Theory – Page 2 – HCSE Blog

21/03/2015

Walk Confidently before Running

Improvement is not a continuous process. It has starts and stops, and ups and downs. Improvement implies change, and that is intentionally disruptive. So the context will determine the progress as much as the change.

A commonly observed behaviour is probably at the root of why the majority of improvements initiatives fail to achieve a significant and sustained improvement. Trying to run before mastering the skill of walking.

An experienced improvement coach will not throw learners into the deep end and watch them sink or swim. That is not coaching; it is cruelty.

So the first improvement projects must be doable and done with lots of hands-off support, encouragement and praise for progress.

This has the benefit of developing confidence and capability.

It has a danger of leading to over-confidence though. Confidence that exceeds capability.

There is a risk that the growing learner will take on a future improvement project that is outside their capability zone.

The danger of doing this is that they fall at the second hurdle and their new confidence can be damaged and even smashed. This can leave the learner feeling less motivated and more fearful than before.

There are a number of ways that an improvement coach can mitigate this risk:

1. Make the learners aware up front that this is a risk.
2. Scope each project to stretch but not scare.
3. Be prepared to stop and reduce scope if necessary.
4. Set the expectation to consolidate the basics by teaching others.

These are not mutually exclusive options. Seeing, doing and teaching can happen in parallel and that is actually the most productive way to learn.

As children we learned to walk with confidence before we learned to run … because falling flat on our face hurts both physically and emotionally!

This is just the same.

21/02/2015

Cumulative Sum

[Bing] Bob logged in for the weekly Webex coaching session. Leslie was not yet on line, but joined a few minutes later.

<Leslie> Hi Bob, sorry I am a bit late, I have been grappling with a data analysis problem and did not notice the time.

<Bob> Hi Leslie. Sounds interesting. Would you like to talk about that?

<Leslie> Yes please! It has been driving me nuts!

<Bob> OK. Some context first please.

<Leslie> Right, yes. The context is an improvement-by-design assignment with a primary care team who are looking at ways to reduce the unplanned admissions for elderly patients by 10%.

<Bob> OK. Why 10%?

<Leslie> Because they said that would be an operationally very significant reduction. Most of their unplanned admissions, and therefore costs for admissions, are in that age group. They feel that some admissions are avoidable with better primary care support and a 10% reduction would make their investment of time and effort worthwhile.

<Bob> OK. That makes complete sense. Setting a new design specification is OK. I assume they have some baseline flow data.

<Leslie> Yes. We have historical weekly unplanned admissions data for two years. It looks stable, though rather variable on a week-by-week basis.

<Bob> So has the design change been made?

<Leslie> Yes, over three months ago – so I expected to be able to see something by now but there are no red flags on the XmR chart of weekly admissions. No change. They are adamant that they are making a difference, particularly in reducing re-admissions. I do not want to disappoint them by saying that all their hard work has made no difference!

<Bob> OK Leslie. Let us approach this rationally. What are the possible causes that the weekly admissions chart is not signalling a change?

<Leslie> If there has not been a change in admissions. This could be because they have indeed reduced readmissions but new admissions have gone up and is masking the effect.

<Bob> Yes. That is possible. Any other ideas?

<Leslie> That their intervention has made no difference to re-admissions and their data is erroneous … or worse still … fabricated!

<Bob> Yes. That is possible too. Any other ideas?

<Leslie> Um. No. I cannot think of any.

<Bob> What about the idea that the XmR chart is not showing a change that is actually there?

<Leslie> You mean a false negative? That the sensitivity of the XmR chart is limited? How can that be? I thought these charts will always signal a significant shift.

<Bob> It depends on the degree of shift and the amount of variation. The more variation there is the harder it is to detect a small shift. In a conventional statistical test we would just use bigger samples, but that does not work with an XmR chart because the run tests are all fixed length. Pre-defined sample sizes.

<Leslie> So that means we can miss small but significant changes and come to the wrong conclusion that our change has had no effect! Isn’t that called a Type 2 error?

<Bob> Yes, it is. And we need to be aware of the limitations of the analysis tool we are using. So, now you know that how might you get around the problem?

<Leslie> One way would be to aggregate the data over a longer time period before plotting on the chart … we know that will reduce the sample variation.

<Bob> Yes. That would work … but what is the downside?

<Leslie> That we have to wait a lot longer to show a change, or not. We do not want that.

<Bob> I agree. So what we do is we use a chart that is much more sensitive to small shifts of the mean. And that is called a cusum chart. These were not invented until 30 years after Shewhart first described his time-series chart. To give you an example, do you recall that the work-in-progress chart is much more sensitive to changes in flow than either demand or activity charts?

<Leslie> Yes, and the WIP chart also reacts immediately if either demand or activity change. It is the one I always look at first.

<Bob> That is because a WIP chart is actually a cusum chart. It is the cumulative sum of the difference between demand and activity.

<Leslie> OK! That makes sense. So how do I create and use a cusum chart?

<Bob> I have just emailed you some instructions and a few examples. You can try with your unplanned admissions data. It should only take a few minutes. I will get a cup of tea and a chocolate Hobnob while I wait.

[Five minutes later]

<Leslie> Wow! That is just brilliant! I can see clearly on the cusum chart when the shifts happened and when I split the XmR chart at those points the underlying changes become clear and measurable. The team did indeed achieve a 10% reduction in admissions just as they claimed they had. And I checked with a statistical test which confirmed that it is statistically significant.

<Bob> Good work. Cusum charts take a bit of getting used to and we have be careful about the metric we are plotting and a few other things but it is a useful trick to have up our sleeves for situations like this.

<Leslie> Thanks Bob. I will bear that in mind. Now I just need to work out how to explain cusum charts to others! I do not want to be accused of using statistical smoke-and-mirrors! I think a golf metaphor may work with the GPs.

24/01/2015

Politicial Purpose

The question that is foremost in the mind of a designer is “What is the purpose?” It is a future-focussed question. It is a question of intent and outcome. It raises the issues of worth and value.

Without a purpose it impossible to answer the question “Is what we have fit-for-purpose?“

And without a clear purpose it is impossible for a fit-for-purpose design to be created and tested.

In the absence of a future-purpose all that remains are the present-problems.

Without a future-purpose we cannot be proactive; we can only be reactive.

And when we react to problems we generate divergence. We observe heated discussions. We hear differences of opinion as to the causes and the solutions. We smell the sadness, anger and fear. We taste the bitterness of cynicism. And we are touched to our core … but we are paralysed. We cannot act because we cannot decide which is the safest direction to run to get away from the pain of the problems we have.

And when the inevitable catastrophe happens we look for somewhere and someone to place and attribute blame … and high on our target-list are politicians.

So the prickly question of politics comes up and we need to grasp that nettle and examine it with the forensic lens of the system designer and we ask “What is the purpose of a politician?” What is the output of the political process? What is their intent? What is their worth? How productive are they? Do we get value for money?

They will often answer “Our purpose is to serve the public“. But serve is a verb so it is a process and not a purpose … “To serve the public for what purpose?” we ask. “What outcome can we expect to get?” we ask. “And when can we expect to get it?”

We want a service (a noun) and as voters and tax-payers we have customer rights to one!

On deeper reflection we see a political spectrum come into focus … with Public at one end and Private at the other. A country generates wealth through commerce … transforming natural and human resources into goods and services. That is the Private part and it has a clear and countable measure of success: profit. The Public part is the redistribution of some of that wealth for the benefit of all – the tax-paying public. Us.

Unfortunately the Public part does not have quite the same objective test of success: so we substitute a different countable metric: votes. So the objectively measurable outcome of a successful political process is the most votes.

But we are still talking about process … not purpose. All we have learned so far is that the politicians who attract the most votes will earn for themselves a temporary mandate to strive to achieve their political purpose. Whatever that is.

So what do the public, the voters, the tax-payers (and remember whenever we buy something we pay tax) … the customers of this political process … actually get for their votes and cash? Are they delighted, satisfied or disappointed? Are they getting value-for-money? Is the political process fit-for-purpose? And what is the purpose? Are we all clear about that?

And if we look at the current “crisis” in health and social care in England then I doubt that “delight” will feature high on the score-sheet for those who work in healthcare or for those that they serve. The patients. The long-suffering tax-paying public.

Are politicians effective? Are they delivering on their pledge to serve the public? What does the evidence show? What does their portfolio of public service improvement projects reveal? Welfare, healthcare, education, police, and so on.

Well the actual evidence is rather disappointing … a long trail of very expensive taxpayer-funded public service improvement failures.

And for an up-to-date list of some of the “eye-wateringly”expensive public sector improvement train-wrecks just read The Whitehall Effect.

But lurid stories of public service improvement failures do not attract precious votes … so they are not aired and shared … and when they are exposed our tax-funded politicians show their true skills and real potential.

Rather than answering the questions they filter, distort and amplify the questions and fire them at each other. And then fall over each other avoiding the finger-of-blame and at the same time create the next deceptively-plausible election manifesto. Their food source is votes so they have to tickle the voters to cough them up. And they are consummate masters of that art.

Politicians sell dreams and serve disappointment.

So when the-most-plausible with the most votes earn the right to wield the ignition keys for the engine of our national economy they deflect future blame by seeking the guidance of experts. And the only place they can realistically look is into the private sector who, in manufacturing anyway, have done a much better job of understanding what their customers need and designing their processes to deliver it. On-time, first-time and every-time.

Politicians have learned to be wary of the advice of academics – they need something more pragmatic and proven. And just look at the remarkable rise of the manufacturing phoenix of Jaguar-Land-Rover (JLR) from the politically embarrassing ashes of the British car industry. And just look at Amazon to see what information technology can deliver!

So the way forward is blindingly obvious … combine manufacturing methods with information technology and build a dumb-robot manned production-line for delivering low-cost public services via a cloud-based website and an outsourced mega-call-centre manned by standard-script-following low-paid operatives.

But here we hit a bit of a snag.

Designing a process to deliver a manufactured product for a profit is not the same as designing a system to deliver a service to the public. Not by a long chalk. Public services are an example of what is now known as a complex adaptive system (CAS).

And if we attempt to apply the mechanistic profit-focussed management mantras of “economy of scale” and “division of labour” and “standardisation of work” to the messy real-world of public service then we actually achieve precisely the opposite of what we intended. And the growing evidence is embarrassingly clear.

We all want safer, smoother, better, and more affordable public services … but that is not what we are experiencing.

Our voted-in politicians have unwittingly commissioned complicated non-adaptive systems that ensure we collectively fail.

And we collectively voted the politicians into power and we are collectively failing to hold them to account.

So the ball is squarely in our court.

Below is a short video that illustrates what happens when politicians and civil servants attempt complex system design. It is called the “Save the NHS Game” and it was created by a surgeon who also happens to be a system designer. The design purpose of the game is to raise awareness. The fundamental design flaw in this example is “financial fragmentation” which is the the use of specific budgets for each part of the system together with a generic, enforced, incremental cost-reduction policy (the shrinking budget). See for yourself what happens …

In health care we are in the improvement business and to do that we start with a diagnosis … not a dream or a decision.

We study before we plan, and we plan before we do.

And we have one eye on the problem and one eye on the intended outcome … a healthier patient. And we often frame improvement in the negative as a ‘we do not want a not sicker patient’ … physically or psychologically. Primum non nocere. First do no harm.

And 99.9% of the time we do our best given the constraints of the system context that the voted-in politicians have created for us; and that their loyal civil servants have imposed on us.

Politicians are not designers … that is not their role. Their part is to create and sell realistic dreams in return for votes.

Civil servants are not designers … that is not their role. Their part is to enact the policy that the vote-seeking politicians cook up.

Doctors are not designers … that is not their role. Their part is to make the best possible clinical decisions that will direct actions that lead, as quickly as possible, to healthier and happier patients.

So who is doing the complex adaptive system design? Whose role is that?

And here we expose a gap. No one. For the simple reason that no one is trained to … so no one is tasked to.

But there is a group of people who are perfectly placed to create the context for developing this system design capability … the commissioners, the executive boards and the senior managers of our public services.

So that is where we might reasonably start … by inviting our leaders to learn about the science of complex adaptive system improvement-by-design.

And there are now quite a few people who can now teach this science … they are the ones who have done it and can demonstrate and describe their portfolios of successful and sustained public service improvement projects.

Would you vote for that?

22/11/2014

Metamorphosis

Some animals undergo a remarkable transformation on their journey to becoming an adult.

This metamorphosis is most obvious with a butterfly: the caterpillar enters the stage and a butterfly emerges.

The capabilities and behaviours of these development stages are very different. A baby caterpillar crawls and feeds on leaves; an adult butterfly flies and feeds on nectar.

There are many similarities to the transformation of an organisation from chaotic to calm; from depressed to enthused; and from struggling to flying.

It is the metamorphosis of individuals within organisations that drives the system change – the transformation from inept sceptics to capable advocates.

