied – there were a few meals out and some BBQ action.

To separate out these two parts of the voice-of-the-process we need to rationally group the data into the Lo-cal days (F) and the OK-cal days (N).

The grouped BaseLine© chart tells a different story. The two groups clearly have a different average and both have a lower variation-over-time than the meaningless mixed-up chart.

And we can now see a flag – on the second F day. That is a prompt for an “investigation” which revealed: will-power failure. Thursday evening beer and peanuts! The counter measure was to avoid Lo-cal on a Thursday!

What we are seeing here is the fifth step of 6M Design® exercise – the Monitor step.

And as well as monitoring the factor we are changing – the cause; we also monitor the factor we want to influence – the effect.

The effect here is weight. And our design includes a way of monitoring that – the daily weighing.

The output metric BaseLine© chart – weight – shows a very different pattern. It is described as “unstable” because there are clusters of flags (red dots) – some at the start and some at the end. The direction of the instability is “falling” – which is the intended outcome.

So we have robust, statistically valid evidence that our modified design is working.

The weight is falling so the energy going in must be less than the energy being put out. I am burning off the excess lard and without doing any extra exercise. The physics of the system mandate that this is the only explanation. And that was my design specification.

So that is good. Our design is working – but is it working as we designed? Does observation match prediction? This is Improvement-by-Design.

Remember that we had to estimate the other parameter to our model – the average daily energy output – and we guessed a value of 2400 kcal per day using generic published data. Now I can refine the model using my specific measured change in weight – and I can work backwards to calculate the third parameter. And when I did that the number came out at 2300 kcal per day. Not a huge difference – the equivalent of one yummy Chocolate Hobnob a day – but the effect is cumulative. Over the 53 days of the 6M Design® project so far that would be a 5300 kcal difference – about 0.6kg of useless blubber.

So now I have refined my personal energy-weight model using the new data and I can update my prediction and create a new chart – a Deviation from Aim chart.

This is the chart I need to watch to see if I am on the predicted track – and it too is unstable -and not a good direction. It shows that the deviation-from-aim is increasing over time and this is because my original guesstimate of an unmeasurable model parameter was too high.

This means that my current design will not get me to where I want to be, when I what to be there. This tells me I need to tweak my design. And I have a list of options.

1) I could adjust the target average calories per day down from 1500 to 1400 and cut out a few more calories; or

2) I could just keep doing what I am doing and accept that it will take me longer to get to the destination; or

3) I could do a bit of extra exercise to burn the extra 100 kcals a day off, or

4) I could do a bit of any or all three.

And because I am comparing experience with expectation using a DFA chart I will know very quickly if the design tweak is delivering.

And because some nice weather has finally arrived so the BBQ will be busy I have chosen to take longer to get there. I will enjoy the weather, have a few beers and some burgers. And that is OK. It is a perfectly reasonable design option – it is a rational and justifiable choice.

And I need to set my next destination – a weight if about 72 kg according to the BMI chart – and with my calibrated Energy-Weight model I will know exactly how to achieve that weight and how long it will take me. And I also know how to maintain it – by increasing my calorie intake. More beer and peanuts – maybe – or the occasional Chocolate Hobnob even. Hurrah! Win-win-win!

This real-life example illustrates 6M Design® in action and demonstrates that it is a generic framework.

The energy-weight model in this case is a very simple one that can be worked out on the back of a beer mat (which is what I did).

It is called a linear model because the relationship between calories-in and weight-out is approximately a straight line.

Most real-world systems are not like this. Inputs are not linearly related to outputs. They are called non-linear systems: and that makes a BIG difference.

A very common error is to impose a “linear model” on a “non-linear system” and it is a recipe for disappointment and disaster. We do that when we commit the Flaw of Averages error. We do it when we plot linear regression lines through time-series data. We do it when we extrapolate beyond the limits of our evidence. We do it when we equate time with money.

The danger of this error is that our linear model leads us to make unwise decisions and we actually make the problem worse – not better. We then give up in frustration and label the problem as “impossible” or “wicked” or get sucked into to various forms of Snake Oil Sorcery.

The safer approach is to assume the system is non-linear and just let the voice of the system talk to us through our BaseLine© charts. The challenge for us is to learn to understand what the system is saying.

That is why the time-series charts are called System Behaviour Charts and that is why they are an essential component of Improvement-by-Design.

However – there is a step that must happen before this – and that is to get the Foundations in place. The foundation of knowledge on which we can build our new learning. That gap must be filled first.

And anyone who wants to invest in learning the foundations of improvement science can now do so at their own convenience and at their own pace because it is on-line …. and it is here.

15/06/2013

Resistance and Persistence

[Bing-Bong]

The email from Leslie was unexpected.

“Hi Bob, can I change the planned topic of our session today to talk about resistance. We got off to a great start with our improvement project but now I am hitting brick walls and we are losing momentum. I am getting scared we will stall. Leslie”

Bob replied immediately – it was only a few minutes until their regular teleconference call.

“Hi Leslie, no problem. Just firing up the Webex session now. Bob”

[Whoop-Whoop]

The sound bite announced Leslie joining in the teleconference.

<Leslie> Hi Bob. Sorry about the last minute change of plan. Can I describe the scenario?

<Bob> Hi Leslie. Please do.

<Leslie> Well we are at stage five of the 6M Design® sequence and we are monitoring the effect of the first set of design changes that we have made. We started by eliminating design flaws that were generating errors and impairing quality. The information coming in confirms what we predicted at stage 3. The problem is that a bunch of “fence-sitters” that said nothing at the start are now saying that the data is a load of rubbish and implying we are cooking the books to make it look better than it is! I am pulling my hair out trying to convince them that it is working.

<Bob> OK. What is your measure for improvement?

<Leslie> The percentage yield from the new quality-by-design process. It is improving. The BaseLine© chart says so.

<Bob> And how is that improvement being reported?

<Leslie> As the average yield per week. I know we should not aggregate for a month because we need to see the impact of the change as it happens and I know there is a seven-day cycle in the system so we set the report interval at one week.

<Bob> Yes. Those are all valid reasons. What is the essence of the argument against your data?

<Leslie> There is no specific argument – it is just being discounted as “rubbish”.

<Bob> So you are feeling resistance?

<Leslie> You betcha!

<Bob> OK. Let us take a different tack on this. How often do you measure the yield?

<Leslie> Daily.

<Bob> And what is the reason you are using the percentage yield as your metric?

<Leslie> So we can compare one day with the next more easily and plot it on a time-series chart. The denominator is different every day so we cannot use just the count of errors.

<Bob> OK. And how do you calculate the weekly average?

<Leslie> From the daily percentage yields. It is not a difficult calculation!

There was a definite hint of irritation and sarcasm in Leslie’s voice.

<Bob> And how confident are you in your answer?

<Leslie> Completely confident. The team are fantastic. They see the value of this and are collecting the data assiduously. They can feel the improvement. They do not need the data to prove it. The feedback is to convince the fence-sitters and skeptics and they are discounting it.

<Bob> OK so you are confident in the quality of the data going in to your calculation – how confident are you in the data coming out?

<Leslie> What do you mean! It is a simple calculation – a 12 year old could do.

<Bob> How are you feeling Leslie?

<Leslie>Irritated!

<Bob> Does it feel as if I am resisting too?

<Leslie>Yes!!

<Bob> Irritation is anger – the sense of loss in the present. What do you feel you are losing?

<Leslie> My patience and my self-confidence.

<Bob> So what might be my reasons for resisting?

<Leslie> You could be playing games or you could have a good reason.

<Bob> Do I play games?

<Leslie> Not so far! Sorry … no. You do not do that.

<Bob> So what could be my good reason?

<Leslie> Um. You can feel or see something that I cannot. An error?

<Bob> Yes. If I just feel something is not right I cannot do much else but say “That does not feel right”. If I can see what I is not right I can explain my rationale for resisting. Can I try to illuminate?

<Leslie> Yes please!

<Bob> OK – have you got a spreadsheet handy?

<Leslie> Yes.

<Bob> OK – create a column of twenty random numbers in the range 20-80 and label them “daily successes”. Next to them create a second column of random numbers in the range 20-100 and label then “daily activity”.

<Leslie> OK – done that.

<Bob> OK – calculate the % yield by day then the average of the column of daily % yield.

<Leslie> OK – that is exactly how I do it.

<Bob> OK – now sum the columns of successes and activities and calculate the average % yield from those two totals.

<Leslie> Yes – I could do that and it will give the same final answer but I do not do that because I cannot use that data on my run chart – for the reasons I said before.

<Bob> Does it give the same answer?

<Leslie> Um – no. Wait. I must have made an error. Let me check. No. I have done it correctly. They are not the same. Uh?

<Bob> What are you feeling?

<Leslie> Confused! But the evidence is right there in front of me.

<Bob> An assumption you have been making has just been exposed to be invalid. Your rhetoric does not match reality.

<Leslie> But everyone does this … it is standard practice.

<Bob> And that makes it valid?

<Leslie> No .. of course not. That is one of the fundamental principles of Improvement Science. Just doing what everyone else does is not necessarily correct.

<Bob> So now we must understand what is happening. Can you now change the Daily Activity column so it is the same every day – say 60.

<Leslie> OK. Now my method works. The yield answers are the same.

<Bob> Yes.

<Leslie> Why is that?

<Bob> The story goes back to 1948 when Claude Shannon described “Information Theory”. When you create a ratio you start with two numbers and end up with only one which implies that information is lost in the conversion. Two numbers can only give one ratio, but that same ratio can be created by an infinite set of two numbers. The relationship is asymmetric. It is not an equality. And it has nothing to do with the precision of the data. When we throw data away we create ambiguity.

<Leslie> And in my data the activity by day does vary. There is a regular weekly cycle and some random noise. So the way I am calculating the average yield is incorrect, and the message I am sharing is distorted, so others can quite reasonably challenge the data, and because I was 100% confident I was correct I have been assuming that their resistance was just due to cussedness!

<Bob> There may be some cussedness too. It is sometimes difficult to separate skepticism and cynicism.

<Leslie> So what is the lesson here? There must be more to your example than just exposing a basic arithmetic error.

<Bob> The message is that when you feel resistance you must accept the possibility that you are making an error that you cannot see. The person demonstrating resistance can feel the emotional pain of a rhetoric-reality mismatch but can not explain the cause. You need to strive to see the problem through their eyes. It is OK to say “With respect I do not see it that way because …”.

<Leslie> So feeling “resistance” signals an opportunity for learning?

<Bob> Yes. Always.

<Leslie> So the better response is to pull back and to check assumptions and not to push forward and make the resistance greater or worse still break through the barrier of resistance, celebrate the victory, and then commit an inevitable and avoidable blunder – and then add insult to injury and blame someone else creating even more cynicism on the future.

<Bob> Yes. Well put.

<Leslie> Wow! And that is why patience and persistence are necessary. Not persistently pushing but persistently searching for the unconscious assumptions that underpin resistance; consistently using Reality as the arbiter; and having enough patience to let Reality tell its own story.

<Bob> Yes. And having the patience and persistence to keep learning from our confusion and to keep learning how to explain what we have discovered better and better.

<Leslie> Thanks Bob. Once again you have opened a new door for me.

<Bob> A door that was always there and yet hidden from view until it was illuminated with an example.

08/06/2013

Middle-Aware

[Dring Dring]

<Bob> Hi Leslie, how are you today?

<Leslie> Really good thanks. We are making progress and it is really exciting to see tangible and measurable improvement in safety, delivery, quality and financial stability.

<Bob> That is good to hear. So what topic shall we explore today?

<Leslie> I would like to return to the topic of engagement.

<Bob> OK. I am sensing that you have a specific Niggle that you would like to share.

<Leslie> Yes. Specifically it is engaging the Board.

<Bob> Ah ha. I wondered when we would get to that. Can you describe your Niggle?

<Leslie> Well, the feeling is fear and that follows from the risk of being identified as a trouble-maker which follows from exposing gaps in knowledge and understanding of seniors.

<Bob> Well put. This is an expected hurdle that all Improvement Scientists have to learn to leap reliably. What is the barrier that you see?

<Leslie> That I do not know how to do it and I have seen a lot of people try and commit career-suicide – like moths on a flame.

<Bob> OK – so it is a real fear based on real evidence. What methods did the “toasted moths” try?

<Leslie> Some got angry and blasted off angry send-to-all emails. They just succeeded in identifying themselves as “terrorists” and were dismissed – politically and actually. Others channeled their passion more effectively by heroic acts that held the system together for a while – and they succeeded in burning themselves out. The end result was the same: toasted!

<Bob> So with your understanding of design principles what does that say?

<Leslie> That the design of their engagement process is wrong.

<Bob> Wrong?

<Leslie> I mean “not fit for purpose”.

<Bob> And the difference is?

<Leslie> “Wrong” is a subjective judgement, “not fit for purpose” is an objective assessment.

<Bob> Yes. We need to be careful with words. So what is the “purpose”?

<Leslie> An organisation that is capable of consistently delivering improvement on all dimensions, safety, delivery, quality and affordability.

<Bob> Which requires?

<Leslie> All the parts working in synergy to a common purpose.

<Bob> So what are the parts?

<Leslie> The departments.

<Bob> They are the stages that the streams cross – they are parts of system structure. I am thinking more broadly.

<Leslie> The workers, the managers and the executives?

<Bob> Yes. And how is that usually perceived?

<Leslie> As a power hierarchy.

<Bob> And do physical systems have power hierarchies?

<Leslie> No … they have components with different and complementary roles.

<Bob> So does that help?

<Leslie> Yes! To achieve synergy each component has to know its complementary role and be competent to do it.

<Bob> And each must understand the roles of the others, respect the difference, and develop trust in their competence.

<Leslie> And the concepts of understanding, respect and trust appears again.

<Bob> Indeed. They are always there in one form or another.

<Leslie> So as learning and improvement is a challenge then engagement is respectful challenge …

<Bob> … uh huh …

<Leslie> … and each part is different so requires a different form of respectful challenge?

<Bob> Yes. And with three parts there are six relationships between them – so six different ways of one part respectfully challenging another. Six different designs that have the same purpose but a different context.

<Leslie> Ah ha! And if we do not use the context-dependent-fit-for-purpose-respectful-challenge-design we do not achieve our purpose?

<Bob> Correct. The principles of design are generic.

<Leslie> So what are the six designs?

<Bob> Let us explore three of them. First the context of a manager respectfully challenging a worker to improve.

<Leslie> That would require some form of training. Either the manager trains the worker or employs someone else to.

<Bob> Yes – and when might a manager delegate training?

<Leslie> When they do not have time to or do not know how to.

<Bob> Yes. So how would the flaw in that design be avoided?

<Leslie> By the manager maintaining their own know-how by doing enough training themselves and delegating the rest.

<Bob> Yup. Well done. OK let us consider a manager respectfully challenging other managers to improve.

<Leslie> I see what you mean. That is a completely different dynamic. The closest I can think of is a coaching arrangement.

<Bob> Yes. Coaching is quite different from training. It is more of a two-way relationship and I prefer to refer to it as “informal co-coaching” because both respectfully challenge each other in different ways; both share knowledge; and both learn and develop.

<Leslie> And that is what you are doing now?

<Bob> Yes. The only difference is that we have agreed a formal coaching contract. So what about a worker respectfully challenging a manager or a manager respectfully challenging an executive?

<Leslie>That is a very different dynamic. It is not training and it is not coaching.

<Bob> What other options are there?

<Leslie>Not formal coaching! An executive is not going to ask a middle manager to coach them!

<Bob> You are right on both counts – so what is the essence of informal coaching?

<Leslie> An informal coach provides a different perspective and will say what they see if asked and will ask questions that help to illustrate alternative perspectives and offer evidence of alternative options. This is just well-structured, judgement-free feedback.

<Bob> Yes. We do it all the time. And we are often “coached” by those much younger than ourselves who have a more modern perspective. Our children for instance.

<Leslie> So the judgement free feedback metaphor is the one that a manager can use to engage an executive.

<Bob> Yes. And look at it from the perspective of the executive – they want feedback that can help them made wiser strategic decisions. That is their role. Boards are always asking for customer feedback, staff feedback and performance feedback. They want to know the Nuggets, the Niggles, the Nice Ifs and the NoNos. They just do not ask for it like that.

<Leslie> So they are no different from the rest of us?

<Bob> Not in respect of an insatiable appetite for unfiltered and undistorted feedback. What is different is their role. They are responsible for the strategic decisions – the ones that affect us all – so we can help ourselves by helping them make those decisions. A well-designed feedback model is fit-for-that-purpose.

<Leslie> And an Improvement Scientist needs to be able to do all three – training, coaching and communicating in a collaborative informal style. Is that leadership?

<Bob> I call it “middle-aware”.

<Leslie> It makes complete sense to me. There is a lot of new stuff here and I will need to reflect on it. Thank you once again for showing me a different perspective on the problem.

<Bob> I enjoyed it too – talking it through helps me to learn to explain it better – and I look forward to hearing the conclusions from your reflections because I know I will learn from that too.

01/06/2013

Closing the Two Loops

Over the past few weeks I have been conducting an Improvement Science Experiment (ISE). I do that a lot. This one is a health improvement experiment. I do that a lot too. Specifically – improving my own health. Ah! Not so diligent with that one.

The domain of health that I am focusing on is weight – for several reasons:
(1) because a stable weight that is within “healthy” limits is a good idea for many reasons and
(2) because weight is very easy to measure objectively and accurately.

But like most people I have constraints: motivation constraints, time constraints and money constraints. What I need is a weight reduction design that requires no motivation, no time, and no money. That sounds like a tough design challenge – so some consideration is needed.

Design starts with a specific purpose and a way of monitoring progress. And I have a purpose – weight within acceptable limits; a method for monitoring progress – a dusty set of digital scales. What I need is a design for delivering the improvement and a method for maintaining it. That is the challenge.

So I need a tested design that will deliver the purpose. I could invent something here but it is usually quicker to learn from others who have done it, or something very similar. And there is lots of knowledge and experience out there. And they fall into two broad schools – Eat Healthier or Exercise More and usually Both.

Eat Healthier is sold as Eat Less of the Yummy Bad Stuff and more of the Yukky Good Stuff. It sounds like a Puritanical Policy and is not very motivating. So with zero motivation as a constraint this is a problem. And Yukky Good Stuff seems to come with a high price tag. So with zero budget as a constraint this is a problem too.

Exercise More is sold as Get off Your Bottom and Go for a Walk. It sounds like a Macho Man Mantra. Not very motivating either. It takes time to build up a “healthy” sweat and I have no desire to expose myself as a health-desperado by jogging around my locality in my moth-eaten track suit. So with zero time as a constraint this is a problem. Gym subscriptions and the necessary hi-tech designer garb do not come cheap. So with a zero budget constraint this is another problem.

So far all the conventional wisdom is failing to meet any of my design constraints. On all dimensions.

Oh dear!

The rhetoric is not working. That packet of Chocolate Hob Nobs is calling to me from the cupboard. And I know I will feel better if I put them out of their misery. Just one will not do any harm. Yum Yum. Arrrgh!!! The Guilt. The Guilt.

OK – get a grip – time for Improvement Scientist to step in – we need some Science.

[Improvement Science hat on]

The physics and physiology are easy on this one:

(a) What we eat provides us with energy to do necessary stuff (keep warm, move about, think, etc). Food energy is measured in “Cals”; work energy is measured in “Ergs”.
(b) If we eat more Cals than we burn as Ergs then the difference is stored for later – ultimately as blubber (=fat).
(c) There are four contributors to or weight: dry (bones and stuff), lean (muscles and glands of various sorts), fluid (blood, wee etc), and blubber (fat).
(d) The sum of the dry, lean, and fluids should be constant – we need them – we do not store energy there.
(e) The fat component varies. It is stored energy. Work-in-progress so to speak.
(f) One kilogram of blubber is equivalent to about 9000 Cals.
(g) An adult of average weight, composition, and activity uses between 2000 and 2500 Cals per day – just to stay at a stable weight.

These facts are all we need to build an energy flow model.

Food Cals = Energy In.
Work Ergs = Energy Out.
Difference between Energy In and Energy Out is converted to-and-from blubber at a rate of 1 gram per 9 Cal.
Some of our weight is the accumulated blubber – the accumulated difference between Cals-In and Ergs-Out

The Laws Of Physics are 100% Absolute and 0% Negotiable. The Behaviours of People are 100% Relative and 100% Negotiable. Weight loss is more about behaviour. Habits. Lifestyle.

Bit more Science needed now:

Which foods have the Cals?

(1) Fat (9 Cal per gram)
(2) Carbs (4 Cal per gram)
(3) Protein (4 Cal per gram)
(4) Water, Vitamins, Minerals, Fibre, Air, Sunshine, Fags, Motivation (0 Cal per gram).

So how much of each do we get from the stuff we nosh?

It is easy enough to work out – but it is very tedious to do so. This is how calorie counting weight loss diets work. You weigh everything that goes in, look up the Cal conversions per gram in a big book, do some maths and come up with a number. That takes lots of time. Then you convert to points and engage in a pseudo-accounting game where you save points up and cash them in as an occasional cream cake. Time is a constraint and Saving-the-Yummies-for-Later is not changing a habit – it is feeding it!

So it is just easier for me to know what a big bowel of tortilla chips translates to as Cals. Then I can make an informed choice. But I do not know that.

Why not?

Because I never invested time in learning. Like everyone else I gossip, I guess, and I generalise. I say “Yummy stuff is bad because it is Hi-Cal; Yukky stuff is good because it is Lo-Cal“. And from this generalisation I conclude “Cutting Cals feels bad“. Which is a problem because my motivation is already rock bottom. So I do nothing, and my weight stays the same, and I still feel bad.

The Get-Thin-Quick industry knows this … so they use Shock Tactics to motivate us. They scare us with stories of fat young people having heart attacks and dying wracked with regret. Those they leave behind are the real victims. The industry bludgeons us into fearful submission and into coughing up cash for their Get Thin Quick Panaceas. Their real goal is the repeat work – the loyal customers. And using scare mongering and a few whale-to-waif conversions as rabble-rousing zealots they cook up the ideal design to achieve that. They know that, for most of us, as soon as the fear subsides, the will weakens, the chips are down (the neck), the blubber builds, and we are back with our heads hung low and our wallets open.

I have no motivation – that is a constraint. So flogging an over-weight and under-motivated middle-aged curmudgeon will only get a more over-weight, ego-bruised-and-depressed, middle-aged cynic. I may even seek solace in the Chocolate Hob Nob jar.

Nah! I need a better design.

[Improvement Scientist hat back on]

First Rule of Improvement – Check the Assumptions.

Assumption 1:
Yummy => Hi-Cal => Bad for Health
Yukky => Lo-Cal => Good for Health

It turns out this is a gross over-simplification. Lots of Yummy things are Lo-Cal; lots of Yukky things are Hi-Cal. Yummy and Yukky are subjective. Cals are not.

OK – that knowledge is really useful because if I know which-is-which then I can made wiser decisions. I can do swaps so that the Yummy Score goes higher and the Cals Score goes lower. That sounds more like it! My Motiv-o-Meter twitches.

Assumption 2:
Hi-Cal => Cheap => Good for Wealth
Lo-Cal => Expensive => Bad for Wealth

This is a gross over-simplification too. Lots of Expensive things are Hi-Cal; lots of Cheap things are Lo-Cal.

OK so what about the combination?

Bingo! There are lots of Yummy+Cheap+Lo-Cal things out there ! So my process is to swap the Lose-Lose-Lose for the Win-Win-Win. I feel a motivation surge. The needle on my Motiv-o-Meter definitely moved this time.

But how much? And for how long? And how will I know if it is working?

[Improvement Science hat back on]

Second Rule of Improvement Science – Work from the Purpose

We need an output specification. What weight reduction in what time-scale?

OK – I work out my target weight – using something called the BMI (body mass index) which uses my height and a recommended healthy BMI range to give a target weight range. I plumb for 75 kg – not just “10% reduction” – I need an absolute goal. (PS. The BMI chart I used is at the end of the blog).

OK – I now I need a time-scale – and I know that motivation theory shows that if significant improvement is not seen within 15 repetitions of a behaviour change then it does not stick. It will not become a new habit. I need immediate feedback. I need to see a significant weight reduction within two weeks. I need a quick win to avoid eroding my fragile motivation. And so long as a get that I will keep going. And how long to get to target weight? One or two lunar cycles feels about right. Let us compromise on six weeks.

And what is a “significant improvement”?

Ah ha! Now I am on familiar ground – I have a tool for answering that question – a system behaviour chart (SBC). I need to measure my weight and plot it on a time-series chart using BaseLine. And I know that I need 9 points to show a significant shift, and I know I must not introduce variation into my measurements. So I do four things – I ensure my scales have high enough precision (+/- 0.1 kg); I do the weighing under standard conditions (same time of day and same state of dress); I weigh myself every day or every other day; and I plot-the-dots.

OK – how am I doing on my design checklist?
1. Purpose – check
2. Process – check
3. Progress – check

Anything missing?

Yes – I need to measure the energy input – the Cals per day going in – but I need a easy, quick and low-cost way of doing it.

Time for some brainstorming. What about an App? That fancy new smartphone can earn its living for a change. Yup – lots of free ones for tracking Cals. Choose one. Works OK. Another flick on the Motiv-o-Meter needle.

OK – next bit of the jigsaw. What is my internal process metric (IPM)? How many fewer Cals per day on average do I need to achieve … quick bit of beer-mat maths … that many kg reduction times Cal per kg of blubber divided by 6 weeks gives … 1300 Cals per day less than now (on average). So what is my daily Cals input now? I dunno. I do not have a baseline. And I do not fancy measuring it for a couple of weeks to get one. My feeble motivation will not last that long. I need action. I need a quick win.

OK – I need to approach this a different way. What if I just change the input to more Yummy+Cheap+Lo-Cal stuff and less Yummy+Cheap+Hi-Cal stuff and just measure what happens. What if I just do what I feel able to? I can measure the input Cals accurately enough and also the output weight. My curiosity is now pricked too and my Inner Nerd starts to take notice and chips in “You can work out the rest from that. It is a simple S&F model” . Thanks Inner Nerd – you do come in handy occasionally. My Motiv-o-Meter is now in the green – enough emotional fuel for a decision and some action.

I have all the bits of the design jigsaw – Purpose, Process, Progress and Pieces. Studying, and Planning over – time for Doing.

So what happened?

It is an ongoing experiment – but so far it has gone exactly as the design dictated (and the nerdy S&F model predicted).

And the experience has helped me move some Get-Thin-Quick mantras to the rubbish bin.

I have counted nine so far:

Mantra 1. Do not weight yourself every day – rubbish – weigh yourself every day using a consistent method and plot the dots.
Mantra 2. Focus on the fat – rubbish – it is Cals that count whatever the source – fat, carbs, protein (and alcohol).
Mantra 3. Five fresh fruit and veg a day – rubbish – they are just Hi-Cost+Low-Cal stocking fillers.
Mantra 4. Only eat balanced meals – rubbish – it is OK to increase protein and reduce both carbs and fat.
Mantra 5. It costs money to get healthy – rubbish – it is possible to reduce cost by switching to Yummy+Cheap+Lo-Cal stuff.
Mantra 6. Cholesterol is bad – rubbish – we make more cholesterol than we eat – just stay inside a recommended range.
Mantra 7. Give up all alcohol – rubbish – just be sensible – just stay inside a recommended range.
Mantra 8. Burn the fat with exercise – rubbish – this is scraping-the-burnt-toast thinking – less Cals in first.
Mantra 9. Eat less every day – rubbish – it is OK to have Lo-Cal days and OK-Cal days – it is the average Cals that count.

And the thing that has made the biggest difference is the App. Just being able to quickly look up the Cals in a “Waitrose Potato Croquette” when-ever and where-ever I want to is what I really needed. I have quickly learned what-is-in-what and that helps me make “Do I need that Chocolate Hob-Nob or not?” decisions on the fly. One tiny, insignificant Chocolate Hob-Nob = 95 Cals. Ouch! Maybe not.

I have been surprised by what I have learned. I now know that before I was making lots of unwise decisions based on completely wrong assumptions. Doh!

The other thing that has helped me build motivation is seeing the effect of those wiser design decisions translated into a tangible improvement – and quickly! With a low-variation and high-precision weight measurement protocol I can actually see the effect of the Cals ingested yesterday on the Weight recorded today. Our bodies obey the Laws of Physics. We are what we eat.

So what is the lesson to take away?

That there are two feedback loops that need to be included in all Improvement Science challenges – and both loops need to be closed so information flows if the Improvement exercise is to succeed and to sustain.

First the Rhetoric Feedback loop – where new, specific, knowledge replaces old, generic gossip. We want to expose the myths and mantras and reveal novel options. Challenge assumptions with scientifically valid evidence. If you do not know then look it up.

Second the Reality Feedback loop – where measured outcomes verifies the wisdom of the decision – the intended purpose was achieved. Measure the input, internal and output metrics and plot all as time-series charts. Seeing is believing.

So the design challenge has been achieved and with no motivation, no time and no budget.

Now where is that packet of Chocolate Hob Nobs. I think I have earned one. Yum yum.

[PS. This is not a new idea – it is called “double loop learning“. Do not know of it? Worth looking it up?]

25/05/2013

Burn-and-Scrape

[Ring Ring]

<Bob> Hi Leslie how are you to today?

<Leslie> I am good thanks Bob and looking forward to today’s session. What is the topic?

<Bob> We will use your Niggle-o-Gram® to choose something. What is top of the list?

<Leslie> Let me see. We have done “Engagement” and “Productivity” so it looks like “Near-Misses” is next.

<Bob> OK. That is an excellent topic. What is the specific Niggle?

<Leslie> “We feel scared when we have a safety near-miss because we know that there is a catastrophe waiting to happen.”

<Bob> OK so the Purpose is to have a system that we can trust not to generate avoidable harm. Is that OK?

<Leslie> Yes – well put. When I ask myself the purpose question I got a “do” answer rather than a “have” one. The word trust is key too.

<Bob> OK – what is the current safety design used in your organisation?

<Leslie> We have a computer system for reporting near misses – but it does not deliver the purpose above. If the issue is ranked as low harm it is just counted, if medium harm then it may be mentioned in a report, and if serious harm then all hell breaks loose and there is a root cause investigation conducted by a committee that usually results in a new “you must do this extra check” policy.

<Bob> Ah! The Burn-and-Scrape model.

<Leslie>Pardon? What was that? Our Governance Department call it the Swiss Cheese model.

<Bob> Burn-and-Scrape is where we wait for something to go wrong – we burn the toast – and then we attempt to fix it – we scrape the burnt toast to make it look better. It still tastes burnt though and badly burnt toast is not salvageable.

<Leslie>Yes! That is exactly what happens all the time – most issues never get reported – we just “scrape the burnt toast” at all levels.

<Bob> One flaw with the Burn-and-Scrape design is that harm has to happen for the design to work.

It is all reactive.

Another design flaw is that it focuses attention on the serious harm first – avoidable mortality for example. Counting the extra body bags completely misses the purpose. Avoidable death means avoidably shortened lifetime. Avoidable non-fatal will also shorten lifetime – and it is even harder to measure. Just consider the cumulative effect of all that non-fatal life-shortening avoidable-but-ignored harm?

Most of the reasons that we live longer today is because we have removed a lot of lifetime shortening hazards – like infectious disease and severe malnutrition.

Take health care as an example – accurately measuring avoidable mortality in an inherently high-risk system is rather difficult. And to conclude “no action needed” from “no statistically significant difference in mortality between us and the global average” is invalid and it leads to a complacent delusion that what we have is good enough. When it comes to harm it is never “good enough”.

<Leslie> But we do not have the resources to investigate the thousands of cases of minor harm – we have to concentrate on the biggies.

<Bob> And do the near misses keep happening?

<Leslie> Yes – that is why they are top rank on the Niggle-o-Gram®.

<Bob> So the Burn-and-Scrape design is not fit-for-purpose.

<Leslie> So it seems. But what is the alternative? If there was one we would be using it – surely?

<Bob> Look back Leslie. How many of the Improvement Science methods that you have already learned are business-as-usual?

<Leslie> Good point. Almost none.

<Bob> And do they work?

<Leslie> You betcha!

<Bob> This is another example. It is possible to design systems to be safe – so the frequent near misses become rare events.

<Leslie> Is it? Wow! That know-how would be really useful to have. Can you teach me?

<Bob> Yes. First we need to explore what the benefits would be.

<Leslie> OK – well first there would be no avoidable serious harm and we could trust in the safety of our system – which is the purpose.

<Bob> Yes …. and?

<Leslie> And … all the effort, time and cost spent “scraping the burnt toast” would be released.

<Bob> Yes …. and?

<Leslie> The safer-by-design processes would be quicker and smoother, a more enjoyable experience for both customers and suppliers, and probably less expensive as well!

<Bob> Yes. So what does that all add up to?

<Leslie> A win-win-win-win outcome!

<Bob> Indeed. So a one-off investment of effort, time and money in learning Safety-by-Design methods would appear to be a wise business decision.

<Leslie> Yes indeed! When do we start?

<Bob> We have already started.

