Tag: System

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Resilience

The rise in the use of the term “resilience” seems to mirror the sense of an accelerating pace of change. So, what does it mean? And is the meaning evolving over time?

One sense of the meaning implies a physical ability to handle stresses and shocks without breaking or failing. Flexible, robust and strong are synonyms; and opposites are rigid, fragile, and weak.

So, digging a bit deeper we know that strong implies an ability to withstand extreme stress while resilient implies the ability to withstanding variable stress. And the opposite of resilient is brittle because something can be both strong and brittle.

This is called passive resilience because it is an inherent property and cannot easily be changed. A ball is designed to be resilient – it will bounce back – and this inherent in the material and the structure. The implication of this is that to improve passive resilience we would need to remove and to replace with something better suited to the range of expected variation.

The concept of passive resilience applies to processes as well, and a common manifestation of a brittle process is one that has been designed using averages.

Processes imply flows. The flow into a process is called demand, while the flow out of the process is called activity. What goes in must come out, so if the demand exceeds the activity then a backlog will be growing inside the process. This growing queue creates a number of undesirable effects – first it takes up space, and second it increases the time for demand to be converted into activity. This conversion time is called the lead-time.

So, to avoid a growing queue and a growing wait, there must be sufficient flow-capacity at each and every step along the process. The obvious solution is to set the average flow-capacity equal to the average demand; and we do this because we know that more flow-capacity implies more cost – and to stay in business we must keep a lid on costs!

This sounds obvious and easy but does it actually work in practice?

The surprising answer is “No”. It doesn’t.

What happens in practice is that the measured average activity is always less than the funded flow-capacity, and so less than the demand. The backlogs will continue to grow; the lead-time will continue to grow; the waits will continue to grow; the internal congestion will continue to grow – until we run out of space. At that point everything can grind to a catastrophic halt. That is what we mean by a brittle process.

This fundamental and unexpected result can easily and quickly be demonstrated in a concrete way on a table top using ordinary dice and tokens. A credible game along these lines was described almost 40 years ago in The Goal by Eli Goldratt, originator of the school of improvement called Theory of Constraints. The emotional impact of gaining this insight can be profound and positive because it opens the door to a way forward which avoids the Flaw of Averages trap. There are countless success stories of using this understanding.

So, when we need to cope with variation and we choose a passive resilience approach then we have to plan to the extremes of the range of variation. Sometimes that is not possible and we are forced to accept the likelihood of failure. Or we can consider a different approach.

Reactive resilience is one that living systems have evolved to use extensively, and is illustrated by the simple reflex loop shown in the diagram.

A reactive system has three components linked together – a sensor (i.e. temperature sensitive nerves endings in the skin), a processor (i.e. the grey matter of the spinal chord) and an effector (i.e. the muscle, ligaments and bones). So, when a pre-defined limit of variation is reached (e.g. the flame) then the protective reaction withdraws the finger before it becomes damaged. The advantage this type of reactive resilience is that it is relatively simple and relatively fast. The disadvantage is that it is not addressing the cause of the problem.

This is called reactive, automatic and agnostic.

The automatic self-regulating systems that we see in biology, and that we have emulated in our machines, are evidence of the effectiveness of a combination of passive and reactive resilience. It is good enough for most scenarios – so long as the context remains stable. The problem comes when the context is evolving, and in that case the automatic/reflex/blind/agnostic approach will fail – at some point.

Survival in an evolving context requires more – it requires proactive resilience.

What that means is that the processor component of the feedback loop gains an extra feature – a memory. The advantage this brings is that past experience can be recalled, reflected upon and used to guide future expectation and future behaviour. We can listen and learn and become proactive. We can look ahead and we can keep up with our evolving context. One might call, this reactive adaptation or co-evolution and it is a widely observed phenomenon in nature.

The usual manifestation is this called competition.

Those who can reactively adapt faster and more effectively than others have a better chance of not failing – i.e. a better chance of survival. The traditional term for this is survival of the fittest but the trendier term for proactive resilience is agile.

And that is what successful organisations are learning to do. They are adding a layer of proactive resilience on top of their reactive resilience and their passive resilience.

All three layers of resilience are required to survive in an evolving context.