The journey starts with the tiny, hungry, baby caterpillar emerging from the egg.

This like a curious new sceptic emerging from denial and tentatively engaging the the process of learning. Usually triggered by seeing or hearing of a significant and sustained success that disproves their ‘impossibility hypothesis’.

A caterpillar is an eating machine. As it grows it sheds its skin and becomes larger. It also changes its appearance and eventually its behaviour.

Our curious improvement sceptic is devouring new information and is visibly growing in knowledge, understanding and confidence.

When the caterpillar sheds the last skin a new form emerges. A pupa. It has a different appearance and behaviour. It is now stationary and it does not move or eat.

This is the contemplative sceptic who appears to have become dormant but is not … they are planning to change. This stage is very variable: it may be minutes or years.

Inside the pupa the solid body of the caterpillar is converted to ‘cellular soup’ and the cells are reassembled into a completely new structure called an adult butterfly.

Our healthy sceptic is dissolving their self-limiting beliefs and restructuring their mental model. It is stage of apparent confusion and success is not guaranteed.

And suddenly the adult butterfly emerges: fully formed but not yet able to fly. Its wings are not yet ready – they need to be inflated, to dry and be flexed.

So it is with our newly hatched improvement practitioner. They need to pause, prepare, and practice before they feel safe to fly solo. They start small but are thinking big.

After a short rest the new wings are fully expanded and able to lift the butterfly aloft to explore the new opportunities that await. A whole new and exciting world full of flowers and nectar.

Our improvement practitioner can also feel when they are ready to explore. And then they fly – right first time.

An active improvement practitioner will inspire others to emerge, and many of those will hatch into improvement caterpillars who will busily munch on the new knowledge and grow in understanding and confidence. Then it goes quiet and, as if by magic, a new generation of improvement butterflies appear. And they continue to spread the word and the knowledge.

That is how Improvement Science grows and spreads – by metamorphosis.

10/11/2014

World Class Improvement

Improvement Science is exactly like a sport: it requires training and practice to do well.

Elite athletes do not just turn up and try hard … they have invested thousands of hours of blood, sweat and tears to even be eligible to turn up.

And their preparation is not random or haphazard … it is structured and scientific. Sport is a science.

So it is well worth using this sporting metaphor to outline some critical-to-success factors … because the statistics on improvement projects is not good.

It is said that over 70% of improvement projects fail to achieve their goals.

That is a shocking statistic. It is like saying 70% of runners who start a race do not finish!

And in sport if you try something that you are not ready for then you can seriously damage your health. So just turning up and trying hard is not enough. In can actually be counter-productive!

Common sense tells us that those fail to complete the course were not well enough prepared to undertake the challenge. We know that only one person can win a race … but everyone else could finish it. And to start and finish a tough race is a major achievement for each participant.

It is actually their primary goal.

Being good enough to when we need to is the actual objective; being the best-on-the-day is a bonus. Not winning is not a failure. Not finishing is.

So how does an Improvement Scientist prepare for the improvement challenge?

First, we need enough intrinsic motivation to get out of bed and to invest the required time and effort. We must have enough passion to get started and to keep going. We must be disappointed enough with past failures to commit to preventing future ones. We must be angry enough with the present problems to take action … not on the people … but on the problem. We must be fearful enough of the future consequences of inaction to force us to act. And we need to be excited enough by the prospect of success to reach out for it.

Second, we need some technical training. How to improve the behaviour and performance of a complex adaptive system is not obvious. If it were we would all know how to do it. Many of the most effective designs appear counter-intuitive at first sight. Many of our present assumptions and beliefs are actually a barrier to change. So we need help and guidance in identifying what assumptions we need to unlearn.

Third, We need to practice what we have learned until it becomes second-nature, and almost effortless. Deceptively easy to the untrained eye. And we develop our capability incrementally by taking on challenges of graded difficulty. Each new challenge is a bit of a stretch, and we build on what we have achieved already. There are no short cuts or quick fixes if we want to be capable and confident at taking on BIG improvement challenges.

And we need a coach as well as a trainer.

The role of a trainer is to teach us technical skills and to develop our physical strength, stamina and resilience.

The role of the coach is to help us develop our emotional stamina and resilience. We need to learn to manage our minds as much as our muscles. We all harbour self-defeating attitudes, beliefs and behaviours. Bad habits that trip us up and cause us to slip, fall and bruise our egos and confidence.

The psychological development is actually more important than the physical … because if is our self-defeating “can’t do” and “yes but” inner voices that sap our intrinsic motivation and prevent us crawling out of bed and getting started.

The UK Cycling Team that won multiple goal medals in the 2012 Olympics did not just train hard and have the latest and best equipment. They also had the support of a very special type of coach. Dr Steve Peters … who showed them how to manage their inner Chimp … and how to develop their mental strength in synergy with their technical ability. The result was a multi-gold medal winning engine.

And we can all benefit from this wisdom just by reading The Chimp Paradox by Dr Steve Peters.

So when we take on a difficult improvement challenge, one that many have tried and failed to overcome, and if we want world class performance as the outcome … then we need to learn the hard-won lessons of the extreme athletes … and we need to model their behaviour.

Because that is what it takes to become an Improvement Science Practitioner.

Our goal is to finish each improvement race that we start … to deliver a significant and sustained improvement. We do not need to be perfect or the best … we just need to start and finish the race.

04/10/2014

Wacky Language

All innovative ideas are inevitably associated with new language.

Familiar words used in an unfamiliar context so that the language sounds ‘wacky’ to those in the current paradigm.

Improvement science is no different.

A problem arises when familiar words are used in a new context and therefore with a different meaning. Confusion.

So we try to avoid this cognitive confusion by inventing new words, or by using foreign words that are ‘correct’ but unfamiliar.

This use of novel and foreign language exposes us to another danger: the evolution of a clique of self-appointed experts who speak the new and ‘wacky’ language.

This self-appointed expert clique can actually hinder change because it can result yet another us-and-them division. Another tribe. More discussion. More confusion. Less improvement.

So it is important for an effective facilitator-of-improvement to define any new language using the language of the current paradigm. This can be achieved by sharing examples of new concepts and their language in familiar contexts and with familiar words, because we learn what words mean from their use-in-context.

For example:

The word ‘capacity’ is familiar and we all know what we think it means. So when we link it to another familiar word, ‘demand’, then we feel comfortable that we understand what the phrase ‘demand-and-capacity’ means.

But do we?

The act of recognising a word is a use of memory or knowledge. Understanding what a word means requires more … it requires knowing the context in which the word is used. It means understanding the concept that the word is a label for.

To a practitioner of flow science the word ‘capacity’ is confusing – because it is too fuzzy. There are many different forms of capacity: flow-capacity, space-capacity, time-capacity, and so on. Each has a different unit and they are not interchangeable. So the unqualified term ‘capacity’ will trigger the question:

What sort of capacity are you referring to?

[And if that is not the reaction then you may be talking to someone who has little understanding of flow science].

Then there are the foreign words that are used as new labels for old concepts.

Lean zealots seem particularly fond of peppering their monologues with Japanese words that are meaningless to anyone else but other Lean zealots. Words like muda and muri and mura which are labels for important and useful flow science concepts … but the foreign name gives no clue as to what that essential concept is!

[And for a bit of harmless sport ask a Lean zealot to explain what these three words actually mean but only using language that you understand. If they cannot to your satisfaction then you have exposed the niggle. And if they can then it is worth asking ‘What is the added value of the foreign language?’]

And for those who are curious to know the essential concepts that these four-letter M words refer to:

muda means ‘waste’ and refers to the effects of poor process design in terms of the extra time (and cost) required for the process to achieve its intended purpose. A linked concept is a ‘niggle’ which is the negative emotional effect of a poor process design.

muri means ‘overburdening’ and can be illustrated with an example. Suppose you work in a system where there is always a big backlog of work waiting to be done … a large queue of patients in the waiting room … a big heap of notes on the trolley. That ‘burden’ generates stress and leads to other risky behaviours such as rushing, corner-cutting, deflection and overspill. It is also an outcome of poor process design, so is avoidable.

mura means variation or uncertainty. Again an example helps. Suppose we are running an emergency service then, by definition, a we have no idea what medical problem the next patient that comes through the door will present us with. It could be trivial or life-threatening. That is unplanned and expected variation and is part of the what we need our service to be designed to handle. Suppose when we arrive for our shift that we have no idea how many staff will be available to do the work because people phone in sick at the last minute and there is no resilience on the staffing capacity. Our day could be calm-and-capable (and rewarding) or chaotic-and-incapable (and unrewarding). It is the stress of not knowing that creates the emotional and cultural damage, and is the expected outcome of incompetent process design. And is avoidable.

And finally we come to words that are not foreign but are not very familiar either.

Words like praxis.

This sounds like ‘practice’ but is not spelt the same. So is the the same?

And it sounds like a medical condition called dyspraxia which means: poor coordination of movement.

And when we look up praxis in an English dictionary we discover that one definition is:

the practice and practical side of a profession or field of study, as opposed to theory.

Ah ah! So praxis is a label for the the concept of ‘how to’ … and someone who has this ‘know how’ is called a practitioner. That makes sense.

On deeper reflection we might then describe our poor collective process design capability as dyspraxic or uncoordinated. That feels about right too.

An improvement science practitioner (ISP) is someone who knows the science of improvement; and can demonstrate their know-how in practice; and can explain the principles that underpin their praxis using the language of the learner. Without any wacky language.

So if we want to diagnose and treat our organisational dyspraxia;

… and if we want smooth and efficient services (i.e. elimination of chaos and reduction of cost);

… and if we want to learn this know-how, practice or praxis;

… then we could study the Foundations of Improvement Science in Healthcare (FISH);

… and we could seek the wisdom of the growing Community of Healthcare Improvement Practitioners (CHIPs).

FISH & CHIPs … a new use for a familiar phrase?

20/09/2014

A Sisyphean Nightmare

[Beep] It was time for the weekly e-mentoring session so Bob switched on his laptop, logged in to the virtual meeting site and found that Lesley was already there.

<Bob> Hi Lesley. What shall we talk about today?

<Lesley> Hello Bob. Another old chestnut I am afraid. Queues. I keep hitting the same barrier where people who are fed up with the perpetual queue chaos have only one mantra “If you want to avoid long waiting times then we need more capacity.“

<Bob> So what is the problem? You know that is not the cause of chronic queues.

<Lesley> Yes, I know that mantra is incorrect – but I do not yet understand how to respectfully challenge it and how to demonstrate why it is incorrect and what the alternative is.

<Bob> OK. I understand. So could you outline a real example that we can work with.

<Lesley> Yes. Another old chestnut: the Emergency Department 4-hour breaches.

<Bob> Do you remember the Myth of Sisyphus?

<Leslie> No, I do not remember that being mentioned in the FISH course.

<Bob> Ho ho! No indeed, it is much older. In Greek mythology Sisyphus was a king of Ephyra who was punished by the Gods for chronic deceitfulness by being compelled to roll an immense boulder up a hill, only to watch it roll back down, and then to repeat this action forever.

<Lesley> Ah! I see the link. Yes, that is exactly how people in the ED feel. Everyday it feels like they are pushing a heavy boulder uphill – only to have to repeat the same labour the next day. And they do not believe it can ever be any better with the resources they have.

<Bob> A rather depressing conclusion! Perhaps a better metaphor is the story in the film “Ground Hog Day” where Bill Murray plays the part of a rather arrogant newsreader who enters a recurring nightmare where the same day is repeated, over and over. He seems powerless to prevent it. He does eventually escape when he learns the power of humility and learns how to behave differently.

<Lesley> So the message is that there is a way out of this daily torture – if we are humble enough to learn the ‘how’.

<Bob> Well put. So shall we start?

<Lesley> Yes please!

<Bob> OK. As you know very well it is important not to use the unqualified term ‘capacity’. We must always state if we are referring to flow-capacity or space-capacity.

<Lesley> Because they have different units and because they are intimately related to lead time by Little’s Law.

<Bob> Yes. Little’s Law is mathematically proven Law of flow physics – it is not negotiable.

<Lesley> OK. I know that but how does it solve problem we started with?

<Bob> Little’s Law is necessary but it is not sufficient. Little’s Law relates to averages – and is therefore just the foundation. We now need to build the next level of understanding.

<Lesley> So you mean we need to introduce variation?

<Bob> Yes. And the tool we need for this is a particular form of time-series chart called a Vitals Chart.

<Lesley> And I am assuming that will show the relationship between flow, lead time and work in progress … over time ?

<Bob> Exactly. It is the temporal patterns on the Vitals Chart that point to the root causes of the Sisyphean Chaos. The flow design flaws.

<Lesley> Which are not lack of flow-capacity or space-capacity.

<Bob> Correct. If the chaos is chronic then there must already be enough space-capacity and flow-capacity. Little’s Law shows that, because if there were not the system would have failed completely a long time ago. The usual design flaw in a chronically chaotic system is one or more misaligned policies. It is as if the system hardware is OK but the operating software is not.