For a real-world example of this approach delivering a significant and sustained improvement in safety click here.

18/05/2013

Invisible Design

Improvement Science is all about making some-thing better in some-way by some-means.

There are lots of things that might be improved – almost everything in fact.

There are lots of ways that those things might be improved. If it was a process we might improve safety, quality, delivery, and productivity. If it was a product we might improve reliability, usability, durability and affordability.

There are lots of means by which those desirable improvements might be achieved – lots of different designs.

Multiply that lot together and you get a very big number of options – so it is no wonder we get stuck in the “what to do first?” decision process.

So how do we approach this problem currently?

We use our intuition.

Intuition steers us to the obvious – hence the phrase intuitively obvious. Which means what looks to our minds-eye to be a good option.And that is OK. It is usually a lot better than guessing (but not always).

However, the problem using “intuitively obvious” is that we end up with mediocrity. We get “about average”. We get “OKish”. We get “satisfactory”. We get “what we expected”. We get “same as always”. We do not get “significantly better-than-average’. We do not get “reliably good”. We do not get improvement. And we do not because anyone and everyone can do the “intuitively obvious” stuff.

To improve we need a better-than-average functional design. We need a Reliably Good Design. And that is invisible.

By “invisible” I mean not immediately obvious to our conscious awareness. We do not notice good functional design because it does not get in the way of achieving our intention. It does not trip us up.

We notice poor functional design because it trips us up. It traps us into making mistakes. It wastes out time. It fails to meet our expectation. And we are left feeling disappointed, irritated, and anxious. We feel Niggled.

We also notice exceptional design – because it works far better than we expected. We are surprised and we are delighted.

We do not notice Good Design because it just works. But there is a trap here. And that is we habitually link expectation to price. We get what we paid for. Higher cost => Better design => Higher expectation.

So we take good enough design for granted. And when we take stuff for granted we are on the slippery slope to losing it. As soon as something becomes invisible it is at risk of being discounted and deleted.

If we combine these two aspects of “invisible design” we arrive at an interesting conclusion.

To get from Poor Design to OK Design and then Good Design we have to think “counter-intuitively”. We have to think “outside the box”. We have to “think laterally”.

And that is not a natural way for us to think. Not for individuals and not for teams. To get improvement we need to learn a method of how to counter our habit of thinking intuitively and we need to practice the method so that we can do it when we need to. When we want to need to improve.

To illustrate what I mean let us consider an real example.

Suppose we have 26 cards laid out in a row on a table; each card has a number on it; and our task is to sort the cards into ascending order. The constraint is that we can only move cards by swapping them. How do we go about doing it?

There are many sorting designs that could achieve the intended purpose – so how do we choose one?

One criteria might be the time it takes to achieve the result. The quicker the better.

One criteria might be the difficulty of the method we use to achieve the result. The easier the better.

When individuals are given this task they usually do something like “scan the cards for the smallest and swap it with the first from the left, then repeat for the second from the left, and so on until we have sorted all the cards“.

This card-sorting-design is fit for purpose. It is intuitively obvious, it is easy to explain, it is easy to teach and it is easy to do. But is it the quickest?

The answer is NO. Not by a long chalk. For 26 randomly mixed up cards it will take about 3 minutes if we scan at a rate of 2 per second. If we have 52 cards it will take us about 12 minutes. Four times as long. Using this intuitively obvious design the time taken grows with the square of the number of cards that need sorting.

In reality there are much quicker designs and for this type of task one of the quickest is called Quicksort. It is not intuitively obvious though, it is not easy to describe, but it is easy to do – we just follow the Quicksort Policy. (For those who are curious you can read about the method here and make up your own mind about how “intuitively obvious” it is. Quicksort was not invented until 1960 so given that sorting stuff is not a new requirement, it clearly was not obvious for a few thousand years).

Using Quicksort to sort our 52 cards would take less than 3 minutes! That is a 400% improvement in productivity when we flip from an intuitive to a counter-intuitive design. And Quicksort was not chance discovery – it was deliberately designed to address a specific sorting problem – and it was designed using robust design principles.

So our natural intuition tends to lead us to solutions that are “effective, easy and inefficient” – and that means expensive in terms of use of resources.

This has an important conclusion – if we are all is given the same improvement assignment and we all used our intuition to solve it then we will get similar and mediocre results. It will feel OK and it will appear obvious but there will be no improvement.

We then conclude that “OK, this is the best we can expect.” which is intuitively obvious, logically invalid, and wrong. It is that sort of intuitive thinking trap that blocked us from inventing Quicksort for thousands of years.

And remember, to decide what is “best” we have to explore all options exhaustively – both intuitively obvious and counter-intuitively obscure. That impossible in practice. This is why “best” and “optimum” are generally unhelpful concepts in the context of improvement science.

So how do we improve when good design is so counter-intuitive?

The answer is that we learn a set of “good designs” from a teacher who knows and understands them, and then we prove them to ourselves in practice. We leverage the “obvious in retrospect” effect. And we practice until we understand. And then we then teach others.

So if we wanted to improve the productivity of our designed-by-intuition card sorting process we could:
(a) consult a known list of proven sorting algorithms,
(b) choose one that meets our purpose (our design specification),
(c) compare the measured performance of our current “intuitively obvious” design with the predicted performance of that “counter-intuitively obscure” design,
(d) set about planning how to implement the higher performance design – possibly as a pilot first to confirm the prediction, reassure the fence-sitters, satisfy the skeptics, and silence the cynics.

So if these proven good designs are counter-intuitive then how do we get them?

The simplest and quickest way is to learn from people who already know and understand them. If we adopt the “not invented by us” attitude and attempt to re-invent the wheel then we may get lucky and re-discover a well-known design, we might even discover a novel design; but we are much more likely to waste a lot of time and end up no better off, or worse. This is called “meddling” and is driven by a combination of ignorance and arrogance.

So who are these people who know and understand good design?

They are called Improvement Scientists – and they have learned one-way-or-another what a good design looks like. That lalso means they can see poor design where others see only-possible design.

That difference of perception creates a lot of tension.

The challenge that Improvement Scientists face is explaining how counter-intuitive good design works: especially to highly intelligent, skeptical people who habitually think intuitively. They are called Academics. And it is a pointless exercise trying to convince them using rhetoric.

Instead our Improvement Scientists side-steps the “theoretical discussion” and the “cynical discounting” by pragmatically demonstrating the measured effect of good design in practice. They use reality to make the case for good design – not rhetoric.

Improvement Scientists are Pragmatists.

And because they have learned how counter-intuitive good design is to the novice – how invisible it is to their intuition – then they are also Voracious Learners. They have enough humility to see themselves as Eternal Novices and enough confidence to be selective students. They will actively seek learning from those who can demonstrate the “what” and explain the “how”. They know and understand it is a much quicker and easier way to improve their knowledge and understanding. It is Good Design.

11/05/2013

“When the Student is ready …”

Improvement Science is not a new idea. The principles are enduring and can be traced back as far as recorded memory – for Millennia. This means that there is a deep well of ancient wisdom that we can draw from. Much of this wisdom is condensed into short sayings which capture a fundamental principle or essence.

One such saying is attributed to Zen Buddhism and goes “When the Student is ready the Teacher will appear.“

This captures the essence of a paradigm shift – a term made popular by Thomas S Kuhn in his seminal 1962 book – The Structure of Scientific Revolutions. It was written just over 50 years ago.

System-wide change takes time and the first stage is the gradual build up of dissatisfaction with the current paradigm. The usual reaction from the Guardians of the Status Quo is to silence the first voices of dissent, often brutally. As the pressure grows there are too many voices to silence individually so more repressive Policies and Policing are introduced. This works for a while but does not dissolve the drivers of dissatisfaction. The pressure builds and the cracks start to appear. This is a dangerous phase.

There are three ways out: repression, revolution, and evolution. The last one is the preferred option – and it requires effective leadership to achieve. Effective leaders are both Teachers and Students. Knowledge and understanding flow through them as they acquire Wisdom.

The first essence of the message is that the solutions to the problems are already known – but the reason they are not widely known and used is our natural affection for the familiar and our distrust of the unfamiliar. If we are comfortable then why change?

It is only when we are uncomfortable enough that we will start to look for ways to regain comfort – physical and psychological.

The second essence of the message is that to change we need to learn something and that means we have to become Students, and to seek the guidance of a Teacher. Someone who understands the problems, their root causes, the solutions, the benefits and most importantly – how to disseminate that knowledge and understanding. A Teacher that can show us how not just tell us what.

The third essence of the message is that the Students become Teachers themselves as they put into practice what they have learned and prove to themselves that it works, and it is workable. The new understanding flows along the Optimism-Skepticism gradient until the Tipping Point is reached. It is then unstoppable and the Paradigm flips. Often remarkably quickly.

The risk is that change means opportunity and there are many who can sniff out an opportunity to cash in on the change chaos. They are the purveyors of Snakeoil – and they prey on the dissatisfied and desperate.

So how does a Student know a True-Teacher from a Snakeoil Salesperson?

Simple – the genuine Teacher will be able to show a portfolio of successes and delighted ex-students; will be able to explain and demonstrate how they were both achieved; will be willing to share their knowledge; and will respectfully decline to teach someone who they feel is not yet ready to learn.

04/05/2013

The Green Shoots of Improvement

Improvement is a form of innovation and it obeys the same Laws of Innovation.

One of these Laws describes how innovation diffuses and it is called Rogers’ Law.

The principle is that innovations diffuse according to two opposing forces – the Force of Optimism and the Force of Skepticism. As individuals we differ in our balance of these two preferences.

When we are in status quo the two forces are exactly balanced.

As the Force of Optimism builds (usually from increasing dissatisfaction with the status quo driving Necessity-the-Mother-of-Invention) then the Force of Skepticism tends to build too. It feels like being in a vice that is slowly closing. The emotional stress builds, the strain starts to show and the cracks begin to appear. Sometimes the Optimism jaw of the vice shatters first, sometimes the Skepticism jaw does – either way the pent-up-tension is relieved. At least for a while.

The way to avoid the Vice is to align the forces of Optimism and Skepticism so that they both pull towards the common goal, the common purpose, the common vision. And there always is one. People want a win-win-win outcome, they vary in daring to dream that it is possible. It is.

The importance of pull is critical. When we have push forces and a common goal we do get movement – but there is a danger – because things can veer out of control quickly. Pull is much easier to steer and control than push. We all know this from our experience of the real world.

And When the status quo starts to move in the direction of the common vision we are seeing tangible evidence of the Green Shoots of Improvement breaking through the surface into our conscious awareness. Small signs first, tender green shoots, often invisible among the overgrowth, dead wood and weeds.

Sometimes the improvement is a reduction of the stuff we do not want – and that can be really difficult to detect if it is gradual because we adapt quickly and do not notice diffuse, slow changes.

We can detect the change by recording how it feels now then reviewing our records later (very few of us do that – very few of us keep a personal reflective journal). We can also detect change by comparing ourselves with others – but that is a minefield of hidden traps and is much less reliable (but we do that all the time!).

Improvement scientists prepare the Soil-of-Change, sow the Seeds of Innovation, and wait for the Spring to arrive. As the soil thaws (the burning platform of a crisis may provide some energy for this) some of the Seeds will germinate and start to grow. They root themselves in past reality and they shoot for the future rhetoric. But they have a finite fuel store for growth – they need to get to the surface and to sunlight before their stored energy runs out. The preparation, planting and timing are all critical.

And when the Green Shoots of Improvement appear the Improvement Scientist switches role from Germinator to Grower – providing the seedlings with emotional sunshine in the form of positive feedback, encouragement, essential training, and guidance. The Grower also has to provide protection from toxic threats that can easily kill a tender improvement seedling – the sources of Cynicide that are always present. The disrespectful sneers of “That will never last!” and “You are wasting your time – nothing good lasts long around here!”

The Improvement Scientist must facilitate harnessing the other parts of the system so that they all pull in the direction of the common vision – at least to some degree. And the other parts add up to about 85% of it so they collectively they have enough muscle to create movement in the direction of the shared vision. If they are aligned.

And each other part has a different, significant and essential role.

The Disruptive Innovators provide the new ideas – they are always a challenge because they are always questioning “Why do we do it that way?” “What if we did it differently?” “How could we change?” We do not want too many disruptive innovators because they are – disruptive. Frustrated disruptive innovations can easily flip to being Cynics – so it is wise not to ignore them.

The Early Adopters provide the filter – they test the new ideas; they reject the ones that do not work; and they shape the ones that do. They provide the robust evidence of possibility. We need more Adopters than Innovators because lots of the ideas do not germinate. Duff seed or hostile soil – it does not matter which. We want Green Shoots of Improvement.

The Majority provide the route to sharing the Adopter-Endorsed ideas, the Green Shoots of Improvement. They will sit on the fence, consider the options, comment, gossip, listen, ponder and eventually they will commit and change. The Early Majority earlier and the Late Majority later. The Late Majority are also known as the Skeptics. They are willing to be convinced but they need the most evidence. They are most risk-averse and for that reason they are really useful – because they can help guide the Shoots of Improvement around the Traps. They will help if asked and given a clear role – “Tell us if you see gaps and risks and tell us why so that we can avoid them at the design and development stage”. And you can tell if they are a True Skeptic or a Cynic-in-Skeptic clothing – because the Cynics will decline to help saying that they are too busy.

The last group, the Cynics, are a threat to significant and sustained improvement. And they can be managed using one or more the these four tactics:

1. Ignore them. This has the advantage of not wasting time but it tends to enrage them and they get noisier and more toxic.
2. Isolate them. This is done by establishing peer group ground rules that are is based on Respectful Challenge.
3. Remove them. This needs senior intervention and a cast-iron case with ample evidence of bad behaviour. Last resort.
4. Engage them. This is the best option if it can be achieved – invite the Cynics to be Skeptics. The choice is theirs.

It is surprising how much improvement follows from just turning blocking some of the sources of Cynicide!

So the take home message is a positive one:

Look for the Green Shoots of Improvement,

Celebrate every one you find,

Nurture and Protect them

and they will grow bigger and stronger and one day will flower, fruit and create their own Seeds of Innovation.

20/04/2013

Do Not Give Up Too Soon

Tangible improvement takes time. Sometimes it takes a long time.

The more fundamental the improvement the more people are affected. The more people involved the greater the psychological inertia. The greater the resistance the longer it takes to show tangible effects.

The advantage of deep-level improvement is that the cumulative benefit is greater – the risk is that the impatient Improvementologist may give up too early – sometimes just before the benefit becomes obvious to all.

The seeds of change need time to germinate and to grow – and not all good ideas will germinate. The green shoots of innovation do not emerge immediately – there is often a long lag and little tangible evidence for a long time.

This inevitable delay is a source of frustration, and the impatient innovator can unwittingly undo their good work. By pushing too hard they can drag a failure from the jaws of success.

Q: So how do we avoid this trap?

The trick is to understand the effect of the change on the system. This means knowing where it falls on our Influence Map that is marked with the Circles of Control, Influence and Concern.

Our Circle of Concern includes all those things that we are aware of that present a threat to our future survival – such as a chunk of high-velocity space rock smashing into the Earth and wiping us all out in a matter of milliseconds. Gulp! Very unlikely but not impossible.

Some concerns are less dramatic – such as global warming – and collectively we may have more influence over changing that. But not individually.

Our Circle of Influence lies between the limit of our individual control and the limit of our collective control. This a broad scope because “collective” can mean two, twenty, two hundred, two thousand, two million, two billion and so on.

Making significant improvements is usually a Circle of Influence challenge and only collectively can we make a difference. But to deliver improvement at this level we have to influence others to change their knowledge, understanding, attitudes, beliefs and behaviour. That is not easy and that is not quick. It is possible though – with passion, plausibility, persistence, patience – and an effective process.

It is here that we can become impatient and frustrated and are at risk of giving up too soon – and our temperaments influence the risk. Idealists are impatient for fundamental change. Rationals, Guardians and Artisans do not feel the same pain – and it is a rich source of conflict.

So if we need to see tangible results quickly then we have to focus closer to home. We have to work inside our Circle of Individual Influence and inside our Circle of Control. The scope of individual influence varies from person-to-person but our Circle of Control is the same for all of us: the outer limit is our skin. We all choose our behaviour and it is that which influences others: for better or for worse. It is not what we think it is what we do. We cannot read or control each others minds. We can all choose our attitudes and our actions.

So if we want to see tangible improvement quickly then we must limit the scope of our action to our Circle of Individual Influence and get started. We do what we can and as soon as we can.

Choosing what to do and what not do requires wisdom. That takes time to develop too.

Making an impact outside the limit of our Circle of Individual Influence is more difficult because it requires influencing many other people.

So it is especially rewarding for to see examples of how individual passion, persistence and patience have led to profound collective improvement. It proves that it is still possible. It provides inspiration and encouragement for others.

One example is the recently published Health Foundation Quality, Cost and Flow Report.

This was a three-year experiment to test if the theory, techniques and tools of Improvement Science work in healthcare: specifically in two large UK acute hospitals – Sheffield and Warwick.

The results showed that Improvement Science does indeed work in healthcare and it worked for tough problems that were believed to be very difficult if not impossible to solve. That is very good news for everyone – patients and practitioners.

But the results have taken some time to appear in published form – so it is really good news to report that the green shoots of improvement are now there for all to see.

The case studies provide hard evidence that win-win-win outcomes are possible and achievable in the NHS.

The Impossibility Hypothesis has been disproved. The cynics can step off the bus. The skeptics have their evidence and can now become adopters.

And the report offers a lot of detail on how to do it including two references that are available here:

These references both describe the fundamentals of how to align financial improvement with quality and delivery improvement to achieve the elusive win-win-win outcome.

A previously invisible door has opened to reveal a new Land of Opportunity. A land inhabited by Improvementologists who mark the path to learning and applying this new knowledge and understanding.

There are many who do not know what to do to solve the current crisis in healthcare – they now have a new vista to explore.

Do not give up too soon – there is a light at the end of the dark tunnel.

And to get there safely and quickly we just need to learn and apply the Foundations of Improvement Science in Healthcare – and we first learn to FISH in our own ponds first.

23/03/2013

Time-Reversed Insight

Thinking-in-reverse sounds like an odd thing to do but it delivers more insight and solves tougher problems than thinking forwards. That is the reason it is called Time-Reversed Insight. And once we have mastered how to do it, we discover that it comes in handy in all sorts of problematic situations where thinking forwards only hits a barrier or even makes things worse.

Time-reversed thinking is not the same thing as undoing what you just did. It is reverse thinking – not reverse acting.

We often hear the advice “Start with the end in mind …” and that certainly sounds like it might be time-reversed thinking, but it is often followed by “… to help guide your first step.” The second part tells us it is not. Jumping from outcome to choosing the first step is actually time-forward thinking.

Time-forward thinking comes in many other disguises: “Seeking your True North” is one and “Blue Sky Thinking” is another. They are certainly better than discounting the future and they certainly do help us to focus and to align our efforts – but they are still time-forward thinking. We know that because the next question is always “What do we do first? And then? And then?” in other words “What is our Plan?”.

This is not time-reversed insightful thinking: it is good old, tried-and-tested, cause-and-effect thinking. Great for implementation but a largely-ineffective, and a hugely-inefficient way to dissolve “difficult” problems. In those situation it becomes keep-busy behaviour. Plan-Do-Plan-Do-Plan-Do ……..

In time-reversed thinking the first question looks similar. It is a question about outcome but it is very specific. It is “What outcome do we want? When do we want it? and How would we know we have got it?” It is not a direction. It is a destination. The second question in time-reversed thinking is the clincher. It is “What happened just before?” and is followed by “And before that? And before that?“.

We actually do this all the time but we do it unconsciously and we do it very fast. It is called the “blindingly obvious in hindsight” phenomenon. What happens is we feel the good or bad outcome and then we flip to the cause in one unconscious mental leap. Ah ha!

And we do this because thinking backwards in a deliberate, conscious, sequential way is counter-intuitive.

Our unconscious mind seems to have no problem doing it though. And that is because it is wired differently. Some psychologists believe that we literally have “two brains”: one that works sequentially in the direction of forward time – and one that works in parallel and in a forward-and backward in time fashion. It is the sequential one that we associate with conscious thinking; it is the parallel one that we associate with unconscious feeling. We do both and usually they work in synergy – but not always. Sometimes they antagonise each other.

The problem is that our sequential, conscious brain does not like working backwards. Just like we do not like walking backwards, or driving backwards. We have evolved to look, think, and move forwards. In time.

So what is so useful about deliberate, conscious, time-reversed thinking?

It can give us an uniquely different perspective – one that generates fresh insight – and that new view enables us to solve problems that we believed were impossible when looked at in a time-forward way.

An example of time-reverse thinking:

The 4N Chart is an emotional mapping tool. More specifically it is an emotion-over-time mapping technique. The way it is used is quite specific and quite counter-intuitive. If we ask ourselves the question “What is my top Niggle?” our reply is usually something like “Not enough time!” or “Person x!” or “Too much work!“. This is not how The 4N Chart is designed to be used. The question is “What is my commonest negative feeling?” and then the question “What happened just before I felt it?“. What was the immediately preceding cause of the Niggle? And then the questions continue deliberately and consciously to think backwards: “And before that?”, “And before that?” until the root causes are laid bare.

A typical Niggle-cause exposing dialog might be:

Q: What is my most commonest negative feeling?
A: I feel angry!
Q: What happened just before?
A: My boss gives me urgent jobs to do at half past 4 on Friday afternoon!
Q: And before that?
A: Reactive crisis management meetings are arranged at very short notice!
Q: And before that?
A: We have regular avoidable crises!
Q: And before that?
A: We are too distracted with other important work to spot each crisis developing!
Q: And before that?
A: We were not able to recruit when a valuable member of staff left.
Q: And before that?
A: Our budget was cut!

This is time-reversed thinking and we can do this reasonably easily because we are working backwards from the present – so we can use our memory to help us. And we can do this individually and collectively. Working backwards from the actual outcome is safer because we cannot change the past.

It is surprisingly effective though because by doing this time-reverse thinking consciously we uncover where best to intervene in the cause-and-effect pathway that generates our negative emotions. Where it crosses the boundary of our Circle of Control. And all of us have the choice to step-in just before the feeling is triggered. We can all choose if we are going to allow the last cause to trigger to a negative feeling in us. We can all learn to dodge the emotional hooks. It takes practice but it is possible. And having deflected the stimulus and avoided being hijacked by our negative emotional response we are then able to focus our emotional effort into designing a way to break the cause-effect-sequence further upstream.

We might leave ourselves a reminder to check on something that could develop into a crisis without us noticing. Averting just one crisis would justify all the checking!

This is what calm-in-a-crisis people do. They disconnect their feelings. It is very helpful but it has a risk.

The downside is that they can disconnect all their feelings – including the positive ones. They can become emotionless, rational, logical, tough-minded robots. And that can be destructive to individual and team morale. It is the antithesis of improvement.

So be careful when disconnecting emotional responses – do it only for defense – never for attack.

A more difficult form of time-reversed thinking is thinking backwards from future-to-present. It is more difficult for many reasons, one of which is because we do not have a record of what actually happened to help us. We do however have experience of similar things from the past so we can make a good guess at the sort of things that could cause a future outcome.

Many people do this sort of thinking in a risk-avoidance way with the objective of blocking all potential threats to safety at an early stage. When taken to extreme it can manifest as turgid, red-taped, blind bureaucracy that impedes all change. For better or worse.

Future-to-present thinking can be used as an improvement engine – by unlocking potential opportunity at an early stage. Innovation is a fragile flower and can easily be crushed. Creative thinking needs to be nurtured long enough to be tested.

Change is deliberately destablising so this positive form of future-to-present thinking can also be counter-productive if taken to extreme when it becomes incessant meddling. Change for change sake is also damaging to morale.

So, either form of future-to-present thinking is OK in moderation and when used in synergy the effect is like magic!

Synergistic future-to-present time-reversed thinking is called Design Thinking and one formulation is called 6M Design.

16/03/2013

What is the Temperamenture?

Tweet
The sound heralded the arrival of a tweet so Bob looked up from his book and scanned the message. It was from Leslie, one of the Improvement Science apprentices.

It said “If your organisation is feeling poorly then do not forget to measure the Temperamenture. You may have Cultural Change Fever.“

Bob was intrigued. This was a novel word and he suspected it was not a spelling error. He know he was being teased. He tapped a reply on his iPad “Interesting word ‘Temperamenture’ – can you expand?”

Ring Ring
<Bob> Hello, Bob here.

There was laughing on the other end of the line – it was Leslie.

<Leslie> Ho Ho. Hi Bob – I thought that might prick your curiosity if you were on line. I know you like novel words.

<Bob> Ah! You know my weakness – I am at your mercy now! So, I am consumed with curiosity – as you knew I would be.

<Leslie> OK. No more games. You know that you are always saying that there are three parts to Improvement Science – Processes, People and Systems – and that the three are synergistic so they need to be kept in balance …

<Bob> Yes.

<Leslie> Well, I have discovered a source of antagonism that creates a lot of cultural imbalance and emotional heat in my organisation.

<Bob> OK. So I take from that you mean an imbalance in the People part that then upsets the Process and System parts.

<Leslie> Yes, exactly. In your Improvement Science course you mentioned the theory behind this but did not share any real examples.

<Bob> That is very possible. Hard evidence and explainable examples are easier for the Process component – the People stuff is more difficult to do that way. Can you be more specific? I think I know where you may be going with this.

<Leslie> OK. Where do you feel I am going with it?

<Bob> Ha! The student becomes the teacher. Excellent response! I was thinking something to do with the Four Temperaments.

<Leslie>Yes. And specifically the conflict that can happen between them. I am thinking of the tension between the Idealists and the Guardians.

<Bob> Ah! Yes. The Bile Wars – Yellow and Black. The Cholerics versus the Melancholics. So do you have hard evidence of this happening in reality rather than just my theoretical rhetoric?

<Leslie> Yes! But the facts do not seem to fit the theory. You know that I work in a hospital. Well one of the most important “engines” of a hospital is the surgical operating suite. Conveniently called the SOS.

<Bob> Yes. It seems to be a frequent source of both Nuggets and Niggles.

<Leslie> Well, I am working with the SOS team at my hospital and I have to say that they are a pretty sceptical bunch. Everyone seems to have strong opinions. Strong but different opinions of what should happen and who should do it. The words someone and should get mentioned a lot. I have not managed to find this elusive “someone” yet. The some-one, no-one, every-one, any-one problem.

<Bob> OK. I have heard this before. I hear that surgeons in particular have strong opinions – and they disagree with each other! I remember watching episodes of “Doctor in the House” many years ago. What was the name of the irascible chief surgeon played by James Robertson Justice? Sir Lancelot Spratt The archetype surgeon. Are they actually like that?

<Leslie> I have not met any as extreme as Sir Lancelot though some do seem to emulate that role model. In reality the surgeons, anaesthetists, nurses, ODPs, and managers all seem to believe there is one way that a theatre should be run, their way, and their separate “one ways” do not line up. Hence the high emotional temperature.

<Bob> OK, so how does the Temperament dimension relate to this? Is there a temperament mismatch between the different tribes in the operating suite as the MBTI theory would suggest?

<Leslie> That was my hypothesis and I decided that the only way I could test it was by mapping the temperaments using the Temperament Sorter from the FISH toolbox.

<Bob> Excellent, but you would need quite a big sample to draw any statistically valid conclusions. How did you achieve that with a group of disparate sceptics?

<Leslie>I know. So I posed this challenge as a research question – and they were curious enough to give it a try. Well, the Surgeons and Anaesthetists were anyway. The Nurses, OPDs and Managers chose to sit on the fence and watch the game.

<Bob>Wow! Now I am really interested. What did you find?

<Leslie>Woah there! I need to explain how we did it first. They have a monthly audit meeting where they all get together as separate groups and after I posed the question they decided to do use the Temperament Sorter at one of those meetings. It was done in a light-hearted way and it was really good fun too. I brought some cartoons and descriptions of the sixteen MBTI types and they tried to guess who was which type.

<Bob>Excellent. So what did you find?

<Leslie>We disproved the hypothesis that there was a Temperament mismatch.

<Bob>Really! What did the data show?

<Leslie> It showed that the Temperament profile for both surgeons and anaesthetists was different from the population average …

<Bob>OK, and …?

<Leslie>… and that there was no statistical difference between surgeons and anaesthetists.

<Bob>Really! So what are they both?

<Leslie>Guardians. The majority of both tribes are SJs.

There was a long pause. Bob was digesting this juicy new fact. Leslie knew that if there was one thing that Bob really liked it was having a theory disproved by reality. Eventually he replied.

<Bob> Clarity of hindsight is a wonderful thing. It makes complete sense that they are Guardians. Speaking as a patient, what I want most is Safety and Predictability which is the ideal context for Guardians to deliver their best. I am sure that neither surgeons nor anaesthetists like “surprises” and I suspect that they both prefer doing things “by the book”. They are sceptical of new ideas by temperament.

<Leslie> And there is more.

<Bob> Excellent! What?

<Leslie> They are tough-minded Guardians. They are STJs.

<Bob> Of course! Having the responsibility of “your life in my hands” requires a degree of tough-mindedness and an ability to not get too emotionally hooked. Sir Lancelot is a classic extrovert tough-minded Guardian! The Rolls-Royce and the ritual humiliation of ignorant underlings all fits. Wow! Well done Leslie. So what have you done with this new knowledge and deeper understanding?

<Leslie>Ouch! You got me! That is why I sent the Tweet. Now what do I do?

<Bob>Ah! I am not sure. We are both in uncharted water now so I suggest we explore and learn together. Let me ponder and do some exploring of the implications of your findings and I will get back to you. Can you do the same?

<Leslie>Good plan. Shall we share notes in a couple of days?

<Bob>Excellent. I look forward to it.

This is not a completely fictional narrative.

In a recent experiment the Temperament of a group of 66 surgeons and 65 anaesthetists was mapped using a standard Myers-Briggs Type Indicator® tool. The data showed that the proportion reporting a Guardian (xSxJ) preference was 62% for the surgeons and 59% for the anaesthetists. The difference was not statistically significant [For the statistically knowledgable the Chi-squared test gave a p-value of 0.84]. The reported proportion of the normal population who have a Guardian temperament is 34% so this is very different from the combined group of operating theatre doctors [Chi-squared test, p<0.0001]. Digging deeper into the data the proportion showing the tough-minded Guardian preference, the xSTJ, was 55% for the Surgeons and 46% for the Anaesthetists whichwas also not significantly different [p=0.34] but compared with a normal population proportion of 24% there are significantly more tough-minded Guardians in the operating theatre [p<0.0001].

So what then is the difference between Surgeons and Anaesthetists in their preferred modes of thinking?

The data shows that Surgeons are more likely to prefer Extraversion – the ESTJ profile – compared with Anaesthetists – who lean more towards Introversion – the ISTJ profile (p=0.12). This p-value means that with the data available there is a one in eight chance that this difference is due to chance. We would needs a bigger set of data to get greater certainty.

The temperament gradient is enough to create a certain degree of tension because although the Guardian temperament is the same, and the tough-mindedness is the same, the dominant function differs between the ESTJ and the ISTJ types. As the Surgeons tend to the ESTJ mode, their dominant function is Thinking Judgement. The Anaesthetists tend to perfer ISTJ so their dominant fuction is Sensed Perceiving. This makes a difference.

And it fits with their chosen roles in the operating theatre. The archetype ESTJ Surgeon is the Supervisor and decides what to do and who does it. The archetype ISTJ Anaesthetist is the Inspector and monitors and maintains safety and stability. This is a sweepig generalisation of course – but a useful one.

The roles are complementary, the minor conflict is inevitable, and the tension is not a “bad” thing – it is healthy – for the patient. But when external forces threaten the safety, predictability and stability the conflict is amplified.

Rather like the weather.

Hot wet air looks clear. Cold dry air looks clear too. When hot-humid air from the tropics meets cold-crisp air from the poles then a band of of fog will be created. We call it a weather front and it generates variation. And if the temperature and humidity difference is excessive then storm clouds will form. The lightning will flash and the thunder will growl as the energy is released.

Clouds obscure clarity of forward vision but clouds also create shade from the sun above; clouds trap warmth beneath; and clouds create rain which is necessary to sustain growth. Clouds are not all bad.

An Improvement Scientist knows that 100% harmony is not the healthiest ratio. Unchallenged group-think is potentially dangerous. Zero harmony is also unhealthy. Open warfare is destructive. Everyone loses. A mixture of temperaments, a bit of fog, and a bit of respectful challenge is healthier than All or None.

It is at the chaotic interface between different temperaments that learning and innovation happens so a slight temperamenture gradient is ideal. The emotometer should not read too cold or too hot.

Understanding this is a big step towards being able to manage the creative tension.

To explore the Temperamenture Map of your team, department and organisation try the Temperament Sorter tool – one of the Improvement Science cultural diagnostic tests.

15/02/2013

What Can I Do To Help?

The growing debate about the safety of our health care systems is gaining momentum.

This is not just a UK phenomenon.

The same question was being asked 10 years ago across the pond by many people – perhaps the most familiar name is Don Berwick.