One manifestation of this is the concept of design which is where we create things with the required resilience before they are needed. This is illustrated by the design squiggle which has time running left to right and shows the design evolving adaptively until there is sufficient clarity to implement and possibly automate.

And one interesting thing about design is that it can be done without an understanding of how something works – just knowing what works is enough. The elegant and durable medieval cathedrals were designed and built by Master builders who had no formal education. They learned the heuristics as apprentices and through experience.

And if we project the word game forwards we might anticipate a form of resilience called proactive adaptation. However, we sense that is a novel thing because there is no proadaptive word in the dictionary.

PS. We might also use the term Anti-Fragile, which is the name of a thought-provoking book that explores this very topic.

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Systemory

How do we remember the vast amount of information that we seem to be capable of?

Our brains are comprised of billions of cells most of which are actually inactive and just there to support the active brain cells – the neurons.

Suppose that the active brain cell part is 50% and our brain has a volume of about 1.2 litres or 1,200 cu.cm or 1,200,000 cu.mm. We know from looking down a microscope that each neuron is about 20/1,000 mm x 20/1,000 mm  x 20/1,000 mm which gives a volume of 8/1,000,000 cu.mm or 125,000 neurons for every cu.mm. The population of a medium sized town in a grain of salt!  This is a concept we can just about grasp. And with these two facts we estimate that there are in the order of 140,000,000,000 neurons in a human brain – 140 billion – about 20 times the population of the whole World. Wow!

But even that huge number is less than the size of the memory on the hard disc of the computer I am writing this blog on – which has 200 gigabytes which is 1,600 gigabits which is 1,600 billion bits. Ten times as many memory cells as there are neurons in a human brain. 

But our brains are not just for storing data – they do all the data processing too – it is an integrated processor-and-memory design completely unlike the separate processor-or-memory design of a digital computer.  Each of our brains is remarkable in its capability, adaptability, and agility – its ability to cope with change – its ability to learn and to change its behaviour while still working.  So how does our biological memory work?

Well not like a digital computer where the zeros and ones, the binary digits (bits) are stored in regular structure of memory cells – a static structural memory – a data prison.  Our biological memory works in a completely different way – it is a temporal memory – it is time dependent. Our memories are not “recalled” like getting a book out of an indexed slot on a numbered in a massive library; are memories are replayed like a recording or rebuilt from a recipe. Time is the critical factor and this concept of temporal memory is a feature of all systems.

And that is not all – the temporal memory is not a library of video tapes – it is the simultaneous collective action of many parts of the system that create the illusion of the temporal memory – we have a parallel-distributed-temporal-memory. More like a video hologram. And it means we cannot point to the “memory” part of our brains – it is distributed throughout the system – and this means that the connections between the parts are as critical a part of the design and the parts themselves. It is a tricky concept to grasp and none of the billions of digital computers that co-inhabit this planet operate this way. They are feeble and fragile in comparison. An inferior design.

The terms distributed-temporal or systemic-memory are a bit cumbersome though so we need a new label – let us call it a systemory.  The properties of a systemory are remarkable – for example it still works when a bit of the systemory is removed.  When a bit of your brain is removed you don’t “forget” a bit of your name or lose the left ear on the mental picture of your friends face – as would happen with a computer.  A systemory is resilient to damage which is a necessary design-for-survival. It also implies that we can build our systemory with imperfect parts and incomplete connections. In a digital computer this would not work: the localised-static or silo-memory has to be perfect because if a single bit gets flipped or a single wire gets fractured it can render the whole computer inoperative useless junk.

Another design-for-survival property of a systemory is that it still works even when it is being changed – it is continuously adaptable and updateable.  Not so a computer – to change the operating system the computer has to be stopped, the old program overwritten by the new one, then the new one started. In fact computers are designed to prevent programs modifying themselves – because it a sure recipe for a critical system failure – the dreaded blue screen!

So if we map our systemory concept across from person to population and we replace neurons with people then we get an inkling of how a society can have a collective memory, a collective intelligence, a collective consciousness even – a social systemory. We might call that property the culture.  We can also see that the relationships that link the people are as critical as the people themselves and that both can be imperfect yet we get stable and reliable behaviour. We can also see that influencing the relationships between people has as much effect on the system behaviour as how the people themselves perform – because the properties of the systemory are emergent. Culture is an output not an input.