<Lesley> So to escape from the Sisyphean Recurring ED 4-Hour Breach Nightmare we just need enough humility and enough time to learn how to diagnose and redesign some of our ED system operating software? Some of our own policies? Some of our own mantras?

<Bob> Yup. And not very much actually. Most of the software is OK. We need to focus on the flaws.

<Lesley> So where do I start?

<Bob> You need to do the ISP-1 challenge that is called Brainteaser 104. That is where you learn how to create a Vitals Chart.

<Lesley> OK. Now I see what I need to do and the reason: understanding how to do that will help me explain it to others. And you are not going to just give me the answer.

<Bob> Correct. I am not going to just give you the answer. You will not fully understand unless you are able to build your own Vitals Chart generator. You will not be able to explain the how to others unless you demonstrate it to yourself first.

<Lesley> And what else do I need to do that?

<Bob> A spreadsheet and your raw start and finish event data.

<Lesley> But we have tried that before and neither I nor the database experts in our Performance Department could work out how to get the real time work in progress from the events – so we assumed we would have to do a head count or a bed count every hour which is impractical.

<Bob> It is indeed possible as you are about to discover for yourself. The fact that we do not know how to do something does not prove that it is impossible … humility means accepting our inevitable ignorance and being open to learning. Those who lack humility will continue to live the Sisyphean Nightmare of ED Ground Hog Day. The choice to escape is ours.

<Lesley> I choose to learn. Please send me BT104.

<Bob> It is on its way …

23/08/2014

Learning in Style

Improvement implies learning – new experiences, new insights, new models and new ways of doing things.

So understanding the process of learning is core to the science of improvement.

What many people do not fully appreciate is that we differ in the way we prefer to learn. These are habitual behaviours that we have acquired.

The diagram shows one model – the Honey and Mumford model that evolved from an earlier model described by Kolb.

One interesting feature of this diagram is the two dimensions – Perception and Processing which are essentially the same as the two core dimensions in the Myers-Briggs Type Index.

What the diagram above does not show so well is that the process of learning is a cycle – the clockwise direction in this diagram – Pragmatist then Activist then Reflector then Theorist and back to Pragmatist.

This is the PART sequence. And it can start at any point … ARTP, RTPA, TPAR.

We all use all of these learning styles – but we have a preference for some more than others – our preferred learning styles are our learning comfort zones.

The large observational studies conducted in the 1980’s using the PART model revealed that most people have moderate to strong preferences for only one or two of these styles. Less than 20% have a preference for three and very few feel equally comfortable with all four.

The commonest patterns are illustrated by the left and right sides of the diagram: the Pragmatist-Activist combination and the Reflector-Theorist combination.

It is not that one is better than the other … all four are synergistic and an effective and efficient learning process requires being comfortable with using all four in a continuous sequence.

Imagine this as a wheel – an imbalance between the four parts represents a distorted wheel. So when this learning wheel ‘turns’ it delivers an emotionally bumpy ‘ride’. Past experience of being pushed through this pain-and-gain process will tend to inhibit or even block learning completely.

So to get a more comfortable learning journey we first need to balance our PART wheel – and that implies knowing what our preferred styles are and then developing the learning styles that we use least to build our competence and confidence with them. And that is possible because these are learned habits. With guidance, focus and practice we can all strengthen our less favoured learning ‘muscles’.

Those with a preference for planning-and-doing would focus on developing their reflection and then their abstraction skills. For example by monitoring the effects of their actions in reality and using that evidence to challenge their underlying assumptions and to generate new ‘theories’ for pragmatic experimentation. Actively seeking balanced feedback and reflecting on it is one way to do that.

Those with a preference for studying-and-abstracting would focus on developing their design and then their delivery skills and become more comfortable with experimenting to test their rhetoric against reality. Actively seeking opportunities to learn-by-doing is one way.

And by creating the context for individuals to become more productive self-learners we can see how learning organisations will follow naturally. And that is what we need to deliver system-wide improvement at scale and pace.

16/08/2014

The 85% Optimum Occupancy Myth

There seems to be a belief among some people that the “optimum” average bed occupancy for a hospital is around 85%.

More than that risks running out of beds and admissions being blocked, 4 hour breaches appearing and patients being put at risk. Less than that is inefficient use of expensive resources. They claim there is a ‘magic sweet spot’ that we should aim for.

Unfortunately, this 85% optimum occupancy belief is a myth.

So, first we need to dispel it, then we need to understand where it came from, and then we are ready to learn how to actually prevent queues, delays, disappointment, avoidable harm and financial non-viability.

Disproving this myth is surprisingly easy. A simple thought experiment is enough.

Suppose we have a policy where we keep patients in hospital until someone needs their bed, then we discharge the patient with the longest length of stay and admit the new one into the still warm bed – like a baton pass. There would be no patients turned away – 0% breaches. And all our the beds would always be full – 100% occupancy. Perfection!

And it does not matter if the number of admissions arriving per day is varying – as it will.

And it does not matter if the length of stay is varying from patient to patient – as it will.

We have disproved the hypothesis that a maximum 85% average occupancy is required to achieve 0% breaches.

The source of this specific myth appears to be a paper published in the British Medical Journal in 1999 called “Dynamics of bed use in accommodating emergency admissions: stochastic simulation model“

So it appears that this myth was cooked up by academic health economists using a computer model.

And then amateur queue theory zealots jump on the band-wagon to defend this meaningless mantra and create a smoke-screen by bamboozling the mathematical muggles with tales of Poisson processes and Erlang equations.

And they are sort-of correct … the theoretical behaviour of the “ideal” stochastic demand process was described by Poisson and the equations that describe the theoretical behaviour were described by Agner Krarup Erlang. Over 100 years ago before we had computers.

BUT …

The academics and amateurs conveniently omit one minor, but annoying, fact … that real world systems have people in them … and people are irrational … and people cook up policies that ride roughshod over the mathematics, the statistics and the simplistic, stochastic mathematical and computer models.

And when creative people start meddling then just about anything can happen!

So what went wrong here?

One problem is that the academic hefalumps unwittingly stumbled into a whole minefield of pragmatic process design traps.

Here are just some of them …

1. Occupancy is a ratio – it is a meaningless number without its context – the flow parameters.

2. Using linear, stochastic models is dangerous – they ignore the non-linear complex system behaviours (chaos to you and me).

3. Occupancy relates to space-capacity and says nothing about the flow-capacity or the space-capacity and flow-capacity scheduling.

4. Space-capacity utilisation (i.e. occupancy) and systemic operational efficiency are not equivalent.

5. Queue theory is a simplification of reality that is needed to make the mathematics manageable.

6. Ignoring the fact that our real systems are both complex and adaptive implies that blind application of basic queue theory rhetoric is dangerous.

And if we recognise and avoid these traps and we re-examine the problem a little more pragmatically then we discover something very useful:

That the maximum space capacity requirement (the number of beds needed to avoid breaches) is actually easily predictable.

It does not need a black-magic-box full of scary queue theory equations or rather complicated stochastic simulation models to do this … all we need is our tried-and-trusted tool … a spreadsheet.

And we need something else … some flow science training and some simulation model design discipline.

When we do that we discover something else …. that the expected average occupancy is not 85% … or 65%, or 99%, or 95%.

There is no one-size-fits-all optimum occupancy number.

And as we explore further we discover that:

The expected average occupancy is context dependent.

And when we remember that our real system is adaptive, and it is staffed with well-intended, well-educated, creative people (who may have become rather addicted to reactive fire-fighting), then we begin to see why the behaviour of real systems seems to defy the predictions of the 85% optimum occupancy myth:

Our hospitals seem to work better-than-predicted at much higher occupancy rates.

And then we realise that we might actually be able to design proactive policies that are better able to manage unpredictable variation; better than the simplistic maximum 85% average occupancy mantra.

And finally another penny drops … average occupancy is an output of the system …. not an input. It is an effect.

And so is average length of stay.

Which implies that setting these output effects as causal inputs to our bed model creates a meaningless, self-fulfilling, self-justifying delusion.

Ooops!

Now our challenge is clear … we need to learn proactive and adaptive flow policy design … and using that understanding we have the potential to deliver zero delays and high productivity at the same time.

And doing that requires a bit more than a spreadsheet … but it is possible.

02/08/2014

Big Data

The Digital Age is changing the context of everything that we do – and that includes how we use information for improvement.

Historically we have used relatively small, but carefully collected, samples of data and we subjected these to rigorous statistical analysis. Or rather the statisticians did. Statistics is a dark and mysterious art to most people.

As the digital age ramped up in the 1980’s the data storage, data transmission and data processing power became cheap and plentiful. The World Wide Web appeared; desktop computers with graphical user interfaces appeared; data warehouses appeared, and very quickly we were all drowning in the data ocean.

Our natural reaction was to centralise but it became quickly obvious that even an army of analysts and statisticians could not keep up.

So our next step was to automate and Business Intelligence was born; along with its beguiling puppy-faced friend, the Performance Dashboard.

The ocean of data could now be boiled down into a dazzling collection of animated histograms, pie-charts, trend-lines, dials and winking indicators. We could slice-and-dice, we could zoom in-and-out, and we could drill up-and-down until our brains ached.

And none of it has helped very much in making wiser decisions that lead to effective actions that lead to improved outcomes.

Why?

The reason is that the missing link was not a lack of data processing power … it was a lack of an effective data processing paradigm.

The BI systems are rooted in the closed, linear, static, descriptive statistics of the past … trend lines, associations, correlations, p-values and so on.

Real systems are open, non-linear and dynamic; they are eternally co-evolving. Nothing stays still.

And it is real systems that we live in … so we need a new data processing paradigm that suits our current reality.

Some are starting to call this the Big Data Era and it is very different.

Business Intelligence uses descriptive statistics and data with high information density to measure things, detect trends etc.;
Big Data uses inductive statistics and concepts from non-linear system identification to infer laws (regressions, non-linear relationships, and causal effects) from large data sets to reveal relationships, dependencies and perform predictions of outcomes and behaviours.

And each of us already has a powerful Big Data processor … the 1.3 kg of caveman wet-ware sitting between our ears.

Our brain processes billions of bits of data every second and looks for spatio-temporal relationships to identify patterns, to derive models, to create action options, to predict short-term outcomes and to make wise survival decisions.

The problem is that our Brainy Big Data Processor is easily tricked when we start looking at time-dependent systems … data from multiple simultaneous flows that are interacting dynamically with each other.

It did not evolve to do that … it evolved to help us to survive in the Wild – as individuals.

And it has been very successful … as the burgeoning human population illustrates.

But now we have a new collective survival challenge and we need new tools … and the out-of-date Business Intelligence Performance Dashboard is just not going to cut the mustard!

Big Data on TED Talks

17/06/2014

A Bit Of A Shock

It comes as a bit of a shock to learn that some of our habitual assumptions and actions are worthless.

Improvement implies change. Change requires doing things differently. That requires making different decisions. And that requires innovative thinking. And that requires new knowledge.

We are comfortable with the idea of adding new knowledge to the vast store we have already accumulated.

We are less comfortable with the idea of removing old knowledge when it has grown out-of-date.

We are shocked when we discover that some of our knowledge is just wrong and it always has been. Since the start of time.

So we need to prepare ourselves for those sorts of shocks. We need to be resilient so that we are not knocked off our feet by them. We need to practice a different emotional reaction to our habitual fright-flight-or-fight reaction.

We need to cultivate our curiosity.

For example:

It comes as a big shock to many when they learn that it is impossible to determine the cause from an analysis of the observed effect. Not just difficult. Impossible.

“No Way!” We shout angrily. “We do that all the time!”

But do we?

What we do is we observe temporal associations. We notice that Y happened after X and we conclude that X caused Y.

This is an incorrect conclusion. We can only conclude from this observation that ‘X may have played a part in causing Y’ but we cannot prove it.

Not by observation alone.

What we can definitely say is that Y did not cause X – because time does not go backwards. At least it does not appear to.

Another thing that does not go backwards is information.

Q: What is 2 + 2? Four. Easy. There is only one answer. Two numbers become one.

Let us try this in reverse …

Q: What two numbers when added together give 4? Tricky. There are countless answers. One number cannot become two without adding uncertainty. Guessing.

So when we look at the information coming out of a system – the effects and we attempt to analyse it to reveal the causes we hit a problem. It is impossible.

And learning that is a big shock to people who describe themselves as ‘information analysts’ …. the whole foundation of what they do appears to evaporate.

So we need to outline what we can reasonably do with the retrospective analysis of effect data.

We can look for patterns.

Patterns that point to plausible causes.

Just like patterns of symptoms that point to possible diseases.

But how do we learn what patterns to look for?

Simple. We experiment. We do things and observe what happens immediately afterwards – the immediate effects. We conduct lots and lots of small experiments. And we learn the repeating patterns. “If the context is this and I do that then I always see this effect”.