The term Improvement Science has been buzzing around for a long time. This is a global – not just a local challenge.

Seeing the shameful reality in black-and-white [the Francis Report] is a nasty shock to everyone. There are no winners here. Our blissful ignorance is gone. Painful awareness has arrived.

The usual emotional reaction to being shoved from blissful ignorance into painful awareness is characteristic; and it does not matter if it is discovering horse in your beef pie or hearing of 1200 avoidable deaths in a UK hospital.

Our emotional reaction is a predictable sequence that goes something like:

Shock => Denial => Anger =>Bargaining =>Depression =>Acceptance

=> Resolution.

It is the psychological healing process that is called the grief reaction and it is a normal part of the human psyche. We all do it. And we do it both individually and collectively. I remember well the global grief reactions that followed the sudden explosion of Challenger; the sudden death of Princess Diana; and the sudden collapse of the Twin Towers.

Fortunately such avoidable tragedies are uncommon.

The same chain-reaction happens to a lesser degree in any sudden change. We grieve the loss of our old way of thinking – we mourn the passing away our comfortable rhetoric that has been rudely and suddenly disproved by harsh reality. This is the Nerve Curve. And learning to ride it safely is a critical-to-survival life skill. Especially in turbulent times.

The UK population has suffered two psychological shocks in recent weeks – the discovery of horse in the beef pie and the fuller public disclosure of the story behind the 1000’s of avoidable deaths in one of our Trust hospitals. Both are now escalating and the finger of blame is pointing squarely at a common cause: the money-tail-wagging-the-safety-dog.

So what will happen next? The Wall of Denial has been dynamited with hard evidence. We are now into the Collective Anger phase.

First there will be widespread righteous indignation and a strong desire to blame, to hunt down the evil ones, and to crucify the responsible and accountable. Partly as punishment, partly as a lesson to others, and partly to prevent them doing harm again. Uncontrolled anger is dangerous especially when there is a lethal weapon to hand. The more controlled, action-oriented and future-focused will want to do something about it. Now! There will be rallies, and soap-boxes, and megaphones. The We-Told-You-So brigade will get shoved aside and trampled in the rush to do something – ANYTHING. Conferences will be hastily arranged and those most fearful for their reputations and jobs will cough up the cash and clear their diaries. They will be expected to be there. They will be. Desperately looking for answers. Anxiously seeking credible leaders. And the snake-oil salesmen will have a bonanza! The calmer, more reflective, phlegmatic, academic types will call for more money for more research so that we can fully analyse and fully understand the problem before we do anything.

And while the noisy bargaining for more cash keeps everyone busy the harm will continue to happen.

Eventually the message will sink in as the majority accept that there is no way to change the past; that we cannot cling to what is out-of-date thinking; and that all of our new-reality-avoiding tactics are fruitless. And we are forced to accept that there is no more cash. Now we are in danger of becoming helpless and hopeless, slipping into depression, and then into despair. We are at risk of giving up and letting ourselves wallow and drown in self-pity. This is a dangerous phase. Depression is understandable but it is avoidable because there is always something than can be done. We can always ask the elephant-in-the-room questions. Inside we usually know the answers.

We accept the new reality; we accept that we cannot change the past, we accept that we have some learning to do; we accept that we have to adjust; and we accept that all of us can do something.

Now we have reached the most important stage – resolution. This is the test of our resolve. Are we all-talk or can we convert talk-to-walk?

We can all ask ourselves one question: “What can I do to help?”

I have asked myself that question and my first answer was “As a system designer I can help by looking at this challenge as a design assignment and describe what I see “.

Design starts with the intended outcome, the vision, the goal, the objective, the specification, the target.

The design goal is: Significant reduction in avoidable harm in the NHS, quickly, and at no extra cost.

[Please note that a design goal is a “what we get” not a “what we do”. It is a purpose and not just a process.]

Now we can invite, gather, dream-up, brain-storm any number of design options and then we can consider logically and rationally how well they might meet our design goal.

What are some of the design options on the table?

Design Option 1. Create a cadre of hospital inspectors.

Nope – that will take time and money and inspection alone does not guarantee better outcomes. We have enough evidence of that.

Design Option 2. Get lots more PhDs funded, do high quality academic research, write papers, publish them and hope the evidence is put into practice.

Nope – that will take time and money too and publication alone does not guarantee adoption of the lessons and delivery of better outcomes. We have enough evidence of that too. What is proven to be efficacious in a research trial is not necessarily effective, practical or affordable in reality.

Design Option 3. Put together conferences and courses to teach/train a new generation of competent healthcare improvement practitioners.

Maybe – it has the potential to deliver the outcome but it too will take time and money. We have been doing conferences and courses for decades – they are not very cost-effective. The Internet may have changed things though.

Design Option 4. All of the above plus broadcast via the Internet the current pragmatic know-how of the basics of safe system design to everyone in the NHS so that they know what is possible and they know how to get started.

Promising – it has the greatest potential to deliver the required outcome, a broadcast will cost nothing and it can start working immediately.

OK – Option 4 it is – here we go …

The Basics of How To Design a Safe System

Definition 1: Safe means free of risk of harm.

Definition 2: Harm is the result of hazards combining with risks.

There are two components to safe system design – the people stuff and the process stuff.

For example a busy main road is designed to facilitate the transport of stuff from A to B. It also represents a hazard – the potential for harm. If the vehicles bump into each other or other things then harm will result. So a lot of the design of the vehicles and the roads is about reducing the risk of bumps or mitigating the effects (e.g. seat-belts).

The risk is multi-factorial. If you drive at high speed, under the influence of recreational drugs, at night, on an icy road then the probability of having a bump is high. If you step into a busy road without looking then the risk of getting bumped into is high too.

So the path to better safety is to eliminate as many hazards as possible and to reduce the risks as much as possible. And we have to do that without unintentionally creating more hazards, higher risks, excessive delays and higher costs.

So how is this done outside healthcare?

One tried-and-tested method for designing safer processes is called FMEA – Failure Modes and Effects Analysis.

Now that sounds really nerdy and it is. It is an attention-to-detail exercise that will make your brain ache and your eyes bleed. But it works – so it is worthwhile learning the basic principles.

For the people part there is the whole body of Human Factors Research to access. This is also a bit nerdy for us hands-on oily-rag pragmatists so if you want something more practical immediately then have a go with The 4N Chart and the Niggle-o-Gram (which is a form of emotional FMEA). This short summary is also free to download, read, print, copy, share, discuss and use.

OK – I am off to design and build something else – an online course for teaching safety-by-design.

What are you going to do to help improve safety in the NHS?

10/02/2013

The Writing on the Wall – Part II

The retrospectoscope is the favourite instrument of the forensic cynic – the expert in the after-the-event-and-I-told-you-so rhetoric. The rabble-rouser for the lynch-mob.

It feels better to retrospectively nail-to-a-cross the person who committed the Cardinal Error of Omission, and leave them there in emotional and financial pain as a visible lesson to everyone else.

This form of public feedback has been used for centuries.

It is called barbarism, and it has no place in a modern civilised society.

A more constructive question to ask is:

“Could the evolving Mid-Staffordshire crisis have been detected earlier … and avoided?”

And this question exposes a tricky problem: it is much more difficult to predict the future than to explain the past. And if it could have been detected and avoided earlier, then how is that done? And if the how-is-known then is everyone else in the NHS using this know-how to detect and avoid their own evolving Mid-Staffs crisis?

To illustrate how it is currently done let us use the actual Mid-Staffs data. It is conveniently available in Figure 1 embedded in Figure 5 on Page 360 in Appendix G of Volume 1 of the first Francis Report. If you do not have it at your fingertips I have put a copy of it below.

The message does not exactly leap off the page and smack us between the eyes does it? Even with the benefit of hindsight. So what is the problem here?

The problem is one of ergonomics. Tables of numbers like this are very difficult for most people to interpret, so they create a risk that we ignore the data or that we just jump to the bottom line and miss the real message. And It is very easy to miss the message when we compare the results for the current period with the previous one – a very bad habit that is spread by accountants.

This was a slowly emerging crisis so we need a way of seeing it evolving and the better way to present this data is as a time-series chart.

As we are most interested in safety and outcomes, then we would reasonably look at the outcome we do not want – i.e. mortality. I think we will all agree that it is an easy enough one to measure.

MS_RawDeaths This is the raw mortality data from the table above, plotted as a time-series chart. The green line is the average and the red-lines are a measure of variation-over-time. We can all see that the raw mortality is increasing and the red flags say that this is a statistically significant increase. Oh dear!

But hang on just a minute – using raw mortality data like this is invalid because we all know that the people are getting older, demand on our hospitals is rising, A&Es are busier, older people have more illnesses, and more of them will not survive their visit to our hospital. This rise in mortality may actually just be because we are doing more work.

Good point! Let us plot the activity data and see if there has been an increase.

Yes – indeed the activity has increased significantly too.

Told you so! And it looks like the activity has gone up more than the mortality. Does that mean we are actually doing a better job at keeping people alive? That sounds like a more positive message for the Board and the Annual Report. But how do we present that message? What about as a ratio of mortality to activity? That will make it easier to compare ourselves with other hospitals.

Good idea! Here is the Raw Mortality Ratio chart.

Ah ha. See! The % mortality is falling significantly over time. Told you so.

Careful. There is an unstated assumption here. The assumption that the case mix is staying the same over time. This pattern could also be the impact of us doing a greater proportion of lower complexity and lower risk work. So we need to correct this raw mortality data for case mix complexity – and we can do that by using data from all NHS hospitals to give us a frame of reference. Dr Foster can help us with that because it is quite a complicated statistical modelling process. What comes out of Dr Fosters black magic box is the Global Hospital Raw Mortality (GHRM) which is the expected number of deaths for our case mix if we were an ‘average’ NHS hospital.

What this says is that the NHS-wide raw mortality risk appears to be falling over time (which may be for a wide variety of reasons but that is outside the scope of this conversation). So what we now need to do is compare this global raw mortality risk with our local raw mortality risk … to give the Hospital Standardised Mortality Ratio.

This gives us the Mid Staffordshire Hospital HSMR chart. The blue line at 100 is the reference average – and what this chart says is that Mid Staffordshire hospital had a consistently higher risk than the average case-mix adjusted mortality risk for the whole NHS. And it says that it got even worse after 2001 and that it stayed consistently 20% higher after 2003.

Ah! Oh dear! That is not such a positive message for the Board and the Annual Report. But how did we miss this evolving safety catastrophe? We had the Dr Foster data from 2001

This is not a new problem – a similar thing happened in Vienna between 1820 and 1850 with maternal deaths caused by Childbed Fever. The problem was detected by Dr Ignaz Semmelweis who also discovered a simple, pragmatic solution to the problem: hand washing. He blew the whistle but unfortunately those in power did not like the implication that they had been the cause of thousands of avoidable mother and baby deaths. Semmelweis was vilified and ignored, and he did not publish his data until 1861. And even then the story was buried in tables of numbers. Semmelweis went mad trying to convince the World that there was a problem. Here is the full story.

Also, time-series charts were not invented until 1924 – and it was not in healthcare – it was in manufacturing. These tried-and-tested safety and quality improvement tools are only slowly diffusing into healthcare because the barriers to innovation appear somewhat impervious.

And the pores have been clogged even more by the social poison called “cynicide” – the emotional and political toxin exuded by cynics.

So how could we detect a developing crisis earlier – in time to avoid a catastrophe?

The first step is to estimate the excess-death-equivalent. Dr Foster does this for you.Here is the data from the table plotted as a time-series chart that shows that the estimated-excess-death-equivalent per year. It has an average of 100 (that is two per week) and the average should be close to zero. More worryingly the number was increasing steadily over time up to 200 per year in 2006 – that is about four excess deaths per week – on average. It is important to remember that HSMR is a risk ratio and mortality is a multi-factorial outcome. So the excess-death-equivalent estimate does not imply that a clear causal chain will be evident in specific deaths. That is a complete misunderstanding of the method.

I am sorry – you are losing me with the statistical jargon here. Can you explain in plain English what you mean?

OK. Let us use an example.

Suppose we set up a tombola at the village fete and we sell 50 tickets with the expectation that the winner bags all the money. Each ticket holder has the same 1 in 50 risk of winning the wad-of-wonga and a 49 in 50 risk of losing their small stake. At the appointed time we spin the barrel to mix up the ticket stubs then we blindly draw one ticket out. At that instant the 50 people with an equal risk changes to one winner and 49 losers. It is as if the grey fog of risk instantly condenses into a precise, black-and-white, yes-or-no, winner-or-loser, reality.

Translating this concept back into HSMR and Mid Staffs – the estimated 1200 deaths are the just the “condensed risk of harm equivalent”. So, to then conduct a retrospective case note analysis of specific deaths looking for the specific cause would be equivalent to trying to retrospectively work out the reason the particular winning ticket in the tombola was picked out. It is a search that is doomed to fail. To then conclude from this fruitless search that HSMR is invalid, is only to compound the delusion further. The actual problem here is ignorance and misunderstanding of the basic Laws of Physics and Probability, because our brains are not good at solving these sort of problems.

But Mid Staffs is a particularly severe example and it only shows up after years of data has accumulated. How would a hospital that was not as bad as this know they had a risk problem and know sooner? Waiting for years to accumulate enough data to prove there was a avoidable problem in the past is not much help.

That is an excellent question. This type of time-series chart is not very sensitive to small changes when the data is noisy and sparse – such as when you plot the data on a month-by-month timescale and avoidable deaths are actually an uncommon outcome. Plotting the annual sum smooths out this variation and makes the trend easier to see, but it delays the diagnosis further. One way to increase the sensitivity is to plot the data as a cusum (cumulative sum) chart – which is conspicuous by its absence from the data table. It is the running total of the estimated excess deaths. Rather like the running total of swings in a game of golf.

This is the cusum chart of excess deaths and you will notice that it is not plotted with control limits. That is because it is invalid to use standard control limits for cumulative data. The important feature of the cusum chart is the slope and the deviation from zero. What is usually done is an alert threshold is plotted on the cusum chart and if the measured cusum crosses this alert-line then the alarm bell should go off – and the search then focuses on the precursor events: the Near Misses, the Not Agains and the Niggles.

I see. You make it look easy when the data is presented as pictures. But aren’t we still missing the point? Isn’t this still after-the-avoidable-event analysis?

Yes! An avoidable death should be a Never-Event in a designed-to-be-safe healthcare system. It should never happen. There should be no coffins to count. To get to that stage we need to apply exactly the same approach to the Near-Misses, and then the Not-Agains, and eventually the Niggles.

You mean we have to use the SUI data and the IR1 data and the complaint data to do this – and also ask our staff and patients about their Niggles?

Yes. And it is not the number of complaints that is the most useful metric – it is the appearance of the cumulative sum of the complaint severity score. And we need a method for diagnosing and treating the cause of the Niggles too. We need to convert the feedback information into effective action.

Ah ha! Now I understand what the role of the Governance Department is: to apply the tools and techniques of Improvement Science proactively. But our Governance Department have not been trained to do this!

Then that is one place to start – and their role needs to evolve from Inspectors and Supervisors to Demonstrators and Educators – ultimately everyone in the organisation needs to be a competent Healthcare Improvementologist.

OK – I now now what to do next. But wait a minute. This is going to cost a fortune!

This is just one small first step. The next step is to redesign the processes so the errors do not happen in the first place. The cumulative cost saving from eliminating the repeated checking, correcting, box-ticking, documenting, investigating, compensating and insuring is much much more than the one-off investment in learning safe system design.

So the Finance Director should be a champion for safety and quality too.

Yup!

Brill. Thanks. And can I ask one more question? I do not want to appear to skeptical but how do we know we can trust that this risk-estimation system has been designed and implemented correctly? How do we know we are not being bamboozled by statisticians? It has happened before!

That is the best question yet. It is important to remember that HSMR is counting deaths in hospital which means that it is not actually the risk of harm to the patient that is measured – it is the risk to the reputation of hospital! So the answer to your question is that you demonstrate your deep understanding of the rationle and method of risk-of-harm estimation by listing all the ways that such a system could be deliberately “gamed” to make the figures look better for the hospital. And then go out and look for hard evidence of all the “games” that you can invent. It is a sort of creative poacher-becomes-gamekeeper detective exercise.

OK – I sort of get what you mean. Can you give me some examples?

Yes. The HSMR method is based on deaths-in-hospital so discharging a patient from hospital before they die will make the figures look better. Suppose one hospital has more access to end-of-life care in the community than another: their HSMR figures would look better even though exactly the same number of people died. Another is that the HSMR method is weighted towards admissions classified as “emergencies” – so if a hospital admits more patients as “emergencies” who are not actually very sick and discharges them quickly then this will inflated their estimated deaths and make their actual mortality ratio look better – even though the risk-of-harm to patients has not changed.

OMG – so if we have pressure to meet 4 hour A&E targets and we get paid more for an emergency admission than an A&E attendance then admitting to an Assessmen Area and discharging within one day will actually reward the hospital financially, operationally and by apparently reducing their HSMR even though there has been no difference at all to the care that patients actually recieve?

Yes. It is an inevitable outcome of the current system design.

But that means that if I am gaming the system and my HSMR is not getting better then the risk-of-harm to patients is actually increasing and my HSMR system is giving me false reassurance that everything is OK. Wow! I can see why some people might not want that realisation to be public knowledge. So what do we do?

Design the system so that the rewards are aligned with lower risk of harm to patients and improved outcomes.

Is that possible?

Yes. It is called a Win-Win-Win design.

How do we learn how to do that?

Improvement Science.

Footnote I:

The graphs tell a story but they may not create a useful sense of perspective. It has been said that there is a 1 in 300 chance that if you go to hospital you will not leave alive for avoidable causes. What! It cannot be as high as 1 in 300 surely?

OK – let us use the published Mid-Staffs data to test this hypothesis. Over 12 years there were about 150,000 admissions and an estimated 1,200 excess deaths (if all the risk were concentrated into the excess deaths which is not what actually happens). That means a 1 in 130 odds of an avoidable death for every admission! That is twice as bad as the estimated average.

The Mid Staffordshire statistics are bad enough; but the NHS-as-a-whole statistics are cumulatively worse because there are 100’s of other hospitals that are each generating not-as-obvious avoidable mortality. The data is very ‘noisy’ so it is difficult even for a statistical expert to separate the message from the morass.

And remember – that the “expected” mortality is estimated from the average for the whole NHS – which means that if this average is higher than it could be then there is a statistical bias and we are being falsely reassured by being ‘not statistically significantly different’ from the pack.

And remember too – for every patient and family that suffers and avoidable death there are many more that have to live with the consequences of avoidable but non-fatal harm. That is called avoidable morbidity. This is what the risk really means – everyone has a higher risk of some degree of avoidable harm. Psychological and physical harm.

This challenge is not just about preventing another Mid Staffs – it is about preventing 1000’s of avoidable deaths and 100,000s of patients avoidably harmed every year in ‘average’ NHS trusts.

It is not a mass conspiracy of bad nurses, bad doctors, bad managers or bad policians that is the root cause.

It is poorly designed processes – and they are poorly designed because the nurses, doctors and managers have not learned how to design better ones. And we do not know how because we were not trained to. And that education gap was an accident – an unintended error of omission.

Our urgently-improve-NHS-safety-challenge requires a system-wide safety-by-design educational and cultural transformation.

And that is possible because the knowledge of how to design, test and implement inherently safe processes exists. But it exists outside healthcare.

And that safety-by-design training is a worthwhile investment because safer-by-design processes cost less to run because they require less checking, less documenting, less correcting – and all the valuable nurse, doctor and manager time freed up by that can be reinvested in more care, better care and designing even better processes and systems.

Everyone Wins – except the cynics who have a choice: to eat humble pie or leave.

Footnote II:

In the debate that has followed the publication of the Francis Report a lot of scrutiny has been applied to the method by which an estimated excess mortality number is created and it is necessary to explore this in a bit more detail.

The HSMR is an estimate of relative risk – it does not say that a set of specific patients were the ones who came to harm and the rest were OK. So looking at individual deaths and looking for the specific causes is to completely misunderstand the method. So looking at the actual deaths individually and looking for identifiable cause-and-effect paths is an misuse of the message. When very few if any are found to conclude that HSMR is flawed is an error of logic and exposes the ignorance of the analyst further.

HSMR is not perfect though – it has weaknesses. It is a benchmarking process the”standard” of 100 is always moving because the collective goal posts are moving – the reference is always changing . HSMR is estimated using data submitted by hospitals themselves – the clinical coding data. So the main weakness is that it is dependent on the quality of the clinicial coding – the errors of comission (wrong codes) and the errors of omission (missing codes). Garbage In Garbage Out.

Hospitals use clinically coded data for other reasons – payment. The way hospitals are now paid is based on the volume and complexity of that activity – Payment By Results (PbR) – using what are called Health Resource Groups (HRGs). This is a better and fairer design because hospitals with more complex (i.e. costly to manage) case loads get paid more per patient on average. The HRG for each patient is determined by their clinical codes – including what are called the comorbidities – the other things that the patient has wrong with them. More comorbidites means more complex and more risky so more money and more risk of death – roughly speaking. So when PbR came in it becamevery important to code fully in order to get paid “properly”. The problem was that before PbR the coding errors went largely unnoticed – especially the comorbidity coding. And the errors were biassed – it is more likely to omit a code than to have an incorrect code. Errors of omission are harder to detect. This meant that by more complete coding (to attract more money) the estimated casemix complexity would have gone up compared with the historical reference. So as actual (not estimated) NHS mortality has gone down slightly then the HSMR yardstick becomes even more distorted. Hospitals that did not keep up with the Coding Game would look worse even though their actual risk and mortality may be unchanged. This is the fundamental design flaw in all types of benchmarking based on self-reported data.

The actual problem here is even more serious. PbR is actually a payment for activity – not a payment for outcomes. It is calculated from what it cost to run the average NHS hospital using a technique called Reference Costing which is the same method that manufacturing companies used to decide what price to charge for their products. It has another name – Absorption Costing. The highest performers in the manufacturing world no longer use this out-of-date method. The implication of using Reference Costing and PbR in the NHS are profound and dangerous:

If NHS hospitals in general have poorly designed processes that create internal queues and require more bed days than actually necessary then the cost of that “waste” becomes built into the future PbR tariff. This means average length of stay (LOS) is financially rewarded. Above average LOS is financially penalised and below average LOS makes a profit. There is no financial pressure to improve beyound average. This is called the Regression to the Mean effect. Also LOS is not a measure of quality – so there is a to shorten length of stay for purely financial reasons – to generate a surplus to use to fund growth and capital investment. That pressure is non-specific and indiscrimiate. PbR is necessary but it is not sufficient – it requires an quality of outcome metric to complete it.

So the PbR system is based on an out-of-date cost-allocation model and therefore leads to the very problems that are contributing to the MidStaffs crisis – financial pressure causing quality failures and increased risk of mortality. MidStaffs may be a chance victim of a combination of factors coming together like a perfect storm – but those same factors are present throughout the NHS because they are built into the current design.

One solution is to move towards a more up-to-date financial model called stream costing. This uses the similar data to reference costing but it estimates the “ideal” cost of the “necessary” work to achieve the intended outcome. This stream cost becomes the focus for improvement – the streams where there is the biggest gap between the stream cost and the reference cost are the focus of the redesign activity. Very often the root cause is just poor operational policy design; sometimes it is quality and safety design problems. Both are solvable without investment in extra capacity. The result is a higher quality, quicker, lower-cost stream. Win-win-win. And in the short term that is rewarded by a tariff income that exceeds cost and a lower HSMR.

Radically redesigning the financial model for healthcare is not a quick fix – and it requires a lot of other changes to happen first. So the sooner we start the sooner we will arrive.

09/02/2013

The Writing On The Wall – Part I

The writing is on the wall for the NHS.

It is called the Francis Report and there is a lot of it. Just the 290 recommendations runs to 30 pages. It would need a very big wall and very small writing to put it all up there for all to see.

So predictably the speed-readers have latched onto specific words – such as “Inspectors“.

Recommendation 137 “Inspection should remain the central method for monitoring compliance with fundamental standards.”

And it goes further by recommending “A specialist cadre of hospital inspectors should be established …”

A predictable wail of anguish rose from the ranks “Not more inspectors! The last lot did not do much good!”

The word “cadre” is not one that is used in common parlance so I looked it up:

Cadre: 1. a core group of people at the center of an organization, especially military; 2. a small group of highly trained people, often part of a political movement.

So it has a military, centralist, specialist, political flavour. No wonder there was a wail of anguish! Perhaps this “cadre of inspectors” has been unconsciously labelled with another name? Persecutors.

Of more interest is the “highly trained” phrase. Trained to do what? Trained by whom? Clearly none of the existing schools of NHS management who have allowed the fiasco to happen in the first place. So who – exactly? Are these inspectors intended to be protectors, persecutors, or educators?

And what would they inspect?

And how would they use the output of such an inspection?

Would the fear of the inspection and its possible unpleasant consequences be the stick to motivate compliance?

Is the language of the Francis Report going to create another brick wall of resistance from the rubble of the ruins of the reputation of the NHS? Many self-appointed experts are already saying that implementing 290 recommendations is impossible.

They are incorrect.

The number of recommendations is a measure of the breadth and depth of the rot. So the critical-to-success factor is to implement them in a well-designed order. Get the first few in place and working and the rest will follow naturally. Get the order wrong and the radical cure will kill the patient.

So where do we start?

Let us look at the inspection question again. Why would we fear an external inspection? What are we resisting? There are three facets to this: first we do not know what is expected of us; second we do not know if we can satisfy the expectation; and third we fear being persecuted for failing to achieve the impossible.

W Edwards Deming used a very effective demonstration of the dangers of well-intended but badly-implemented quality improvement by inspection: it was called the Red Bead Game. The purpose of the game was to illustrate how to design an inspection system that actually helps to achieve the intended goal. Sustained improvement.

This is applied Improvement Science and I will illustrate how it is done with a real and current example.

I am assisting a department in a large NHS hospital to improve the quality of their service. I have been sent in as an external inspector. The specific quality metric they have been tasked to improve is the turnaround time of the specialist work that they do. This is a flow metric because a patient cannot leave hospital until this work is complete – and more importantly it is a flow and quality metric because when the hospital is full then another patient, one who urgently needs to be admitted, will be waiting for the bed to be vacated. One in one out.

The department have been set a standard to meet, a target, a specification, a goal. It is very clear and it is easily measurable. They have to turnaround each job of work in less than 2 hours. This is called a lead time specification and it is arbitrary. But it is not unreasonable from the perspective of the patient waiting to leave and for the patient waiting to be admitted. Neither want to wait.

The department has a sophisticated IT system that measures their performance. They use it to record when each job starts and when each job is finished and from those two events the software calculates the lead time for each job in real-time. At the end of each day the IT system counts how many jobs were completed in less than 2 hours and compares this with how many were done in total and calculates a ratio which it presents as a percentage in the range of 0 and 100. This is called the process yield. The department are dedicated and they work hard and they do all the work that arrives each day the same day – no matter how long it takes. And at the end of each day they have their score for that day. And it is almost never 100%. Not never. Almost never. But it is not good enough and they are being blamed for it. In turn they blame others for making their job more difficult. It is a blame-game and it has been going on for years.

So how does an experienced Improvement Science-trained Inspector approach this sort of “wicked” problem?

First we need to get the writing on the wall – we need to see the reality – we need to “plot the dots” – we need to see what the performance is doing over time – we need to see the voice of the process. And that requires only their data, a pencil, some paper and for the chart to be put on the on the wall where everyone can see it.

This is what their daily % yield data for three consecutive weeks looked like as a time-series chart. The thin blue line is the 100% yield target.

The 100% target was only achieved on three days – and they were all Sundays. On the other Sunday it was zero (which may mean that there was no data to calculate a ratio from).

There is wide variation from one day to the next and it is the variation as well as the average that is of interest to an improvement scientist. What is the source of the variation it? If 100% yield can be achieved some days then what is different about those days?

So our Improvement science-trained Inspector will now re-plot the data in a different way – as rational groups. This exposes the issue clearly. The variation on Weekends is very wide and the performance during the Weekdays is much less variable. What this says is that the weekend system and the weekday system are different. This means that it is invalid to combine the data for both.

It also raises the question of why there is such high variation in yield only at weekends? The chart cannot answer the question, so our IS-trained Inspector digs a bit deeper and discovers that the volume of work done at the weekend is low, the staffing of the department is different, and that the recording of the events is less reliable. In short – we cannot even trust the weekend data – so we have two reasons to justify excluding it from our chart and just focusing on what happens during the week.

We re-plot our chart, marking the excluded weekend data as not for analysis.

We can now see that the weekday performance of our system is visible, less variable, and the average is a long way from 100%.

The team are working hard and still only achieving mediocre performance. That must mean that they need something that is missing. Motivating maybe. More people maybe. More technology maybe. But there is no more money for more people or technology and traditional JFDI motivation does not seem to have helped.

This looks like an impossible task!

So what does our Inspector do now? Mark their paper with a FAIL and put them on the “To Be Sacked for Failing to Meet an Externally Imposed Standard“ heap?

Nope.

Our IS-trained Inspector calculates the limits of expected performance from the data and plots these limits on the chart – the red lines. The computation is not difficult – it can be done with a calculator and the appropriate formula. It does not need a sophisticated IT system.

What this chart now says is “The current design of this process is capable of delivering between 40% and 85% yield. To expect it do do better is unrealistic”. The implication for action is “If we want 100% yield then the process needs to be re-designed.” Persecution will not work. Blame will not work. Hoping-for-the-best will not work. The process must be redesigned.

Our improvement scientist then takes off the Inspector’s hat and dons the Designer’s overalls and gets to work. There is a method to this and it is called 6M Design®.

First we need to have a way of knowing if any future design changes have a statistically significant impact – for better or for worse. To do this the chart is extended into the future and the red lines are projected forwards in time as the black lines called locked-limits. The new data is compared with this projected baseline as it comes in. The weekends and bank holidays are excluded because we know that they are a different system. On one day (20/12/2012) the yield was surprisingly high. Not 100% but more than the expected upper limit of 85%.

The alerts us to investigate and we found that it was a ‘hospital bed crisis’ and an ‘all hands to the pumps’ distress call went out.

Extra capacity was pulled to the process and less urgent work was delayed until later. It is the habitual reaction-to-a-crisis behaviour called “expediting” or “firefighting”. So after the crisis had waned and the excitement diminished the performance returned to the expected range. A week later the chart signals us again and we investigate but this time the cause was different. It was an unusually quiet day and there was more than enough hands on the pumps.

Both of these days are atypically good and we have an explanation for each of them. This is called an assignable cause. So we are justified in excluding these points from our measure of the typical baseline capability of our process – the performance the current design can be expected to deliver.

An inexperienced manager might conclude from these lessons that what is needed is more capacity. That sounds and feels intuitively obvious and it is correct that adding more capacity may improve the yield – but that does not prove that lack of capacity is the primary cause. There are many other causes of long lead times just as there are many causes of headaches other than brain tumours! So before we can decide the best treatment for our under-performing design we need to establish the design diagnosis. And that is done by inspecting the process in detail. And we need to know what we are looking for; the errors of design commission and the errors of design omission. The design flaws.

Only a trained and experienced process designer can spot the flaws in a process design. Intuition will trick the untrained and inexperienced.

Once the design diagnosis is established then the redesign stage can commence. Design always works to a specification and in this case it was clear – to significantly improve the yield to over 90% at no cost. In other words without needing more people, more skills, more equipment, more space, more anything. The design assignment was made trickier by the fact that the department claimed that it was impossible to achieve significant improvement without adding extra capacity. That is why the Inspector had been sent in. To evaluate that claim.

The design inspection revealed a complex adaptive system – not a linear, deterministic, production-line that manufactures widgets. The department had to cope with wide variation in demand, wide variation in quality of request, wide variation in job complexity, and wide variation in urgency – all at the same time. But that is the nature of healthcare and acute hospital work. That is the expected context.

The analysis of the current design revealed that it was not well suited for this requirement – and the low yield was entirely predictable. The analysis also revealed that the root cause of the low yield was not lack of either flow-capacity or space-capacity.

This insight led to the suggestion that it would be possible to improve yield without increasing cost. The department were polite but they did not believe it was possible. They had never seen it, so why should they be expected to just accept this on faith?

So, the next step was to develop, test and demonstrate a new design and that was done in three stages. The final stage was the Reality Test – the actual process design was changed for just one day – and the yield measured and compared with the predicted improvement.

This was the validity test – the proof of the design pudding. And to visualise the impact we used the same technique as before – extending the baseline of our time-series chart, locking the limits, and comparing the “after” with the “before”.

The yellow point marks the day of the design test. The measured yield was well above the upper limit which suggested that the design change had made a significant improvement. A statistically significant improvement. There was no more capacity than usual and the day was not unusually quiet. At the end of the day we held a team huddle.

Our first question was “How did the new design feel?” The consensus was “Calmer, smoother, fewer interruptions” and best of all “We finished on time – there was no frantic catch up at the end of the day and no one had to stay late to complete the days work!”

