Archive for the ‘Three Wins’ Category

SFQP_enter_circle_middle_15576For a system to be both effective and efficient the parts need to work in synergy. This requires both alignment and collaboration.

Systems that involve people and processes can exhibit complex behaviour. The rules of engagement also change as individuals learn and evolve their beliefs and their behaviours.

The values and the vision should be more fixed. If the goalposts are obscure or oscillate then confusion and chaos is inevitable.

So why is collaborative alignment so difficult to achieve?

One factor has been mentioned. Lack of a common vision and a constant purpose.

Another factor is distrust of others. Our fear of exploitation, bullying, blame, and ridicule.

Distrust is a learned behaviour. Our natural inclination is trust. We have to learn distrust. We do this by copying trust-eroding behaviours that are displayed by our role models. So when leaders display these behaviours then we assume it is OK to behave that way too.  And we dutifully emulate.

The most common trust eroding behaviour is called discounting.  It is a passive-aggressive habit characterised by repeated acts of omission:  Such as not replying to emails, not sharing information, not offering constructive feedback, not asking for other perspectives, and not challenging disrespectful behaviour.

There are many causal factors that lead to distrust … so there is no one-size-fits-all solution to dissolving it.

One factor is ineptitude.

This is the unwillingness to learn and to use available knowledge for improvement.

It is one of the many manifestations of incompetence.  And it is an error of omission.

Whenever we are unable to solve a problem then we must always consider the possibility that we are inept.  We do not tend to do that.  Instead we prefer to jump to the conclusion that there is no solution or that the solution requires someone else doing something different. Not us.

The impossibility hypothesis is easy to disprove.  If anyone has solved the problem, or a very similar one, and if they can provide evidence of what and how then the problem cannot be impossible to solve.

The someone-else’s-fault hypothesis is trickier because proving it requires us to influence others effectively.  And that is not easy.  So we tend to resort to easier but less effective methods … manipulation, blame, bullying and so on.

A useful way to view this dynamic is as a set of four concentric circles – with us at the centre.

The outermost circle is called the ‘Circle of Ignorance‘. The collection of all the things that we do not know we do not know.

Just inside that is the ‘Circle of Concern‘.  These are things we know about but feel completely powerless to change. Such as the fact that the world turns and the sun rises and falls with predictable regularity.

Inside that is the ‘Circle of Influence‘ and it is a broad and continuous band – the further away the less influence we have; the nearer in the more we can do. This is the zone where most of the conflict and chaos arises.

The innermost is the ‘Circle of Control‘.  This is where we can make changes if we so choose to. And this is where change starts and from where it spreads.

SFQP_enter_circle_middle_15576So if we want system-level improvements in safety, flow, quality and productivity (or cost) then we need to align these four circles. Or rather the gaps in them.

We start with the gaps in our circle of control. The things that we believe we cannot do … but when we try … we discover that we can (and always could).

With this new foundation of conscious competence we can start to build new relationships, develop trust and to better influence others in a win-win-win conversation.

And then we can collaborate to address our common concerns – the ones that require coherent effort. We can agree and achieve our common purpose, vision and goals.

And from there we will be able to explore the unknown opportunities that lie beyond. The ones we cannot see yet.

media_video_icon_anim_150_wht_14142In a recent blog we explored the subject of learning styles and how a balance of complementary learning styles is needed to get the wheel-of-change turning.

Experience shows that many of us show a relative weakness in the ‘Activist’ quadrant of the cycle.

That implies we are less comfortable with learning-by-doing. Experimenting.

This behaviour is driven by a learned fear.  The fear-of-failure.

So when did we learn this fear?

Typically it is learned during childhood and is reinforced throughout adulthood.

The fear comes not from the failure though  … it comes from the emotional reaction of others to our supposed failure. The emotional backlash of significant others. Parents and parent-like figures such as school teachers.

Children are naturally curious and experimental and fearless.  That is how they learn. They make lots of mistakes – but they learn from them. Walking, talking, tying a shoelace, and so on.  Small mistakes do not created fear. We learn fear from others.

Full-of-fear others.