If we observe a young child learning that is what we see … they are experimenting all the time. They are curious. They delight in discovery. Novelty is fun. Learning to walk is a game. Learning to talk is a game. Learning to be a synergistic partner in a social group is a game.

And that same child-like curiosity is required for effective improvement.

And we know when we are doing improvement right: it feels good. It is fun. Learning is fun.

24/05/2014

Ratio Hazards

[Bzzzzz Bzzzzz] Bob’s phone was on silent but the desktop amplified the vibration and heralded the arrival of Leslie’s weekly ISP coaching call.

<Bob> Hi Leslie. How are you today and what would you like to talk about?

<Leslie> Hi Bob. I am well and I have an old chestnut to roast today … target-driven-behaviour!

<Bob> Excellent. That is one of my favorite topics. Is there a specific context?

<Leslie> Yes. The usual desperate directive from on-high exhorting everyone to “work harder to hit the target” and usually accompanied by a RAG table of percentages that show just who is failing and how badly they are doing.

<Bob> OK. Red RAGs irritating the Bulls eh? Percentages eh? Have we talked about Ratio Hazards?

<Leslie> We have talked about DRATs … Delusional Ratios and Arbitrary Targets as you call them. Is that the same thing?

<Bob> Sort of. What happened when you tried to explain DRATs to those who are reacting to these ‘desperate directives’?

<Leslie> The usual reply is ‘Yes, but that is how we are required to report our performance to our Commissioners and Regulatory Bodies.’

<Bob> And are the key performance indicators that are reported upwards and outwards also being used to manage downwards and inwards? If so, then that is poor design and is very likely to be contributing to the chaos.

<Leslie> Can you explain that a bit more? It feels like a very fundamental point you have just made.

<Bob> OK. To do that let us work through the process by which the raw data from your system is converted into the externally reported KPI. Choose any one of your KPIs

<Leslie> Easy! The 4-hour A&E target performance.

<Bob> What is the raw data that goes in to that?

<Leslie> The percentage of patients who breach 4-hours per day.

<Bob> And where does that ratio come from?

<Leslie> Oh! I see what you mean. That comes from a count of the number of patients who are in A&E for more than 4 hours divided by a count of the number of patients who attended.

<Bob> And where do those counts come come from?

<Leslie> We calculate the time the patient is in A&E and use the 4-hour target to label them as breaches or not.

<Bob> And what data goes into the calculation of that time?

<Leslie>The arrival and departure times for each patient. The arrive and depart events.

<Bob>OK. Is that the raw data?

<Leslie>Yes. Everything follows from that.

<Bob> Good. Each of these two events is a time – which is a continuous metric. In principle, we could in record it to any degree of precision we like – milliseconds if we had a good enough enough clock.

<Leslie> Yes. We record it to an accuracy of of seconds – it is when the patient is ‘clicked through’ on the computer.

<Bob> Careful Leslie, do not confuse precision with accuracy. We need both.

<Leslie> Oops! Yes I remember we had that conversation before.

<Bob> And how often is the A&E 4-hour target KPI reported externally?

<Leslie> Quarterly. We either succeed or fail each quarter of the financial year.

<Bob> That is a binary metric. An “OK or not OK”. No gray zone.

<Leslie> Yes. It is rather blunt but that is how we are contractually obliged to report our performance.

<Bob> OK. And how many patients per day on average come to A&E?

<Leslie> About 200 per day.

<Bob> So the data analysis process is boiling down about 36,000 pieces of continuous data into one Yes-or-No bit of binary data.

<Leslie> Yes.

<Bob> And then that one bit is used to drive the action of the Board: if it is ‘OK last quarter’ then there is no ‘desperate directive’ and if it is a ‘Not OK last quarter’ then there is.

<Leslie> Yes.

<Bob> So you are throwing away 99.9999% of your data and wondering why what is left is not offering much insight in what to do.

<Leslie>Um, I guess so … when you say it like that. But how does that relate to your phrase ‘Ratio Hazards’?

<Bob> A ratio is just one of the many ways that we throw away information. A ratio requires two numbers to calculate it; and it gives one number as an output so we are throwing half our information away. And this is an irreversible act. Two specific numbers will give one ratio; but that ratio can be created by an infinite number possible pairs of numbers and we have no way of knowing from the ratio what specific pair was used to create it.

<Leslie> So a ratio is an exercise in obfuscation!

<Bob> Well put! And there is an even more data-wasteful behaviour that we indulge in. We aggregate.

<Leslie> By that do you mean we summarise a whole set of numbers with an average?

<Bob> Yes. When we average we throw most of the data away and when we average over time then we abandon our ability to react in a timely way.

<Leslie>The Flaw of Averages!

<Bob> Yes. One of them. There are many.

<Leslie>No wonder it feels like we are flying blind and out of control!

<Bob> There is more. There is an even worse data-wasteful behaviour. We threshold.

<Leslie>Is that when we use a target to decide if the lead time is OK or Not OK.

<Bob> Yes. And using an arbitrary target makes it even worse.

<Leslie> Ah ha! I see what you are getting at. The raw event data that we painstakingly collect is a treasure trove of information and potential insight that we could use to help us diagnose, design and deliver a better service. But we throw all but one single solitary binary digit when we put it through the DRAT Processor.

<Bob> Yup.

<Leslie> So why could we not do both? Why could we not use use the raw data for ourselves and the DRAT processed data for external reporting.

<Bob> We could. So what is stopping us doing just that?

<Leslie> We do not know how to effectively and efficiently interpret the vast ocean of raw data.

<Bob> That is what a time-series chart is for. It turns the thousands of pieces of valuable information onto a picture that tells a story – without throwing the information away in the process. We just need to learn how to interpret the pictures.

<Leslie> Wow! Now I understand much better why you insist we ‘plot the dots’ first.

<Bob> And now you understand the Ratio Hazards a bit better too.

<Leslie> Indeed so. And once again I have much to ponder on. Thank you again Bob.

17/05/2014

The Learning Labyrinth

There is an amazing phenomenon happening right now – a whole generation of people are learning to become system designers and they are doing it by having fun.

There is a game called Minecraft which millions of people of all ages are rapidly discovering. It is creative, fun and surprisingly addictive.

This is what it says on the website.

“Minecraft is a game about breaking and placing blocks. At first, people built structures to protect against nocturnal monsters, but as the game grew players worked together to create wonderful, imaginative things.”

The principle is that before you can build you have to dig … you have to gather the raw materials you need … and then you have to use what you have gathered in novel and imaginative ways. You need tools too, and you need to learn what they are used for, and what they are useless for. And the quickest way to learn the necessary survival and creative skills is by exploring, experimenting, seeking help, and sharing your hard-won knowledge and experience with others.

The same principles hold in the real world of Improvement Science.

The treasure we are looking for is less tangible though … but no less difficult to find … unless you know where to look.

The treasure we seek is learning; how to achieve significant and sustained improvement on all dimensions.

And there is a mountain of opportunity that we can mine into. It is called Reality.

And when we do that we uncover nuggets of knowledge, jewels of understanding, and pearls of wisdom.

There are already many tunnels that have been carved out by others who have gone before us. They branch and join to form a vast cave network. A veritable labyrinth. Complicated and not always well illuminated or signposted.

And stored in the caverns is a vast treasure trove of experience we can dip into – and an even greater horde of new treasure waiting to be discovered.

But even now there there is no comprehensive map of the labyrinth. So it is easy to get confused and to get lost. Not all junctions have signposts and not all the signposts are correct. There are caves with many entrances and exits, there are blind-ending tunnels, and there are many hazards and traps for the unwary.

So to enter the Learning Labyrinth and to return safety with Improvement treasure we need guides. Those who know the safe paths and the unsafe ones. And as we explore we all need to improve the signage and add warning signs where hazards lurk.

And we need to work at the edge of knowledge to extend the tunnels further. We need to seal off the dead-ends, and to draw and share up-to-date maps of the paths.

We need to grow a Community of Improvement Science Minecrafters.

And the first things we need are some basic improvement tools and techniques … and they can be found here.

26/04/2014

Synchronicity

[Beep, Beep, Beep, Beep, Beeeeep] The reminder roused Bob from deep reflection and he clicked the Webex link on his desktop to start the meeting. Leslie was already online.

<Bob> Hi Leslie. How are you? And what would you like to share and explore today?

<Leslie> Hi Bob, I am well thank you and I would like to talk about chaos again.

<Bob> OK. That is always a rich mine of new insights! Is there a specific reason?

<Leslie>Yes. The story I want to share is of the chaos that I have been experiencing just trying to get a new piece of software available for my team to use. You would not believe the amount of time, emails, frustration and angst it has taken to negotiate this through the ‘proper channels’.

<Bob> Let me guess … about six months?

<Leslie> Spot on! How did you know?

<Bob> Just prior experience of similar stories. So what is your diagnosis of the cause of the chaos?

<Leslie> My intuition shouts at me that people are just being deliberately difficult and that makes me feel angry and want to shout at them … but I have learned that behaviour is counter-productive.

<Bob> So what did you do?

<Leslie> I escalated the ‘problem’ to my line manager.

<Bob> And what did they do?

<Leslie> I am not sure, I was not copied in, but it seemed to clear the ‘obstruction’.

<Bob> And were the ‘people’ you mentioned suddenly happy and willing to help?

<Leslie> Not really … they did what we needed but they did not seem very happy about it.

<Bob> OK. You are describing a Drama Triangle, a game, and your behaviour was from the Persecutor role.

<Leslie>What! But I deliberately did not send any ANGRY emails or get into a childish argument. I escalated the issue I could not solve because that is what we are expected to do.

<Bob> Yes I know. If you had engaged in a direct angry conversation, by whatever means, that would have been an actively aggressive act. By escalating the issue and someone Bigger having the angry conversation you have engaged in a passive aggressive act. It is still playing the game from the Persecutor role and in fact is the more common mode of Persecution.

<Leslie> But it got the barrier cleared and the problem sorted?

<Bob> And did it leave everyone feeling happier than before?

<Leslie> I guess not. I certainly felt like a bit of a ‘tale teller’ and the IT technician probably hates me and fears for his job, and the departmental heads probably distrust each other even more than before.

<Bob> So this approach may appear to work in the short term but it creates a much bigger long term problem – and it is that long term problem of ‘distrust’ that creates the chaos. So it is a self-sustaining design.

<Leslie> Oh dear! Is there a way to avoid this and to defuse the chronic distrust?

<Bob> Yes. You have demonstrated a process that you would like to improve – you want the same short term outcome, your software installed and working, and you want it quicker and with less angst and leaving everyone feeling good about how they have played a part in achieving that objective.

<Leslie>Yes. That would be my ideal.

<Bob>So what is different between what you did and your ‘ideal’ scenario? What did you do that you should not have and what did you not do that you could have?

<Leslie> Well I triggered off a drama triangle which I should not have. I also assumed that the IT people would know what to do because I do not understand the technical nuances of getting new software procured and installed. What I could have done is make it much clearer for them what I needed, why I needed it and how and when I needed it. I could have done a lot more homework before asking them for assistance. I could also have given my inner Chimp a banana and gone to talk to them face-to-face and ask their opinion early on so I could see the problem from their perspective as well as mine.

<Bob> Yes – that all sounds reasonable and respectful. What you are doing is ‘synchronising‘. You are engaging in understanding the process well enough so that you can align all the actions that need to be done, in the correct order and then sharing that. It is rather like being the composer of a piece of music – you share the score so that the individual players know what to do and when. There is one other task you need to do.

<Leslie>I need to be the conductor!

<Bob> Yes. You are the metronome. You set the pace and guide the orchestra. They are the specialists with their instruments – that is not your role.

<Leslie> And when I do that then the music is harmonious and pleasing-to-the-ear; not a chaotic cacophony!

<Bob> Indeed … and the music is the voice of the system – and is the feedback that everyone hears – and not only do the musicians derive pleasure from contributing then the wider audience will hear what can be achieved and see how it is achieved.

<Leslie> Wow! That musical metaphor works really well for me. Thanks Bob, I need to go and work on my communicating, composing and conducting capabilities.

19/04/2014

SuDoKu

An Improvement-by-Design challenge is very like a Sudoku puzzle. The rules are deceptively simple but the solving the puzzle is not so simple.

For those who have never tried a Sudoku puzzle the objective is to fill in all the empty boxes with a number between 1 and 9. The constraint is that each row, column and 3×3 box (outlined in bold) must include all the numbers between 1 and 9 i.e. no duplicates.

What you will find when you try is that, at each point in the puzzle solving process there are more than one choice for most empty cells.

The trick is to find the empty cells that have only one option and fill those in. That changes the puzzle and makes it ‘easier’.

And when you keep following this strategy, and so long as you do not make any mistakes, then you will solve the puzzle. It just takes concentration, attention to detail, and discipline.