The next question was “Do we want to continue tomorrow with this new design or revert back to the old one?” The answer was clear “Keep going with the new design. It feels better.”

The same chart was used to show what happened over the next few days – excluding the weekends as before. The improvement was sustained – it did not revert to the original because the process design had been changed. Same work, same capacity, different process – higher yield. The red flags on the charts mark the statistically significant evidence of change and the cluster of red flags is very strong statistical evidence that the improvement is not due to chance.

The next phase of the 6M Design® method is to continue to monitor the new process to establish the new baseline of expectation. That will require at least twelve data points and it is in progress. But we have enough evidence of a significant improvement. This means that we have no credible justification to return to the old design, and it also implies that it is no longer valid to compare the new data against the old projected limits. Our chart tells us that we need to split the data into before-and-after and to calculate new averages and limits for each segment separately. We have changed the voice of the process by changing the design.

And when we split the data at the point-of-change then the red flags disappear – which means that our new design is stable. And it has a new capability – a better one. We have moved closer to our goal of 100% yield. It is still early days and we do not really have enough data to calculate the new capability.

What we can say is that we have improved average quality yield from 63% to about 90% at no cost using a sequence of process diagnose, design, deliver. Study-Plan-Do.

And we have hard evidence that disproves the impossibility hypothesis.

And that was the goal of the first design change – it was not to achieve 100% yield in one jump. Our design simulation had predicted an improvement to about 90%. And there are other design changes to follow that need this stable foundation to build on. The order of implementation is critical – and each change needs time to bed in before the next change is made. That is the nature of the challenge of improving a complex adaptive system.

The cost to the department was zero but the benefit was huge. The bigger benefit to the organisation was felt elsewhere – the ‘customers’ saw a higher quality, quicker process – and there will be a financial benefit for the whole system. It will be difficult to measure with our current financial monitoring systems but it will be real and it will be there – lurking in the data.

The improvement required a trained and experienced Inspector/Designer/Educator to start the wheel of change turning. There are not many of these in the NHS – but the good news is that the first level of this training is now available.

What this means for the post-Francis Report II NHS is that those who want to can choose to leap over the wall of resistance that is being erected by the massing legions of noisy cynics. It means we can all become our own inspectors. It means we can all become our own improvers. It means we can all learn to redesign our systems so that they deliver higher safety, better quality, more quickly and at no extra one-off or recurring cost. We all can have nothing to fear from the Specialist Cadre of Hospital Inspectors.

The writing is on the wall.

15/02/2013 – Two weeks in and still going strong. The yield has improved from 63% to 92% and is stable. Improvement-by-design works.

10/03/2013 – Six weeks in and a good time to test if the improvement has been sustained.

The chart is the weekly performance plotted for 17 weeks before the change and for 5 weeks after. The advantage of weekly aggregated data is that it removes the weekend/weekday 7-day cycle and reduces the effect of day-to-day variation.

The improvement is obvious, significant and has been sustained. This is the objective improvement. More important is the subjective improvement.

Here is what Chris M (departmental operational manager) wrote in an email this week (quoted with permission):

Hi Simon

It is I who need to thank you for explaining to me how to turn our pharmacy performance around and ultimately improve the day to day work for the pharmacy team (and the trust staff). This will increase job satisfaction and make pharmacy a worthwhile career again instead of working in constant pressure with a lack of achievement that had made the team feel rather disheartened and depressed. I feel we can now move onwards and upwards so thanks for the confidence boost.

Best wishes and many thanks

Chris

This is what Improvement Science is all about!

02/02/2013

Kicking the Habit

It is not easy to kick a habit. We all know that. And for some reason the ‘bad’ habits are harder to kick than the ‘good’ ones. So what is bad about a ‘bad habit’ and why is it harder to give up? Surely if it was really bad it would be easier to give up?

Improvement is all about giving up old ‘bad’ habits and replacing them with new ‘good’ habits – ones that will sustain the improvement. But there is an invisible barrier that resists us changing any habit – good or bad. And it is that barrier to habit-breaking that we need to understand to succeed. Luck is not a reliable ally.

What does that habit-breaking barrier look like?

The problem is that it is invisible – or rather it is emotional – or to be precise it is chemical.

Our emotions are the output of a fantastically complex chemical system – our brains. And influencing the chemical balance of our brains can have a profound effect on our emotions. That is how anti-depressants work – they very slightly adjust the chemical balance of every part of our brains. The cumulative effect is that we feel happier. Nicotine has a similar effect.

And we can achieve the same effect without resorting to drugs or fags – and we can do that by consciously practising some new mental habits until they become ingrained and unconscious. We literally overwrite the old mental habit.

So how do we do this?

First we need to make the mental barrier visible – and then we can focus our attention on eroding it. To do that we need to remove the psychological filter that we all use to exclude our emotions. It is rather like taking off our psychological sunglasses.

When we do that the invisible barrier jumps into view: illuminated by the glare of three negative emotions. Sadness, fear, and anxiety. So whenever we feel any of these we know there is a barrier to improvement hiding the emotional smoke. This is the first stage: tune in to our emotions.

The next step is counter-intuitive. Instead of running away from the negative feeling we consciously flip into a different way of thinking. We actively engage with our negative feelings – and in a very specific way. We engage in a detached, unemotional, logical, rational, analytical ‘What caused that negative feeling?’ way.

We then focus on the causes of the negative emotions. And when we have the root causes of our Niggles we design around them, under them, and over them. We literally design them out of our heads.

The effect is like magic.

And this week I witnessed a real example of this principle in action.

One team I am working with experienced the Power of Improvementology. They saw the effect with their own eyes. There were no computers in the way, no delays, no distortion and no deletion of data to cloud the issue. They saw the performance of their process jump dramatically – from a success rate of 60% to 96%! And not just the first day, the second day too. “Surprised and delighted” sums up their reaction.

So how did we achieve this miracle?

We just looked at the process through a different lens – one not clouded and misshapen by old assumptions and blackened by ignorance of what is possible. We used the 6M Design® lens – and with the clarity of insight it brings the barriers to improvement became obvious. And they were dissolved. In seconds.

Success then flowed as the Dam of Disbelief crumbled and was washed away.

The chaos has gone. The interruptions have gone. The expediting has gone. The firefighting has gone. The complaining has gone. These chronic Niggles have have been replaced by the Nuggets of calm efficiency, new hope and visible excitement.

And we know that others have noticed the knock-on effect because we got an email from our senior executive that said simply “No one has moaned about TTOs for two days … something has changed.”

That is Improvementology-in-Action.

26/01/2013

Curing Chronic Carveoutosis

Last week the Ray Of Hope briefly illuminated a very common system design disease called carveoutosis. This week the RoH will tarry a little longer to illuminate an example that reveals the value of diagnosing and treating this endemic process ailment.

Do you remember the days when we used to have to visit the Central Post Office in our lunch hour to access a quality-of-life-critical service that only a Central Post Office could provide – like getting a new road tax disc for our car? On walking through the impressive Victorian entrances of these stalwart high street institutions our primary challenge was to decide which queue to join.

In front of each gleaming mahogony, brass and glass counter was a queue of waiting customers. Behind was the Post Office operative. We knew from experience that to be in-and-out before our lunch hour expired required deep understanding of the ways of people and processes – and a savvy selection. Some queues were longer than others. Was that because there was a particularly slow operative behind that counter? Or was it because there was a particularly complex postal problem being processed? Or was it because the customers who had been waiting longer had identified that queue was fast flowing and had defected to it from their more torpid streams? We know that size is not a reliable indicator of speed or quality.

The social pressure is now mounting … we must choose … dithering is a sign of weakness … and swapping queues later is another abhorrent behaviour. So we employ our most trusted heuristic – we join the end of the shortest queue. Sometimes it is a good choice, sometimes not so good! But intuitively it feels like the best option.

Of course if we choose wisely and we succeed in leap-frogging our fellow customers then we can swagger (just a bit) on the way out. And if not we can scowl and mutter oaths at others who (by sheer luck) leap frog us. The Post Office Game is fertile soil for the Aint’ It Awful game which we play when we arrive back at work.

But those days are past and now we are more likely to encounter a single-queue when we are forced by necessity to embark on a midday shopping sortie. As we enter we see the path of the snake thoughtfully marked out with rope barriers or with shelves hopefully stacked with just-what-we-need bargains to stock up on as we drift past. We are processed FIFO (first-in-first-out) which is fairer-for-all and avoids the challenge of the dreaded choice-of-queue. But the single-queue snake brings a new challenge: when we reach the head of the snake we must identify which operative has become available first – and quickly!

Because if we falter then we will incur the shame of the finger-wagging or the flashing red neon arrow that is easily visible to the whole snake; and a painful jab in the ribs from the impatient snaker behind us; and a chorus of tuts from the tail of the snake. So as we frantically scan left and right along the line of bullet-proof glass cells looking for clues of imminent availability we run the risk of developing acute vertigo or a painful repetitive-strain neck injury!

So is the single-queue design better? Do we actually wait less time, the same time or more time? Do we pay a fair price for fair-for-all queue design? The answer is not intuitively obvious because when we are forced to join a lone and long queue it goes against our gut instinct. We feel the urge to push.

The short answer is “Yes”. A single-queue feeding tasks to parallel-servers is actually a better design. And if we ask the Queue Theorists then they will dazzle us with complex equations that prove it is a better design – in theory. But the scary-maths does not help us to understand how it is a better design. Most of us are not able to convert equations into experience; academic rhetoric into pragmatic reality. We need to see it with our own eyes to know it and understand it. Because we know that reality is messier than theory.

And if it is a better design then just how much better is it?

To illustrate the potential advantage of a single-queue design we need to push the competing candiates to their performance limits and then measure the difference. We need a real example and some real data. We are Improvementologists!

First we need to map our Post Office process – and that reveals that we have a single step process – just the counter. That is about as simple as a process gets. Our map also shows that we have a row of counters of which five are manned by fully trained Post Office service operatives.

Now we can measure our process and when we do that we find that we get an average of 30 customers per hour walking in the entrance and and average of 30 cusomers an hour walking out. Flow-out equals flow-in. Activity equals demand. And the average flow is one every 2 minutes. So far so good. We then observe our five operatives and we find that the average time from starting to serve one customer to starting to serve the next is 10 minutes. We know from our IS training that this is the cycle time. Good.

So we do a quick napkin calculation to check and that the numbers make sense: our system of five operatives working in parallel, each with an average cycle time of 10 minutes can collectively process a customer on average every 2 minutes – that is 30 per hour on average. So it appears we have just enough capacity to keep up with the flow of work – we are at the limit of efficiency. Good.

We also notice that there is variation in the cycle time from customer to customer – so we plot our individual measurements asa time-series chart. There does not seem to be an obvious pattern – it looks random – and BaseLine says that it is statistically stable. Our chart tells us that a range of 5 to 15 minutes is a reasonable expectation to set.

We also observe that there is always a queue of waiting customers somewhere – and although the queues fluctuate in size and location they are always there.

So there is always a wait for some customers. A variable wait; an unpredictable wait. And that is a concern for us because when the queues are too numerous and too long then we see customers get agitated, look at their watches, shrug their shoulders and leave – taking their custom and our income with them and no doubt telling all their friends of their poor experience. Long queues and long waits are bad for business.

And we do not want zero queues either because if there is no queue and our operatives run out of work then they become under-utilised and our system efficiency and productivity falls. That means we are incurring a cost but not generating an income. No queues and idle resources are bad for business too.

And we do not want a mixture of quick queues and slow queues because that causes complaints and conflict. A high-conflict customer complaint experience is bad for business too!

What we want is a design that creates small and stable queues; ones that are just big enough to keep our operatives busy and our customers not waiting too long.

So which is the better design and how much better is it? Five-queues or a single-queue? Carve-out or no-carve-out?

To find the answer we decide to conduct a week-long series of experiments on our system and use real data to reveal the answer. We choose the time from a customer arriving to the same customer leaving as our measure of quality and performance – and we know that the best we can expect is somewhere between 5 and 15 minutes. We know from our IS training that is called the Lead Time.

On day #1 we arrange our Post Office with five queues – clearly roped out – one for each manned counter. We know from our mapping and measuring that customers do not arrive in a steady stream and we fear that may confound our experiment so we arrange to admit only one of our loyal and willing customers every 2 minutes. We also advise our loyal and willing customers which queue they must join before they enter to avoid the customer choice challenges. We decide which queue using a random number generator – we toss a dice until we get a number between 1 and 5. We record the time the customer enters on a slip of paper and we ask the customer to give it to the operative and we instruct our service operatives to record the time they completed their work on the same slip and keep it for us to analyse later. We run the experiment for only 1 hour so that we have a sample of 30 slips and then we collect the slips, calculate the difference between the arrival and departure times and plot them on a time-series chart in the order of arrival.

This is what we found. Given that the time at the counter is an average of 10 minutes then some of these lead times seem quite long. Some customers spend more time waiting than being served. And we sense that the performance is getting worse over time.

So for the next experiment we decide to open a sixth counter and to rope off a sixth queue. We expect that increasing capacity will reduce waiting time and we confidently expect the performance to improve.

On day #2 we run our experiment again, letting customers in one every 2 minutes as before and this time we use all the numbers on the dice to decide which queue to direct each customer to. At the end of the hour we collect the slips, calculate the lead times and plot the data – on the same chart.

This is what we see.

It does not look much better and that is big surprise!

The wide variation from customer to customer looks about the same but with the Eye of Optimism we get a sense that the overall performance looks a bit more stable.

So we conclude that adding capacity (and cost) may make a small difference.

But then we remember that we still only served 30 customers – which means that our income stayed the same while our cost increased by 20%. That is definitely NOT good for business: it is not goiug to look good in a business case “possible marginally better quality and 20% increase in cost and therefore price!”

So on day #3 we change the layout. This time we go back to five counters but we re-arrange the ropes to create a single-queue so the customer at the front can be ‘pulled’ to the first available counter. Everything else stays the same – one customer arriving every 2 minutes, the dice, the slips of paper, everything. At the end of the hour we collect the slips, do our sums and plot our chart.

And this is what we get! The improvement is dramatic. Both the average and the variation has fallen – especially the variation. But surely this cannot be right. The improvement is too good to be true. We check our data again. Yes, our customers arrived and departed on average one every 2 minutes as before; and all our operatives did the work in an average of 10 minutes just as before. And we had the exactly the same capacity as we had on day #1. And we finished on time. It is correct. We are gobsmaked. It is like a magic wand has been waved over our process. We never would have predicted that just moving the ropes around to could have such a big impact. The Queue Theorists were correct after all!

But wait a minute! We are delivering a much better customer experience in terms of waiting time and at the same cost. So could we do even better with six counters open? What will happen if we keep the single-queue design and open the sixth desk? Before it made little difference but now we doubt our ability to guess what will happen. Our intuition seems to keep tricking us. We are losing our confidence in predicting what the impact will be. We are in counter-intuitive land! We need to run the experiment for real.

So on day #4 we keep the single-queue and we open six desks. We await the data eagerly.

And this is what happened. Increasing the capacity by 20% has made virtually no difference – again. So we now have two pieces of evidence that say – adding extra capacity did not make a difference to waiting times. The variation looks a bit less though but it is marginal.

It was changing the Queue Design that made the difference! And that change cost nothing. Rien. Nada. Zippo!

That will look much better in our report but now we have to face the emotional discomfort of having to re-evaluate one of our deepest held assumptions.

Reality is telling us that we are delivering a better quality experience using exactly the same resources and it cost nothing to achieve. Higher quality did NOT cost more. In fact we can see that with a carve-out design when we added capacity we just increased the cost we did NOT improve quality. Wow! That is a shock. Everything we have been led to believe seems to be flawed.

Our senior managers are not going to like this message at all! We will be challening their dogma directly. And they do not like that. Oh dear!

Now we can see how much better a no-carveout single-queue pull-design can work; and now we can explain why single-queue designs are used; and now we can show others our experiment and our data and if they do not believe us they can repeat the experiment themselves. And we can see that it does not need a real Post Office – a pad of Post It® Notes, a few stopwatches and some willing helpers is all we need.

And even though we have seen it with our own eyes we still struggle to explain how the single-queue design works better. What actually happens? And we still have that niggling feeling that the performance on day #1 was unstable. We need to do some more exploring.

So we run the day#1 experiment again – the five queues – but this time we run it for a whole day, not just an hour.

Ah ha! Our hunch was right. It is an unstable design. Over time the variation gets bigger and bigger.

But how can that happen?

Then we remember. We told the customers that they could not choose the shortest queue or change queue after they had joined it. In effect we said “do not look at the other queues“.

And that happens all the time on our systems when we jealously hide performance data from each other! If we are seen to have a smaller queue we get given extra work by the management or told to slow down by the union rep!

So what do we do now? All we are doing is trying to improve the service and all we seem to be achieving is annoying more and more people.

What if we apply a maximum waiting time target, say of 1 hour, and allow customers to jump to the front of their queue if they are at risk if breaching the target? That will smooth out spikes and give everyone a fair chance. Customers will understand. It is intuitively obvious and common sense. But our intuition has tricked us before …

So we run the experiment again and this time we tell our customers that if they wait 50 minutes then they can jump to the front of their queue. They appreciate this because they now have a upper limit on the time they will wait.

And this is what we observe. It looks better than before, at least initially, and then it goes pear-shaped.

All we have done with our ‘carve-out and-expedite-the-long-waiters’ design is to defer the inevitable – the crunch. We cannot keep our promise. By the end everyone is pushing to the frontof the queue. It is a riot!

And there is more. Look at the lead time for the last few customers – two hours. Not only have they waited a long time, but we have had to stay open for two hours longer. That is a BIG cost pessure in overtime payments.

So, whatever way we look at it: a single-queue design is better. And no one loses out! The customers have a short and predictable waiting time; the operatives are kept occupied and go home on time; and the executives bask in the reflected glory of the excellent customer feedback. It is a Three Wins® design.

Seeing is believing – and we now know that it is worth diagnosing and treating carveoutosis.

And the only thing left to do is to explain is how a single-queue design works better. It is not obvious is it?

And the best way to do that is to play the Post Office Game and see what actually happens.

A big light-bulb moment awaits!

Update: My little Sylvanian friends have tried the Post Office Game and kindly sent me this video of the before Sylvanian Post Office Before and the after Sylvanian Post Office After. They say they now know how the single-queue design works better.

19/01/2013

A Ray Of Hope

It does not seem to take much to bring a real system to an almost standstill. Six inches of snow falling between 10 AM and 2 PM in a Friday in January seems to be enough!

It was not so much the amount of snow – it was the timing. The decision to close many schools was not made until after the pupils had arrived – and it created a logistical nightmare for parents.

Many people suddenly needed to get home before they expected which created an early rush hour and gridlocked the road system.

The same number of people travelled the same distance in the same way as they would normally – it just took them a lot longer. And the queues created more problems as people tried to find work-arounds to bypass the traffic jams.

How many thousands of hours of life-time was wasted sitting in near-stationary queues of cars? How many millions of poundsworth of productivity was lost? How much will the catchup cost?

And yet while we grumble we shrug our shoulders and say “It is just one of those things. We cannot control the weather. We just have to grin and bear it.”

Actually we do not have to. And we do not need a weather machine to control the weather. Mother Nature is what it is.

Exactly the same behaviour happens in many systems – and our conclusion is the same. We assume the chaos and queues are inevitable.

They are not.

They are symptoms of the system design – and specifically they are the inevitable outcomes of the time-design.

But it is tricky to visualise the time-design of a system. We can see the manifestations of the poor time-design, the queues and chaos, but we do not so easily perceive the causes. So the poor time-design persists. We are not completely useless though; there are lots of obvious things we can do. We can devise ingenious ways to manage the queues; we can build warehouses to hold the queues; we can track the jobs in the queues using sophisticated and expensive information technology; we can identify the hot spots; we can recruit and deploy expediters, problem-solvers and fire-fighters to facilitate the flow through the hottest of them; and we can pump capacity and money into defences, drains and dramatics. And our efforts seem to work so we congratulate ourselves and conclude that these actions are the only ones that work. And we keep clamouring for more and more resources. More capacity, MORE capacity, MORE CAPACITY.

Until we run out of money!

And then we have to stop asking for more. And then we start rationing. And then we start cost-cutting. And then the chaos and queues get worse.

And all the time we are not aware that our initial assumptions were wrong.

The chaos and queues are not inevitable. They are a sign of the time-design of our system. So we do have other options. We can improve the time-design of our system. We do not need to change the safety-design; nor the quality-design; nor the money-design. Just improving the time-design will be enough. For now.

So the $64,000,000 question is “How?”

Before we explore that we need to demonstrate What is possible. How big is the prize?

The class of system design problem that cause particular angst are called mixed-priority mixed-complexity crossed-stream designs. We encounter dozens of them in our daily life and we are not aware of it. One of particular interest to many is called a hospital. The mixed-priority dimension is the need to manage some patients as emergencies, some as urgent and some as routine. The mixed-complexity dimension is that some patients are easy and some are complex. The crossed-stream dimension is the aggregation of specialised resources into departments. Expensive equipment and specific expertise. We then attempt to push patients with different priorites long different paths through these different departments . And it is a management nightmare!

Our usual and “obvious” response to this challenge is called a carve-out design. And that means we chop up our available resource capacity into chunks. And we do that in two ways: chunks of time and chunks of space. We try to simplify the problem by dissecting it into bits that we can understand. We separate the emergency departments from the planned-care facilities. We separate outpatients from inpatients. We separate medicine from surgery – and we then intellectually dissect our patients into organ systems: brains, lungs, hearts, guts, bones, skin, and so on – and we create separate departments for each one. Neurology, Respiratory, Cardiology, Gastroenterology, Orthopaedics, Dermatology to list just a few. And then we become locked into the carve-out design silos like prisoners in cages of our own making.

And so it is within the departments that are sub-systems of the bigger system. Simplification, dissection and separation. Ad absurdam.

The major drawback with our carve-up design strategy is that it actually makes the system more complicated. The number of necessary links between the separate parts grows exponentially. And each link can hold a small queue of waiting tasks – just as each side road can hold a queue of waiting cars. The collective complexity is incomprehensible. The cumulative queue is enormous. The opportunity for confusion and error grows exponentially. Safety and quality fall and cost rises. Carve-out is an inferior time-design.

But our goal is correct: we do need to simplify the system so that means simplifying the time-design.

To illustrate the potential of this ‘simplify the time-design’ approach we need a real example.

One way to do this is to create a real system with lots of carve-out time-design built into it and then we can observe how it behaves – in reality. A carefully designed Table Top Game is one way to do this – one where the players have defined Roles and by following the Rules they collectively create a real system that we can map, measure and modify. With our Table Top Team trained and ready to go we then pump realistic tasks into our realistic system and measure how long they take in reality to appear out of the other side. And we then use the real data to plot some real time-series charts. Not theoretical general ones – real specific ones. And then we use the actual charts to diagnose the actual causes of the actual queues and actual chaos.

This is the time-series chart of a real Time-Design Game that has been designed using an actual hospital department and real observation data. Which department it was is not of importance because it could have been one of many. Carve-out is everywhere.

During one run of the Game the Team processed 186 tasks and the chart shows how long each task took from arriving to leaving (the game was designed to do the work in seconds when in the real department it took minutes – and this was done so that one working day could be condensed from 8 hours into 8 minutes!)

There was a mix of priority: some tasks were more urgent than others. There was a mix of complexity: some tasks required more steps that others. The paths crossed at separate steps where different people did defined work using different skills and special equipment. There were handoffs between all of the steps on all of the streams. There were lots of links. There were many queues. There were ample opportunities for confusion and errors.

But the design of the real process was such that the work was delivered to a high quality – there were very few output errors. The yield was very high. The design was effective. The resources required to achieve this quality were represented by the hours of people-time availability – the capacity. The cost. And the work was stressful, chaotic, pressured, and important – so it got done. Everyone was busy. Everyone pulled together. They helped each other out. They were not idle. They were a good team. The design was efficient.

The thin blue line on the time-series chart is the “time target” set by the Organisation. But the effective and efficient system design only achieved it 77% of the time. So the “obvious” solution was to clamour for more people and for more space and for more equipment so that the work can be done more quickly to deliver more jobs on-time. Unfortunately the Rules of the Time-Design Game do not allow this more-money option. There is no more money.

To succeed at the Time-Design Game the team must find a way to improve their delivery time performance with the capacity they have and also to deliver the same quality. But this is impossible! If it were possible then the solution would be obvious and they would be doing it already. No one can succeed on the Time-Design Game.

Wrong. It is possible. And the assumption that the solution is obvious is incorrect. The solution is not obvious – at least to the untrained eye.

To the trained eye the time-series chart shows the characteristic signals of a carve-out time-design. The high task-to-task variation is highly suggestive as is the pattern of some of the earlier arrivals having a longer lead time. An experienced system designer can diagnose a carve-out time-design from a set of time-series charts of a process just as a doctor can diagnose the disease from the vital signs chart for a patient. And when the diagnosis is confirmed with a verification test then the time-Redesign phase can start.

This chart shows what happened after the time-design of the system was changed – after some of the carve-out design was modified. The Y-axis scale is the same as before – and the delivery time improvement is dramatic. The Time-ReDesigned system is now delivering 98% achievement of the “on time target”.

The important thing to be aware of is that exactly the same work was done, using exactly the same steps, and exactly the same resources. No one had to be retrained, released or recruited. The quality was not impaired. And the cost was actually less because less overtime was needed to mop up the spillover of work at the end of the day.

And the Time-ReDesigned system feels better to work in. It is not chaotic; flow is much smoother; and it is busy yet relaxed and even fun. The same activity is achieved by the same people doing the same work in the same sequence. Only the Time-Design has changed. A change that delivered a win for the workers!

What was the impact of this cost-saving improvement on the customers of this service? They can now be 98% confident that they will get their task completed correctly in less than 120 minutes. Before the Time-Redesign the 98% confidence limit was 470 minutes! So this is a win for the customers too!

And the Time-ReDesigned system is less expensive so it is a win for whoever is paying.

Same safety and quality, quicker with less variation, and at lower cost. Win-Win-Win.

And the usual reaction to playing the Time-ReDesign Game is incredulous disbelief. Some describe it as a “light bulb” moment when they see how the diagnosis of the carve-out time-design is made and and how the Time-ReDesign is done. They say “If I had not seen it with my own eyes I would not have believed it.” And they say “The solutions are simple but not obvious!” And they say “I wish I had learned this years ago!” And thay apologise for being so skeptical before.

And there are those who are too complacent, too careful or too cynical to play the Time-ReDesign Game (which is about 80% of people actually) – and who deny themselves the opportunity of a win-win-win outcome. And that is their choice. They can continue to grin and bear it – for a while longer.

And for the 20% who want to learn how to do Time ReDesign for real in their actual systems there is now a Ray Of Hope.

And the Ray of Hope is illuminating a signpost on which is written “This Way to Improvementology“.

12/01/2013

Quality First or Time First?

Before we explore this question we need to establish something. If the issue is Safety then that always goes First – and by safety we mean “a risk of harm that everyone agrees is unacceptable”.

Many Improvement Zealots state dogmatically that the only way reach the Nirvanah of “Right Thing – On Time – On Budget” is to focus on Quality First.

This is incorrect. And what makes it incorrect is the word only.

Experience teaches us that it is impossible to divert people to focus on quality when everyone is too busy just keeping afloat. If they stop to do something else then they will drown. And they know it.

The critical word here is busy.

‘Busy’ means that everyone is spending all their time doing stuff – important stuff – the work, the checking, the correcting, the expediting, the problem solving, and the fire-fighting. They are all busy all of the time.

So when a Quality Zealot breezes in and proclaims ‘You should always focus on quality first … that will solve all the problems’ then the reaction they get is predictable. The weary workers listen with their arms-crossed, roll-their eyes, exchange knowing glances, sigh, shrug, shake their heads, grit their teeth, and trudge back to fire-fighting. Their scepticism and cynicism has been cut a notch deeper. And the weary workers get labelled as ‘Not Interested In Quality’ and ‘Resisting Change’ and ‘Laggards’ by the Quality Zealot who has spent more time studying and regurgitating rhetoric than investing time in observing and understanding reality.

The problem here is the seemingly innocuous word ‘always’. It is too absolute. Too black-and-white. Too dogmatic. Too simple.

Sometimes focussing on Quality First is a wise decision. And that situation is when there is low-quality and idle-time. There is some spare capacity to re-invest in understanding the root causes of the quality issues, in designing them out of the process, and in implementing the design changes.

But when everyone is busy – when there is no idle-time – then focussing on quality first is not a wise decision because it can actually make the problem worse!

[The Quality Zealots will now be turning a strange red colour, steam will be erupting from their ears and sparks will be coming from their finger-tips as they reach for their keyboards to silence the heretical anti-quality lunatic. “Burn, burn, burn” they rant].

When everyone is busy then the first thing to focus on is Time.

And because everyone is busy then the person doing the Focus-on-Time stuff must be someone else. Someone like an Improvementologist. The Quality Zealot is a liability at this stage – but they become an asset later when the chaos has calmed.

And what our Improvementologist is looking for are queues – also known as Work-in-Progress or WIP.

Why WIP? Why not where the work is happening? Why not focus on resource utilisation? Isn’t that a time metric?

Yes, resource utilisation is a time-related metric but because everyone is busy then resource utilisation will be high. So looking at utilisation will only confirm what we already know. And everyone is busy doing important stuff – they are not stupid – they are busy and they are doing their best given the constraints of their process design.

The queue is where an Improvementologist will direct attention first. And the specific focus of their attention is the cause of the queue.

This is because there is only one cause of a queue: a mismatch-over-time between demand and activity.

So, the critical first step to diagnosing the cause of a queue is to make the flow visible – to plot the time-series charts of demand, activity and WIP. Until that is done then no progress will be made with understanding what is happening and it wil be impossible to decide what to do. We need a diagnosis before we can treat. And to get a diagnosis we need data from an examination of our process; and we need data on the history of how it has developed. And we need to know how to convert that data into information, and then into understanding, and then into design options, and then into a wise decision, and then into action, and then into improvement.

And we now know how to spot an experienced Improvementologist because the first thing they will look for are the Queues not the Quality.

But why bother with the flow and the queues at all? Customers are not interested in them! If time is the focus then surely it is turnaround times and waiting times that we need to measure! Then we can compare our performance with our ‘target’ and if it is out of range we can then apply the necessary ‘pressure’!

This is indeed what we observe. So let us explore the pros and cons of this approach with an example.

We are the manager of a support department that receives requests, processes them and delivers the output back to the sender. We could be one of many support departments in an organisation: human resources, procurement, supplies, finance, IT, estates and so on. We are the Backroom Brigade. We are the unsung heros and heroines.

The requests for our service come in different flavours – some are easy to deal with, others are more complex. They also come with different priorities – urgent, soon and routine. And they arrive as a mixture of dribbles and deluges. Our job is to deliver high quality work (i.e. no errors) within the delivery time expected by the originator of the request (i.e. on time). If we do that then we do not get complaints (but we do not get compliments either).

From the outside things look mostly OK. We deliver mostly on quality and mostly on time. But on the inside our department is in chaos! Every day brings a new fire to fight. Everyone is busy and the pressure and chaos are relentless. We are keeping our head above water – but only just. We do not enjoy our work-life. It is not fun. Our people are miserable too. Some leave – others complain – others just come to work, do stuff, take the money and go home – like Zombies. They comply.

Once in the past we were were seduced by the sweet talk of a Quality Zealot. We were promised Nirvanah. We were advised to look at the quality of the requests that we get. And this suggestion resonated with us because we were very aware that the requests were of variable quality. Our people had to spend time checking-and-correcting them before we could process them. The extra checking had improved the quality of what we deliver – but it had increased our costs too. So the Quality Zealot told us we should work more closely with our customers and to ‘swim upstream’ to prevent the quality problems getting to us in the first place. So we sent some of our most experienced and most expensive Inspectors to paddle upstream. But our customers were also very busy and, much as they would have liked, they did not have time to focus on quality either. So our Inspectors started doing the checking-and-correcting for our customers. Our people are now working for our customers but we still pay their wages. And we do not have enough Inspectors to check-and-correct all the requests at source so we still need to keep a skeleton crew of Inspectors in the department. And these stay-at-home Inspectors are stretched too thin and their job is too pressured and too stressful. So no one wants to do it.And given the choice they would all rather paddle out to the customers first thing in the morning to give them as much time as possible to check-and-correct the requests so the days work can be completed on time. It all sounds perfectly logical and rational – but it does not seem to have worked as promised. The stay-at-home Inspectors can only keep up with the more urgent work, delivery of the less urgent work suffers and the chronic chaos and fire-fighting are now aggravated by a stream of interruptions from customers asking when their ‘non-urgent’ requests will be completed.

The Quality Zealot insisted we should always answer the phone to our customers – so we take the calls – we expedite the requests – we solve the problems – and we fight-the-fire. Day, after day, after day.

We now know what Purgatory means. Retirement with a pension or voluntary redundancy with a package are looking more attractive – if only we can keep going long enough.

And the last thing we need is more external inspection, more targets, and more expensive Quality Zealots telling us what to do!