So in the World – the development of global communication systems means that all 7 billion people in the global social systemory can, in principle, connect to each other and can collectively learn and change faster and faster as the technology to connect more widely and more quickly develops. The rate of culture change is no longer governed by physical constraints such as geographic location, orand temporal constraints such as how long a letter takes to be delivered.

Perhaps the most challenging implication is that a systemory does not have a “point of control” – there is no librarian who acts as a gatekeeper to the data bank, no guard on the data prison.  The concept of “control” in a systemory is different – it is global not local – and it is influence not control.  The rapid development of mobile communication technology and social networking gives ample evidence – we would now rather communicate with a familar on the other side of the world than with a stranger standing next to us in the lunch queue. We have become tweeting and texting daemons.  Our emotional relationships are more important than our geographical ones. And if enough people can connect to each other they can act in a collective, coordinated, adaptive and agile way that no command-and-control system can either command or control. The recent events in the Middle East are ample evidence of the emergent effectiveness of a social systemory.

Our insight exposes a weakness of a social systemory – it is possible to adversely affect the whole by introducing a behavioural toxin that acts at the social connection level – on the relationships between people. The behavioural toxin needs only to have a weak and apparently harmless effect but when disseminated globally the cumulative effect creates cultural dysfunction.  It is rather like the effect of alcohol and other recreational chemical substances on the brain – it cause a temporary systemory dysfunction – but one that in an over-stressed psychological system paradoxically results in pleasure; or rather stress release. Hence the self-reinforcing nature of the addiction.  

Effective leaders are intuitively aware that just their behaviour can be a tonic or a toxin for the whole system: organisations are the the same emotional boat as their leader.

Effective leaders use their behaviour to steer the systemory of the organisation along a path of improvement and their behaviour is the output of their personal systemory.

Leaders have to be the change that they want their organisations to achieve.

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Focus-on-the-Flow

One of the foundations of Improvement Science is visualisation – presenting data in a visual format that we find easy to assimilate quickly – as pictures.

We derive deeper understanding from observing how things are changing over time – that is the reality of our everyday experience.

And we gain even deeper understanding of how the world behaves by acting on it and observing the effect of our actions. This is how we all learned-by-doing from day-one. Most of what we know about people, processes and systems we learned long before we went to school.

When I was at school the educational diet was dominated by rote learning of historical facts and tried-and-tested recipes for solving tame problems. It was all OK – but it did not teach me anything about how to improve – that was left to me.

More significantly it taught me more about how not to improve – it taught me that the delivered dogma was not to be questioned. Questions that challenged my older-and-better teachers’ understanding of the world were definitely not welcome.

Young children ask “why?” a lot – but as we get older we stop asking that question – not because we have had our questions answered but because we get the unhelpful answer “just because.”

When we stop asking ourselves “why?” then we stop learning, we close the door to improvement of our understanding, and we close the door to new wisdom.

So to open the door again let us leverage our inborn ability to gain understanding from interacting with the world and observing the effect using moving pictures.

Unfortunately our biology limits us to our immediate space-and-time, so to broaden our scope we need to have a way of projecting a bigger space-scale and longer time-scale into the constraints imposed by the caveman wetware between our ears.

Something like a video game that is realistic enough to teach us something about the real world.

If we want to understand better how a health care system behaves so that we can make wiser decisions of what to do (and what not to do) to improve it then a real-time, interactive, healthcare system video game might be a useful tool.

So, with this design specification I have created one.

The goal of the game is to defeat the enemy – and the enemy is intangible – it is the dark cloak of ignorance – literally “not knowing”.

Not knowing how to improve; not knowing how to ask the “why?” question in a respectful way.  A way that consolidates what we understand and challenges what we do not.

And there is an example of the Health Care System Flow Game being played here.

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Synigence

The “Qualigence, Quantigence and Synergence” blopic has generated some interesting informal feedback and since being more attuned to this concept I have seen evidence of it at work in practice. My own reflection is that synergence does not quite hit the spot because syn-erg-gence can be translated as “knowing how to work together” and from this small niggle a new word was born – synigence – which I feel captures the concept better. It is an improvement. 