In the example above, the top-right cell in the left-box on the middle-row can only hold a 6; and the top-middle cell in the middle-box on the bottom-row must be a 3.

So we can see already there are three ways ‘into’ the solution – put the 6 in and see where that takes us; put the 3 in and see where that takes us; or put both in and see where that takes us.

The final solution will be the same – so there are multiple paths from where we are to our objective. Some may involve more mental work than others but all will involve completing the same number of empty cells.

What is also clear is that the sequence order that we complete the empty cells is not arbitrary. Usually the boxes and rows with the fewest empty cells get competed earlier and those with the most empty cells at the start get completed later.

And even if the final configuration is the same, if we start with a different set of missing cells the solution path will be different. It may be very easy, very hard or even impossible without some ‘guessing’ and hoping for the best.

Exactly the same is true of improvement-by-design challenges.

The rules of flow science are rather simple; but when we have a system of parallel streams (the rows) interacting with parallel stages (the columns); and when we have safety, delivery, and economy constraints to comply with at every part of the system … then finding and ‘improvement plan’ that will deliver our objective is a tough challenge.

But it is possible with concentration, attention-to-detail and discipline; and that requires some flow science training and some improvement science practice.

OK – I am off for lunch and then maybe indulge in a Sudoku puzzle or two – just for fun – and then maybe design an improvement plan to two – just for fun!

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.

01/03/2014

Miserable or Motivated?

[Beep Beep] The alarm on Bob’s smartphone was the reminder that in a few minutes his e-mentoring session with Lesley was due. Bob had just finished the e-mail he was composing so he sent it and then fired-up the Webex session. Lesley was already logged in and on line.

<Bob> Hi Lesley. What aspect of Improvement Science shall we talk about today? What is next on your map?

<Lesley> Hi Bob. Let me see. It looks like ‘Employee Engagement‘ is the one that we have explored least yet – and it links to lots of other things.

<Bob> OK. What would you say the average level of Employee Engagement is in your organisation at the moment? On a scale of zero to ten where zero is defined as ‘complete apathy’.

<Lesley> Good question. I see a wide range of engagement and I would say the average is about four out of ten. There are some very visible, fully-engaged, energetic, action-focused movers-and-shakers. There are many more nearer the apathy end of the spectrum. Most employees seem to turn up, do their jobs well enough to avoid being disciplined, and then go home.

<Bob> OK. And do you feel that is a problem?

<Lesley> You betcha! Improvement means change and change means action. Disengaged employees are a deadweight. They do not actively block change – they will go along with it if pushed – but they do not contribute to making it happen. And that creates a different problem. The movers-and-shakers get frustrated and eventually get tired trying to move the deadweight up hill and give up and then can become increasingly critical and then cynical. After they give up in despair they then actively block any new ideas saying – “Do not try you will fail.”

<Bob> So how would you describe the emotional state of those you describe as “disengaged”?

<Lesley> Miserable.

<Bob> And who is making them feel miserable?

<Lesley> That is another good question. They appear to be making themselves feel miserable. And it is not what is happening that triggers this emotion. It is what is not happening. Apathy seems to be self-sustaining.

<Bob> Can you explain in a bit more about what you mean by that and maybe share an example?

<Lesley> An example is easier. I have reflected on this a bit and I have used one of the 6M Design® techniques to help me understand it better. I used a Right-2-Left® map to compare a personal example of when I felt really motivated and delivered a significant and measurable improvement; with one where I felt miserable and no change happened.

<Bob> Excellent. What did you discover?

<Lesley> I discovered that there were four classes of difference between the two examples. And I then understood what you mean by ‘Acts and Errors of Omission and Commission’.

<Bob> OK. And which was the commonest of the four combinations in your example?

<Lesley> The Errors of Omission. And within just that group there were three different types that were most obvious.

<Bob> Can you list them for me?

<Lesley> For sure. The first is the miserableness I felt when what I was doing felt to me that it was irrelevant. When what I was being asked to do had no worthwhile purpose that I was aware of.

<Bob> So which was it? No worth or not being aware of the worth?

<Lesley>Me not being aware of the worth. I hoped it was of value to someone higher up the corporate food chain otherwise I would not have been asked to do it! But I was never sure. And that uncertainty generated some questions. What if what I am doing is of no worth to anyone? What if I am just wasting my lifetime doing it? That fearful thought left me feeling more miserable than motivated.

<Bob> OK. What was the second Error of Omission?

<Lesley> It is linked to the first one. I had no objective way of knowing if I was doing a worthwhile job. And the word objective is important. I am not asking for subjective feedback – there is too much expectation, variation, assumption, prejudgement and politics mixed up in opinions of what I achieve. I needed specific, objective and timely feedback. I associated my feeling of miserableness with not getting objective feedback that told me what I was doing was making a worthwhile difference to someone else. Anyone else!

<Bob> I thought that you get a lot of objective feedback on a whole raft of organisational performance metrics?

<Lesley> Oh yes! We do!! The problem is that it is high level, aggregated, anonymous, and delayed. To get a copy of a report that says as an organisation we did or did not meet last quarters arbitrary performance target for x, y or z usually generates a ‘So what? How does that relate to what I do?’ reaction. I need objective, specific and timely feedback about the effects of my work. Good or bad.

<Bob> OK. And Error of Omission Three?

<Lesley> This was the trickiest one to nail down. What it came down to was being treated as a process and not as a person. I felt anonymous. I was just a headcount, a number on a payroll ledger, an overhead cost. That feeling was actually the most demotivating of all.

<Bob> And did it require all Three Errors of Omission to be present for the ‘miserableness’ to become manifest?

<Lesley> Alas no! Any one of them was enough. The more of them at the same time the deeper the feeling of misery the less motivated I felt.

<Bob> Thank you for being so frank and open. So what have you ‘abstracted’ from your ‘reflection’?

<Lesley> That employee engagement requires that these Three Errors of Omission must be deliberately checked for and proactively addressed if discovered.

<Bob> And who would, could or should do this check-and-correct work?

<Lesley> H’mm. Another very god question. The employee could do it but it is difficult for them because a lot of the purpose-setting and feedback comes from outside their circle of control and from higher up. Approaching a line-manager with a list of their Errors of Omission will be too much of a challenge!

<Bob> So?

<Lesley> The manager should do it. They should ask themselves these questions. Only they can correct their own Errors of Omission. I doubt if that would happen spontaneously though! Humility seems a bit of a rare commodity.

<Bob> I agree. So what can the employee do to help their boss?

<Lesley> They could ask how they can be of most value to their boss and they could ask for objective and timely feedback on how well they are performing as an individual on those measures of worth. It sounds so simple and obvious when said out loud. So why does no one do it?

<Bob> A very good question. Some do and they are the often described as ‘motivating leaders’. So does this insight suggest to you any strategies for grasping the ‘Employee Engagement’ nettle without getting stung?

<Lesley> Yes indeed! I am already planning my next action. A chat with my line-manager about what I could do. Thanks Bob.

<Bob> My pleasure. And remember that the same principle works for everyone that we work directly with – especially those immediately ‘upstream’ and ‘downstream’ of us in our daily work.

25/01/2014

Our Irrational Inner Chimp

The modern era in science started about 500 years ago when an increasing number of people started to challenge the dogma that our future is decided by Fates and Gods. That we had no influence. And to appease the ‘Gods’ we had to do as we were told. That was our only hope of Salvation.

This paradigm came under increasing pressure as the evidence presented by Reality did not match the Rhetoric. Many early innovators paid for their impertinence with their fortunes, their freedom and often their future. They were burned as heretics.

When the old paradigm finally gave way and the Age of Enlightenment dawned the pendulum swung the other way – and the new paradigm became the ‘mechanical universe’. Isaac Newton showed that it was possible to predict, with very high accuracy, the motion of the planets just by adopting some simple rules and a new form of mathematics called calculus. This opened a door into a more hopeful world – if Nature follows strict rules and we know what they are then we can learn to control Nature and get what we need without having to appease any Gods (or priests).

This was the door to the Industrial Revolutions – there have been more that one – each lasting about 100 years (18th C, 19th C and 20th C). Each was associated with massive population growth as we systematically eliminated the causes of early mortality – starvation and infectious disease.

But not everything behaved like the orderly clockwork of the planets and the pendulums. There was still the capricious and unpredictable behaviour that we call Lady Luck. Had the Gods retreated but were still playing dice?

Progress was made here too – and the history of the ‘understanding of chance’ is peppered with precocious and prickly mathematical savants who discovered that chance follows rules too. Probability theory was born and that spawned a troublesome child called Statistics. This was a trickier one to understand. To most people statistics is just mathematical gobbledygook.

But from that emerged a concept called the Rational Man – which underpinned the whole of Economic Theory for 250 years. Until very recently. The RM hypothesis stated that we make unconscious but rational judgements when presented with uncertain win/lose choices. And from that seed sprouted concepts such as the Law of Supply and Demand – when the supply of things we demand are limited then we (rationally) value them more and will choose to pay more so prices go up so fewer can afford them so demand drops. Foxes and Rabbits. A negative feedback loop. The economic system becomes self-adjusting and self-stabilising. The outcome of this assumption is a belief that ‘because people are collectively rational the economic system will be self-stabilising and it will correct the adverse short term effects of any policy blunders we make‘. The ‘let-the-market-decide’ belief that experimental economic meddling is harmless over the long term and what is learned from ‘laissez-faire’ may even be helpful. It is a no-lose long term improvement strategy. Losers are just unlucky, stupid or both.

In 2002 the Nobel Prize for Economics was not awarded to an economist. It was awarded to a psychologist – Daniel Kahneman – who showed that the model of the Rational Man did not stand up to rigorous psychological experiment. Reality demonstrated we are Irrational Chimps. The economists had omitted to test their hypothesis. Oops!

This lesson has many implications for the Science of Improvement. One of which is a deeper understanding of the nemesis of improvement – resistance to change.

One of the surprising findings is that our judgements are biased – and our bias operates at an unconscious level – what Kahneman describes as the System One level. Chimp level. We are not aware we are making biased decisions.

For example. Many assume that we prefer certainty to uncertainty. We fear the unpredictable. We avoid it. We seek the predictable and the stable. And we will put up with just about anything so long as it is predictable. We do not like surprises. And when presented with that assertion most people nod and say ‘Yes’ – that feels right.

We also prefer gain to loss. We love winning. We hate losing. This ‘competitive spirit’ is socially reinforced from day one by our ‘pushy parents’ – we all know the ones – but we all do it to some degree. Do better! Work harder! Be a success! Optimize! Be the best! Be perfect! Be Perfect! BE PERFECT.

So which is more important to us? Losing or uncertainty? This is one question that Kahneman asked. And the answer he discovered was surprising – because it effectively disproved the Rational Man hypothesis. And this is how a psychologist earned a Nobel Prize for Economics.

Kahneman discovered that loss is more important to us than uncertainty.

To demonstrate this he presented subjects with a choice between two win/lose options; and he presented the choice in two ways. To a statistician and a Rational Man the outcomes were exactly the same in terms of gain or loss. He designed the experiment to ensure that it was the unconscious judgement that was being measured – the intuitive gut reaction. So if our gut reactions are Rational then the choice and the way the choice was presented would have no significant effect.

There was an effect. The hypothesis was disproved.

The evidence showed that our gut reactions are biased … and in an interesting way.

If we are presented with the choice between a certain gain and an uncertain gain/loss (so the average gain is the same) then we choose the certain gain much more often. We avoid uncertainty. Uncertainly =1 Loss=0.

BUT …

If we are presented with a choice between certain loss and an uncertain loss/gain (so the average outcome is again the same) then we choose the uncertain option much more often. This is exactly the opposite of what was expected.

And it did not make any difference if the subject knew the results of the experiment before doing it. The judgement is made out of awareness and communicated to our consciousness via an emotion – a feeling – that biases our slower, logical, conscious decision process.

This means that the sense of loss has more influence on our judgement than the sense of uncertainty.

This behaviour is hard-wired. It is part of our Chimp brain design. And once we know this we can see the effect of it everywhere.

1. We will avoid the pain of uncertainty and resist any change that might deliver a gain when we believe that future loss is uncertain. We are conservative and over-optimistic.

2. We will accept the pain of uncertainty and only try something new (and risky) when we believe that to do otherwise will result in certain loss. The Backs Against The Wall scenario. The Cornered Rat is Unpredictable and Dangerous scenario.

This explains why we resist any change right up until the point when we see Reality clearly enough to believe that we are definitely going to lose something important if we do nothing. Lose our reputation, lose our job, lose our security, lose our freedom or lose our lives. That is a transformational event. A Road to Damascus moment.

Understanding that we behave like curious, playful, social but irrational chimps is one key to unlocking significant and sustained improvement.

We need to celebrate our inner chimp – it is key to innovation.

And we need to learn how to team up with our inner chimp rather than be hijacked by it.