To an adult who has learned how to do many things it becomes easy to be impatient with the trial-and-error approach of a child … and typically we react in three ways:

1) We say “Don’t do that” when we see our child attempt something in a way we believe will not work or we believe could cause an accident. We teach them our fears.

2) We say “No” when we disagree with an idea or an answer that a child has offered. We discount them by discounting their ideas.

3) We say “I’ll do it” when we see a child try and fail. We discount their ability to learn how to solve problems and we discount our ability to let them.

Our emotional reaction is negative in all three cases and that is what teaches our child the fear of failure.

So they stop trying as hard.

And bit-by-bit they lose their curiosity and their courage.

We have now put them on the path to scepticism and cynicism.  Which is how we were taught.

This fear-of-failure brainwashing continues at school.

But now it is more than just fear of disappointing our parents; now it is fear of failing tests and exams … fear of the negative emotional backlash from peers, teachers and parents.

Some give up: they flee.  Others become competitive: they fight.

Neither strategies dissolve the source of the fear though … they just exacerbate it.

So it is rather too common to see very accomplished people paralysed with fear when circumstances dictate that they need to change in some way … to learn a new skill for example … to self-improve maybe.

Their deeply ingrained fear-of-failure surfaces and takes over control – and the fright/flight/fight behaviour is manifest.

So to get to the elusive win-win-win outcomes we want we have to weaken the fear-of-failure reflex … we need to develop a new habit … learning-by-doing.

The trick to this is to focus on things that fall 100% inside our circle of control … the Niggles that rank highest on our Niggle-o-Gram®.

And when we Study the top niggle; and then Plan the change; and then Do what we planned, and then Study effect of our action … then we learn-by-doing.

But not just by doing …. by Studying, Planning, Doing and Studying again.

Actions Speak not just to us but to everyone else too.

stick_figure_liking_it_150_wht_9170Common-sense tells us that to achieve system-wide improvement we need to grasp the “culture nettle”.

Most of us believe that culture drives attitudes; and attitudes drive behaviour; and behaviour drives improvement.

Therefore to get improvement we must start with culture.

And that requires effective leadership.

So our unspoken assumptions about how leaders motivate our behaviour seem rather important to understand.

In 1960 a book was published with the  title “The Human Side of Enterprise” which went right to the heart of this issue.   The author was Doug McGregor who was a social scientist and his explanation of why improvement appears to be so difficult in large organisations was a paradigm shift in thinking.  His book inspired many leaders to try a different approach – and they discovered that it worked and that enterprise-wide transformation followed.  The organisations that these early-adopters led evolved into commercial successes and more enjoyable places to work.

The new leaders learned to create the context for change – not to dictate the content.

Since then social scientists have disproved many other ‘common sense’ beliefs by applying a rigorous scientific approach and using robust evidence.

They have busted the culture-drives-change myth …. the evidence shows that it is the other way around … change drives culture.

And what changes first is behaviour.

We are social  animals …. most of us are much more likely to change our behaviour if we see other people doing the same.  We do not like being too different.

As we speak there is a new behaviour spreading – having a bucket of cold water tipped over your head as part of a challenge to raise money for charity.

This craze has a positive purpose … feeling good about helping others through donating money to a worthwhile cause … but most of us need a nudge to get us to do it.

Seeing well-known public figures having iced-water dumped on them on a picture or video shared through multiple, parallel, social media channels is a powerful cultural signal that says “This new behaviour is OK”.

Exhortation and threats are largely ineffective – fear will move people – it will scatter them, not align them. Shaming-and-blaming into behaving differently is largely ineffective too – it generates short-term anger and long-term resentment.

This is what Doug McGregor highlighted over half a century ago … and his message is timeless.

“.. the research evidence indicates quite clearly that skillful and sensitive membership behaviour is the real clue to effective group operation“.

Appreciating this critical piece of evidence opens a new door to system-wide improvement … one that we can all walk through:  Sharing improvement stories.

Sharing stories of actions that others have done and the benefits they achieved as a result; and also sharing stories of things that we ourselves have done and achieved.