Improvement Science always considers a challenge from three perspectives – quality, delivery and quantity. The delivery dimension involves time and can be viewed both qualitatively and quantitatively.  The pure qualitative dimension is the subjective experience (feelings) and the pure quanitative dimension is the objective evidence (facts) – very often presented in the Universal Language of Money (ULM). The diagram attempts to capture this idea of three perspectives and that there is common ground between all three;  the soil in which the seeds of improvement take root. There is more to it though – this common ground/vision/goal/sense does not look the same from different perspectives and for synergy to develop the synigent facilitator needs to be capable of translating the one vision into three languages. It is rather like the Rosetta Stone an ancient Egyptian grandiorite stele inscribed with a decree issued at Memphis, Egypt in 196 BC on behalf of King Ptolemy V. The decree appears in three scripts: Ancient Egyptian hieroglyphs, Demotic Egyptian script, and Ancient Greek and, as it presents essentially the same text in all three scripts, it provided the key to the modern understanding of Egyptian hieroglyphs.  With this key the wisdom of the Ancient Egyptians was unlocked.

My learning this week is that this is less on an exercise in how to influence others and more of an exercise in how to influence oneself and by that route the sum can become greater than the parts.  Things that looked impossible for either working alone (or more often in conflict) now become not only possible but also inevitable.  Once we have seen we cannot forget – and once we believe we cannot understand that it is not obvious to everyone else: and there lurks a trap for the unsynigent – it is not obvious – if it were we would have seen it sooner ourselves.

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Do You Have A Miserable Job?

If you feel miserable at work and do not know what to do then then take heart because you could be suffering from a treatable organisational disease called CRAP (cynically resistant arrogant pessimism).

To achieve a healthier work-life then it is useful to understand the root cause of CRAP and the rationale of how to diagnose and treat it.

Organisations have three interdependent dimensions of performance: value, time and money.  All organisations require both the people and the processes to be working in synergy to reliably deliver value-for-money over time.  To create a productive system it is necessary to understand the relationships between  value, money and time. Money is easier because it is tangible and durable; value is harder because it is intangible and transient. This means that the focus of attention is usually on the money – and it is often assumed that if the money is OK then the value must be OK too.  This assumption is incorrect.

Value and money are interdependent but have different “rates of change”  and can operate in different “directions”.  A common example is when a dip in financial performance triggers an urgent “drive” to improve the “bottom line”.  Reactive revenue generation and cost cutting results in a small, quick, and tangible improvement on the money dimension but at the same time sets off a large, slow, and intangible deterioration on the value dimension.  Money, time and  value are interdependent and the inevitable outcome is a later and larger deterioration in the money – as illustrated in the doodle. If only money is measured the deteriorating value is not detected, and by the time the money starts to falter the momentum of the falling value is so great that even heroic efforts to recover are futile. As the money starts to fall the value falls even further and even faster – the lose-lose-lose spiral of organisational failure is now underway.

People who demonstrate in their attitude and behaviour that they are miserable at work provide the cardinal sign of falling system value. A miserable, sceptical and cynical employee poisons the emotional atmosphere for everyone around them. Misery is both defective and infective.  The primary cause of a miserable job is the behaviour exhibited by people in positions of authority – and the more the focus is only on money the more misery their behaviour generates.

Fortunately there is an antidote; a way to break out of the vicious tail spin – measure both value and money, focus on improving value and observe the positive effect on the money.  The critical behaviour is to actively test the emotional temperature and to take action to keep it moving in a positive direction.  “The Three Signs of a Miserable Job” by Patrick Lencioni tells a story of how an experienced executive learns that the three things a successful managerial leader must do to achieve system health are:
1) ensure employees know their unique place, role and value in the whole system;
2) ensure employees can consciously connect their work with a worthwhile system goal; and
3) ensure employees can objectively measure how they are doing.

Miserable jobs are those where the people feel anonymous, where people feel their work is valueless, and where people feel that they get no feedback from their seniors, peers or juniors. And it does not matter if it is the cleaner or the chief executive – everyone needs a role, a goal and to know all their interdependencies.