If we do not we will fail – the Laws of Physics, Probability and Psychology decree it.

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.

28/12/201306/01/2024

The Time Trap

[Hmmmmmm] The desk amplified the vibration of Bob’s smartphone as it signaled the time for his planned e-mentoring session with Leslie.

[Dring Dring]

<Bob> Hi Leslie, right-on-time, how are you today?

<Leslie> Good thanks Bob. I have a specific topic to explore if that is OK. Can we talk about time traps.

<Bob> OK – do you have a specific reason for choosing that topic?

<Leslie> Yes. The blog last week about ‘Recipe for Chaos‘ set me thinking and I remembered that time-traps were mentioned in the FISH course but I confess, at the time, I did not understand them. I still do not.

<Bob> Can you describe how the ‘Recipe for Chaos‘ blog triggered this renewed interest in time-traps?

<Leslie> Yes – the question that occurred to me was: ‘Is a time-trap a recipe for chaos?’

<Bob> A very good question! What do you feel the answer is?

<Leslie>I feel that time-traps can and do trigger chaos but I cannot explain how. I feel confused.

<Bob>Your intuition is spot on – so can you localize the source of your confusion?

<Leslie>OK. I will try. I confess I got the answer to the MCQ correct by guessing – and I wrote down the answer when I eventually guessed correctly – but I did not understand it.

<Bob>What did you write down?

<Leslie>“The lead time is independent of the flow”.

<Bob>OK. That is accurate – though I agree it is perhaps a bit abstract. One source of confusion may be that there are different causes of of time-traps and there is a lot of overlap with other chaos-creating policies. Do you have a specific example we can use to connect theory with reality?

<Leslie> OK – that might explain my confusion. The example that jumped to mind is the RTT target.

<Bob> RTT?

<Leslie> Oops – sorry – I know I should not use undefined abbreviations. Referral to Treatment Time.

<Bob> OK – can you describe what you have mapped and measured already?

<Leslie> Yes. When I plot the lead-time for patients in date-of-treatment order the process looks stable but the histogram is multi-modal with a big spike just underneath the RTT target of 18 weeks. What you describe as the ‘Horned Gaussian’ – the sign that the performance target is distorting the behaviour of the system and the design of the system is not capable on its own.

<Bob> OK and have you investigated why there is not just one spike?

<Leslie> Yes – the factor that best explains that is the ‘priority’ of the referral. The ‘urgents’ jump in front of the ‘soons’ and both jump in front of the ‘routines’. The chart has three overlapping spikes.

<Bob> That sounds like a reasonable policy for mixed-priority demand. So what is the problem?

<Leslie> The ‘Routine’ group is the one that clusters just underneath the target. The lead time for routines is almost constant but most of the time those patients sit in one queue or another being leap-frogged by other higher-priority patients. Until they become high-priority – then they do the leap frogging.

<Bob> OK – and what is the condition for a time trap again?

<Leslie> That the lead time is independent of flow.

<Bob>Which implies?

<Leslie> Um. let me think. That the flow can be varying but the lead time stays the same?

<Bob> Yup. So is the flow of routine referrals varying?

<Leslie> Not over the long term. The chart is stable.

<Bob> What about over the short term? Is demand constant?

<Leslie>No of course not – it varies – but that is expected for all systems. Constant means ‘over-smoothed data’ – the Flaw of Averages trap!

<Bob>OK. And how close is the average lead time for routines to the RTT maximum allowable target?

<Leslie> Ah! I see what you mean. The average is about 17 weeks and the target is 18 weeks.

<Bob>So what is the flow variation on a week-to-week time scale?

<Leslie>Demand or Activity?

<Bob>Both.

<Leslie>H’mm – give me a minute to re-plot flow as a weekly-aggregated chart. Oh! I see what you mean – both the weekly activity and demand are both varying widely and they are not in sync with each other. Work in progress must be wobbling up and down a lot! So how can the lead time variation be so low?

<Bob>What do the flow histograms look like?

<Leslie> Um. Just a second. That is weird! They are both bi-modal with peaks at the extremes and not much in the middle – the exact opposite of what I expected to see! I expected a centered peak.

<Bob>What you are looking at is the characteristic flow fingerprint of a chaotic system – it is called ‘thrashing’.

<Leslie> So I was right!

<Bob> Yes. And now you know the characteristic pattern to look for. So what is the policy design flaw here?

<Leslie>The DRAT – the delusional ratio and arbitrary target?

<Bob> That is part of it – that is the external driver policy. The one you cannot change easily. What is the internally driven policy? The reaction to the DRAT?

<Leslie> The policy of leaving routine patients until they are about to breach then re-classifying them as ‘urgent’.

<Bob>Yes! It is called a ‘Prevarication Policy’ and it is surprisingly and uncomfortably common. Ask yourself – do you ever prevaricate? Do you ever put off ‘lower priority’ tasks until later and then not fill the time freed up with ‘higher priority tasks’?

<Leslie> OMG! I do that all the time! I put low priority and unexciting jobs on a ‘to do later’ heap but I do not sit idle – I do then focus on the high priority ones.

<Bob> High priority for whom?

<Leslie> Ah! I see what you mean. High priority for me. The ones that give me the biggest reward! The fun stuff or the stuff that I get a pat on the back for doing or that I feel good about.

<Bob> And what happens?

<Leslie> The heap of ‘no-fun-for-me-to-do’ jobs gets bigger and I await the ‘reminders’ and then have to rush round in a mad panic to avoid disappointment, criticism and blame. It feels chaotic. I get grumpy. I make more mistakes and I deliver lower-quality work. If I do not get a reminder I assume that the job was not that urgent after all and if I am challenged I claim I am too busy doing the other stuff.

<Bob> Have you avoided disappointment?

<Leslie> Ah! No – that I needed to be reminded meant that I had already disappointed. And when I do not get a reminded does not prove I have not disappointed either. Most people blame rather than complain. I have just managed to erode other people’s trust in my reliability. I have disappointed myself. I have achieved exactly the opposite of what I intended. Drat!

<Bob> So what is the reason that you work this way? There will be a reason. A good reason.

<Leslie> That is a very good question! I will reflect on that because I believe it will help me understand why others behave this way too.

<Bob> OK – I will be interested to hear your conclusion. Let us return to the question. What is the downside of a ‘Prevarication Policy’?

<Leslie> It creates stress, chaos, fire-fighting, last minute changes, increased risk of errors, more work and it erodes both quality, confidence and trust.

<Bob>Indeed so – and the impact on productivity?

<Leslie> The activity falls, the system productivity falls, revenue falls, queues increase, waiting times increase and the chaos increases!

<Bob> And?

<Leslie> We treat the symptoms by throwing resources at the problem – waiting list initiatives – and that pushes our costs up. Either way we are heading into a spiral of decline and disappointment. We do not address the root cause.

<Bob> So what is the way out of chaos?

<Leslie> Reduce the volume on the destabilizing feedback loop? Stop the managers meddling!

<Bob> Or?

<Leslie> Eh? I do not understand what you mean. The blog last week said management meddling was the problem.

<Bob> It is a problem. How many feedback loops are there?

<Leslie> Two – that need to be balanced.

<Bob> So what is another option?

<Leslie> OMG! I see. Turn UP the volume of the stabilizing feedback loop!

<Bob> Yup. And that is a lot easier to do in reality. So that is your other challenge to reflect on this week. And I am delighted to hear you using the terms ‘stabilizing feedback loop’ and ‘destabilizing feedback loop’.

<Leslie> Thank you. That was a lesson for me after last week – when I used the terms ‘positive and negative feedback’ it was interpreted in the emotional context – positive feedback as encouragement and negative feedback as criticism. So ‘reducing positive feedback’ in that sense is the exact opposite of what I was intending. So I switched my language to using ‘stabilizing and destabilizing’ feedback loops that are much less ambiguous and the confusion and conflict disappeared.

<Bob> That is very useful learning Leslie … I think I need to emphasize that distinction more in the blog. That is one advantage of online media – it can be updated!

<Leslie> Thanks again Bob! And I have the perfect opportunity to test a new no-prevarication-policy design – in part of the system that I have complete control over – me!

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.

25/10/2013

The Black Curtain

A couple of weeks ago an important event happened. A Masterclass in Demand and Capacity for NHS service managers was run by an internationally renown and very experienced practitioner of Improvement Science.

The purpose was to assist the service managers to develop their capability for designing quality, flow and cost improvement using tried and tested operations management (OM) theory, techniques and tools.

It was assumed that as experienced NHS service managers that they already knew the basic principles of OM and the foundation concepts, terminology, techniques and tools.

It was advertised as a Masterclass and designed accordingly.

On the day it was discovered that none of the twenty delegates had heard of two fundamental OM concepts: Little’s Law and Takt Time.

These relate to how processes are designed-to-flow. It was a Demand and Capacity Master Class; not a safety, quality or cost one. The focus was flow.

And it became clear that none of the twenty delegates were aware before the day that there is a well-known and robust science to designing systems to flow.

So learning this fact came as a bit of a shock.

The implications of this observation are profound and worrying:

“if a significant % of senior NHS operational managers are unaware of the foundations of operations management then the NHS may have problem it was not aware of …“

because …

“if transformational change of the NHS into a stable system that is fit-for-purpose (now and into the future) requires the ability to design processes and systems that deliver both high effectiveness and high efficiency ...”

then …

“it raises the question of whether the current generation of NHS managers are fit-for-this-future-purpose“.

No wonder that discovering a Science of Improvement actually exists came as a bit of a shock!

And saying “Yes, but clinicians do not know this science either!” is a defensive reaction and not a constructive response. They may not but they do not call themselves “operational managers”.

[PS. If you are reading this and are employed by the NHS and do not know what Little’s Law and Takt Time are then it would be worth doing that first. Wikipedia is a good place to start].

And now we have another question:

“Given there are thousands of operational managers in the NHS; what does one sample of 20 managers tell us about the whole population?”

Now that is a good question.

It is also a question of statistics. More specifically quite advanced statistics.

And most people who work in the NHS have not studied statistics to that level. So now we have another do-not-know-how problem.

But it is still an important question that we need to understand the answer to – so we need to learn how and that means taking this learning path one step at a time using what we do know, rather than what we do not.

Step 1:

What do we know? We have one sample of 20 NHS service managers. We know something about our sample because our unintended experiment has measured it: that none of them had heard of Little’s Law or Takt Time. That is 0/20 or 0%.

This is called a “sample statistic“.

What we want to know is “What does this information tell us about the proportion of the whole population of all NHS managers who do have this foundation OM knowledge?”

This proportion of interest is called the unknown “population parameter“.

And we need to estimate this population parameter from our sample statistic because it is impractical to measure a population parameter directly: That would require every NHS manager completing an independent and accurate assessment of their basic OM knowledge. Which seems unlikely to happen.

The good news is that we can get an estimate of a population parameter from measurements made from small samples of that population. That is one purpose of statistics.

Step 2:

But we need to check some assumptions before we attempt this statistical estimation trick.

Q1: How representative is our small sample of the whole population?

If we chose the delegates for the masterclass by putting the names of all NHS managers in a hat and drawing twenty names out at random, as in a tombola or lottery, than we have what is called a “random sample” and we can trust our estimate of the wanted population parameter. This is called “random sampling”.

That was not the case here. Our sample was self-selecting. We were not conducting a research study. This was the real world … so there is a chance of “bias”. Our sample may not be representative and we cannot say what the most likely bias is.

It is possible that the managers who selected themselves were the ones struggling most and therefore more likely than average to have a gap in their foundation OM knowledge. It is also possible that the managers who selected themselves are the most capable in their generation and are very well aware that there is something else that they need to know.

We may have a biased sample and we need to proceed with some caution.

Step 3:

So given the fact that none of our possibly biased sample of mangers were aware of the Foundation OM Knowledge then it is possible that no NHS service managers know this core knowledge. In other words the actual population parameter is 0%. It is also possible that the managers in our sample were the only ones in the NHS who do not know this. So, in theory, the sought-for population parameter could be anywhere between 0% and very nearly 100%. Does that mean it is impossible to estimate the true value?

It is not impossible. In fact we can get an estimate that we can be very confident is accurate. Here is how it is done.

Statistical estimates of population parameters are always presented as ranges with a lower and an upper limit called a “confidence interval” because the sample is not the population. And even if we have an unbiased random sample we can never be 100% confident of our estimate. The only way to be 100% confident is to measure the whole population. And that is not practical.

So, we know the theoretical limits from consideration of the extreme cases … but what happens when we are more real-world-reasonable and say – “let us assume our sample is actually a representative sample, albeit not a randomly selected one“. How does that affect the range of our estimate of the elusive number – the proportion of NHS service managers who know basic operation management theory?

Step 4:

To answer that we need to consider two further questions:

Q2. What is the effect of the size of the sample? What if only 5 managers had come and none of them knew; what if had been 50 or 500 and none of them knew?