Stories of small changes that delivered big benefits for others and for ourselves.  Win-win-wins. Stories of things that took little time and little effort to do because they fell inside our circles of control.

See-and-Share is an example of skillful and sensitive membership behaviour.

Effective leaders are necessary … yes … they are needed to create the context for change. It is we members who create and share the content.

Conscious_and_CompetentThis week I was made mindful again of a simple yet powerful model that goes a long way to explaining why we find change so difficult.

It is the conscious-competent model.

There are two dimensions which gives four combinations that are illustrated in the diagram.

We all start in the bottom left corner. We do not know what we do not know.  We are ignorant and incompetent and unconscious of the  fact.

Let us call that Blissful Ignorance.

Then suddenly we get a reality check. A shock. A big enough one to start us on the emotional roller coaster ride we call the Nerve Curve.

We become painfully aware of our ignorance (and incompetence). Conscious of it.

That is not a happy place to be and we have a well-developed psychological first line of defence to protect us. It is called Denial.

“That’s a load of rubbish!” we say.

But denial does not change reality and eventually we are reminded. Reality does not go away.

Our next line of defence is to shoot the messenger. We get angry and aggressive.

Who the **** are you to tell me that I do not know what I am doing!” we say.

Sometimes we are openly aggressive.  More often we use passive aggressive tactics. We resort to below-the-belt behind-the-back corridor-gossip behaviour.

But that does not change reality either.  And we are slowly forced to accept that we need to change. But not yet …

Our next line of defence is to bargain for more time (in the hope that reality will swing back in our favour).

There may be something in this but I am too busy at the moment … I will look at this  tomorrow/next week/next month/after my holiday/next quarter/next financial year/in my next job/when I retire!” we wheedle.

Our strategy usually does not work – it just wastes time – and while we prevaricate the crisis deepens. Reality is relentless.

Our last line of defence has now been breached and now we sink into depression and despair.

It is too late. Too difficult for me. I need rescuing. Someone help me!” we wail.

That does not work either. There is no one there. It is up to us. It is sink-or-swim time.

What we actually need now is a crumb of humility.

And with that we can start on the road to Know How. We start by unlearning the old stuff and then we can  replace it with the new stuff.  Step-by-step we climb out of the dark depths of Painful Awareness.

And then we get a BIG SURPRISE.

It is not as difficult as we assumed. And we discover that learning-by-doing is fun. And we find that demonstrating to others what we are learning is by far the most effective way to consolidate our new conscious competence.

And by playing to our strengths, with persistence, with practice and with reality-feedback our new know how capability gradually becomes second nature. Business as usual. The way we do things around here. The culture.

Then, and only then, will the improvement sustain … and spread … and grow.


Chimp_BattleImprovement 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“.

Chimp_Paradox_SmallSteve 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.

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.

There are three necessary parts before ANY improvement-by-design effort will gain traction. Omit any one of them and nothing happens.


1. A clear purpose and an outline strategic plan.

2. Tactical measurement of performance-over-time.

3. A generic Improvement-by-Design framework.

These are necessary minimum requirements to be able to safely delegate the day-to-day and week-to-week tactical stuff the delivers the “what is needed”.

These are necessary minimum requirements to build a self-regulating, self-sustaining, self-healing, self-learning win-win-win system.

And this is not a new idea.  It was described by Joseph Juran in the 1960’s and that description was based on 20 years of hands-on experience of actually doing it in a wide range of manufacturing and service organisations.

That is 20 years before  the terms “Lean” or “Six Sigma” or “Theory of Constraints” were coined.  And the roots of Juran’s journey were 20 years before that – when he started work at the famous Hawthorne Works in Chicago – home of the Hawthorne Effect – and where he learned of the pioneering work of  Walter Shewhart.

And the roots of Shewhart’s innovations were 20 years before that – in the first decade of the 20th Century when innovators like Henry Ford and Henry Gantt were developing the methods of how to design and build highly productive processes.

Ford gave us the one-piece-flow high-quality at low-cost production paradigm. Toyota learned it from Ford.  Gantt gave us simple yet powerful visual charts that give us an understanding-at-a-glance of the progress of the work.  And Shewhart gave us the deceptively simple time-series chart that signals when we need to take more notice.