We do not have to endure a Miserable Job – we all have the power to transform it into Worthwhile Work.

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The Rubik Cube Problem

Look what popped out of Santa’s sack!

I have not seen one of these for years and it brought back memories of hours of frustration and time wasted in attempting to solve it myself; a sense of failure when I could not; a feeling of envy for those who knew how to; and a sense of indignation when they jealously guarded the secret of their “magical” power.

The Rubik Cube got me thinking – what sort of problem is this?

At first it is easy enough but it becomes quickly apparent that it becomes more difficult the closer we get to the final solution – because our attempts to reach perfection undo our previous good work.  It is very difficult to maintain our initial improvement while exploring new options. 

This insight struck me as very similar to many of the problems we face in life and the sense of futility that creates a powerful force that resists further attempts at change.  Fortunately, we know that it is possible to solve the Rubik cube – so the question this raises is “Is there a way to solve it in a rational, reliable and economical way from any starting point?”

One approach is to try every possible combination of moves until we find the solution. That is the way a computer might be programmed to solve it – the zero intelligence or brute force approach.

The problem here is that it works in theory but fails in practice because of the number of possible combinations of moves. At each step you can move one of the six faces in one of two directions – that is 12 possible options; and for each of these there are 12 second moves or 12 x 12 possible two-move paths; 12 x 12 x 12 = 1728 possible three-move paths; about 3 million six-move paths; and nearly half a billion eight-move paths!

You get the idea – solving it this way is not feasible unless you are already very close to the solution.

So how do we actually solve the Rubik Cube?  Well, the instructions that come with a new one tells you – a combination of two well-known ingredients: strategy and tactics. The strategy is called goal-directed and in my instructions the recommended strategy is to solving each layer in sequence. The tactics are called heuristics: tried-tested-and-learned sequences of actions that are triggered by specific patterns.

At each step we look for a small set of patterns and when we find one we follow the pre-designed heuristic and that moves us forward along the path towards the next goal. Of the billions of possible heuristics we only learn, remember, use and teach the small number that preserve the progress we have already made – these are our magic spells.

So where do these heuristics come from?

Well, we can search for them ourselves or we can learn them from someone else.  The first option holds the opportunity for new insights and possible breakthroughs – the second option is quicker!  Someone who designs or discovers a better heuristic is assured a place in history – most of us only ever learn ones that have been discovered or taught by others – it is a much quicker way to solve problems.  

So, for a bit of fun I compared the two approaches using a computer: the competitive-zero-intelligence-brute-force versus the collaborative-goal-directed-learned-and-shared-heuristics.  The heuristic method won easily every time!

The Rubik Cube is an example of a mechanical system: each of the twenty-six parts are interdependent, we cannot move one facet independently of the others, we can only move groups of nine at a time. Every action we make has nine consequences – not just one.  To solve the whole Rubik Cube system problem we must be mindful of the interdependencies and adopt methods that preserve what works while improving what does not.

The human body is a complex biological system. In medicine we have a phrase for this concept of preserving what works while improving what does not: “primum non nocere” which means “first of all do no harm”.  Doctors are masters of goal-directed heuristics; the medical model of diagnosis before prognosis before treatment is a goal-directed strategy and the common tactic is to quickly and accurately pattern-match from a small set of carefully selected data. 

In reality we all employ goal-directed-heuristics all of the time – it is the way our caveman brains have evolved.  Relative success comes from having a more useful set of heuristics – and these can be learned.  Just as with the Rubik Cube – it is quicker to learn what works from someone who can demonstrate that it works and can explain how it works – than to always laboriously work it out for ourselves.

An organisation is a bio-psycho-socio-economic system: a set of interdependent parts called people connected together by relationships and communication processes we call culture.  Improvement Science is a set of heuristics that have been discovered or designed to guide us safely and reliably towards any goal we choose to select – preserving what has been shown to work and challenging what does not.  Improvement Science does not define the path it only helps us avoid getting stuck, or going around in circles, or getting hopelessly lost while we are on the life-journey to our chosen goal.

And Improvement Science is learnable.

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More than the Sum or Less?

It is often assumed that if you combine world-class individuals into a team you will get a world-class team.