And when we go and look we see a workplace that appears just as chaotic and stressful and angry as we feel. There are heaps of work in progress everywhere – the phone is always ringing – and our people are running around like headless chickens, expediting, fire-fighting and getting burned-out: physically and emotionally. And we feel powerless to stop it. So we hide.

Does this fictional fiasco feel familiar? It is called the Miserable Job Purgatory Vortex.

Now we know the characteristic pattern of symptoms and signs: constant pressure of work, ever present threat of quality failure, everyone busy, just managing to cope, target-stick-and-carrot management, a miserable job, and demotivated people.

The issue here is that the queues are causing some of the low quality. It is not always low quality that causes all of the queues.

Queues create delays, which generate interruptions, which force investigation, which generates expediting, which takes time from doing the work, which consumes required capacity, which reduces activity, which increases the demand-activity mismatch, which increases the queue, which increases the delay – and so on. It is a vicious circle. And interruptions are a fertile source of internally generated errors which generates even more checking and correcting which uses up even more required capacity which makes the queues grow even faster and longer. Round and round. The cries for ‘we need more capacity’ get louder. It is all hands to the pump – but even then eventually there is a crisis. A big mistake happens. Then Senior Management get named-blamed-and shamed, money magically appears and is thrown at the problem, capacity increases, the symptoms settle, the cries for more capacity go quiet – but productivity has dropped another notch. Eventually the financial crunch arrives.

One symptom of this ‘reactive fire-fight design’ is that people get used to working late to catch up at the end of the day so that the next day they can start the whole rollercoaster ride again. And again. And again. At least that is a form of stability. We can expect tomorrow to be just a s miserable as today and yesterday and the day before that. But TOIL (Time Off In Lieu) costs money.

The way out of the Miserable Job Purgatory Vortex is to diagnose what is causing the queue – and to treat that first.

And that means focussing on Time first – and that means Focussing on Flow first. And by doing that we will improve delivery, improve quality and improve cost because chaotic systems generate errors which need checking and correcting which costs more. Time first is a win-win-win strategy too.

And we already have everything we need to start. We can easily count what comes in and when and what goes out and when.

The first step is to plot the inflow over time (the demand), the outflow over time (the activity), and from that we work out and plot the Work-in-Progress over time. With these three charts we can start the diagnostic process and by that path we can calm the chaos.

And then we can set to work on the Quality Improvement.

13/01/2013 – Newspapers report that 17 hospitals are “dangerously understaffed” Sound familiar?

Next week we will explore how to diagnose the root cause of a queue using Time charts.

For an example to explore please play the SystemFlow Game by clicking here

22/12/2012

Shifting, Shaking and Shaping

Stop Press: For those who prefer cartoons to books please skip to the end to watch the Who Moved My Cheese video first.

In 1962 – that is half a century ago – a controversial book was published. The title was “The Structure of Scientific Revolutions” and the author was Thomas S Kuhn (1922-1996) a physicist and historian at Harvard University. The book ushered in the concept of a ‘paradigm shift’ and it upset a lot a people.

In particular it upset a lot of scientists because it suggested that the growth of knowledge and understanding is not smooth – it is jerky. And Kuhn showed that the scientists were causing the jerking.

Kuhn described the process of scientific progress as having three phases: pre-science, normal science and revolutionary science. Most of the work scientists do is normal science which means exploring, consolidating, and applying the current paradigm. The current conceptual model of how things work. Anyone who argues against the paradigm is regarded as ‘mistaken’ because the paradigm represents the ‘truth’. Kuhn draws on the history of science for his evidence, quoting examples of how innovators such as Galileo, Copernicus, Newton, Einstein and Hawking radically changed the way that we now view the Universe. But their different models were not accepted immediately and ethusiastically because they challenged the status quo. Galileo was under house arrest for much of his life because his ‘heretical’ writings challenged the Church.

Each revolution in thinking was both disruptive and at the same time constructive because it opened a door to allow rapid expansion of knowledge and understanding. And that foundation of knowledge that has been built over the centuries is one that we all take for granted. It is a fragile foundation though. It could be all lost and forgotten in one generation because none of us are born with this knowledge and understanding. It is not obvious. We all have to learn it. Even scientists.

Kuhn’s book was controversial because it suggested that scientists spend most of their time blocking change. This is not necessarily a bad thing. Stability for a while is very useful and the output of normal science is mostly positive. For example the revolution in thinking introduced by Isaac Newton (1643-1727) led directly to the Industrial Revolution and to far-reaching advances in every sphere of human knowledge. Most of modern engineering is built on Newtonian mechanics and it is only at the scales of the very large, the very small and the very quick that it falls over. Relativistic and quantum physics are more recent and very profound shifts in thinking and they have given us the digital computer and the information revolution. This blog is a manifestation of the quantum paradigm.

Kuhn concluded that the progess of change is jerky because scientists create resistance to change to create stability while doing normal science experiments. But these same experiments produce evidence that suggest that the current paradigm is flawed. Over time the pressure of conflicting evidence accumulates, disharmony builds, conflict is inevitable and intellectual battle lines are drawn. The deeper and more fundamental the flaw the more bitter the battle.

In contrast, newcomers seek harmony in the cacophony and propose new theories that explain both the old and the new. New paradigms. The stage is now set for a drama and the public watch bemused as the academic heavyweights slug it out. Eventually a tipping point is reached and one of the new paradigms becomes dominant. Often the transition is triggered by one crucial experiment.

There is a sudden release of the tension and a painful and disruptive conceptual lurch – a paradigm shift. Then the whole process starts over again. The creators of the new paradigm become the consolidators and in time the defenders and eventually the dogmatics! And it can take decades and even generations for the transition to be completed.

It is said that Albert Einstein (1879-1955) never fully accepted quantum physics even though his work planted the seeds for it and experience showed that it explained the experimental observations better. [For more about Einstein click here].

The message that some take from Kuhn’s book is that paradigm shifts are the only way that knowledge can advance. With this assumption getting change to happen requires creating a crisis – a burning platform. Unfortunatelty this is an error of logic – it is a unverified generalisation from an observed specific. The evidence is growing that this we-always-need-a-burning-platform assumption is incorrect. It appears that the growth of knowledge and understanding can be smoother, less damaging and more effective without creating a crisis.

So what is the evidence that this is possible?

Well, what pattern would you look for to illustrate that it is possible to improve smoothly and continually? A smooth growth curve of some sort? Yes – but it is more than that. It is a smooth curve that is steeper than anyone else’s and one that is growing steeper over time. Evidence that someone is learning to improve faster than their peers – and learning painlessly and continuously without crises; not painfully and intermittently using crises.

Two examples are Toyota and Apple.

Toyota is a Japanese car manufacturer that has out-performed other car manufacturers consistently for 40 years – despite the global economic boom-bust cycles. What is their secret formula for their success?

We need a bit of history. In the 1980’s a crisis-of-confidence hit the US economy. It was suddenly threatened by higher-quality and lower-cost imported Japanese products – for example cars.

The switch to buying Japanese cars had been triggered by the Oil Crisis of 1973 when the cost of crude oil quadrupled almost overnight – triggering a rush for smaller, less fuel hungry vehicles. This is exactly what Toyota was offering.

This crisis was also a rude awakening for the US to the existence of a significant economic threat from their former adversary. It was even more shocking to learn that W Edwards Deming, an American statistician, had sown the seed of Japan’s success thirty years earlier and that Toyota had taken much of its inspiration from Henry Ford. The knee-jerk reaction of the automotive industry academics was to copy how Toyota was doing it, the Toyota Production System (TPS) and from that the school of Lean Tinkering was born.

This knowledge transplant has been both slow and painful and although learning to use the Lean Toolbox has improved Western manufacturing productivity and given us all more reliable, cheaper-to-run cars – no other company has been able to match the continued success of Japan. And the reason is that the automotive industry academics did not copy the paradigm – the intangible, subjective, unspoken mental model that created the context for success. They just copied the tangible manifestation of that paradigm. The tools. That is just cynically copying information and knowledge to gain a competitive advantage – it is not respecfully growing understanding and wisdom to reach a collaborative vision.

Apple is now one of the largest companies in the world and it has become so because Steve Jobs (1955-2011), its Californian, technophilic, Zen Bhuddist, entrepreneurial co-founder, had a very clear vision: To design products for people. And to do that they continually challenged their own and their customers paradigms. Design is a logical-rational exercise. It is the deliberate use of explicit knowledge to create something that delivers what is needed but in a different way. Higher quality and lower cost. It is normal science.

Continually challenging our current paradigm is not normal science. It is revolutionary science. It is deliberately disruptive innovation. But continually challenging the current paradigm is uncomfortable for many and, by all accounts, Steve Jobs was not an easy person to work for because he was future-looking and demanded perfection in the present. But the success of this paradigm is a matter of fact:

“In its fiscal year ending in September 2011, Apple Inc. hit new heights financially with $108 billion in revenues (increased significantly from $65 billion in 2010) and nearly $82 billion in cash reserves. Apple achieved these results while losing market share in certain product categories. On August 20, 2012 Apple closed at a record share price of $665.15 with 936,596,000 outstanding shares it had a market capitalization of $622.98 billion. This is the highest nominal market capitalization ever reached by a publicly traded company and surpasses a record set by Microsoft in 1999.”

And remember – Apple almost went bust. Steve Jobs had been ousted from the company he co-founded in a boardroom coup in 1985. After he left Apple floundered and Steve Jobs proved it was his paradigm that was the essential ingredient by setting up NeXT computers and then Pixar. Apple’s fortunes only recovered after 1998 when Steve Jobs was invited back. The rest is history so click to see and hear Steve Jobs describing the Apple paradigm.

So the evidence states that Toyota and Apple are doing something very different from the rest of the pack and it is not just very good product design. They are continually updating their knowledge and understanding – and they are doing this using a very different paradigm. They are continually challenging themselves to learn. To illustrate how they do it – here is a list of the five principles that underpin Toyota’s approach:

Challenge
Improvement
Go and see
Teamwork
Respect

This is Win-Win-Win thinking. This is the Science of Improvement. This is Improvementology®.

So what is the reason that this proven paradigm seems so difficult to replicate? It sounds easy enough in theory! Why is it not so simple to put into practice?

The requirements are clearly listed: Respect for people (challenge). Respect for learning (improvement). Respect for reality (go and see). Respect for systems (teamwork).

In a word – Respect.

Respect is a big challenge for the individualist mindset which is fundamentally disrespectful of others. The individualist mindset underpins the I-Win-You-Lose Paradigm; the Zero-Sum -Game Paradigm; the Either-Or Paradigm; the Linear-Thinking Paradigm; the Whole-Is-The-Sum-Of-The-Parts Paradigm; the Optimise-The-Parts-To-Optimise-The-Whole Paradigm.

Unfortunately these are the current management paradigms in much of the private and public worlds and the evidence is accumulating that this paradigm is failing. It may have been adequate when times were better, but it is inadequate for our current needs and inappropriate for our future needs.

So how can we avoid having to set fire to the current failing management paradigm to force a leap into the cold and uninviting reality of impending global economic failure? How can we harness our burning desire for survival, security and stability? How can we evolve our paradigm pro-actively and safely rather than re-actively and dangerously?

We need something tangible to hold on to that will keep us from drowning while the old I-am-OK-You-are-Not-OK Paradigm is dissolved and re-designed. Like the body of the caterpillar that is dissolved and re-assembled inside the pupa as the body of a completely different thing – a butterfly.

We need a robust and resilient structure that will keep us safe in the transition from old to new and we also need something stable that we can steer to a secure haven on a distant shore.

We need a conceptual lifeboat. Not just some driftwood, a bag of second-hand tools and no instructions! And we need that lifeboat now.

But why the urgency?

The answer is basic economics.

The UK population is growing and the proportion of people over 65 years old is growing faster. Advances in healthcare means that more of us survive age-related illnesses such as cancer and heart disease. We live longer and with better quality of life – which is great.

But this silver-lining hides a darker cloud.

The proportion of elderly and very elderly will increase over the next 20 years as the post WWII baby-boom reaches retirement age. The number of people who are living on pensions is increasing and the demands on health and social services is increasing. Pensions and public services are not paid out of past savings they are paid out of current earnings. So the country will need to earn more to pay the bills. The UK economy will need to grow.

But the UK economy is not growing. Our Gross Domestic Product (GDP) is currently about £380 billion and flat as a pancake. This sounds like a lot of dosh – but when shared out across the population of 56 million it gives a more modest figure of just over £100 per person per week. And the time-series chart for the last 20 years shows that the past growth of about 1% per quarter took a big dive in 2008 and went negative! That means serious recession. It recovered briefly but is now sagging towards zero.

So we are heading for a big economic crunch and hiding our heads in the sand and hoping for the best is not a rational strategy. The only way to survive is to cut public services or for tax-funded services to become more productive. And more productive means increasing the volume of goods and services for the same cost. These are the services that we will need to support the growing population of dependents but without increasing the cost to the country – which means the taxpayer.

The success of Toyota and Apple stemmed from learning how to do just that: how to design and deliver what is needed; and how to eliminate what is not; and how to wisely re-invest the released cash. The difference can translate into higher profit, or into growth, or into more productivity. It just depends on the context. Toyota and Apple went for profit and growth. Tax-funded public services will need to opt for productivity.

And the learning-productivity-improvement-by-design paradigm will be a critical-to-survival factor in tax-payer funded public services such as the NHS and Social Care. We do not have a choice if we want to maintain what we take for granted now. We have to proactively evolve our out-of-date public sector management paradigm. We have to evolve it into one that can support dramatic growth in productivity without sacrificing quality and safety.

We cannot use the burning platform approach. And we have to act with urgency.

We need a lifeboat!

Our current public sector management paradigm is sinking fast and is being defended and propped up by the old school managers who were brought up in it. Unfortunately the evidence of 500 years of change says that the old school cannot unlearn. Their mental models go too deep. The captains and their crews will go down with their ships. [Remember the Titanic the unsinkable ship that sank in 1912 on the maiden voyage. That was a victory of reality over rhetoric.]

Those of us who want to survive are the ‘rats’. We know when it is time to leave the sinking ship. We know we need lifeboats because it could be a long swim! We do not want to freeze and drown during the transition to the new paradigm.

So where are the lifeboats?

One possibility is an unfamiliar looking boat called “6M Design”. This boat looks odd when viewed through the lens of the conventional management paradigm because it combines three apparently contradictiry things: the rational-logical elements of system design; the respect-for-people and learning-through-challenge principles embodied by Toyota and Apple; and the counter-intuitive technique of systems thinking.

Another reason it feel odd is because “6M Design” is not a solution; it is a meta-solution. 6M Design is a way of creating a good-enough-for-now solution by changing the current paradigm a bit at a time. It is a-how-to-design framework; it is not the-what-to-do solution. 6M Design is a paradigm shaper – not a paradigm shaker or a paradigm shifter.

And there is yet another reason why 6M Design does not float the current management boat. It does not need to be controlled by self-appointed experts. Business schools and management consultants, who have a vested interest in defending the current management paradigm, cannot make a quick buck from it because they are irrelevant. 6M Design is intended to be used by anyone and everyone as a common language for collectively engaging in respectful challenge and lifelong learning. Anyone can learn to use it. Anyone.

We do not need a crisis to change. But without changing we will get the crisis we do not want. If we choose to change then we can choose a safer and smoother path of change.

The choice seems clear. Do you want to go down with the ship or stay afloat aboard an innovation boat?

And we will need something to help us navigate our boat.

If you are a reflective, conceptual learner then you might ike to read a synopsis of Thomas Kuhn’s book. You can download a copy here. [There is also a 50 year anniversary edition of the original that was published this year].

And if you prefer learning from stories then there is an excellent one called “Who Moved My Cheese” that describes the same challenge of change. And with the power of the digital paradigm you can watch the video here.

15/12/2012

Defusing Trust Eroders – Part II

? Hello Leslie. How are you today?

Hi Bob – I am OK. Thank you for your time today. Is 15 minutes going to be enough?

? Yes. There is evidence that the ideal chunk of time for effective learning is around 15 minutes.

OK. I said I would read the material you sent me and reflect on it.

? Yes. Can you retell your Nerve Curve as a storyboard and highlight your ‘ah ha’ moments?

OK. And that was the first ‘ah ha’. I found the storyboard format a really effective way to capture my sequence of emotional states.

?Yes. There are very close links between stories, communication, learning and improvement. Before we learned to write we used campfire stories to pass collective knowledge from generation to generation. It is an ancient, in-built skill we all have and we all enjoy a good story.

Yes. My first reaction was to the way you described the Victim role. It really resonated with how I was feeling and how I was part of the dynamic. You were spot on with the feelings that dominated my thinking – anxiety and fear. The big ‘ah ha’ for me was to understand the discount that I was making. Not of others – of myself.

? OK. What was the image that you sketched on your storyboard?

I am embarrased to say – you will think I am silly.

? I will not think you are silly.

Ouch! I know. And I knew that as soon as I said it. I think I was actually saying it to myself – or part of myself. Like I was trying to appease part of myself. Anyway, the picture I sketched was me as a small child at school standing with my head down, hands by my sides, and being told off in front of the whole class for getting a sum wrong. I was crying. I was not very good at maths and even now my mind sort of freezes and I get tears in my eyes and feel scared whenever someone tries to explain something using equations! I can feel the terror starting to well up just talking about it.

? OK. Do not panic. The story you have told is very common. Many of our fears of failure originate from early memories of experiencing ‘education by humiliation’. It is a blunt motivational tool that causes untold and long lasting damage. It is a symptom of a low quality education system design. Education is an exercise in improvement of knowledge and understanding. The unintended outcome of this clumsy educational tactic is a belief that we cannot solve problems ourselves and it is that invalid belief that creates the self-fulfilling prophecy of repeated failure.

Yes! And I know I can solve maths problems – I do it all the time – and I help my children with their maths homework. So it is not the maths that is triggering my fear. What is it?

? The answer to your question will become clear. What is the next picture on your storyboard?

The next picture was of the teacher who was telling me off. Or rather the face of the teacher. It was a face of frustration and anger. I drew a thought bubble and wrote in it “This small, irritating child cannot solve even a simple maths problem and is slowing down the whole lesson by bursting into tears everytime they get stuck. I blame the parents who are clearly too soft. They all need to learn some discipline – the hard way.“

? Does this shed any light on your question?

Wow! Yes! It is not the maths that I am reacting to – it is the behaviour of the teacher. I am scared of the behaviour. I feel powerless. They are the teacher, I am just a small, incompetent, stupid, blubbing child. They do not care that I do not understand the question, and that I am in distress, and that I am scared that I will be embarassed in front of the whole class, and that I am scared that my parents will see a bad mark on my school report. And I feel trapped. I need to rationalise this. To make sense of it. Maybe I am stupid? That would explain why I cannot solve the mths problem. Maybe I should just give in and accept that I am a failure and to stupid to do maths?

There was a pause. Then Leslie continued in a different tone. A more determined tone.

But I am not a failure. This is just my knee jerk habitual reaction to an authority figure displaying anger towards me. I can decide how I react. I have complete control over that. I can disconnect the behaviour I experience and my reaction to it. I can choose. Wow!

? OK. How are you feeling right now? Can you describe it using a visual metaphor?

Um – weird. Mixed feelings. I am picturing myself sitting on a giant catapault. The ends of the huge elastic bands are anchored in the present and I am sitting in the loop but it is stretched way back into the past. There is something formless in the past that has been holding me back and the tension has been slowly building over time. And it feels that I have just cut that tie to the past, and I am free, and I am now being accelerated into the future. I did that. I am in control of my own destiny and it suddenly feels fun and exciting.

? OK. How do you feel right now about the memory of the authority figure from the past?

OK actually. That is really weird. I thought that I would feel angry but I do not. I just feel free. It was not them that was the problem. Their behaviour was not my fault – and it was my reaction to their behaviour that was the issue. My habitual behaviour. No, wait a second. Our habitual behaviour. It is a dynamic. It takes both people to play the game.

There was a pause. Leslie sensed that Bob knew that some time was needed to let the emotions settle a bit.

? Are you OK to continue with your storyboard?

Yes. The next picture is of the faces of my parents. They are looking at my school report. They look sad and are saying “We always dreamed that Leslie would be a doctor or something like that. I suppose we will have to settle for something less ambitious. Do not worry Leslie, it is not your fault, it will be OK, we will help you.” I felt like I had let them down and I had shattered their dream. I felt so ashamed. They had given me everything I had ever asked for. I also felt angry with myself and with them. And that is when I started beating myself up. I no longer needed anyone else to do that! I could persecute myself. I could play both parts of the game in my own head. That is what I did just now when it felt like I was talking to myself.

? OK. You have now outlined the three roles that together create the dynamic for a stable system of learned behaviour. A system that is very resistant to change. It is like a triangular role-playing-game. We pass the role-hats as we swap places in the triangle and we do it in collusion with others and ourselves and we do it unconsciously. The purpose of the game is to create opportunities for social interaction – which we need and crave – the process has a clear purpose. The unintended outcome of this design is that it generates bad feelings, it erodes trust and it blocks personal and organisational development and improvement. We get stuck in it – rather like a small boat in a whirlpool. And we cannot see that we are stuck in it. We just feel bad as we spin around in an emotional maelstrom. And we feel cheated out of something better but we do not know what it is and how to get it.

There was a long pause. Leslie’s mind was racing. The world had just changed. The pieces had been blown apart and were now re-assembling in a different configuration. A simpler, clearer and more elegant design.

So, tell me if I have this right. Each of the three roles involves a different discount?

?Yes.

And each discount requires a different – um – tactic to defuse?

?Yes.

So the way to break out of this trust eroding behavioural hamster-wheel is to learn to recognise which role we are in and to consciously deploy the discount defusing tactic.

? Yes.

And by doing that enough times we learn how to spot the traps that other people are creating and avoid getting sucked into them.

? Yes. And we also avoid starting them ourselves.

Of course! And by doing that we develop growing respect for ourselves and for each other and a growing level of trust in ourselves and in others? We have started to defuse the trust eroding behaviour and that lowers the barrier to personal and organisational development and improvement.

? Yes.

So what are the three discount defusing tactics?

There was a pause. Leslie knew what was coming next. It would be a question.

? What role are you in now?

Oh! Yes. I see. I am still feeling like that small school child at school but now I am asking for the answer and I am discounting myself by assuming that I cannot solve this problem myself. I am assuming that I need you to rescue me by telling me the answer. I am still in the trust eroding game, I do not trust myself and I am inviting you to play too, and to reinforce my belief that I cannot solve the problem.

? And do you need me to tell you the answer?

No. I can probably work this out myself. And if I do get stuck then I can ask for hints or nudges – not for the answer. I need to do the learning work.

? OK. I will commit to hinting and nudging if asked and if I do not know the answer I will say so.

Phew! That was definitely a rollercoaster ride on the Nerve Curve. Looking back it all makes complete sense and I now know what to do – but at the start it felt like I was heading into the Dark Unknown. You are right. It is liberating and exhilarating!

? That feeling of clarity of hindsight and exhilaration from learning is what we always strive for. Both as educators and educatees.

You mean it is the same for you? You are still riding the Nerve Curve? Still feeling surprised, confused, scared, resolved, enlightened then delighted?

? Yes. Every day. It is fun. I believe that there is No Limit to Learning so there is an inexhaustible Font of Fun.

Wow! I am off to have more Fun from Learning. Thank you so much yet again.

? Thank you Leslie.

Defusing Trust Eroders – Part I

Defusing Trust Eroders – Part III

13/12/2012

Defusing Trust Eroders – Part I

Bob heard the beep and looked at his phone. There was a text message from Leslie, one of his Improvementology mentees.

It said:

Hi Bob, Do you have time to help me with a behaviour barrier that I keep hitting and cannot see a way around?

Bob thumbed his reply:

?Yes. I am free at the moment – please feel free to call.

?Hello Leslie. How can I help?

Hi Bob. I really hope you can help me with this recurring Niggle. I have looked through my Foundation notes and I cannot see where it is described and it does not seem to be a Nerve Curve problem.

?I will do my best. Can you outline the context or give me an example?

It is easier to give you an example. This week I was working with a team in my organisation who approached me to help them with recurring niggles in their process. I went to see for myself and I mapped their process and identified where their niggles were and what was driving them. That was the easy bit. But when I started to make suggestions of what they could do to resolve their problems they started to give me a hard time and kept saying ‘Yes, but …”. It was as if they were asking for help but did not really want it. They kept emphasising that all their problems were caused by other people outside their department and kept asking me what I could do about it. I felt as if they were pushing the problem onto me and I was also feeling guilty for not being able to sort it out for them.

There was a pause. Then Bob said.

?You are correct Leslie. This is not a Nerve Curve issue. It is a different people-related system issue. It is ubiquitous and it is a potentially deadly organisational disease. We call it Trust Eroding Behaviour.

That sounds exactly how it felt for me. I went to help in good faith and quickly started to feel distrustful of their motives. It was not a good feeling and I do not know if I want to go back. One part of me says ‘ It is your duty – you have made a commitment’ and another part of me says ‘Stop – you are being suckered.’ What is happening?

?Do you remember that the Improvement Science framework has three parts – Processes, People and Systems?

Yes.

?OK. This is part of the People component and it is similar to but different from the Nerve Curve. The Nerve Curve is a hard-wired emotional response to any change. The Fright, Fight, Flight response. It is just the way we are and it is not ‘correctable’. This is different. This is a learned behaviour. Which means it can be unlearned.

Unlearned? That is not a concept that I am familiar with. Can you explain? Is it the same as forgetting?

?Forgetting means that you cannot bring something to conscious awareness. Unlearning is different – it operates at a deeper psychological and emotional level. Have you ever tried to change a bad habit?

Yes I have. I used to smoke which is definitely a bad habit and I managed to give up but it was really tough.

?What you did was to unlearn the smoking habit. You did not forget about smoking. You could not because you are repeatedly reminded by other people who still indulge in the habit.

Ah ha! I see what you mean. Yes – after I kicked the habit I became a bit of a Stop-Smoking evangelist. I even had a tee shirt. It did not seem to make much impact on the still-smokers though. If anything it seemed to make them more determined to keep doing it – just to spite me!

?Yes. What you describe is what many people report. It is part if the same learned behaviour patterns. The habit that is causing the issue is rather like smoking because it causes short-term pleasure and long-term pain. It is both attractive and destructive. The behaviour feels good briefly but it is toxic to trust which is why we call it the Trust Eroding Behaviour.

What is the habit? I do not recognise the behaviour that you are referring to.

?The habit is called discounting. The reason we are not aware of it is we do it unconsciously.

What is it that we do?

?It is easier to give you some examples. How do you feel when all the feedback you get is silence? How do you feel when someone complains that their mistake was not their fault? How do you feel when you try to help but you hit invisible barriers that block your progess?

Ouch! Those are uncomfortable questions. When I get no feedback I feel anxious and even fearful that I have made a mistake, and no one is telling me, and a nasty surprise is on its way. When someone keeps complaining that even though they made the mistake they are not to blame I feel angry. When I try to help others and fail I feel sad because my reputation, credibility and self-confidence is damaged.

?OK. Do not panic. These negative emotional reactions are the normal reaction to discounting behaviour. Another word for discounting is disrespect. The three primary emotions we feel are fear, anger and sadness. Fear is the sense of impending loss; anger is the sense of present loss; and sadness is the sense of past loss. They are the same emotions that we feel on the Nerve Curve. What is different is the cause. Discounting is a learned disrepectful behaviour.

Oooo! That really resonates with me. Just reflecting on one day at work I can think of lots of examples of all of those negative feelings. So when do we learn this discounting habit?

?It is believed that we learn this behaviour when we are very young – before the age of seven. And because we learn it so young we internalise it and we become unaware of it. It then becomes a habit that is reinforced with years of practice.

Wow! That rings true for me – and it may explain why I actively avoided some people at school – they were just toxic. But they had friends, went to college, got jobs, married andstarted families – just like me. Does that mean we grow out of it?

?Most people unlearn some of these behavioural habits because life-experience teaches them that they are counter-productive. We all carry some of them though and they tend to emerge when we are tired and under pressure. Some people get sort of stuck and carry these behaviours into their adult life. Their behaviour can be toxic to organisations.

I definitely resonate with that statement! Is there a way to unlearn this discounting habit?

?Yes – just becoming aware of its existence is the first step. There are some strategies that we can learn, practice and use to defuse the discounting behaviour and over time our bad habit can be kicked.”

Wow! That sounds really useful. And not just at work – I can see benefits in other areas of my life too.

?Yes. Improvement science is powerful medicine.

So what do I need to do?

?You have learned the 6M Design framework for resolving process niggles. There is an equivalent one for dissolving people niggles. I will send you some material to read and then we can talk again.

Will it help me resolve the problem that I have with the department that asked for my help who are behaving like Victims?

?Yes.

OK – please send me the material. I promise to read it, reflect on it and I will arrange another conversation. I cannot wait to learn how to nail this niggle! I can see a huge win-win-win opportunity here.

?OK. The material is on its way. I look forward to our next conversation.

Defusing Trust Eroders – Part I

Defusing Trust Eroders – Part II

Defusing Trust Eroders – Part III

01/12/2012

The F Word

There is an F-word that organisations do not like to use – except maybe in conspiratorial corridor conversations.

What word might that be? What are good candidates for it?

Finance perhaps?

Certainly a word that many people do not want to utter – especially when the financial picture is not looking very rosy. And when the word finance is mentioned in meetings there is usually a groan of anguish. So yes, finance is a good candidate – but it is not the F-word.

Failure maybe?

Yes – definitely a word that is rarely uttered openly. The concept of failure is just not acceptable. Organisations must succeed, sustain and grow. Talk of failure is for losers not for winners. To talk about failure is tempting fate. So yes, another excellent candidate – but it is not the F-word.

OK – what about Fear?

That is definitely something no one likes to admit to. Especially leaders. They are expected to be fearless. Fear is a sign of weakness! Once you start letting the fear take over then panic starts to set in – then rash decisions follow then you are really on the slippery slope. Your organisation fragments into warring factions and your fate is sealed. That must be the F-word!

Nope. It is another very worthy candidate but it is not the F-word.

[reveal heading=”Click here to reveal the F-word“]

The dreaded F-word is Feedback.

We do not like feedback. We do not like asking for it. We do not like giving it. We do not like talking about it. Our systems seem to be specifically designed to exclude it. Potentially useful feedback information is kept secret, confidential, for-our-eyes only. And if it is shared it is emasculated and anonymized.

And the brave souls who are prepared to grasp the nettle – the 360 Feedback Zealots – are forced to cloak feedback with secrecy and confidentiality. We are expected to ask for feedback, to take it on the chin, but not to know who or where it came from. So to ease the pain of anonymous feedback we are allowed to choose our accusers. So we choose those who we think will not point out our blindspot. Which renders the whole exercise worthless.

And when we actually want feedback we extract it mercilessly – like extracting blood from a reluctant stone. And if you do not believe me then consider this question: Have you ever been to a training course where your ‘certificate of attendance’ was with-held until you had completed the feedback form? The trainers do this for good reason. We just hate giving feedback. Any feedback. Positive or negative. So if they do not extract it from us before we leave they do not get any.

Unfortunately by extracting feedback from us under coercion is like acquiring a confession under torture – it distorts the message and renders it worthless.

What is the problem here? What are we scared of?

We all know the answer to the question. We just do not want to point at the elephant in the room.

We are all terrified of discovering that we have the organisational equivalent of body-odour. Something deeply unpleasant about our behaviour that we are blissfully unaware of but that everyone else can see as plain as day. Our behaviour blindspot. The thing we would cringe with embarrassment about if we knew. We are social animals – not solitary ones. We need on feedback yet we fear it too.

We lack the courage and humility to face our fear so we resort to denial. We avoid feedback like the plague. Feedback becomes the F-word.

But where did we learn this feedback phobia?

Maybe we remember the playground taunts from the Bullies and their Sychophants? From the poisonous Queen-Bees and their Wannabees? Maybe we tried to protect ourselves with incantations that our well-meaning parents taught us. Spells like “Sticks and stones may break my bones but names will never hurt me“. But being called names does hurt. Deeply. And it hurts because we are terrified that there might be some truth in the taunt.

Maybe we learned to turn a blind-eye and a deaf-ear; to cross the street at the first sign of trouble; to turn the other cheek? Maybe we just learned to adopt the Victim role? Maybe we were taught to fight back? To win at any cost? Maybe we were not taught how to defuse the school yard psycho-games right at the start? Maybe our parents and teachers did not know how to teach us? Maybe they did not know themselves? Maybe the ‘innocent’ schoolyard games are actually much more sinister? Maybe we carry them with us as habitual behaviours into adult life and into our organisations? And maybe the bullies and Queen-Bees learned something too? Maybe they learned that they could get away with it? Maybe they got to like the Persecutor role and its seductive musk of power? If so then then maybe the very last thing the Bullies and Queen-Bees will want to do is to encourage open, honest feedback – especially about their behaviour. Maybe that is the root cause of the conspiracy of silence? Maybe?

But what is the big deal here?