These nuggets of pragmatic golden knowledge have been buried for decades under a deluge of academic mud.  It is nigh time to clear away the detritus and get back to the bedrock of pragmatism. The “how-to-do-it” of improvement. Just reading Juran’s 1964 “Managerial Breakthrough” illustrates just how much we now take for granted. And how ignorant we have allowed ourselves to become.

Acquired Arrogance is a creeping, silent disease – we slip from second nature to blissful ignorance without noticing when we divorce painful reality and settle down with our own comfortable collective rhetoric.

The wake-up call is all the more painful as a consequence: because it is all the more shocking for each one of us; and because it affects more of us.

The pain is temporary – so long as we treat the cause and not just the symptom.

The first step is to acknowledge the gap – and to start filling it in. It is not technically difficult, time-consuming or expensive.  Whatever our starting point we need to put in place the three foundation stones above:

1. Common purpose.
2. Measurement-over-time.
3. Method for Improvement.

Then the rubber meets the road (rather than the sky) and things start to improve – for real. Lots of little things in lots of places at the same time – facilitated by the Junior Managers. The cumulative effect is dramatic. Chaos is tamed; calm is restored; capability builds; and confidence builds. The cynics have to look elsewhere for their sport and the skeptics are able to remain healthy.

Then the Middle Managers feel the new firmness under their feet – where before there were shifting sands. They are able to exert their influence again – to where it makes a difference. They stop chasing Scotch Mist and start reporting real and tangible improvement – with hard evidence. And they rightly claim a slice of the credit.

And the upwelling of win-win-win feedback frees the Senior Managers from getting sucked into reactive fire-fighting and the Victim Vortex; and that releases the emotional and temporal space to start learning and applying System-level Design.  That is what is needed to deliver a significant and sustained improvement.

And that creates the stable platform for the Executive Team to do Strategy from. Which is their job.

It all starts with the Three Essentials:

1. A Clear and Common Constancy of Purpose.
2. Measurement-over-time of the Vital Metrics.
3. A Generic Method for Improvement-by-Design.

Black_Curtain_and_DoorA 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


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:


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.

This week I heard an inspiring story of applied Improvement Science that has delivered a win-win-win result. Not in a hospital. Not in a factory. In the red-in-tooth-and-claw reality of rural Kenya.

Africa has vast herds of four-hoofed herbivors called zebra and wildebeast who are accompanied by clever and powerful carnivors – called lions. The sun and rain make the grass grow; the herbivors eat the grass and the carnivors eat the herbivors. It is the way of Nature – and has been so for millions of years.

Enter Man a few thousand years ago with his domesticated cattle and the scene is set for conflict.  Domestic cattle are easy pickings for a hungry lion. Why spend a lot of energy chasing a lively zebra or wildebeast and run the risk of injury that would spell death-by-starvation? Lions are strong and smart but they do not have a social security system to look after the injured and sick. So why not go for the easier option?

Maasai_WarriorsSo Man protects his valuable cattle from hungry lions. And Man is inventive.  The cattle need to eat and sleep like the rest of us – so during the day the cattle are guarded by brave Maasai warriors armed with spears; and at night the cattle are herded into acacia thorn-ringed kraals and watched over by the boys of the tribe.

The lions come at night. Their sense of smell and sight is much better developed than Man’s.

The boys job is to deter the lions from killing the cattle.

And this conflict has been going on for thousands of years.

So when a hungry lion kills a poorly guarded cow or bull – then Man will get revenge and kill the lion.  Everyone loses.

But the application of Improvement Science is changing that ancient conflict.  And it was not done by a scientist or an animal welfare evangelist or a trained Improvementologist. It was done by young Maasai boy called Richard Turere.

He describes the why, the what and the how  … HERE.

Richard_TurereSo what was his breakthrough?

It was noticing that walking about with a torch  was a more effective lion deterrent than a fire or a scarecrow.

That was the chance discovery.  Chance favours the prepared mind.

So how do we create a prepared mind that is receptive to the hints that chance throws at us?