Meredith Belbin showed 30 years ago that you do not and it was a big shock at the time!

So, if world class individuals are not enough, what are the necessary and sufficient conditions for a world-class team?

The late Russell Ackoff described it perfectly – he said that if you take the best parts of all the available cars and put them together you do not get the best car – you do not even get a car. The parts are necessary but they are not sufficient – how the parts connect to each other and how they influence each other is more important.  These interdependencies are part of the system – and to understand a system requires understanding both the parts and their relationships.

A car is a mechanical system; the human body is a biological system; and a team is a social system. So to create a high performance, healthy, world class team requires that both the individuals and their relationships with each other are aligned and resonant.

When the parts are aligned we get more than the sum of the parts; and when they are not we get less.

If we were to define intelligence quotient as “an ability to understand and solve novel problems” then the capability of a team to solve novel problems is the collective intelligence.  Experience suggests that a group can appear to be less intelligent than any of the individual members.  The problem here is with the relationships between the parts – and the term that is often applied is “dysfunctional”.

The root cause is almost always distrustful attitudes which lead from disrespectful prejudices and that lead to discounting behaviour.  We learn these prejudices, attitudes and behaviours from each other and we reinforce them with years of practice.  But if they are learned then they can be un-learned. It is simple in theory, and it is possible in practice, but it is not easy.

So if we want to (dis)solve complex, novel problems thenwe need world-class problem solving teams; and to transform our 3rd class dysfunctional teams we must first learn to challenge respectfully our disrespectful behaviour.

The elephant is in the room!

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Can We See the Wood for the Trees?

“The Map is not the Territory” but it is a very useful because it provides a sense of perspective; the bigger picture; where you are; and what you would need to do to get from A to B.  A map can also provide the the fine detail, they way-points on your journey, and what to expect to see along the way.  I remember the first computer programs that would find a route from A to B for me and present it as a printed recipe for the journey; how far it was and, best of all, how long it would take – so I knew when to set off to be reasonably confident I could arrive on time.  Of course, there might always be unexpected holdups along the way but it was a big step forward. One problem was using the recipe as I drove, and another was when I accidently took a wrong turn, which is easy in unfamiliar surroundings with only a list of instructions to go by.  If I came off the intended track I would get lost – so I still needed the paper map as a backup. The trouble now was I did not alwasy know where I was on the map – because I was lost.  Two steps forward and one step backwards.  Now we have Google Maps and we can see what we will actually see on the way – before we even leave home!  And with SatNav we can get this map-reading-and-route-planning done for us in real time so if we choose to, are forced to, or accidentially take a wrong turn it can get us back-on-track. The days of heated debate between the map reader and the map needer have gone and it seems the only need we have for a map now is as a backup if the SatNav breaks down. (This did happen to me once, I didn’t have a map in the car and the only information I had was the postcode of my destination. I was pressed for time so I drove around randomly until I passed a shop that sold SatNavs and bought a new/spare one – entered the postcode and arrived at my intended destination just in time!).

So is the map dead?  Not at all – the value of a map in providing a sense of perspective, context and location is just as useful as ever. And there are many sorts of maps apart from the static, structural, geographical maps ones we are used to.  The really exciting maps are the dynamic ones – the functional maps.  These are maps that show how things are working and flowing, not only where they are.  Imagine if your SatNav had both a static map and was able to access a real time dynamic map of traffic flow. Just think how much more useful it could be? However, to achieve that implies that each person on the road would have to contribute both their position and their intended destination to a central system – isn’t that Big Brother back. Air traffic control (ATC) systems have done this for years for a very good reason: aeroplanes full of passengers are perishable goods – they can’t land anywhere they like and they can’t stay up there waiting to land for ever.  You can’t afford to have traffic jams with aeroplanes – so every pilot has to file a flight plan and will only be given ATC clearance to take off if their destination is capable of offering them a landing slot in an acceptable time frame – i.e. before the plane runs out of fuel! Static maps will always be needed to provide us with a sense of perspective – and in the future dynamic maps will revolutionise the way that we do everything – but only if we are prepared to behave collectively and share our data.  We want to see the wood, the trees and even the breeze through the leaves!