The ‘big deal’ is that this cultural conspiracy of silence is toxic. It is toxic to trust. It is toxic to teams. It is toxic to morale. It is toxic to motivation. It is toxic to innovation. It is toxic to improvement. It is so toxic that it kills organisations – from the inside. Slowly.

Ouch! That feels uncomfortably realistic. So what is the problem again – exactly?

The problem is a deliberate error of omission – the active avoidance of feedback.

So ….. if it were that – how would we prove that is the root cause? Eh?

By correcting the error of omission and then observing what happens.

And this is where it gets dangerous for leaders. They are skating on politically thin ice and they know it.

Subjective feedback is very emotive. If we ask ten people for their feedback on us we will get ten different replies – because no two people perceive the world (and therefore us) the same way. So which is ‘right’? Which opinions do we take heed of and which ones do we discount? It is a psycho-socio-political minefield. So no wonder we avoid stepping onto the cultural barbed-wire!

There is an alternative. Stick to reality and avoid rhetoric. Stick to facts and avoid feelings. Feed back the facts of how the organisational system is behaving to everyone in the organisation.

And the easiest way to do that is with three time-series charts that are updated and shared at regular and frequent intervals.

First – the count of safety and quality failure near-misses for each interval – for at least 50 intervals.

Second – the delivery time of our product or service for each customer over the same time period.

Third – the revenue generated and the cost incurred for each interval for the same 50 intervals.

No ratios, no targets, no balanced scorecard.

Just the three charts that paint the big picture of reality. And it might not be a very pretty picture.

But why at least 50 intervals?

So we can see the long term and short term variation over time. We need both … because …

Our Safety Chart shows that near misses keep happening despite all the burden of inspection and correction.

Our Delivery Chart shows that our performance is distorted by targets and the Horned Gaussian stalks us.

Our Viability Chart shows that our costs are increasing as we pay dearly for past mistakes and our revenue is decreasing as our customers protect their purses and their persons by staying away.

That is the not-so-good news.

The good news is that as soon as we have a multi-dimensional-frequent-feedback loop installed we will start to see improvement. It happens like magic. And the feedback accelerates the improvement.

And the news gets better.

To make best use of this frequent feedback we just need to include in our Constant Purpose – to improve safety, delivery and viability. And then the final step is to link the role of every person in the organisation to that single win-win-win goal. So that everyone can see how they contribute and how their job is worthwhile.

Shared Goals, Clear Roles and Frequent Feedback.

And if you resonate with this message then you will resonate with “The Three Signs of Miserable Job” by Patrick Lencioni.

And if you want to improve your feedback-ability then a really simple and effective feedback tool is The 4N Chart

And please share your feedback.

[/reveal]

24/11/2012

The Three R’s

Processes are like people – they get poorly – sometimes very poorly.

Poorly processes present with symptoms. Symptoms such as criticism, complaints, and even catastrophes.

Poorly processes show signs. Signs such as fear, queues and deficits.

So when a process gets very poorly what do we do?

We follow the Three R’s

1-Resuscitate
2-Review
3-Repair

Resuscitate means to stabilize the process so that it is not getting sicker.

Review means to quickly and accurately diagnose the root cause of the process sickness.

Repair means to make changes that will return the process to a healthy and stable state.

So the concept of ‘stability’ is fundamental and we need to understand what that means in practice.

Stability means ‘predictable within limits’. It is not the same as ‘constant’. Constant is stable but stable is not necessarily constant.

Predictable implies time – so any measure of process health must be presented as time-series data.

We are now getting close to a working definition of stability: “a useful metric of system performance that is predictable within limits over time”.

So what is a ‘useful metric’?

There will be at least three useful metrics for every system: a quality metric, a time metric and a money metric.

Quality is subjective. Money is objective. Time is both.

Time is the one to start with – because it is the easiest to measure.

And if we treat our system as a ‘black box’ then from the outside there are three inter-dependent time-related metrics. These are external process metrics (EPMs) – sometimes called Key Performance Indicators (KPIs).

Flow in – also called demand
Flow out – also called activity
Delivery time – which is the time a task spends inside our system – also called the lead time.

But this is all starting to sound like rather dry, conceptual, academic mumbo-jumbo … so let us add a bit of realism and drama – let us tell this as a story …

[reveal heading=”Click here to reveal the story …“]

Picture yourself as the manager of a service that is poorly. Very poorly. You are getting a constant barrage of criticism and complaints and the occasional catastrophe. Your service is struggling to meet the required delivery time performance. Your service is struggling to stay in budget – let alone meet future cost improvement targets. Your life is a constant fire-fight and you are getting very tired and depressed. Nothing you try seems to make any difference. You are starting to think that anything is better than this – even unemployment! But you have a family to support and jobs are hard to come by in austere times so jumping is not an option. There is no way out. You feel you are going under. You feel are drowning. You feel terrified and helpless!

In desperation you type “Management fire-fighting” into your web search box and among the list of hits you see “Process Improvement Emergency Service”. That looks hopeful. The link takes you to a website and a phone number. What have you got to lose? You dial the number.

It rings twice and a calm voice answers.

?“You are through to the Process Improvement Emergency Service – what is the nature of the process emergency?”

“Um – my service feels like it is on fire and I am drowning!”

The calm voice continues in a reassuring tone.

?“OK. Have you got a minute to answer three questions?”

“Yes – just about”.

?“OK. First question: Is your service safe?”

“Yes – for now. We have had some catastrophes but have put in lots of extra safety policies and checks which seems to be working. But they are creating a lot of extra work and pushing up our costs and even then we still have lots of criticism and complaints.”

?“OK. Second question: Is your service financially viable?”

“Yes, but not for long. Last year we just broke even, this year we are projecting a big deficit. The cost of maintaining safety is ‘killing’ us.”

?“OK. Third question: Is your service delivering on time?”

“Mostly but not all of the time, and that is what is causing us the most pain. We keep getting beaten up for missing our targets. We constantly ask, argue and plead for more capacity and all we get back is ‘that is your problem and your job to fix – there is no more money’. The system feels chaotic. There seems to be no rhyme nor reason to when we have a good day or a bad day. All we can hope to do is to spot the jobs that are about to slip through the net in time; to expedite them; and to just avoid failing the target. We are fire-fighting all of the time and it is not getting better. In fact it feels like it is getting worse. And no one seems to be able to do anything other than blame each other.”

There is a short pause then the calm voice continues.

?“OK. Do not panic. We can help – and you need to do exactly what we say to put the fire out. Are you willing to do that?”

“I do not have any other options! That is why I am calling.”

The calm voice replied without hesitation.

?“We all always have the option of walking away from the fire. We all need to be prepared to exercise that option at any time. To be able to help then you will need to understand that and you will need to commit to tackling the fire. Are you willing to commit to that?”

You are surprised and strangely reassured by the clarity and confidence of this response and you take a moment to compose yourself.

“I see. Yes, I agree that I do not need to get toasted personally and I understand that you cannot parachute in to rescue me. I do not want to run away from my responsibility – I will tackle the fire.”

?“OK. First we need to know how stable your process is on the delivery time dimension. Do you have historical data on demand, activity and delivery time?”

“Hey! Data is one thing I do have – I am drowning in the stuff! RAG charts that blink at me like evil demons! None of it seems to help though – the more data I get sent the more confused I become!”

?“OK. Do not panic. The data you need is very specific. We need the start and finish events for the most recent one hundred completed jobs. Do you have that?”

“Yes – I have it right here on a spreadsheet – do I send the data to you to analyse?”

?“There is no need to do that. I will talk you through how to do it.”

“You mean I can do it now?”

?“Yes – it will only take a few minutes.”

“OK, I am ready – I have the spreadsheet open – what do I do?”

?“Step 1. Arrange the start and finish events into two columns with a start and finish event for each task on each row.

You copy and paste the data you need into a new worksheet.

“OK – done that”.

?“Step 2. Sort the two columns into ascending order using the start event.”

“OK – that is easy”.

?“Step 3. Create a third column and for each row calculate the difference between the start and the finish event for that task. Please label it ‘Lead Time’”.

“OK – do you want me to calculate the average Lead Time next?”

There was a pause. Then the calm voice continued but with a slight tinge of irritation.

?“That will not help. First we need to see if your system is unstable. We need to avoid the Flaw of Averages trap. Please follow the instructions exactly. Are you OK with that?”

This response was a surprise and you are starting to feel a bit confused.

“Yes – sorry. What is the next step?”

?“Step 4: Plot a graph. Put the Lead Time on the vertical axis and the start time on the horizontal axis”.

“OK – done that.”

?“Step 5: Please describe what you see?”

“Um – it looks to me like a cave full of stalagtites. The top is almost flat, there are some spikes, but the bottom is all jagged.”

?“OK. Step 6: Does the pattern on the left-side and on the right-side look similar?”

“Yes – it does not seem to be rising or falling over time. Do you want me to plot the smoothed average over time or a trend line? They are options on the spreadsheet software. I do that use all the time!”

The calm voice paused then continued with the irritated overtone again.

?“No. There is no value is doing that. Please stay with me here. A linear regression line is meaningless on a time series chart. You may be feeling a bit confused. It is common to feel confused at this point but the fog will clear soon. Are you OK to continue?”

An odd feeling starts to grow in you: a mixture of anger, sadness and excitement. You find yourself muttering “But I spent my own hard-earned cash on that expensive MBA where I learned how to do linear regression and data smoothing because I was told it would be good for my career progression!”

?“I am sorry I did not catch that? Could you repeat it for me?”

“Um – sorry. I was talking to myself. Can we proceed to the next step?”

?”OK. From what you say it sounds as if your process is stable – for now. That is good. It means that you do not need to Resuscitate your process and we can move to the Review phase and start to look for the cause of the pain. Are you OK to continue?”

An uncomfortable feeling is starting to form – one that you cannot quite put your finger on.

“Yes – please”.

?Step 7: What is the value of the Lead Time at the ‘cave roof’?”

“Um – about 42”

?“OK – Step 8: What is your delivery time target?”

“42”

?“OK – Step 9: How is your delivery time performance measured?”

“By the percentage of tasks that are delivered late each month. Our target is better than 95%. If we fail any month then we are named-and-shamed at the monthly performance review meeting and we have to explain why and what we are going to do about it. If we succeed then we are spared the ritual humiliation and we are rewarded by watching others else being mauled instead. There is always someone in the firing line and attendance at the meeting is not optional!”

You also wanted to say that the data you submit is not always completely accurate and that you often expedite tasks just to avoid missing the target – in full knowkedge that the work had not been competed to the required standard. But you hold that back. Someone might be listening.

There was a pause. Then the calm voice continued with no hint of surprise.

?“OK. Step 10. The most likely diagnosis here is a DRAT. You have probably developed a Gaussian Horn that is creating the emotional pain and that is fuelling the fire-fighting. Do not panic. This is a common and curable process illness.”

You look at the clock. The conversation has taken only a few minutes. Your feeling of panic is starting to fade and a sense of relief and curiosity is growing. Who are these people?

“Can you tell me more about a DRAT? I am not familiar with that term.”

?“Yes. Do you have two minutes to continue the conversation?”

“Yes indeed! You have my complete attention for as long as you need. The emails can wait.”

The calm voice continues.

?“OK. I may need to put you on hold or call you back if another emergency call comes in. Are you OK with that?”

“You mean I am not the only person feeling like this?”

?“You are not the only person feeling like this. The process improvement emergency service, or PIES as we call it, receives dozens of calls like this every day – from organisations of every size and type.”

“Wow! And what is the outcome?”

There was a pause. Then the calm voice continued with an unmistakeable hint of pride.

?“We have a 100% success rate to date – for those who commit. You can look at our performance charts and the client feedback on the website.”

“I certainly will! So can you explain what a DRAT is?”

And as you ask this you are thinking to yourself ‘I wonder what happened to those who did not commit?’

The calm voice interrupts your train of thought with a well-practiced explanation.

?“DRAT stands for Delusional Ratio and Arbitrary Target. It is a very common management reaction to unintended negative outcomes such as customer complaints. The concept of metric-ratios-and-performance-specifications is not wrong; it is just applied indiscriminately. Using DRATs can drive short-term improvements but over a longer time-scale they always make the problem worse.”

One thought is now reverberating in your mind. “I knew that! I just could not explain why I felt so uneasy about how my service was being measured.” And now you have a new feeling growing – anger. You control the urge to swear and instead you ask:

“And what is a Horned Gaussian?”

The calm voice was expecting this question.

?“It is easier to demonstrate than to explain. Do you still have your spreadsheet open and do you know how to draw a histogram?”

“Yes – what do I need to plot?”

?“Use the Lead Time data and set up ten bins in the range 0 to 50 with equal intervals. Please describe what you see”.

It takes you only a few seconds to do this. You draw lots of histograms – most of them very colourful but meaningless. No one seems to mind though.

“OK. The histogram shows a sort of heap with a big spike on the right hand side – at 42.”

The calm voice continued – this time with a sense of satisfaction.

?“OK. You are looking at the Horned Gaussian. The hump is the Gaussian and the spike is the Horn. It is a sign that your complex adaptive system behaviour is being distorted by the DRAT. It is the Horn that causes the pain and the perpetual fire-fighting. It is the DRAT that causes the Horn.”

“Is it possible to remove the Horn and put out the fire?”

?“Yes.”

This is what you wanted to hear and you cannot help cutting to the closure question.

“Good. How long does that take and what does it involve?”

The calm voice was clearly expecting this question too.

?“The Gaussian Horn is a non-specific reaction – it is an effect – it is not the cause. To remove it and to ensure it does not come back requires treating the root cause. The DRAT is not the root cause – it is also a knee-jerk reaction to the symptoms – the complaints. Treating the symptoms requires learning how to diagnose the specific root cause of the lead time performance failure. There are many possible contributors to lead time and you need to know which are present because if you get the diagnosis wrong you will make an unwise decision, take the wrong action and exacerbate the problem.”

Something goes ‘click’ in your head and suddently your fog of confusion evaporates. It is like someone just switched a light on.

“Ah Ha! You have just explained why nothing we try seems to work for long – if at all. How long does it take to learn how to diagnose and treat the specific root causes?”

The calm voice was expecting this question and seemed to switch to the next part of the script.

?“It depends on how committed the learner is and how much unlearning they have to do in the process. Our experience is that it takes a few hours of focussed effort over a few weeks. It is rather like learning any new skill. Guidance, practice and feedback are needed. Just about anyone can learn how to do it – but paradoxically it takes longer for the more experienced and, can I say, cynical managers. We believe they have more unlearning to do.”

You are now feeling a growing sense of urgency and excitement.

“So it is not something we can do now on the phone?”

?“No. This conversation is just the first step.”

You are eager now – sitting forward on the edge of your chair and completely focussed.

“OK. What is the next step?”

There is a pause. You sense that the calm voice is reviewing the conversation and coming to a decision.

?“Before I can answer your question I need to ask you something. I need to ask you how you are feeling.”

That was not the question you expected! You are not used to talking about your feelings – especially to a complete stranger on the phone – yet strangely you do not sense that you are being judged. You have is a growing feeling of trust in the calm voice.

You pause, collect your thoughts and attempt to put your feelings into words.

“Er – well – a mixture of feelings actually – and they changed over time. First I had a feeling of surprise that this seems so familiar and straightforward to you; then a sense of resistance to the idea that my problem is fixable; and then a sense of confusion because what you have shown me challenges everything I have been taught; and then a feeling distrust that there must be a catch and then a feeling of fear of embarassement if I do not spot the trick. Then when I put my natural skepticism to one side and considered the possibility as real then there was a feeling of anger that I was not taught any of this before; and then a feeling of sadness for the years of wasted time and frustration from battling something I could not explain. Eventually I started to started to feel that my cherished impossibility belief was being shaken to its roots. And then I felt a growing sense of curiosity, optimism and even excitement that is also tinged with a feeling of fear of disappointment and of having my hopes dashed – again.”

There was a pause – as if the calm voice was digesting this hearty meal of feelings. Then the calm voice stated:

?“You are experiencing the Nerve Curve. It is normal and expected. It is a healthy sign. It means that the healing process has already started. You are part of your system. You feel what it feels – it feels what you do. The sequence of negative feelings: the shock, denial, anger, sadness, depression and fear will subside with time and the positive feelings of confidence, curiosity and excitement will replace them. Do not worry. This is normal and it takes time. I can now suggest the next step.”

You now feel like you have just stepped off an emotional rollercoaster – scary yet exhilarating at the same time. A sense of relief sweeps over you. You have shared your private emotional pain with a stranger on the phone and the world did not end! There is hope.

“What is the next step?”

This time there was no pause.

?“To commit to learning how to diagnose and treat your process illnesses yourself.”

“You mean you do not sell me an expensive training course or send me a sharp-suited expert who will come tell me what to do and charge me a small fortune?”

There is an almost sarcastic tone to your reply that you regret as soon as you have spoken.

Another pause. An uncomfortably long one this time. You sense the calm voice knows that you know the answer to your own question and is waiting for you to answer it yourself.

You answer your own question.

“OK. I guess not. Sorry for that. Yes – I am definitely up for learning how! What do I need to do.”

?“Just email us. The address is on the website. We will outline the learning process. It is neither difficult nor expensive.”

The way this reply was delivered – calmly and matter-of-factly – was reassuring but it also promoted a new niggle – a flash of fear.

“How long have I got to learn this?”

This time the calm voice had an unmistakable sense of urgency that sent a cold prickles down your spine.

?”Delay will add no value. You are being stalked by the Horned Gaussian. This means your system is on the edge of a catastrophe cliff. It could tip over any time. You cannot afford to relax. You must maintain all your current defenses. It is a learning-by-doing process. The sooner you start to learn-by-doing the sooner the fire starts to fade and the sooner you move away from the edge of the cliff.”

“OK – I understand – and I do not know why I did not seek help a long time ago.”

The calm voice replied simply.

?”Many people find seeking help difficult. Especially senior people”.

Sensing that the conversation is coming to an end you feel compelled to ask:

“I am curious. Where do the DRATs come from?”

?“Curiosity is a healthy attitude to nurture. We believe that DRATs originated in finance departments – where they were originally called Fiscal Averages, Ratios and Targets. At some time in the past they were sucked into operations and governance departments by a knowledge vacuum created by an unintended error of omission.”

You are not quite sure what this unfamiliar language means and you sense that you have strayed outside the scope of the “emergency script” but the phrase ‘error of omission sounds interesting’ and pricks your curiosity. You ask:

“What was the error of omission?”

?“We believe it was not investing in learning how to design complex adaptive value systems to deliver capable win-win-win performance. Not investing in learning the Science of Improvement.”

“I am not sure I understand everything you have said.”

?“That is OK. Do not worry. You will. We look forward to your email. My name is Bob by the way.”

“Thank you so much Bob. I feel better just having talked to someone who understands what I am going through and I am grateful to learn that there is a way out of this dark pit of despair. I will look at the website and send the email immediately.”

?”I am happy to have been of assistance.”

[/reveal]

17/11/2012

Systems within Systems

Each of us is a small part of a big system. Each of us is a big system made of smaller parts. The concept of a system is the same at all scales – it is called scale invariant.

When we put a system under a microscope we see parts that are also systems. And when we zoom in on those we see their parts are also systems. And if we look outwards with a telescope we see that we are part of a bigger system which in turn is part of an even bigger system.

This concept of systems-within-systems has a down-side and an up-side.

The down-side is that it quickly becomes impossible to create a mental picture of the whole system-of-systems. Our caveman brains are just not up to the job. So we just focus our impressive-but-limited cognitive capacity on the bit that affects us most. The immediate day-to-day people-and-process here-and-now stuff. And we ignore the ‘rest’. We deliberately become ignorant – and for good reason. We do not ask about the ‘rest’ because we do not want to know because we cannot comprehend the complexity. We create cognitive comfort zones and personal silos.

And we stay inside our comfort zones and we hide inside our silos.

Unfortunately – ignoring the ‘rest’ does not make it go away.

We are part of a system – we are affected by it and it is affected by us. That is how systems work.

The up-side is that all systems behave in much the same way – irrespective of the level. This is very handy because if we can master a method for understanding and improving a system at one level – then we can use the same method at any level. The only change is the degree of detail. We can chunk up and down and still use the same method.

The improvement scientist needs to be a master of one method and to be aware of three levels: the system level, the stream level and the step level.

The system provides the context for the streams. The steps provide the content of the streams.

Direction operates at the system level.
Delivery operates at the stream level.
Doing operates at the step level.

So an effective and efficient improvement science method must work at all three levels – and one method that has been demonstrated to do that is called 6M Design®.

6M Design® is not the only improvement science method, and it is not intended to be the best. Being the best is not the purpose because it is not necessary. Having better than what we had before is the purpose because it is sufficient. That is improvement.

6M Design® works at all three levels. It is sufficient for system-wide and system-deep improvement. So that is what I use.

The first M stands for Map.

Maps are designed to be visual and two-dimensional because that is how our Mark-I eyeballs abd visual sensory systems work. Our caveman brains are good at using pictures and in extraction meaning from the detail. It is a survival skill.

All real systems have a lot more than two dimensions. Safety, Quality, Flow and Cost are four dimensions to start with, and there are many more. So we need lots of maps. Each one looking at just two of the dimensions. It is our set of maps that provide us with a multi-dimensional picture of the system we want to improve.

One dimension features more often in the maps than any other – and that dimension is time.

The Western cultural convention is to put time on the horizonal axis with past in the left and future on the right. Left-to-right means looking forward in time. Right-to-left means looking backwards in time.

We have already seen one of the time-dependent maps – The 4N Chart®.

It is a Emotion-Time map. How do we feel now and why? What do we want to feel in the futrure and why? It is a status-at-a-glance map. A static map. A snapshot.

The emotional roller coaster of change – the Nerve Curve – is an Emotion-Time map too. It is a dynamic map – an expected trajectory map. The emotional ups and downs that we expect to encounter when we engage in significant change.

Change usually involves several threads at the same time – each with its own Nerve Curve.

The 4N Charts® are snapshots of all the parallel threads of change – they evolve over time – they are our day-to-day status-at-a-glance maps – and they guide us to which Nerve Curve to pay attention to next and what to do.

The map that links the three – the purposes, the pathways and the parts – is the map that underpins 6M Design®. A map that most people are not familiar with because it represents a counter-intuitive way of thinking.

And it is that critical-to-success map which differentiates innovative design from incremental improvement.

And using that map can be learned quite quickly – if you have a guide – an Improvement Scientist.

09/11/2012

The Four Parts of Purpose

Mission Statements are often ridiculed and discounted by the very people they are designed for.

Their intention appears positive yet they often seem ineffective and even counter-productive.

Why is that?

In essence the Mission Statement is a declaration of the organisations purpose and provides a context for the formulation of strategy. Very often they are ambiguous, emotive and sort of yingy-yangy. More marketing gimmick than management goal.

The output of Improvement Science is a system designed to deliver its value purpose. So a clear and realistic purpose is the first requirement for an effective system design.

For example:

Global Fast Food Inc – “To provide fast-food prepared in the same high-quality manner world-wide that is tasty, reasonably-priced and delivered consistently in a low-key décor and friendly atmosphere.”

This is a clear purpose specification – and it has all the Three Wins® design elements of quality, delivery and money. It is necessary but it is not yet sufficient.

What is missing?

First we need to be clear what a poor purpose statement design looks like. They contain the word “best”. They are poor designs because just using the word “best” makes them aspirations not specifications. Dreams rather than deliverables. Only one organisation can actually be “the best” so adopting impossible purpose condemns the majority of organisations to failure-to-achieve-their-purpose. And everyone in the organisation knows that. So they give up emotionally at the start. They know that achieving the stated purpose is impossible.

Not having a Statement of Purpose (SoP) at all is even worse because the message this broadcasts is that the organisation cannot articulate its purpose – its reason for existing – where it derives its sense of value and worth. Purposeless organisations are chaotic and demotivating places to work in because the emotional vacuum is filled with something much more toxic – organisational politics.

So we do need some form of Statement of Purpose and one reason that the what-we-will-do design feels incomplete is because it only covers a quarter of the requirements for a system purpose specification. And it is the missing three-quarters that causes the problems. They are difficult to articulate but we can feel the gap that we cannot see.

A statement of purpose is a cultural contract – is operates at the people and psychological level – not at the legal level. It is a collective pledge. It is a statement of expectation.

So when observed behaviour falls short of expected behaviour then disappointment and anger results. After that comes sadness – for the loss of hope – then fear of what the failure implies and what will come next. Fear of the rhetoric-reality mismatch; the small white lies that feed on fear and grow into the big fat porkie-pies; the secrecy and hoarding of knowledge; the hidden agendas; and the behind-closed door wheeling and dealing; the fait accomplis and the handed down JFDI Policies. All untrustworthy behaviours. And all blindingly obvious to everyone. Trust is eroded, optimism turns to skepticism and then cynicism. The toxic emotional swamp deepens. Who would want to invest their lifetime there? The savvy sensitive ones escape. The emotionally thick-skinned species of employee survive. A few noisy idealists may stay out of a misplaced sense of loyality but usually even they fall silent as the toxic swamp overwhelmes them. Not a very rosy picture is it?

So what does a full Statement of Purpose look like?

Firstly there are two Acts:

1. The Acts of Commission – the things that we say we will commit to do.
2. The Acts of Omission – the things that we say we will commit NOT to do.

Both are required.

These are made explicit using a Pledge. The pledge is the output if a formal design exercise – like a blueprint.

Secondly there are the two Defences against Errors. These are made explicit using a Plan. It too requires design.

When we fail to deliver on our commitments as individuals (and we all do because we are all human) then we make two different types of error. I- the Error of Commission or II – the Error of Omission.

The Error of Commission is when we do the wrong thing (or we try to do the right thing but do it wrong). The first is failure of efficacy the second is failure of effectiveness. So first we need to be able to decide what is the right thing and then we need the capability to deliver it right. For that we need to know what to do and how to do it. We need both knowledge and understanding. We need to know what and why.

Errors erode trust. And one of the commonest errors of commission is to assume ineffectiveness (or inefficiency) when the actual cause is poor strategic decisions. The effect of this error is to add more and more bureaucracy. Checking that we have done what we should and done it right. Inspection-and-Correction, Supervision-and-Surveillance, Audits-and-Reports. Waiting for a failure and then sniffing like hounds up the trail of spilt blood and breadcrumbs. Right back to the individual who committed the sinof commission and then to expose and punish them. To weed out the bad apples in the barrel. Bureaucracy is not the solution – it is the symptom of poor strategic decisions.

And some people are naturally drawn to the Inspection, Supervision and Protection roles – the ISP functions – because their temperaments are suited to it. And that is OK so long as the Purpose is valid. When the Purpose is invalid the ISP army will enforce an ineffective strategic plan and the problem will be magnified. Invalid purposes are a symptom of a lack of collective strategic wisdom – which is why the design of the Statement of Purpose is critical to long term success.

The world is always changing – so even when the Purpose is valid and does not change – what was a well designed Policy a decade ago may easily be a poor design of Policy now. But the role of the Inspectors, Supervisors and Protectors is to maintain stability – and that is good. We need that. The danger comes silently and slowly as the Reality changes and the Rhetoric does not. The ISP army grows, the bureaucracy and bullying grows, and the costs escalate. The mismatch is exposed eventually – there is a crisis – often of catastrophic proportions. The longer the delay the bigger the catastrophe. And the bigger the catastrophe the more people get caught in the cross-fire.

So the fourth part is the Defence against Errors of Omission.

An Error of Omission is when we do not do something that we should have. When we did not say “That is not OK” when we could clearly see that something was not OK. The Error of Omission is the more dangerous error because it is invisible. There is nothing to see. There is no blood or breadcrumb trail for the faithful hounds to follow. There is no evidence trail leading to the bad outcome so the hounds follow any trail that they find and either scapegoat the wrong person or go around in circles and eventually conclude “it was a system problem”. They are correct. It is. A system design problem.

The individual errors of omission are bad enough – the collective errors of omission are worse.

And they are driven by two forces. Ignorance and Fear.

160 years ago in Vienna the doctors did not know that not washing their hands when entering the labour ward was an Error of Omission. They were ignorant of the fact. And as a result hundreds of young women and their new babies died of Childbed Fever. The people knew this and it is said that husbands would rather their wives give birth on the street than go to hospital when the doctors were on duty for the day. At its worse the death rate was 30% per month! Now we do know that to not disinfect our hands between patients is an error of omission and we understand the reason – we understand how we unintentionally spread invisible germs on our hands.

Knowledge is the antidote to ignorance and knowledge needs to be shared to be effective – because we are all ignorant until educated. And we are ignorant of our ignorance. We do not now what we do not know. Tackling our ignorance requires humility. The willingness to expose our own knowledge gaps. The willingness to learn – continuously – because reality is always evolving.

The more usual driver of the collective error of omission is fear. Fear of persecution if we break ranks and make ourselves conspicuous by saying “This is not OK”. And the people who perscute us the most are our peers. Their collective fear of their own failures of purpose creates a much greater emotional barrier than the fear of an autocratic ISP bully. We also fear the mob. The dangerously unpredictable blinded-by-anger mob that becomes collectively enraged by their loss of trust and who stone-to-death anything that resembles the threat.

We fear and we turn away so we cannot see; we cover our ears so we cannot hear; and we say and do nothing. That is the Collective Error of Omission.

What then is the way forward?

Fill in the missing pieces.

Ensure that our Statement of Purpose has Four Parts.

1. What we will do and why. The Intended Acts of Commission.

2. What we will not do and why. The Intended Acts of Omission.

3. How we will know we have made an Error of Commission. The Defence against Type I Errors.

4. How we will know we have made an Error of Omission. The Defence against Type II Errors.

The Acts are designs for Trust, the Defences are designs for Feedback – the two essential components of an effective value system design.

03/11/2012

A Recipe for Improvement PIE.

Most of us are realists. We have to solve problems in the real world so we prefer real examples and step-by-step how-to-do recipes.

A minority of us are theorists and are more comfortable with abstract models and solving rhetorical problems.

Many of these Improvement Science blog articles debate abstract concepts – because I am a strong iNtuitor by nature. Most realists are Sensors – so by popular request here is a “how-to-do” recipe for a Productivity Improvement Exercise (PIE)

Step 1 – Define Productivity.

There are many definitions we could choose because productivity means the results delivered divided by the resources used. We could use any of the three currencies – quality, time or money – but the easiest is money. And that is because it is easier to measure and we have well established department for doing it – Finance – the guardians of the money. There are two other departments who may need to be involved – Governance (the guardians of the safety) and Operations (the guardians of the delivery).

So the definition we will use is productivity = revenue generated divided cost incurred.

Step 2 – Draw a map of the process we want to make more productive.

This means creating a picture of the parts and their relationships to each other – in particular what the steps in the process are; who does what, where and when; what is done in parallel and what is done in sequence; what feeds into what and what depends on what. The output of this step is a diagram with boxes and arrows and annotations – called a process map. It tells us at a glance how complex our process is – the number of boxes and the number of arrows. The simpler the process the easier it is to demonstrate a productivity improvement quickly and unambiguously.

Step 3 – Decide the objective metrics that will tell us our productivity.

We have chosen a finanical measure of productivity so we need to measure revenue and cost over time – and our Finance department do that already so we do not need to do anything new. We just ask them for the data. It will probably come as a monthly report because that is how Finance processes are designed – the calendar month accounting cycle is not negotiable.

We will also need some internal process metrics (IPMs) that will link to the end of month productivity report values because we need to be observing our process more often than monthly. Weekly, daily or even task-by-task may be necessary – and our monthly finance reports will not meet that time-granularity requirement.

These internal process metrics will be time metrics.

Start with objective metrics and avoid the subjective ones at this stage. They are necessary but they come later.

Step 4 – Measure the process.

There are three essential measures we usually need for each step in the process: A measure of quality, a measure of time and a measure of cost. For the purposes of this example we will simplify by making three assumptions. Quality is 100% (no mistakes) and Predictability is 100% (no variation) and Necessity is 100% (no worthless steps). This means that we are considering a simplified and theoretical situation but we are novices and we need to start with the wood and not get lost in the trees.

The 100% Quality means that we do not need to worry about Governance for the purposes of this basic recipe.

The 100% Predictability means that we can use averages – so long as we are careful.

The 100% Necessity means that we must have all the steps in there or the process will not work.

The best way to measure the process is to observe it and record the events as they happen. There is no place for rhetoric here. Only reality is acceptable. And avoid computers getting in the way of the measurement. The place for computers is to assist the analysis – and only later may they be used to assist the maintenance – after the improvement has been achieved.

Many attempts at productivity improvement fail at this point – because there is a strong belief that the more computers we add the better. Experience shows the opposite is usually the case – adding computers adds complexity, cost and the opportunity for errors – so beware.

Step 5 – Identify the Constraint Step.

The meaning of the term constraint in this context is very specific – it means the step that controls the flow in the whole process. The critical word here is flow. We need to identify the current flow constraint.

A tap or valve on a pipe is a good example of a flow constraint – we adjust the tap to control the flow in the whole pipe. It makes no difference how long or fat the pipe is or where the tap is, begining, middle or end. (So long as the pipe is not too long or too narrow or the fluid too gloopy because if they are then the pipe will become the flow constraint and we do not want that).