Q3. What if we repeated the experiment more times? With the same or different sample sizes? What could we learn from that?

Our intuition tells us that the larger the sample size and the more often we do the experiment then the more confident we will be of the result. In other words narrower the range of the confidence interval around our sample statistic.

Our intuition is correct because if our sample was 100% of the population we could be 100% confident.

So given we have not yet found an NHS service manager who has the OM Knowledge then we cannot exclude 0%. Our challenge narrows to finding a reasonable estimate of the upper limit of our confidence interval.

Step 5

Before we move on let us review where we have got to already and our purpose for starting this conversation: We want enough NHS service managers who are knowledgeable enough of design-for-flow methods to catalyse a transition to a fit-for-purpose and self-sustaining NHS.

One path to this purpose is to have a large enough pool of service managers who do understand this Science well enough to act as advocates and to spread both the know-of and the know-how. This is called the “tipping point“.

There is strong evidence that when about 20% of a population knows about something that is useful for the whole population – then that knowledge will start to spread through the grapevine. Deeper understanding will follow. Wiser decisions will emerge. More effective actions will be taken. The system will start to self-transform.

And in the Brave New World of social media this message may spread further and faster than in the past. This is good.

So if the NHS needs 20% of its operational managers aware of the Foundations of Operations Management then what value is our morsel of data from one sample of 20 managers who, by chance, were all unaware of the Knowledge. How can we use that data to say how close to the magic 20% tipping point we are?

Step 6:

To do that we need to ask the question in a slightly different way.

Q4. What is the chance of an NHS manager NOT knowing?

We assume that they either know or do not know; so if 20% know then 80% do not.

This is just like saying: if the chance of rolling a “six” is 1-in-6 then the chance of rolling a “not-a-six” is 5-in-6.

Next we ask:

Q5. What is the likelihood that we, just by chance, selected a group of managers where none of them know – and there are 20 in the group?

This is rather like asking: what is the likelihood of rolling twenty “not-a-sixes” in a row?

Our intuition says “an unlikely thing to happen!”

And again our intuition is sort of correct. How unlikely though? Our intuition is a bit vague on that.

If the actual proportion of NHS managers who have the OM Knowledge is about the same chance of rolling a six (about 16%) then we sense that the likelihood of getting a random sample of 20 where not one knows is small. But how small? Exactly?

We sense that 20% is too a high an estimate of a reasonable upper limit. But how much too high?

The answer to these questions is not intuitively obvious.

We need to work it out logically and rationally. And to work this out we need to ask:

Q6. As the % of Managers-who-Know is reduced from 20% towards 0% – what is the effect on the chance of randomly selecting 20 all of whom are not in the Know? We need to be able to see a picture of that relationship in our minds.

The good news is that we can work that out with a bit of O-level maths. And all NHS service managers, nurses and doctors have done O-level maths. It is a mandatory requirement.

The chance of rolling a “not-a-six” is 5/6 on one throw – about 83%;
and the chance of rolling only “not-a-sixes” in two throws is 5/6 x 5/6 = 25/36 – about 69%
and the chance of rolling only “not-a-sixes” in three throws is 5/6 x 5/6 x 5/6 – about 58%… and so on.

[This is called the “chain rule” and it requires that the throws are independent of each other – i.e. a random, unbiased sample]

If we do this 20 times we find that the chance of rolling no sixes at all in 20 throws is about 2.6% – unlikely but far from impossible.

We need to introduce a bit of O-level algebra now.

Let us call the proportion of NHS service managers who understand basic OM, our unknown population parameter something like “p”.

So if p is the chance of a “six” then (1-p) is a chance of a “not-a-six”.

Then the chance of no sixes in one throw is (1-p)

and no sixes after 2 throws is (1-p)(1-p) = (1-p)^2 (where ^ means raise to the power)

and no sixes after three throws is (1-p)(1-p)(1-p) = (1-p)^3 and so on.

So the likelihood of “no sixes in n throws” is (1-p)^n

Let us call this “t”

So the equation we need to solve to estimate the upper limit of our estimate of “p” is

t=(1-p)^20

Where “t” is a measure of how likely we are to choose 20 managers all of whom do not know – just by chance. And we want that to be a small number. We want to feel confident that our estimate is reasonable and not just a quirk of chance.

So what threshold do we set for “t” that we feel is “reasonable”? 1 in a million? 1 in 1000? 1 in 100? 1 in10?

By convention we use 1 in 20 (t=0.05) – but that is arbitrary. If we are more risk-averse we might choose 1:100 or 1:1000. It depends on the context.

Let us be reasonable – let is say we want to be 95% confident our our estimated upper limit for “p” – which means we are calculating the 95% confidence interval. This means that will accept a 1:20 risk of our calculated confidence interval for “p” being wrong: a 19:1 odds that the true value of “p” falls outside our calculated range. Pretty good odds! We will be reasonable and we will set the likelihood threshold for being “wrong” at 5%.

So now we need to solve:

0.05= (1-p)^20

And we want a picture of this relationship in our minds so let us draw a graph of t for a range of values of p.

We know the value of p must be between 0 and 1.0 so we have all we need and we can generate this graph easily using Excel. And every senior NHS operational manager knows how to use Excel. It is a requirement. Isn’t it?

The Excel-generated chart shows the relationship between p (horizontal axis) and t (vertical axis) using our equation:

t=(1-p)^20.

Step 7

Let us first do a “sanity check” on what we have drawn. Let us “check the extreme values”.

If 0% of managers know then a sample of 20 will always reveal none – i.e. the leftmost point of the chart. Check!

If 100% of managers know then a sample of 20 will never reveal none – i.e. way off to the right. Check!

What is clear from the chart is that the relationship between p and t is not a straight line; it is non-linear. That explains why we find it difficult to estimate intuitively. Our brains are not very good at doing non-linear analysis. Not very good at all.

So we need a tool to help us. Our Excel graph. We read down the vertical “t” axis from 100% to the 5% point, then trace across to the right until we hit the line we have drawn, then read down to the corresponding value for “p”. It says about 14%.

So that is the upper limit of our 95% confidence interval of the estimate of the true proportion of NHS service managers who know the Foundations of Operations Management. The lower limit is 0%.

And we cannot say better than somewhere between 0%-14% with the data we have and the assumptions we have made.

To get a more precise estimate, a narrower 95% confidence interval, we need to gather some more data.

[Another way we can use our chart is to ask “If the actual % of Managers who know is x% the what is the chance that no one of our sample of 20 will know?” Solving this manually means marking the x% point on the horizontal axis then tracing a line vertically up until it crosses the drawn line then tracing a horizontal line to the left until it crosses the vertical axis and reading off the likelihood.]

So if in reality 5% of all managers do Know then the chance of no one knowing in an unbiased sample of 20 is about 35% – really quite likely.

Now we are getting a feel for the likely reality. Much more useful than just dry numbers!

But we are 95% sure that 86% of NHS managers do NOT know the basic language of flow-improvement-science.

And what this chart also tells us is that we can be VERY confident that the true value of p is less than 2o% – the proportion we believe we need to get to transformation tipping point.

Now we need to repeat the experiment experiment and draw a new graph to get a more accurate estimate of just how much less – but stepping back from the statistical nuances – the message is already clear that we do have a Black Curtain problem.

A Black Curtain of Ignorance problem.

Many will now proclaim angrily “This cannot be true! It is just statistical smoke and mirrors. Surely our managers do know this by a different name – how could they not! It is unthinkable to suggest the majority of NHS manages are ignorant of the basic science of what they are employed to do!“

If that were the case though then we would already have an NHS that is fit-for-purpose. That is not what reality is telling us.

And it quickly become apparent at the master class that our sample of 20 did not know-this-by-a-different-name.

The good news is that this knowledge gap could hiding the opportunity we are all looking for – a door to a path that leads to a radical yet achievable transformation of the NHS into a system that is fit-for-purpose. Now and into the future.

A system that delivers safe, high quality care for those who need it, in full, when they need it and at a cost the country can afford. Now and for the foreseeable future.

And the really good news is that this IS knowledge gap may be and extensive deep but it is not wide … the Foundations are is easy to learn, and to start applying immediately. The basics can be learned in less than a week – the more advanced skills take a bit longer. And this is not untested academic theory – it is proven pragmatic real-world problem solving know-how. It has been known for over 50 years outside healthcare.

Our goal is not acquisition of theoretical knowledge – is is a deep enough understanding to make wise enough decisions to achieve good enough outcomes. For everyone. Starting tomorrow.

And that is the design purpose of FISH. To provide those who want to learn a quick and easy way to do so.

Stop Press: Further feedback from the masterclass is that some of the managers are grasping the nettle, drawing back their own black curtains, opening the door that was always there behind it, and taking a peek through into a magical garden of opportunity. One that was always there but was hidden from view.

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.

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.

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.

02/03/201311/01/2022

The Seventh Flow

Bing Bong

Bob looked up from the report he was reading and saw the SMS was from Leslie, one of his Improvement Science Practitioners.

It said “Hi Bob, would you be able to offer me your perspective on another barrier to improvement that I have come up against.”

Bob thumbed a reply immediately “Hi Leslie. Happy to help. Free now if you would like to call. Bob“

Ring Ring

<Bob> Hello, Bob here.

<Leslie> Hi Bob. Thank you for responding so quickly. Can I describe the problem?

<Bob> Hi Leslie – Yes, please do.

<Leslie> OK. The essence of it is that I have discovered that our current method of cash-flow control is preventing improvements in safety, quality, delivery and paradoxically in productivity too. I have tried to talk to the Finance department and all I get back is “We have always done it this way. That is what we are taught. It works. The rules are not negotiable and the problem is not Finance“. I am at a loss what to do.

<Bob> OK. Do not worry. This is a common issue that every ISP discovers at some point. What led you to your conclusion that the current methods are creating a barrier to change?

<Leslie> Well, the penny dropped when I started using the modelling tools you have shown me. In particular when predicting the impact of process improvement-by-design changes on the financial performance of the system.

<Bob> OK. Can you be more specific?

<Leslie> Yes. The project was to design a new ambulatory diagnostic facility that will allow much more of the complex diagnostic work to be done on an outpatient basis. I followed the 6M Design approach and looked first at the physical space design. We needed that to brief the architect.

<Bob> OK. What did that show?

<Leslie> It showed that the physical layout had a very significant impact on the flow in the process and that by getting all the pieces arranged in the right order we could create a physical design that felt spacious without actually requiring a lot of space. We called it the “Tardis Effect“. The most marked impact was on the size of the waiting areas – they were really small compared with what we have now which are much bigger and yet still feel cramped and chaotic.

<Bob> OK. So how does that physical space design link to the finance question?

<Leslie> Well, the obvious links were that the new design would have a smaller physical foot-print and at the same time give a higher throughput. It will cost less to build and will generate more activity than if we just copied the old design into a shiny new building.

<Bob> OK. I am sure that the Capital Allocation Committee and the Revenue Generation Committee will have been pleased with that outcome. What was the barrier?

<Leslie> Yes, you are correct. They were delighted because it left more in the Capital Pot for other equally worthy projects. The problem was not capital it was revenue.

<Bob> You said that activity was predicted to increase. What was the problem?

<Leslie>Yes – sorry, I was not clear – it was not the increased activity that was the problem – it was how to price the activity and how to distribute the revenue generated. The Reference Cost Committee and Budget Allocation Committee were the problem.

<Bob> OK. What was the problem?

<Leslie> Well the estimates for the new operational budgets were basically the current budgets multiplied by the ratio of the future planned and historical actual activity. The rationale was that the major costs are people and consumables so the running costs should scale linearly with activity. They said the price should stay as it is now because the quality of the output is the same.

<Bob> OK. That does sound like a reasonable perspective. The variable costs will track with the activity if nothing else changes. Was it apportioning the overhead costs as part of the Reference Costing that was the problem?

<Leslie> No actually. We have not had that conversation yet. The problem was more fundamental. The problem is that the current budgets are wrong.

<Bob> Ah! That statement might come across as a bit of a challenge to the Finance Department. What was their reaction?

<Leslie> To para-phrase it was “We are just breaking even in the current financial year so the current budget must be correct. Please do not dabble in things that you clearly do not understand.”

<Bob> OK. You can see their point. How did you reply?

<Leslie> I tried to explain the concepts of the Cost-Of-The-Queue and how that cost was incurred by one part of the system with one budget but that the queue was created by a different part of the system with a different budget. I tried to explain that just because the budgets were 100% utilised does not mean that the budgets were optimal.

<Bob> How was that explanation received?

<Leslie> They did not seem to understand what I was getting at and kept saying “Inventory is an asset on the balance sheet. If profit is zero we must have planned our budgets perfectly. We cannot shift money between budgets within year if the budgets are already perfect. Any variation will average out. We have to stick to the financial plan and projections for the year. It works. The problem is not Finance – the problem is you.“

<Bob> OK. Have you described the Seventh Flow and put it in context?