The way to identify the constraint in the system is to look at the time measurements. The step that shows the same flow as the output is the constraint step. (And remember we are using the simplified example of no errors and no variation – in real life there is a bit more to identifying the constraint step).

Step 6 – Identify the ideal place for the Constraint Step.

This is the critical-to-success step in the PIE recipe. Get this wrong and it will not work.

This step requires two pieces of measurement data for each step – the time data and the cost data. So the Operational team and the Finance team will need to collaborate here. Tricky I know but if we want improved productivity then there is no alternative.

Lots of productivity improvement initiatives fall at the Sixth Fence – so beware. If our Finance and Operations departments are at war then we should not consider even starting the race. It will only make the bad situation even worse!

If they are able to maintain an adult and respectful face-to-face conversation then we can proceed.

The time measure for each step we need is called the cycle time – which is the time interval from starting one task to being ready to start the next one. Please note this is a precise definition and it should be used exactly as defined.

The money measure for each step we need is the fully absorbed cost of time of providing the resource. Your Finance department will understand that – they are Masters of FACTs!

The magic number we need to identify the Ideal Constraint is the product of the Cycle Time and the FACT – the step with the highest magic number should be the constraint step. It should control the flow in the whole process. (In reality there is a bit more to it than this but I am trying hard to stay out of the trees).

Step 7 – Design the capacity so that the Ideal Constraint is the Actual Constraint.

We are using a precise definition of the term capacity here – the amount of resource-time available – not just the number of resources available. Again this is a precise definition and should be used as defined.

The capacity design sequence means adding and removing capacity to and from steps so that the constraint moves to where we want it.

The sequence is:
7a) Set the capacity of the Ideal Constraint so it is capable of delivering the required activity and revenue.
7b) Increase the capacity of the all the other steps so that the Ideal Constraint actually controls the flow.
7c) Reduce the capacity of each step in turn, a click at a time until it becomes the constraint then back off one click.

Step 8 – Model your whole design to predict the expected productivity improvement.

This is critical because we are not interested in suck-it-and-see incremental improvement. We need to be able to decide if the expected benefit is worth the effort before we authorise and action any changes. And we will be asked for a business case. That necessity is not negotiable either.

Lots of productivity improvement projects try to dodge this particularly thorny fence behind a smoke screen of a plausible looking business case that is more fiction than fact. This happens when any of Steps 2 to 7 are omitted or done incorrectly. What we need here is a model and if we are not prepared to learn how to build one then we should not start. It may only need a simple model – but it will need one. Intuition is too unreliable.

A model is defined as a simplified representation of reality used for making predictions.

All models are approximations of reality. That is OK.

The art of modeling is to define the questions the model needs to be designed to answer (and the precision and accuracy needed) and then design, build and test the model so that it is just simple enough and no simpler. Adding unnecessary complexity is difficult, time consuming, error prone and expensive. Using a computer model when a simple pen-and-paper model would suffice is a good example of over-complicating the recipe!

Many productivity improvement projects that get this far still fall at this fence. There is a belief that modeling can only be done by Marvins with brains the size of planets. This is incorrect. There is also a belief that just using a spreadsheet or modelling software is all that is needed. This is incorrect too. Competent modelling requires tools and training – and experience because it is as much art as science.

Step 9 – Modify your system as per the tested design.

Once you have demonstrated how the proposed design will deliver a valuable increase in productivity then get on with it.

Not by imposing it as a fait accompli – but by sharing the story along with the rationale, real data, explanation and results. Ask for balanced, reasoned and respectful feedback. The question to ask is “Can you think of any reasons why this would not work?” Very often the reply is “It all looks OK in theory but I bet it won’t work in practice but I can’t explain why”. This is an emotional reaction which may have some basis in fact. It may also just be habitual skepticism/cynicism. Further debate is usually worthless – the only way to know for sure is by doing the experiment. As an experiment – as a small-scale and time-limited pilot. Set the date and do it. Waiting and debating will add no value. The proof of the pie is in the eating.

Step 10 – Measure and maintain your system productivity.

Keep measuring the same metrics that you need to calculate productivity and in addition monitor the old constraint step and the new constraint steps like a hawk – capturing their time metrics for every task – and tracking what you see against what the model predicted you should see.

The correct tool to use here is a system behaviour chart for each constraint metric. The before-the-change data is the baseline from which improvement is measured over time; and with a dot plotted for each task in real time and made visible to all the stakeholders. This is the voice of the process (VoP).

A review after three months with a retrospective financial analysis will not be enough. The feedback needs to be immediate. The voice of the process will dictate if and when to celebrate. (There is a bit more to this step too and the trees are clamoring for attention but we must stay out of the wood a bit longer).

And after the charts-on-the-wall have revealed the expected improvement has actually happened; and after the skeptics have deleted their ‘we told you so’ emails; and after the cynics have slunk off to sulk; and after the celebration party is over; and after the fame and glory has been snatched by the non-participants – after all of that expected change management stuff has happened …. there is a bit more work to do.

And that is to establish the new higher productivity design as business-as-usual which means tearing up all the old policies and writing new ones: New Policies that capture the New Reality. Bin the out-of-date rubbish.

This is an essential step because culture changes slowly. If this step is omitted then out-of-date beliefs, attitudes, habits and behaviours will start to diffuse back in, poison the pond, and undo all the good work. The New Policies are the reference – but they alone will not ensure the improvement is maintained. What is also needed is a PFL – a performance feedback loop.

And we have already demonstrated what that needs to be – the tactical system behaviour charts for the Intended Constraint step.

The finanical productivity metric is the strategic output and is reported monthly – as a system behaviour chart! Just comparing this month with last month is meaningless. The tactical SBCs for the constraint step must be maintained continuously by the people who own the constraint step – because they control the productivity of the whole process. They are the guardians of the productivity improvement and their SBCs are the Early Warning System (EWS).

If the tactical SBCs set off an alarm then investigate the root cause immediately – and address it. If they do not then leave it alone and do not meddle.

This is the simplified version of the recipe. The essential framework.

Reality is messier. More complicated. More fun!

Reality throws in lots of rusty spanners so we do also need to understand how to manage the complexity; the unnecessary steps; the errors; the meddlers; and the inevitable variation. It is possible (though not trivial) to design real systems to deliver much higher productivity by using the framework above and by mastering a number of other tools and techniques. And for that to succeed the Governance, Operations and Finance functions need to collaborate closely with the People and the Process – initially with guidance from an experienced and competent Improvement Scientist. But only initially. This is a learnable skill. And it takes practice to master – so start with easy ones and work up.

If any of these bits are missing or are dysfunctional the recipe will not work. So that is the first nettle the Executive must grasp. Get everyone who is necessary on the same bus going in the same direction – and show the cynics the exit. Skeptics are OK – they will counter-balance the Optimists. Cynics add no value and are a liability.

What you may have noticed is that 8 of the 10 steps happen before any change is made. 80% of the effort is in the design – only 20% is in the doing.

If we get the design wrong the the doing will be an ineffective and inefficient waste of effort, time and money.

The best complement to real Improvement PIE is a FISH course.

27/10/2012

The First Step Looks The Steepest

Getting started on improvement is not easy.

It feels like we have to push a lot to get anywhere and when we stop pushing everything just goes back to where it was before and all our effort was for nothing.

And it is easy to become despondent. It is easy to start to believe that improvement is impossible. It is easy to give up. It is not easy to keep going.

One common reason for early failure is that we often start by trying to improve something that we have little control over. Which is natural because many of the things that niggle us are not of our making.

But not all Niggles are like that; there are also many Niggles over which we have almost complete control.

It is these close-to-home Niggles that we need to start with – and that is surprisingly difficult too – because it requires a bit of time-investment.

The commonest reason for not investing time in improvement is: “I am too busy.”

Q: Too busy doing what – specifically?

This simple question is a good place to start because just setting aside a few minutes each day to reflect on where we have been spending our time is a worthwhile task.

And the output of our self-reflection is usually surprising.

We waste lifetime every day doing worthless work.

Then we complain that we are too busy to do the worthwhile stuff.

Q: So what are we scared of? Facing up to the uncomfortable reality of knowing how much lifetime we have wasted already?

We cannot change the past. We can only influence the future. So we need to learn from the past to make wiser choices.

Lifetime is odd stuff. It both is and is not like money.

We can waste lifetime and we can waste money. In that respect they are the same. Money we do not use today we can save for tomorrow, but lifetime not used today is gone forever.

We know this, so we have learned to use up every last drop of lifetime – we have learned to keep ourselves busy.

And if we are always busy then any improvement will involve a trade-off: dis-investing and re-investing our lifetime. This implies the return on our lifetime re-investment must come quickly and predictably – or we give up.

One tried-and-tested strategy is to start small and then to re-invest our time dividend in the next cycle of improvement. An if we make wise re-investment choices, the benefit will grow exponentially.

Successful entrepreneurs do not make it big overnight.

If we examine their life stories we will find a repeating cycle of bigger and bigger business improvement cycles.

The first thing successful entrepreneurs learn is how to make any investment lead to a return – consistently. It is not luck. They practice with small stuff until they can do it reliably.

Successful entrepreneurs are disciplined and they only take calculated risks.

Unsuccessful entrepreneurs are more numerous and they have a different approach.

They are the get-rich-quick brigade. The undisciplined gamblers. And the Laws of Probability ensure that they all will fail eventually.

Sustained success is not by chance, it is by design.

The same is true for improvement. The skill to learn is how to spot an opportunity to release some valuable time resource by nailing a time-sapping-niggle; and then to reinvest that time in the next most promising cycle of improvement – consistently and reliably. It requires discipline and learning to use some novel tools and techniques.

This is where Improvement Science helps – because the tools and techniques apply to any improvement. Safety. Flow. Quality. Productivity. Stability. Reliability.

In a nutshell … trustworthy.

The first step looks the steepest because the effort required feels high and the benefit gained looks small. But it is climbing the first step that separates the successful from the unsuccessful. And successful people are self-disciplined people.

After a few invest-release-reinvest cycles the amount of time released exceeds the amount needed to reinvest. It is then we have time to spare – and we can do what we choose with that.

Ask any successful athlete or entrepreneur – they keep doing it long after they need to – just for the “rush” it gives them.

The tool I use, because it is quick, easy and effective, is called The 4N Chart®. And it has a helpful assistant called a Niggle-o-Gram®. Together they work like a focusing lens – they show where the most fertile opportunity for improvement is – the best return on an investment of time and effort.

And when we have proved to yourself that the first step of improvement is not as steep as you believed – then we have released some time to re-invest in the next cycle of improvement – and in sharing what we have discovered.

That is where the big return comes from.

10/11/2012: Feedback from people who have used The 4N Chart and Niggle-o-Gram for personal development is overwhelmingly positive.

20/10/201206/01/2024

Look Out For The Time Trap!

There is a common system ailment which every Improvement Scientist needs to know how to manage.

In fact, it is probably the commonest.

The Symptoms: Disappointingly long waiting times and all resources running flat out.

The Diagnosis? 90%+ of managers say “It is obvious – lack of capacity!”.

The Treatment? 90%+ of managers say “It is obvious – more capacity!!”

Intuitively obvious maybe – but unfortunately these are incorrect answers. Which implies that 90%+ of managers do not understand how their systems work. That is a bit of a worry. Lament not though – misunderstanding is a treatable symptom of an endemic system disease called agnosia (=not knowing).

The correct answer is “I do not yet have enough information to make a diagnosis“.

This answer is more helpful than it looks because it prompts four other questions:

Q1. “What other possible system diagnoses are there that could cause this pattern of symptoms?”
Q2. “What do I need to know to distinguish these system diagnoses?”
Q3. “How would I treat the different ones?”
Q4. “What is the risk of making the wrong system diagnosis and applying the wrong treatment?”

Before we start on this list we need to set out a few ground rules that will protect us from more intuitive errors (see last week).

The first Rule is this:

Rule #1: Data without context is meaningless.

For example 130 is a number – it is data. 130 what? 130 mmHg. Ah ha! The “mmHg” is the units – it means millimetres of mercury and it tells us this data is a pressure. But what, where, when,who, how and why? We need more context.

“The systolic blood pressure measured in the left arm of Joe Bloggs, a 52 year old male, using an Omron M2 oscillometric manometer on Saturday 20th October 2012 at 09:00 is 130 mmHg”.

The extra context makes the data much more informative. The data has become information.

To understand what the information actually means requires some prior knowledge. We need to know what “systolic” means and what an “oscillometric manometer” is and the relevance of the “52 year old male”. This ability to extract meaning from information has two parts – the ability to recognise the language – the syntax; and the ability to understand the concepts that the words are just labels for; the semantics.

To use this deeper understanding to make a wise decision to do something (or not) requires something else. Exploring that would distract us from our current purpose. The point is made.

Rule #1: Data without context is meaningless.

In fact it is worse than meaningless – it is dangerous. And it is dangerous because when the context is missing we rarely stop and ask for it – we rush ahead and fill the context gaps with assumptions. We fill the context gaps with beliefs, prejudices, gossip, intuitive leaps, and sometimes even plain guesses.

This is dangerous – because the same data in a different context may have a completely different meaning.

To illustrate. If we change one word in the context – if we change “systolic” to “diastolic” then the whole meaning changes from one of likely normality that probably needs no action; to one of serious abnormality that definitely does. If we missed that critical word out then we are in danger of assuming that the data is systolic blood pressure – because that is the most likely given the number. And we run the risk of missing a common, potentially fatal and completely treatable disease called Stage 2 hypertension.

There is a second rule that we must always apply when using data from systems. It is this:

Rule #2: Plot time-series data as a chart – a system behaviour chart (SBC).

The reason for the second rule is because the first question we always ask about any system must be “Is our system stable?”

Q: What do we mean by the word “stable”? What is the concept that this word is a label for?

A: Stable means predictable-within-limits.

Q: What limits?

A: The limits of natural variation over time.

Q: What does that mean?

A: Let me show you.

Joe Bloggs is disciplined. He measures his blood pressure almost every day and he plots the data on a chart together with some context . The chart shows that his systolic blood pressure is stable. That does not mean that it is constant – it does vary from day to day. But over time a pattern emerges from which Joe Bloggs can see that, based on past behaviour, there is a range within which future behaviour is predicted to fall. And Joe Bloggs has drawn these limits on his chart as two red lines and he has called them expectation lines. These are the limits of natural variation over time of his systolic blood pressure.

If one day he measured his blood pressure and it fell outside that expectation range then he would say “I didn’t expect that!” and he could investigate further. Perhaps he made an error in the measurement? Perhaps something else has changed that could explain the unexpected result. Perhaps it is higher than expected because he is under a lot of emotional stress a work? Perhaps it is lower than expected because he is relaxing on holiday?

His chart does not tell him the cause – it just flags when to ask more “What might have caused that?” questions.

If you arrive at a hospital in an ambulance as an emergency then the first two questions the emergency care team will need to know the answer to are “How sick are you?” and “How stable are you?”. If you are sick and getting sicker then the first task is to stabilise you, and that process is called resuscitation. There is no time to waste.

So how is all this relevant to the common pattern of symptoms from our sick system: disappointingly long waiting times and resources running flat out?

Using Rule#1 and Rule#2: To start to establish the diagnosis we need to add the context to the data and then plot our waiting time information as a time series chart and ask the “Is our system stable?” question.

Suppose we do that and this is what we see. The context is that we are measuring the Referral-to-Treatment Time (RTT) for consecutive patients referred to a single service called X. We only know the actual RTT when the treatment happens and we want to be able to set the expectation for new patients when they are referred – because we know that if patients know what to expect then they are less likely to be disappointed – so we plot our retrospective RTT information in the order of referral. With the Mark I Eyeball Test (i.e. look at the chart) we form the subjective impression that our system is stable. It is delivering a predictable-within-limits RTT with an average of about 15 weeks and an expected range of about 10 to 20 weeks.

So far so good.

Unfortunately, the purchaser of our service has set a maximum limit for RTT of 18 weeks – a key performance indicator (KPI) target – and they have decided to “motivate” us by withholding payment for every patient that we do not deliver on time. We can now see from our chart that failures to meet the RTT target are expected, so to avoid the inevitable loss of income we have to come up with an improvement plan. Our jobs will depend on it!

Now we have a problem – because when we look at the resources that are delivering the service they are running flat out – 100% utilisation. They have no spare flow-capacity to do the extra work needed to reduce the waiting list. Efficiency drives and exhortation have got us this far but cannot take us any further. We conclude that our only option is “more capacity”. But we cannot afford it because we are operating very close to the edge. We are a not-for-profit organisation. The budgets are tight as a tick. Every penny is being spent. So spending more here will mean spending less somewhere else. And that will cause a big argument.

So the only obvious option left to us is to change the system – and the easiest thing to do is to monitor the waiting time closely on a patient-by-patient basis and if any patient starts to get close to the RTT Target then we bump them up the list so that they get priority. Obvious!

WARNING: We are now treating the symptoms before we have diagnosed the underlying disease!

In medicine that is a dangerous strategy. Symptoms are often not-specific. Different diseases can cause the same symptoms. An early morning headache can be caused by a hangover after a long night on the town – it can also (much less commonly) be caused by a brain tumour. The risks are different and the treatment is different. Get that diagnosis wrong and disappointment will follow. Do I need a hole in the head or will a paracetamol be enough?

Back to our list of questions.

What else can cause the same pattern of symptoms of a stable and disappointingly long waiting time and resources running at 100% utilisation?

There are several other process diseases that cause this symptom pattern and none of them are caused by lack of capacity.

Which is annoying because it challenges our assumption that this pattern is always caused by lack of capacity. Yes – that can sometimes be the cause – but not always.

But before we explore what these other system diseases are we need to understand why our current belief is so entrenched.

One reason is because we have learned, from experience, that if we throw flow-capacity at the problem then the waiting time will come down. When we do “waiting list initiatives” for example. So if adding flow-capacity reduces the waiting time then the cause must be lack of capacity? Intuitively obvious.

Intuitively obvious it may be – but incorrect too. We have been tricked again. This is flawed causal logic. It is called the illusion of causality.

To illustrate. If a patient complains of a headache and we give them paracetamol then the headache will usually get better. That does not mean that the cause of headaches is a paracetamol deficiency. The headache could be caused by lots of things and the response to treatment does not reliably tell us which possible cause is the actual cause. And by suppressing the symptoms we run the risk of missing the actual diagnosis while at the same time deluding ourselves that we are doing a good job.

If a system complains of long waiting times and we add flow-capacity then the long waiting time will usually get better. That does not mean that the cause of long waiting time is lack of flow-capacity. The long waiting time could be caused by lots of things. The response to treatment does not reliably tell us which possible cause is the actual cause – so by suppressing the symptoms we run the risk of missing the diagnosis while at the same time deluding ourselves that we are doing a good job.

The similarity is not a co-incidence. All systems behave in similar ways. Similar counter-intuitive ways.

So what other system diseases can cause a stable and disappointingly long waiting time and high resource utilisation?

The commonest system disease that is associated with these symptoms is a time trap – and they have nothing to do with capacity or flow.

They are part of the operational policy design of the system. And we actually design time traps into our systems deliberately! Oops!

We create a time trap when we deliberately delay doing something that we could do immediately – perhaps to give the impression that we are very busy or even overworked! We create a time trap whenever we deferring until later something we could do today.

If the task does not seem important or urgent for us then it is a candidate for delaying with a time trap.

Unfortunately it may be very important and urgent for someone else – and a delay could be expensive for them.

Creating time traps gives us a sense of power – and it is for that reason they are much loved by bureaucrats.

To illustrate how time traps cause these symptoms consider the following scenario:

Suppose I have just enough resource-capacity to keep up with demand and flow is smooth and fault-free. My resources are 100% utilised; the flow-in equals the flow-out; and my waiting time is stable. If I then add a time trap to my design then the waiting time will increase but over the long term nothing else will change: the flow-in, the flow-out, the resource-capacity, the cost and the utilisation of the resources will all remain stable. I have increased waiting time without adding or removing capacity. So lack of resource-capacity is not always the cause of a longer waiting time.

This new insight creates a new problem; a BIG problem.

Suppose we are measuring flow-in (demand) and flow-out (activity) and time from-start-to-finish (lead time) and the resource usage (utilisation) and we are obeying Rule#1 and Rule#2 and plotting our data with its context as system behaviour charts. If we have a time trap in our system then none of these charts will tell us that a time-trap is the cause of a longer-than-necessary lead time.

Aw Shucks!

And that is the primary reason why most systems are infested with time traps. The commonly reported performance metrics we use do not tell us that they are there. We cannot improve what we cannot see.

Well actually the system behaviour charts do hold the clues we need – but we need to understand how systems work in order to know how to use the charts to make the time trap diagnosis.

Q: Why bother though?

A: Simple. It costs nothing to remove a time trap. We just design it out of the process. Our flow-in will stay the same; our flow-out will stay the same; the capacity we need will stay the same; the cost will stay the same; the revenue will stay the same but the lead-time will fall.

Q: So how does that help me reduce my costs? That is what I’m being nailed to the floor with as well!

A: If a second process requires the output of the process that has a hidden time trap then the cost of the queue in the second process is the indirect cost of the time trap. This is why time traps are such a fertile cause of excess cost – because they are hidden and because their impact is felt in a different part of the system – and usually in a different budget.

To illustrate. Suppose that 60 patients per day are discharged from our hospital and each one requires a prescription of to-take-out (TTO) medications to be completed before they can leave. Suppose that there is a time trap in this drug dispensing and delivery process. The time trap is a policy where a porter is scheduled to collect and distribute all the prescriptions at 5 pm. The porter is busy for the whole day and this policy ensures that all the prescriptions for the day are ready before the porter arrives at 5 pm. Suppose we get the event data from our electronic prescribing system (EPS) and we plot it as a system behaviour chart and it shows most of the sixty prescriptions are generated over a four hour period between 11 am and 3 pm. These prescriptions are delivered on paper (by our busy porter) and the pharmacy guarantees to complete each one within two hours of receipt although most take less than 30 minutes to complete. What is the cost of this one-delivery-per-day-porter-policy time trap? Suppose our hospital has 500 beds and the total annual expense is £182 million – that is £0.5 million per day. So sixty patients are waiting for between 2 and 5 hours longer than necessary, because of the porter-policy-time-trap, and this adds up to about 5 bed-days per day – that is the cost of 5 beds – 1% of the total cost – about £1.8 million. So the time trap is, indirectly, costing us the equivalent of £1.8 million per annum. It would be much more cost-effective for the system to have a dedicated porter working from 12 am to 5 pm doing nothing else but delivering dispensed TTOs as soon as they are ready! And assuming that there are no other time traps in the decision-to-discharge process; such as the time trap created by batching all the TTO prescriptions to the end of the morning ward round; and the time trap created by the batch of delivered TTOs waiting for the nurses to distribute them to the queue of waiting patients!

Q: So how do we nail the diagnosis of a time trap and how do we differentiate it from a Batch or a Bottleneck or Carveout?

A: To learn how to do that will require a bit more explanation of the physics of processes.

And anyway if I just told you the answer you would know how but might not understand why it is the answer. Knowledge and understanding are not the same thing. Wise decisions do not follow from just knowledge – they require understanding. Especially when trying to make wise decisions in unfamiliar scenarios.

It is said that if we are shown we will understand 10%; if we can do we will understand 50%; and if we are able to teach then we will understand 90%.

So instead of showing how instead I will offer a hint. The first step of the path to knowing how and understanding why is in the following essay:

A Study of the Relative Value of Different Time-series Charts for Proactive Process Monitoring. JOIS 2012;3:1-18

Click here to visit JOIS

06/10/2012

Intuitive Counter

If it takes five machines five minutes to make five widgets how long does it take ten machines to make ten widgets?

If the answer “ten minutes” just popped into your head then your intuition is playing tricks on you. The correct answer is “five minutes“.

Let us try another.

If the lily leaves on the surface of a lake double in area every day and if it takes 48 days to cover the whole lake then how long did it take to cover half the lake? Twenty four days? Nope. The correct answer is 47 days and once again our intuition has tricked us. It is obvious in hindsight though – just not so obvious before.

We all make thousands of unconscious, intuitive decisions every day so if we make unintended errors like this then they must be happening all the time and we do not realise.

OK one more and really concentrate this time.

If we have a three-step sequential process and the chance of a significant safety error at each step is 10%, 30% and 20% respectively then what is the overall error rate for the process? A: (10%+30%+20%) /3 = 60%/3 = 20%? Nope. Um 30%? Nope. What about 60%? Nope. The answer is 49.6%. And it is not intuitively obvious how that is the correct answer.

When it comes to numbers, counting, and anything to do with chance and probability then our intuition is not a safe and reliable tool. But we rely on it all the time and we are not aware of the errors we are making. And it is not just numbers that our intuition trips us up over!

A lot of us are intuitive thinkers … about 40% in fact. The majority of leaders and executives are categorised as iNtuitors when measured using a standard psychological assessment tool. And remember – they are the ones making the Big Decisions that effect us all. So if their intuition is tripping them up then their decisions are likely to be a bit suspect.

Fortunately there is a group of people who do not fall into these hidden cognitive counting traps so easily. They have Books of Rules of how to do numbers correctly – and they are called Accountants. When they have the same standard assessment a lot of them pop up at the other end of the iNtuitor dimension. They are called Sensors. Not because they are sensitive (which of course they are) but because they rank reality more trustworthy than rhetoric. They trust what they see – the facts – the numbers. And money is a number. And numbers add up exactly so that everything is neat, tidy, and auditable down to the last penny. Ahhhh – Blisse is Balanced Books and Budgets.

This is why the World is run by Accountants. They nail our soft and fuzzy intuitive rhetoric onto the hard and precise fiscal reality. And in so doing a big and important piece of the picture is lost. The fuzzy bit,

Intuitors have a very important role. They are able to think outside the Rule Book Box. They are comfortable working with fuzzy concepts and in abstract terms and their favourite sport is intuitive leaping. It is a high risk sport though because sometimes Reality reminds them that the Laws of Physics are not optional or subject to negotiation and innovation. Ouch! But the iNtuitors ability to leap about conceptuallycomes in very handy when the World is changing unpredictably – because it allows the Books of Rules to be challenged and re-written as new discoveries are made. The first Rule is usually “Do not question the Rules” so those who follow Rules are not good at creating new ones. And those who write the rules are not good at sticking to them.

So, after enough painful encounters with Reality the iNtuitors find their comfort zones in board rooms, academia and politics – where they can avoid hard Reality and concentrate on soft Rhetoric. Here they can all have a different conceptual abstract mental model and can happily discuss, debate and argue with each other for eternity. Of course the rest of the Universe is spectacularly indifferent to board room, academic and political rhetoric – but the risk to the disinterested is when the influential iNtuitors impose their self-generated semi-delusional group-think on the Real World without a doing a Reality Check first. The outcome is entirely predictable ….

And as the hot rhetoric meets cold reality the fog of disillusionment forms.

So if we wish to embark on a Quest for Improvement then it is really helpful to know where on the iNtuitor-Sensor dimension each of us prefers to sit. Intuitors need Sensors to provide a reality check and Sensors need Intuitors to challenge the status quo. We are not nailed to our psychological perches – we can shuffle up and down if need be – we do have a favourite spot though; our comfort zone.

To help answer the “Where am I on the NS dimension?” question here is a Temperament Self-Assessment Tool that you can use. It is based on the Jungian, Myers-Briggs and Keirsey models. Just run the programme, answer the 72 questions and you will get your full 4-dimensional profile and your “centre” on each. Then jot down the results on a scrap of paper.

There is a whole industry that has sprung up out these (and other) psychological assessment tools. They feed our fascination with knowing what makes us tick and the role of the psychoexpert is to de-mystify the assessments for us and to explain the patterns in the tea leaves (for a fee of course because it takes years of training to become a Demystifier). Disappointingly, my experience is that almost every person I have asked if they know their Myers-Briggs profile say “Oh yes, I did that years ago, it is SPQR or something like that but I have no idea what it means“. Maybe they should ask for their Demystification Fee to be returned?

Anyway – here is the foundation level demystification guide to help you derive meaning from what is jotted on the scrap of paper.

First look at the N-S (iNtuitor-Sensor) dimension. If you come out as N then look at the T-F (Thinking-Feeling) dimension – and together they will give an xNTx preference or an xNFx preference. People with these preferences are called Rationals and Idealists respectively. If you prefer the S end of the N-S dimension then look at the J-P (Judging-Perceiving) result and this will give an xSxJ or xSxP preference. These are the Guardians and the Artisans. Those are the Four Temperaments described by David Keirsey in “Please Understand Me II“. If you are near the middle of any of the dimensions then you will show a blend of temperaments. And please note – it is not an either-or category – it is a continuous spectrum.

How we actually manifest our innate personality preferences depends on our education, experiences and the exact context. This makes it a tricky to interpret the specific results for an individual – hence the Tribe of Demystificationists. And remember – these are not intelligence tests, and there are no good/bad or right/wrong answers. They are gifts – or rather gifts differing.

So how does all this psychobabble help us as Improvement Scientists?

Much of Improvement Science is just about improving awareness and insight – so insight into ourselves is of value.

Rationals (xNTx) are attracted to occupations that involve strategic thinking and making rational, evidence based decisions: such as engineers and executives. The Idealists (xNFx) are rarer, more sensitive, and attracted to occupations such as teaching, counselling, healing and being champions of good causes. The Guardians (xSxJ) are particularly numerous and are attracted to occupations that form the stable bedrock of society – administrators, inspectors, supervisors, providers and protectors. They value the call-of-duty and sticking-to-the-rules for the good-of-all. Artisans (SPs) are the risk-takers and fun-makers; the promotors, the entertainers, the explorers, the dealers, the artists, the marketeers and the salespeople.

These are the Four Temperaments that form the basic framework of the sixteen Myers-Briggs polarities. And this is not a new idea – it has been around for millenia – just re-emerging with different names in different paradigms. In the Renaissance the Galenic Paradigm held sway and they were called the Phlegmatics (NT), the Cholerics (NF), the Melancholics (SJ) and the Sangines (SP) – depending on which of the four body fluids were believed to be out of balance (phlegm, yellow bile, black bile or blood). So while the paradigms have changed, the empirical reality appears to have endured the ages.

The message for the Improvement Scientist is two-fold:

1. Know your own temperament and recognise the strengths and limitations of it. They all have a light and dark side.
2. Understand that the temperaments of groups of people can be both synergistic and antagonistic.

It is said that birds of a feather flock together and the collective behaviour of departments in large organisations tend to form around the temperament that suits that organisational function. The character of the Finance department is usually very different to that of Operations, or Human Resources – and sparks can (and do) fly when they engage each other. No wonder chief executives have a short half-life and an effective one is worth its weight in gold!

The interdepartmental discord that is commonly observed in large organisations follows more from ignorance (unawareness of the reality of a spectrum of innate temperaments) and arrogance (expecting everyone to think the same way as we do). Antagonism is not an inevitable consequence though – it is just the default outcome in the absence of awareness and effective leadership.

This knowledge highlights two skills that an effective Improvement Scientist needs to master:

1. Respectful Educator (drawing back the black curtain of ignorance) and
2. Respectful Challenger (using reality to illuminate holes in the rhetoric).

Intuitive counter or counter intuitive?

29/09/2012

Structure Time to Fuel Improvement

The expected response to any suggestion of change is “Yes, but I am too busy – I do not have time.”

And the respondent is correct. They do not.

All their time is used just keeping their head above water or spinning the hamster wheel or whatever other metaphor they feel is appropriate. We are at an impasse. A stalemate. We know change requires some investment of time and there is no spare time to invest so change cannot happen. Yes? But that is not good enough – is it?

Well-intended experts proclaim that “I’m too busy” actually means “I have other things to do that are higher priority“. And by that we mean ” … that are a greater threat to my security and to what I care about“. So to get our engagement our well-intended expert pours emotional petrol on us and sets light to it. They show us dramatic video evidence of how our “can’t do” attitude and behaviour is part of the problem. We are the recalcitrant child who is standing in the way of change and we need to have our face rubbed in our own cynical poo.

Now our platform is really burning. Inflamed is exactly what we are feeling – angry in fact. “Thanks-a-lot. Now #!*@ off!” And our well-intentioned expert retreats – it is always the same. The Dinosaurs and the Dead Wood are clogging the way ahead.

Perhaps a different perspective might be more constructive.

It is not just how much time we have that is most important – it is how our time is structured.

Humans hate unstructured time. We like to be mentally active for all of our waking moments.

To test this hypothesis try this demonstration of our human need to fill idle time with activity. When you next talk to someone you know well – at some point after they have finished telling you something just say nothing; keep looking at them; and keep listening – and say nothing. For up to twenty seconds if necessary. Both you and they will feel an overwhelming urge to say something, anything – to fill the silence. It is called the “pregnant pause effect” and most people find even a gap of a second or two feels uncomfortable. Ten seconds would be almost unbearable. Hold your nerve and stay quiet. They will fill the gap.