<Leslie> Arrrgh! No! Of course! That is how I should have approached it. Budgets are Cash-Inventories and what we need is Cash-Flow to where and when it is needed and in just the right amount according to the Principle of Parsimonious Pull. Thank you. I knew you would ask the crunch question. That has given me a fresh perspective on it. I will have another go.

<Bob> Let know how you get on. I am curious to hear the next instalment of the story.

<Leslie> Will do. Bye for now.

Drrrrrrrr

Creating a productive and stable system design requires considering Seven Flows at the same time. The Seventh Flow is cash flow.

Cash is like energy – it is only doing useful work when it is flowing.

Energy is often described as two forms – potential energy and and kinetic energy. The ‘doing’ happens when one form is being converted from potential to kinetic. Cash in the budget is like potential energy – sitting there ready to do some business. Cash flow is like kinetic energy – it is the business.

The most versatile form of energy that we use is electrical energy. It is versatile because it can easily be converted into other forms – e.g. heat, light and movement. Since the late 1800’s our whole society has become highly dependent on electrical energy. But electrical energy is tricky to store and even now our battery technology is pretty feeble. So, if we want to store energy we use a different form – chemical energy. Gas, oil and coal – the fossil fuels – are all ancient stores of chemical energy that were originally derived from sunlight captured by vast carboniferous forests over millions of years. These carbon-rich fossil fuels are convenient to store near where they are needed, and when they are needed. But fossil fuels have a number of drawbacks: One is that they release their stored carbon when they are “burned”. Another is that they are not renewable. So, in the future we will need to develop better ways to capture, transport, use and store the energy from the Sun that will flow in glorious abundance for millions of years to come.

Plants discovered millions of years ago how to do this sunlight-to-chemical energy conversion and that biological legacy is built into every cell in every plant on the planet. Animals just do the reverse trick – they convert chemical-to-electrical. Every cell in every animal on the planet is a microscopic electrical generator that “burns” chemical fuel – carbohydrate. The other products are carbon dioxide and water. Plants use sunlight to recycle and store the carbon dioxide. It is a resilient and sustainable design.

Plants seemingly have it easy – the sunlight comes to them – they just sunbathe all day! The animals have to work a bit harder – they have to move about gathering their chemical fuel. Some animals just feed on plants, others feed on other animals, and we do a bit of both. This food-gathering is a more complicated affair – and it creates a problem. Animals need a constant supply of energy – so they have to carry a store of chemical fuel around with them. That store is heavy so it needs energy to move it about. Herbivors can be bigger and less intelligent because their food does not run away. Carnivors need to be more agile; both physically and mentally. A balance is required. A big enough fuel store but not too big. So, some animals have evolved additional strategies. Animals have become very good at not wasting energy – because the more that is wasted the more food that is needed and the greater the risk of getting eaten or getting too weak to catch the next meal.

To illustrate how amazing animals are at energy conservation we just need to look at an animal structure like the heart. The heart is there to pump blood around. Blood carries chemical nutrients and waste from one “department” of the body to another – just like ships, rail, roads and planes carry stuff around the world.

Blood is a sticky, viscous fluid that requires considerable energy to pump around the body and, because it is pumped continuously by the heart, even a small improvement in the energy efficiency of the circulation design has a big long-term cumulative effect. The flow of blood to any part of the body must match the requirements of that part. If the blood flow to your brain slows down for even few seconds the brain cannot work properly and you lose consciousness – it is called “fainting”.

If the flow of blood to the brain is stopped for just a few minutes then the brain cells actually die. That is called a “stroke”. Our brains use a lot of electrical energy to do their job and our brain cells do not have big stores of fuel – so they need constant re-supply. And our brains are electrically active all the time – even when we are sleeping.

Other parts of the body are similar. Muscles for instance. The difference is that the supply of blood that muscles need is very variable – it is low when resting and goes up with exercise. It has been estimated that the change in blood flow for a muscle can be 30 fold! That variation creates a design problem for the body because we need to maintain the blood flow to brain at all times but we only want blood to be flowing to the muscles in just the amount that they need, where they need it and when they need it. And we want to minimise the energy required to pump the blood at all times. How then is the total and differential allocation of blood flow decided and controlled? It is certainly not a conscious process.

The answer is that the brain and the muscles control their own flow. It is called autoregulation. They open the tap when needed and just as importantly they close the tap when not needed. It is called the Principle of Parsimonious Pull. The brain directs which muscles are active but it does not direct the blood supply that they need. They are left to do that themselves.

So, if we equate blood-flow and energy-flow to cash-flow then we arrive at a surprising conclusion. The optimal design, the most energy and cash efficient, is where the separate parts of the system continuously determine the energy/cash flow required for them to operate effectively. They control the supply. They autoregulate their cash-flow. They pull only what they need when they need it.

BUT

For this to work then every part of the system needs to have a collaborative and parsimonious pull-design philosophy – one that wastes as little energy and cash as possible. Minimum waste of energy requires careful design – it is called ergonomic design. Minimum waste of cash requires careful design – it is called economic design.

Many socioeconomic systems are fragmented and have parts that behave in a “greedy” manner and that compete with each other for resources. It is a dog-eat-dog design. They would use whatever resources they can get for fear of being starved. Greed is Good. Collaboration is Weak. In such a competitive situation a rigid-budget design is a requirement because it helps prevent one part selfishly and blindly destabilising the whole system for all. The problem is that this rigid financial design blocks change so it blocks improvement.

This means that greedy, competitive, selfish systems are unable to self-improve.

So, when the world changes too much and their survival depends on change then they risk becoming extinct just as the dinosaurs did.

Many will challenge this assertion by saying “But competition drives up performance“. Actually, it is not as simple as that. Competition will weed out the weakest who “die” and remove themselves from the equation – apparently increasing the average. What actually drives improvement is customer choice. Organisations that are able to self-improve will create higher-quality and lower-cost products and in a globally-connected-economy the customers will vote with their wallets. The greedy and selfish competition lags behind.

So, to ensure survival in a global economy the Seventh Flow cannot be rigidly restricted by annually allocated departmental budgets. It is a dinosaur design.

And there is no difference between public and private organisations. The laws of cash-flow physics are universal.

How then is the cash flow controlled?

The “trick” is to design a monitoring and feedback component into the system design. This is called the Sixth Flow – and it must be designed so that just the right amount of cash is pulled to the just the right places and at just the right time and for just as long as needed to maximise the revenue. The rest of the design – First Flow to Fifth Flow ensure the total amount of cash needed is a minimum. All Seven Flows are needed.

So the essential ingredient for financial stability and survival is Sixth and Seventh Flow Design capability. That skill has another name – it is called Value Stream Accounting which is a component of complex adaptive systems engineering (CASE).

What? Never heard of Value Stream Accounting?

Maybe that is just another Error of Omission?

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.

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.

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.

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.

01/09/2012

Little and Often

There seem to be two extremes to building the momentum for improvement – One Big Whack or Many Small Nudges.

The One Big Whack can come at the start and is a shock tactic designed to generate an emotional flip – a Road to Damascus moment – one that people remember very clearly. This is the stuff that newspapers fall over themselves to find – the Big Front Page Story – because it is emotive so it sells newspapers. The One Big Whack can also come later – as an act of desperation by those in power who originally broadcast The Big Idea and who are disappointed and frustrated by lack of measurable improvement as the time ticks by and the money is consumed.

Many Small Nudges do not generate a big emotional impact; they are unthreatening; they go almost unnoticed; they do not sell newspapers, and they accumulate over time. The surprise comes when those in power are delighted to discover that significant improvement has been achieved at almost no cost and with no cajoling.

So how is the Many Small Nudge method implemented?

The essential element is The Purpose – and this must not be confused with A Process. The Purpose is what is intended; A Process is how it is achieved. And answering the “What is my/our purpose?” question is surprisingly difficult to do.

For example I often ask doctors “What is our purpose?” The first reaction is usually “What a dumb question – it is obvious”. “OK – so if it is obvious can you describe it?” The reply is usually “Well, err, um, I suppose, um – ah yes – our purpose is to heal the sick!” “OK – so if that is our purpose how well are we doing?” Embarrassed silence. We do not know because we do not all measure our outcomes as a matter of course. We measure activity and utilisation – which are measures of our process not of our purpose – and we justify not measuring outcome by being too busy – measuring activity and utilisation.

Sometimes I ask the purpose question a different way. There is a Latin phrase that is often used in medicine: primum non nocere which means “First do no harm”. So I ask – “Is that our purpose?”. The reply is usually something like “No but safety is more important than efficiency!” “OK – safety and efficiency are both important but are they our purpose?”. It is not an easy question to answer.

A Process can be designed – because it has to obey the Laws of Physics. The Purpose relates to People not to Physics – so we cannot design The Purpose, we can only design a process to achieve The Purpose. We can define The Purpose though – and in so doing we achieve clarity of purpose. For a healthcare organisation a possible Clear Statement of Purpose might be “WE want a system that protects, improves and restores health“.

Purpose statements state what we want to have. They do not state what we want to do, to not do or to not have. This may seem like a splitting hairs but it is important because the Statement of Purpose is key to the Many Small Nudges approach.

Whenever we have a decision to make we can ask “How will this decision contribute to The Purpose?”. If an option would move us in the direction of The Purpose then it gets a higher ranking to a choice that would steer us away from The Purpose. There is only one On Purpose direction and many Off Purpose ones – and this insight explains why avoiding what we do not want (i.e. harm) is not the same as achieving what we do want. We can avoid doing harm and yet not achieve health and be very busy all at the same time.

Leaders often assume that it is their job to define The Purpose for their Organisation – to create the Vision Statement, or the Mission Statement. Experience suggests that clarifying the existing but unspoken purpose is all that is needed – just by asking one little question – “What is our purpose?” – and asking it often and of everyone – and not being satisfied with a “process” answer.

26/08/2012

The Essential Role of the Credible Skeptic

All improvement implies change – some may be incremental elimination of current Niggles; other may be breakthrough achievement of future NiceIfs.

Change is an uphill struggle and the inevitable friction generates heat and sparks which dissipate some of the energy.

People throw spanners into the wheel which may eventually grind to a halt. Experts talk about “oiling the wheels of change” and generating momentum. The mechanical metaphors are numerous and have a common thread – that change requires pushing.

The unstated assumption is that resistance is “bad” and any means to overcome or bypass resistance is therefore justified – but this assumption is one-sided and discounts the possibility that there is a “good” side to resistance.

Suppose a design is proposed that would be effective (it would do the right thing) then resistance-to-change would be counter-improvement. Suppose the proposed design would be ineffective (it would not do the right thing and might even lead to the wrong thing) then resistance-to-change would be protective. The difference is the effectiveness of the design – not the presence of resistance-to-change.

Effectiveness has two components – effective in theory and effective in practice. Demonstrating effectiveness in theory is the purpose of pure research; delivering effectiveness in practice is the purpose of applied research. Both are embraced in Improvement Science.

Who is best placed to decide what will work in theory? An academic.

Who is best placed to decide what can work in practice? A pragmatist.

So we need both doing the parts that they do best. And we need them doing it at the same time … not in sequence … not theory and then practice.

It is a common assumption that novel designs are created sequentially – working from big conceptual chunks in stages of increasing detail to the final blueprints.

Reality is a bit messier than this!

An experienced design team will flip between broad-brush and fine-detail and they know the importance of including both theorists and pragmatists in the team. This is where the practical challenge comes because most people have a preference for one or the other modes of thinking.

Coordinating the effective-design-conversation requires awareness by everyone of the value of both. This is not discussion, instruction, manipulation, or facilitation – it is education. The role of the design team leader is to create the context to allow the learning to flow and the synergy to emerge.

The symptoms and signs associated with inexperienced design teams are:

Design done behind closed doors by strategists with the assistance of theoretical advisors called management consultants.
Design decisions are delivered as a “fait accompli” to those expected to “operationalise” them.
Language such as “herding cats” is used to refer to the influential skeptics who represent the “front line barrier to change”.

These symptoms are harbingers of failure – poor designs that flounder on the Rocks of Don’t Do and good designs that get stuck on the Sands of Won’t Do.

The experienced design team knows these hidden dangers and has learned how to steer around them by demonstrating respect for the theory and for the practice and staying in the Channel to Success. There need to be respected Optics (visionary optimists) and credible Skeptics (respectful pessimists) at both the academic and the pragmatic poles to generate creative resonance. Synergy. An effective design team includes the role of Credible Skeptic.

And there are no chairs at the effective design table for the Politics (egocentric activists) and the Cynics (disrespectful pessimists). Their beliefs, attitudes and behaviours generate dissonance and turbulence which dissipates and wastes the effort, time and money of everyone else.

And we must always remember that effective design comes before efficient design. Doing the wrong thing efficiently makes it wronger! First do the right thing – then do it better. That is a design where everyone benefits.

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.