This technique is used by cognitive behavioural therapists, counsellors and coaches to help us reveal stuff about ourselves to ourselves – and it works incredibly well. It is also used for less altrusitic purposes by some – so when you feel the pain of the pregnant pause just be aware of what might be going on and counter with a question.

If we have no imposed structure for our time then we will create one – because we feel better for it. We have a name for these time-structuring behaviours: habits, past-times and rituals. And they are very important to us because they reduce anxiety.

There is another name for a pre-meditated time-structure: it is called a plan or a process design. Many people hate not having a plan – and to them any plan is better than none. So in the absence of an imposed alternative we habitually make do with time-wasting plans and poorly designed processes. We feel busy because that is the purpose of our time-structuring behaviour – and we look busy too – which is also important. This has an important lesson for all improvement scientists: Using a measure of “business” such as utilisation as a measure of efficiency and productivity is almost meaningless. Utilisation does not distinguish between useful busi-ness and useless busi-ness.

We also time-structure our non-working lives. Reading a newspaper, doing the crossword, listening to the radio, watching television, and web-browsing are all time-structuring behaviours.

This insight into our need for structured time leads to a rational way to release time for change and improvement – and that is to better structure some of our busy time.

A useful metaphor for a time-structure is a tangible structure – such as a building. Buildings have two parts – a supporting, load bearing, structural framework and the functional fittings that are attached to it. Often the structural framework is invisible in the final building – invisible but essential. That is why we need structural engineers. The same is true for time-structuring: the supporting form should be there but it should not not get in the way of the intended function. That is why we need process design engineers too. Good process design is invisible time-structuring.

One essential investment of time in all organisations is communication. Face-to-face talking, phone calls, SMS, emails, reports, meetings, presentations, webex and so on. We spend more time communicating with each other than doing anything else other than sleeping. And more niggles are generated by poorly designed and delivered communication processes than everything else combined. By a long way.

As an example let us consider management meetings.

From a process design perspective mmany management meetings are both ineffective and inefficient. They are unproductive. So why do we still have them?

One possibkle answer is because meetings have two other important purposes: first as a tool for social interaction, and second as a way to structure time. It turns out that we dislike loneliness even more than idleness – and we can meet both needs at the same time by having a meeting. Productivity is not the primary purpose.

So when we do have to communicate effectively and efficiently in order to collectively resolve a real and urgent problem then we are ill prepared. And we know this. We know that as soon as Crisis Management Committees start to form then we are in really big trouble. What we want in a time of crisis is for someone to structure time for us. To tell us what to do.

And some believe that we unconsciously create crisis after crisis for just that purpose.

Recently I have been running an improvement experiment. I have been testing the assumption that we have to meet face-to-face to be effective. This has big implications for efficiency because I work in a multi-site organisation and to attend a meeting on another site implies travelling there and back. That travel takes one hour in each direction when all the separate parts are added together. It has two other costs. The financial cost of the fuel – which is a variable cost – if I do not travel then I do not incur the cost. And there is an emotional cost – I have to concentrate on driving and will use up some of my brain-fuel in doing so. There are three currencies – emotional, temporal and financial.

The experiment was a design change. I changed the design of the communication process from at-the-same-place-and-time to just at-the-same-time. I used an internet-based computer-to-computer link (rather like Skype or FaceTime but with some other useful tools like application sharing).

It worked much better than I expected.

There was the anticipated “we cannot do this because we do not have webcams and no budget for even pencils“. This was solved by buying webcams from the money saved by not burning petrol. The conversion rate was one webcam per four trips – and the webcam is a one off capital cost not a recurring revenue cost. This is accpiuntant-speak for “the actual cash released will fund the change“. No extra budget is required. And combine the fuel savings for everyone, and parking charges and the payback time is even shorter.

There were also the anticipated glitches as people got used to the unfamiliar technology (they did not practice of course because they were too busy) but the niggles go away with a few iterations.

So what were the other benefits?

Well one was the travel time saved – two hours per meeting – which was longer than the meeting! The released time cannot be stored and used later like the money can – it has to be reinvested immediately. I reinvested it in other improvement work. So the benefit was amplified.

Another was the brain-fuel saved from not having to drive – which I used to offset my cumuative brain-fuel deficit called chronic fatigue. The left over was re-invested in the improvement work. 100% recycled. Nothing was wasted.

The unexpected benefit was the biggest one.

The different communication design of a virtual meeting required a different form of meeting structure and discipline. It took a few iterations to realise this – then click – both effectiveness and efficiency jumped up. The time became even better structured, more productive and released even more time to reinvest. Wow!

And the whole thing funded itself.

08/09/2012

Predictable and Explainable – or Not

It is a common and intuitively reasonable assumption to believe that if something is explainable then it is predictable; and if it is not explainable then it is not predictable. Unfortunately this beguiling assumption is incorrect. Some things are explainable but not predictable; and some others are predictable but not explainable. Believe me? Of course not. We are all skeptics when our intuitively obvious assumptions and conclusions are challenged! We want real and rational evidence not rhetorical exhortation.

OK. Explainable means that the principles that guide the process are conceptually simple. We can explain the parts in detail and we can explain how they are connected together in detail. Predictable implies that if we know the starting point in detail, and the intervention in detail, then we can predict what the outcome will be – in detail.

Let us consider an example. Say we know how much we have in our bank account, and we know how much we intend to spend on that new whizzo computer, then we can predict what will be left in out bank account when the payment has been processed. Yes. This is an explainable and predictable system. It is called a linear system.

Let us consider another example. Say we know we have six dice each with numbers 1 to 6 printed on them and we throw them at the same time. Can we predict where they will land and what the final sum will be? No. We can say that it will be between 6 and 36 but that is all. And after we have thrown the dice we will not be able to explain, in detail, how they came to rest exactly where they did. This is an unpredictable and unexplainable system. It is called a random system.

This is a picture of a conceptually simple system. It is a novelty toy and it comprises two thin sheets of glass held a few millimetres apart by some curved plastic spacers. The narrow space is filled with green coloured oil, some coarse black volcanic sand, and some fine white coral sand. That is all. It is a conceptually simple toy. I have (by some magical means) layered the sand so that the coarse black sand is at the bottom and the fine white sand is on top. It is stable arrangement – and explainable. I then tipped the toy on its side – I rotated it through 90 degrees. It is a simple intervention – and explainable.

My intervention has converted a stable system to an unstable one and I confidently predict that the sand and oil will flow under the influence of gravity. There is no randomness here – I do not jiggle the toy – so the outcome should be predictable because I can explain all the parts in detail before we start; and I can explain the process in detail; and I can explain precisely what my intervention will be. So I should be able to predict the final configuration of the sand when this simple and explainable system finally settles into a new stable state again. Yes?

Well, I cannot. I can make some educated guesses – some plausible projections. But the only way to find out precisely what will happen is by doing the experiment and observing what actually happens.

This is what happened.

The final, stable configuration of the coarse black and fine white sand has a strange beauty in the way the layers are re-arranged. The result is not random – it has structure. And with the benefit of hindsight I feel I can work backwards and understand how it might have come about. It is explainable in retrospect but I could not predict it in prospect – even with a detailed knowledge of the starting point and the process.

This is called a non-linear system. Explainable in concept but difficult to predict in practice. The weather is another example of a non-linear system – explainable in terms of the physics but not precisely predictable. How reliable are our long range weather forecasts – or the short range ones for that matter?

Non-linear systems exhibit complex and unpredictable behaviour – even though they may be simple in concept and uncomplicated in construction. Randomness is usually present in real systems but it is not the cause of the complex behaviour, and making our systems more complicated seems likely to result in more unpredictable behaviour – not less.

If we want the behaviour of our system to be predictable and our system has non-linear parts and relationships in it – then we are forced to accept two Universal Truths.

1. That our system behaviour will only be predictable within limits (even if there is little or no randomness in it).

2. That to keep the behaviour within acceptable limits then we need to be careful how we arrange the parts and how they relate to each other.

This challenge of creating a predictable-within-acceptable-limits system from non-linear parts is called resilient design.

We have a fourth option to consider: a system that has a predictable outcome but an unexplainable reason.

We make predictions two ways – by working out what will happen or by remembering what has happened before. The second method is much easier so it is the one we use most of the time: it is called re-cognition. We call it knowledge.

If we have a black box with inputs on one side and outputs on the other, and we observe that when we set the inputs to a specific configuration we always get the same output – then we have a predicable system. We cannot explain how the inputs result in the output because the inner workings are hidden. It could be very simple – or it could be fiendishly complicated – we do not know.

It this situation we have no choice but to accept the status quo – and we have to accept that to get a predictable outcome we have to follow the rules and just do what we have always done before. It is the creed of blind acceptance – the If you always do what you have always done you will always get what you always got. It is knowledge but it is not understanding. New knowledge can only be found by trial and error. It is not wisdom, it is not design, it is not curiosity and it is not Improvement Science.

If our systems are non-linear (which they are) and we want predictable and acceptable performance (which we do) then we must strive to understand them and then to design them to be as simple as possible (which is difficult) so that we have the greatest opportunity to improve their performance by design (which is called Improvement Science).

This is a snapshot of the evolving oil-and-sand system. Look at that weird wine-glass shaped hole in the top section caused by the black sand being pulled down through the gap in the spacer then running down the slope of the middle section to fill a white sand funnel and then slip through the next hole onto the top of the white sand pyramid created by the white sand in the middle section that slipped through earlier onto the top of the sliding sand in the lowest section. Did you predict that? I suspect not. Me neither. But I can explain it – with the benefit of hindsight.

So what is it that is causing this complex behaviour? It is the spacers – the physical constraints to the flow of the sand and oil. And the same is true of systems – when the process hits a constraint then the behaviour suddenly changes and complex behaviour emerges. And there is more to it than even this. It is the gaps between the spacers that is creating the complex behaviour. The flow from one compartment leaking into the next and influencing its behaviour, and then into the next. This is what happens in all systems – the more constraints that are added to force the behaviour into predictable channels, and the more gaps that exist in the system of constraints then the more complex and unpredictable the system behaviour becomes. Which is exactly the opposite of the intended outcome.

The lesson that this simple toy can teach us is that if we want stable and predictable (i.e. non-complex) behaviour from our complicated systems then we must design them to operate inside the constraints so that they just never quite touch them. That requires data, information, knowledge, understanding and wise design. That is called Improvement Science.

But if, in an act of desperation, we force constraints onto the system we will make the system less stable, less predictable, less safe, less productive, less enjoyable and less affordable. That is called tampering.

18/08/2012

Disappointers, Delighters and Satisfiers.

There are two broad approaches to improvement. One is to start with what we have got now and tinker with it in the hope it will get better. When this is done well it is effective albeit slow. When it is done badly it amounts to dangerous meddling. The more interconnected the system we are trying to improve the more likely our well intentioned tinkering will create a bigger problem in the future than we have now.

Another approach is to start with what-we-want-to-have in the future and then design-to-deliver it. Our starting point is not an aspirational dream vision, also known as an hallucination, it is a clear performance specification with four dimensions: safety, delivery, quality and affordability. This is called a SFQP specification.

The first one to focus on is safety … and what we usually find is that risk of harm is usually a knock-on effect of delivery and quality design problems.

The easiest one is delivery – because it is the application of process physics. The next easiest one is affordability because that is the application of value system accounting.

The tricky one is quality because that implies subjectivity, people, psychology, behaviour and politics. When we add quality to our design challenge we rack up the wickedness score!

So, how do we create a clear and realistic output quality performance specification?

If we draw up a chart with Subjective Quality on the Y-axis and Objective Performance on the X-axis, we can plot all the characteristics of our current and future design on this chart. And when we do that we discover some surprising things.

First – some factors go unnoticed until the performance drops. Said another way we do not notice when it is working – we only only notice when it is not. These factors are called Disappointers. We take for granted that things work 99% of the time – the sun comes up every morning; there is 21% of oxygen in the atmosphere; the air temperature is OK; the electricity is on; the milk, paper and post gets delivered; the car starts and so on. We take it all for granted and we complain when it unexpectedly does not.

So if we ask our customers what they want from an improved service they do not spontaneously volunteer what is currently working well and that they take for granted – because it is out of their awareness. This is what Henry Ford implied when he said “If I asked the customer what they wanted I would have got a faster horse“. It is also the reason why a Three Wins design starts with The 4N Chart® – and specifically the Nuggets corner. We need to make conscious what works well because when we plan improvement we do not want to unintentionally discard the baby with the bath water!

Second – some factors go unnoticed until performance exceeds a minimum threshold. They are not expected so we do not mind if they are not provided – but if they are unexpectedly provided then we are surprised and Delighted. The first time. Once we know what is possible we come to expect it again, and eventually every time.

A common design error is to try to use a Delighter to compensate for a Disappointer.

Suppose we walked into our hotel room and found a complimentary bottle of wine that we were not expecting and then we discovered that there was no toilet paper and the shower was cold. The bottle of wine would not compensate for our disappointment and it might even irritate us because we conclude that the management does not care about our basic needs. Our trust is eroded and our feedback reflects that.

Effective design for trusted quality starts by eliminating the possibility of disappointment. We design it so the expected essentials are “right first time and every time“. Our measure of success is not praise – it is absence of complaints. A deafening silence. It is what does not happen that is important. Good expected essential design is invisible – because it never intrudes on our awareness. And for this reason it is surprisingly difficult to do. It requires pro-action not re-action.

The third type of factor is the Satisfier – and these are the ones that our customers will volunteer because they are aware of them. Lower performance giving lower perceived quality scores and higher performance giving higher. These are the “you get what you pay for” factors. A better designed car is expected to be more comfortable, quieter, easier to drive, safer, more reliable, more effort-saving gadgets and so on. Price is a satisfier. Cost is not. Cost is an output of the design process. So the better the design the greater the gap can be between cost and price.

This method is called Kano Analysis and an understanding of it is essential for effective quality improvement. And like so much of Improvement Science it appears counter-intuitive at first, common-sense when explained, and blindingly obvious when experienced.

11/08/2012

Patience: Necessary but Not Sufficient

The words innovation, invention, and improvement are often used as alternatives for creativity – but there important differences between these concepts.

Creativity refers to any “out of the box” thinking – where assumptions are challenged and changed then the implications are explored. The classic “thought experiment”. It was one of those that led Albert Einstein to the radical idea that our perception of time as separate from space was inaccurate. He asked the question “If I was sitting on a light beam what would I see?” Creative thinking happens inside the head – and creative play happens when groups engage in creative thinking together. Children do it naturally and spontaneously – in the playground. In the classroom play is discouraged – that is where work happens. So as educated adults we separate work-time from play-time and creativity at work is lost. But far more than just that is sacrificed. Creativity is fun – so when we forbid creativity we exclude fun.

An invention is a novel combination of known parts. Invention is an act of design that arises from new insight which comes from creatively challenging assumptions and playing with ideas. Inventions are not accidents – they require deliberate, conscious activity. Inventions are creativity converted to action. And creating an invention is hard work! Inventors are often depicted as driven, hard-working, loners who the rest of society do not understand – but groups can be much more inventive than individuals. Have you ever wondered why children have so much fun when working together to build a sandcastle on the beach or a den in the forest?

Innovation is when you actually do anything new. It does not need to be novel or inventive – just new for you. Anyone can be innovative and everyone is. Adopting a creative-play mode of thinking at work may be innovative; it may lead to a new insights; which may lead to new designs and new inventions. It is also fun to do – especially as a group.

Improvement is what happens when the output of the innovation-creativity-insight-design-invention process is implemented in practice. The improvement is the measured change in a valued characteristic of a system. An actual improvement. Not just the thought of improvement, or the talk of improvement or even the walk of improvement. The the hard evidence of improvement – the evaluation.

This innovation-to-improvement sequence requires time. And one of the important habits that an effective Improvement Scientist must cultivate is patience. Improvements take time to cook – especially when they come from disruptive innovation. That is innovation that challenges deeper held, unconscious, assumptions. Such as “Time is Absolute”.

But patience alone is not enough – it is necessary but it is not sufficient.

The effective Improvement Scientist understands that sustained benefit is more than just a good idea. For a good idea to become established practice then many other people may need to change some of their assumptions, beliefs and behaviours. To achieve that sort of requires other skills – of which personal mastery, respectful challenge and pragmatic assertion are essential.

But there are traps for the unwary and the inexperienced. One danger is for the impatient Improvement Scientist to give their innovation away to the first investor that shows interest. An experienced Improvement Scientist is a serial innovator who can generate good ideas at will. Many must be put on the shelf and wait for the right time – like Cheddar cheeses slowly maturing in an ancient underground river cut cave.

And when the time is right for the seed of innovation to germinate then the Improvement Scientist must step up, be assertive, and state what, declare why and show how.

04/08/2012

The Challenge of Wicked Problems

“Wicked problem” is a phrase used to describe a problem that is difficult or impossible to solve because of incomplete, contradictory, and changing requirements that are often not recognised.

The term ‘wicked’ is used, not in the sense of evil, but rather in the sense that it is resistant to resolution.

The complex inter-dependencies imply that an effort to solve one aspect of a wicked problem may reveal or create other problems.

System-level improvement is a very common example of a wicked problem, so an Improvement Scientist needs to be able to sort the wicked problems from the tame ones.

Tame problems can be solved using well known and understood methods and the solution is either right or wrong. For example – working out how much resource capacity is needed to deliver a defined demand is a tame problem. Designing a booking schedule to avoid excessive waiting is a tame problem. The fact that many people do not know how to solve these tame problems does not make them wicked ones. Ignorance in not that same as intransigence.

Wicked problems do not have right or wrong solutions – they have better or worse outcomes. Wicked problems cannot be precisely defined, dissected, analysed and solved. They are messy. They are more than complicated – they are complex. A mechanical clock is a complicated mechanism but designing, building, operating and even repairing a clock is a tame problem not a wicked one.

So how can we tell a wicked problem from a tame one?

If a problem has been solved and there is a known and repeatable solution then it is, by definition, a tame problem. If a problem has never been solved then it might be tame – and the only way to find out is to try solving it.

The barrier we then discover is that each of us gets stuck in the mud of our habitual, unconscious assumptions. Experience teaches us that just taking a different perspective can be enough to create the breakthrough insight – the “Ah ha!” moment. Seeking other perspectives and opinions is an effective strategy when stuck.

So, if two-heads-are-better-than-one then many heads must be even better! Do we need a committee to solve wicked problems?

Experience teaches us that when we try it we find that it often does not work!

The different perspectives also come with different needs, different assumptions, and different agendas and we end up with a different wicked problem. The committee is rendered ineffective and inefficient by rhetorical discussion and argument.

This is where a very useful Improvement Science technique comes in handy. It is called Argument Free Problem Solving (AFPS) and it was intentionally designed to facilitate groups working on complex problems.

The trick to AFPS is to understand what generates the arguments and to design these causes out of the problem solving process. There are several contributors.

First there is just good old fashioned disrespectful skepticism – otherwise known as cynicism. The antidote to this poison is to respectfully challenge the disrespectful component of the cynical behaviour – the personal discounting bit. And it is surprisingly effective!

Second there is the well known principle that different people approach life and problems in different ways. Some call this temperament and others call it personality. Whatever the label, knowing our preferred style and how different styles can conflict is useful because it leads to mutual respect for our different gifts. One tried and tested method is Jungian Typology which comes in various brands such as the MBTI® (Myers Briggs Type Indicator).

Third there is the deepening understanding of how the 1.3 kg of caveman wetware between our ears actually works. The ongoing advances in neuroscience are revealing fascinating insights into how “irrational” we really are and how easy it is to fool the intuition. Stage magicians and hypnotists make a living out of this inherent “weakness”. One of the lessons from neuroscience is that we find it easier to communicate when we are all in the same mental state – even if we have different temperaments. It is called cognitive resonance. Being on the same wavelength. Arguments arise when different people are in conflicting mental states – cognitive dissonance.

So an effective problem solving team is more akin to a flock of birds or a shoal of fish – that can change direction quickly and as one – without a committee, without an argument, and without creating chaos. For birds and fish it is an effective survival strategy because it confounds the predators. The ones that do not join in … get eaten!

When a group are able to change perspective together and still stay focused on the problem then the tame ones get resolved and the wicked ones start to be dissolved.

And that is all we can expect for wicked problems.

The AFPS method can be learned quickly – and experience shows that just one demonstration is usually enough to convince the participants when a team is hopelessly entangled in a wicked-looking problem!

07/07/2012

Iconoclasts and Iconoblasts

The human body is an amazing self-repairing system. It does this by being able to detect damage and to repair just the damaged part while still continuing to function. One visible example of this is how it repairs a broken bone. The skeleton is the hard, jointed framework that protects and supports the soft bits. Some of the soft bits, the muscles, both stablise and move this framework of bones. Together they form the musculoskeletal system that gives us the power to move ourselves. So when, by accident, we break a bone how do we repair the damage? The secret is in the microscopic structure of the bone. Bone is not like concrete, solid and inert, it is a living tissue. Two of the microsopic cells that live in the bone are the osteoclasts and the osteoblasts (osteo- is Greek for “bone”; -clast is Greek for “break” and -blast is Greek for “germ” in the sense of something that grows). Osteoclasts dissolve the old bone and osteoblasts deposit new bone – so when they work together they can create bone, remodel bone, and repair bone. It is humbling when we consider that millions of microscopic cells are able to coordinate this continuous, dynamic, adaptive, reparative behaviour with no central command-and-control system, no decision makers, no designers, no blue-prints, no project managers. How is this biological miracle achieved? We are not sure – but we know that there must be a process.

Organisations are systems that face a similar challenge. They have relatively rigid operational and cultural structures of roles, responsibilities, lines of accountability, rules, regulations, values, beliefs, attitudes and behaviours. These formal and informal structures are the conceptual “bones” of the organisation – the structure that enables the organisation to function. Organisations also need to grow and to develop – which means that their virtual bones need to be remodelled continuously. Occasionally organisations have accidents – and their bones break – and sometimes the breaks are deliberate: it is called “re-structuring”.

There are people within organisations that have the same role as the osteoblast in the body. These people are called iconoclasts and what they do is dissolve dogma. They break up the rigid rules and regulations that create the corporate equivalent of concrete – but they are selective. Iconoclasts are sensitive to stress and to strain and they only dissolve the cultural concrete where it is getting in the way of improvement. That is where dogma is blocking innovation. Iconoclasts question the status quo, and at the same time explain how it is causing a problem, offer alternatives, and predict the benefits of the innovation. Iconoclasts are not skeptics or cynics – they prepare the ground for change – they are facilitators.

There is a second group people who we could call the iconoblasts. They are the ones who create the new rules, the new designs, the new recipes, the new processes, the new operating standards – and they work alongside the iconoclasts to ensure the structure remains strong and stable as it evolves. The iconoblasts are called Improvement Scientists.

Improvement Scientists are like builders – they use the raw materials of ideas, experience, knowledge, understanding, creativity and enthusiasm and assemble them into new organisational structures. In doing so they fully accept that one day these structures will in turn be dismantled and rebuilt. That is the way of improvement. The dogma is relative and temporary rather than absolute and permanent. And the faster the structures can be disassembled and reassembled the more agile the organisation becomes and the more able it is to survive change.

So how are the iconoclasts and iconoblasts coordinated? Can they also work effectively and efficiently without a command-and-control system? If millions if microscopic cells in our bones can achieve it then maybe the individuals within organisations can do it too. We just need to understand what makes an iconoclast and an iconoblast and effective partnership and an essential part of an organisation.

23/06/2012

Productivity Improvement Science

Very often there is a requirement to improve the productivity of a process and operational managers are usually measured and rewarded for how well they do that. Their primary focus is neither safety nor quality – it is productivity – because that is their job.

For-profit organisations see improved productivity as a path to increased profit. Not-for-profit organisations see improved productivity as a path to being able to grow through re-investment of savings. The goal may be different but the path is the same – productivity improvement.

First we need to define what we mean by productivity: it is the ratio of a system output to a system input. There are many input and output metrics to choose from and a convenient one to use is the ratio of revenue to expenses for a defined period of time. Any change that increases this ratio represents an improvement in productivity on this purely financial dimension and we know that this financial data is measured. We just need to look at the bank statement.

There are two ways to approach productivity improvement: by considering the forces that help productivity and the forces that hinder it. This force-field metaphor was described by the psychologist Kurt Lewin (1890-1947) and has been developed and applied extensively and successfully in many organisations and many scenarios in the context of change management.

Improvement results from either strengthening helpers or weakening hinderers or both – and experience shows that it is often quicker and easier to focus attention on the hinderers because that leads to both more improvement and to less stress in the system. Usually it is just a matter of alignment. Two strong forces in opposition results in high stress and low motion; but in alignment creates low stress and high acceleration.

So what hinders productivity?

Well, anything that reduces or delays workflow will reduce or delay revenue and therefore hinder productivity. Anything that increases resource requirement will increase cost and therefore hinder productivity. So looking for something that causes both and either removing or realigning it will have a Win-Win impact on productivity!

A common factor that reduces and delays workflow is the design of the process – in particular a design that has a lot of sequential steps performed by different people in different departments. The handoffs between the steps are a rich source of time-traps and bottlenecks and these both delay and limit the flow. A common factor that increases resource requirement is making mistakes because errors generate extra work – to detect and to correct. And there is a link between fragmentation and errors: in a multi-step process there are more opportunities for errors – particularly at the handoffs between steps.

So the most useful way to improve the productivity of a process is to simplify it by combining several, small, separate steps into single large ones.

A good example of this can be found in healthcare – and specifically in the outpatient department.

Traditionally visits to outpatients are defined as “new” – which implies the first visit for a particular problem – and “review” which implies the second and subsequent visits. The first phase is the diagnostic work and this often requires special tests or investigations to be performed (such as blood tests, imaging, etc) which are usually done by different departments using specialised equipment and skills. The design of departmental work schedules requires a patient to visit on a separate occasion to a different department for each test. Each of these separate visits incurs a delay and a risk of a number of errors – the commonest of which is a failure to attend for the test on the appointed day and time. Such did-not-attend or DNA rates are surprisingly high – and values of 10% are typical in the NHS.

The cumulative productivity hindering effect of this multi-visit diagnostic process design is large. Suppose there are three steps: New-Test-Review and each step has a 10% DNA rate and a 4 week wait. The quickest that a patient could complete the process is 12 weeks and the chance of getting through right first time (the yield) is about 90% x 90% x 90% = 73% which implies that 27% extra resource is needed to correct the failures. Most attempts to improve productivity focus on forcing down the DNA rate – usually with limited success. A more effective approach is to redesign process by combining the three New-Test-Review steps into one visit. Exactly the same resources are needed to do the work as before but now the minimum time would be 4 weeks, the right-first-time yield would increase to 90% and the extra resources required to manage the two handoffs, the two queues, and the two sources of DNAs would be unnecessary. The result is a significant improvement in productivity at no cost. It is also an improvement in the quality of the patient experience but that is a unintended bonus.

So if the solution is that obvious and that beneficial then why are we not doing this everywhere? The answer is that we do in some areas – in particular where quality and urgency is important such as fast-track one-stop clinics for suspected cancer. However – we are not doing it as widely as we could and one reason for that is a hidden hinderer: the way that the productivity is estimated in the business case and measured in the the day-to-day business.

Typically process productivity is estimated using the calculated unit price of the product or service. The unit price is arrived at by adding up the unit costs of the steps and adding an allocation of the overhead costs (how overhead is allocated is subject to a lot of heated debate by accountants!). The unit price is then multiplied by expected activity to get expected revenue and divided by the total cost (or budget) to get the productivity measure. This approach is widely taught and used and is certainly better than guessing but it has a number of drawbacks. Firstly, it does not take into account the effects of the handoffs and the queues between the steps and secondly it drives step-optimisation behaviour. A departmental operational manager who is responsible and accountable for one step in the process will focus their attention on driving down costs and pushing up utilisation of their step because that is what they are performance managed on. This in itself is not wrong – but it can become counter-productive when it is done in isolation and independently of the other steps in the process. Unfortunately our traditional management accounting methods do not prevent this unintentional productivity hindering behaviour – and very often they actually promote it – literally!

This insight is not new – it has been recognised by some for a long time – so we might ask ourselves why this is still the case? This is a very good question that opens another “can of worms” which for the sake of brevity will be deferred to a later conversation.

So, when applying Improvement Science in the domain of financial productivity improvement then the design of both the process and of the productivity modelling-and-monitoring method may need addressing at the same time. Unfortunately this does not seem to be common knowledge and this insight may explain why productivity improvements do not happen more often – especially in publically funded not-for-profit service organisations such as the NHS.

17/06/2012

Pruning the Niggle Tree

Sometimes our daily existence feels like a perpetual struggle between two opposing forces: the positive force of innovation, learning, progress and success; and the opposing force of cynicism, complacency, stagnation and failure. Often the balance-of-opposing-forces is so close that even small differences of opinion can derail us – especially if they are persistent. And we want to stay on course to improvement.

Niggles are the irritating things that happen every day. Day after day. Niggles are persistent. So when we are in our “ying-yang” equilibrium and “balanced on the edge” then just one extra niggle can push us off our emotional tight-rope. And we know it. The final straw!

So to keep ourselves on track to success we need to “nail” niggles. But which ones? There seem to be so many! Where do we start?

If we recorded just one day and from that we listed all the positive things that happened on green PostIt® notes and all the negatives things on red ones – then we would be left with a random-looking pile of red and green notes. Good days would have more green, and bad days would have more red – and all days would have both. And that is just the way it is. Yes? But are they actually random? Is there a deeper connection?

Experience teaches us that when we Investigate-a-Niggle we find it is connected to other niggles. The “cannot find a parking place” niggle is because of the “car park is full” niggle which also causes the “someone arrived late for my important meeting” niggle. The red leaf is attached to a red twig which in turn sprouts other red leaves. The red leaves connect to other red leaves; not to green ones.

If we tug on a green leaf – a Nugget – we find that it too is connected to other nuggets. The “congratulations on a job well done” nugget is connected to the the “feedback is important” nugget from which sprouts the “opportunities for learning” nugget. Our green leaf is attached, indirectly, to many other green leaves; not to red ones.

It seems that our red leaves (niggles) and our green leaves (nuggets) are connected – but not directly to each other. It is as if we have two separate but tightly intertwined plants competing with each other for space and light. So if we want a tree that is more green than red and if we want to progress steadily in the direction of sustained improvement – then we need to prune the niggle tree (red leaves) and leave the nugget tree (green leaves) unscathed.

The problem is that if we just cut off one or two red leaves new ones sprout quickly from the red twigs to replace them. We quickly learn that this apprach is futile. We suspect that if we were able to cut all the red leaves off at once then the niggle tree might shrivel and die – but that looks impossible. We need to be creative and we need to search deeper. With the knowledge that the red-leaves are part of one tree and we can remove multiple red leaves in one snip by working our way back from the leaves, up the red twigs and to the red branches. If we prune far enough back then we can expect a large number of interconnected red leaves to wither and fall off – leaving the healthy green leaves more space and more light to grow on that part of the tree.

Improvement Science is about pruning the Niggle tree to make space for the Nugget tree to grow. It is about creating an environment for the Green shoots of innovation to sprout. Most resistance comes from those who feed on the Red leaves – the Cynics – and if we remove enough red branches then they will go hungry. And now the Cynics have a choice: learn to taste and appreciate the Green leaves or “find another tree”.

We want a Greener tree- with fewer poisonous Red leaves on it.

03/06/2012

Negotiate, Negotiate, Negotiate.

One of the most important skills that an Improvement Scientist needs is the ability to negotiate. We are all familiar with one form of negotiaton which is called distributive negotiation which is where the parties carve up the pie in a low trust compromise. That is not the form we need – what we need is called integrative negotiation. The goal of integrative negotiation is to join several parts into a greater whole and it implies a higher level of trust and a greater degree of collaboration.

Organisations of more than about 90 people are usually split into departments – and for good reasons. The complex organisation requires specialist aptitudes, skills, and know-how and it is easier to group people together who share the specialist skills needed to deliver that service to the organisation – such as financial services in the accounts department. The problem is that this division also creates barriers and as the organisation increases in size these barriers have a cumulative effect that can severely limit the capability of the organisation. The mantra that is often associated with this problem is “communication, communication, communication” … which is too non-specific and therefore usually ineffective.

The products and services that an organisation is designed to deliver are rarely the output of one department – so the parts need to align and to integrate to create an effective and efficient delivery system. This requires more than just communication – it requires integrative negotiation – and it is not a natural skill or one that is easy to develop. It requires investment of effort and time.

To facilitate the process we need to provide three things: a common goal, a common language and a common ground. The common goal is what all parts of the system are aligned to; the common language is how the dialog is communicated; and the common ground is our launch pad.

Integrative negotiation starts with finding the common ground – the areas of agreement. Very often these are taken for granted because we are psychologically tuned to notice differences rather than similarities. We have to make the “assumed” and “obvious” explicit before we turn our attention on our differences.

Integrative negoation proceeds with defining the common niggles and nice-ifs that could be resolved by a single change; the win-win-win opportunities.

Integrative negotiation concludes with identifying changes that are wholly within the circle of influence of the parties involved – the changes that they have the power to make individually and collectively.

After negotiation comes decision and after decision comes action and that is when improvement happens.