That is one purpose of learning Improvement Science.

What came after the discovery was not luck … it was design.

Richard used what was to hand to design a solution that achieved the required purpose – an effective lion deterrent – in a way that was also an efficient use of his lifetime.

He had bigger dreams than just protecting his tribe’s cattle. His dream was to fly in one of those silver things that he saw passing high over the savannah every day.

And sitting up every night waving a torch to deter hungry lions from eating his father’s cattle was not going to deliver that dream.

So he had to nail that Niggle before he could achieve his Nice If.

Like many budding inventors and engineers Richard is curious about how things work – and he learned a lot about electronics by dismantling his mother’s radio! It got him into a lot of trouble – but the knowledge and understanding that he gained was put to good use when he designed his “lion lights”.

This true story captures the essence of Improvement Science better than any blog, talk, lecture, course or book could.

That is why it was shared by those who learned of his improvement; then to TED; then to the World; then passed to me and I am passing it on too.  It is an inspiring story. It says that anyone can do this sort of thing if they choose to.

And it shows how Improvement Science spreads.  Through the grapevine.  And understanding how that works is part of the Science.

[Beep Beep] Bob tapped the “Answer” button on his smartphone – it was Lesley calling in for their regular ISP coaching session.

<Bob>Hi Lesley. How are you today? And which tunnel in the ISP Learning Labyrinth shall we explore today?

<Lesley>Hi Bob. I am OK thank you. Can we invest some time in the Engagement Maze?

<Bob>OK. Do you have a specific example?

<Lesley>Sort of. This week I had a conversation with our Chief Executive about the potential of Improvement Science and the reply I got was “I am convinced by what you say but it is your colleagues who need to engage. If you have not succeeded in convincing them then how can I?” I was surprised by that response and slightly niggled because it had an uncomfortable nugget of truth in it.

<Bob>That sounds like the sound a wise leader who understands that the “power” to make things happen does not sit wholly in the lap of those charged with accountability.

<Lesley> I agree. And at the same time everything that the “Top Team” suggest gets shot down in flames by a small and very vocal group of my more skeptical colleagues.

<Bob>Ah ha!  It sounds like the Victim Vortex is causing trouble here.

<Lesley>The Victim Vortex?

<Bob>Yes. Let me give you an example. One of the common initiators of the Victim Vortex is the data flow part of a complex system design. The Sixth Flow. So can I ask you: “How are new information systems developed in your organization?

<Lesley>Wow! You hit the nail on the head first time!  Just this week there has been another firestorm of angry emails triggered by yet another silver-bullet IT system being foisted on us!

<Bob>Interesting use of language Lesley. You sound quite “niggled”.

<Lesley>I am.  Not by the constant “drizzle of IT magic” – that is irritating enough – but more by the cynical reaction of my peers.

<Bob>OK. This sounds like good enough example of the Victim Vortex. What do you expect the outcome will be?

<Lesley>Well if past experience is a predictor for future performance – an expensive failure, more frustration and a deeper well of cynicism.

<Bob>Frustrating for whom?

<Lesley>Everyone. The IT department as well. It feels like we are all being sucked into a lose-lose-lose black hole of depression and despair!

<Bob>A very good description of the Victim Vortex.

<Lesley>So the Victim Vortex is an example of the Drama Triangle acting on an organizational level?

tornada_150_wht_10155<Bob>Yes. Visualize a cultural tornado. The energy that drives it is the emotional  currency spent in playing the OK – Not OK Games.  It is a self-fueling system,  a stable design, very destructive and very resistant to change.

<Lesley>That metaphor works really well for me!

<Bob>A similar one is a whirlpool – a water vortex. If you were out swimming and were caught up in a whirlpool what are your exit strategy options?

<Lesley>An interesting question.  I have never had that experience and would not want it – it sounds rather hazardous. Let me think.  If I do nothing I will just get swept around in the chaos and I am at risk of  getting bashed, bruised and then sucked under.

<Bob>Yes – you would probably spend all your time and energy just treading water and dodging the flotsam and jetsam that has been sucked into the Vortex. That is what most people do. It is called the Hamster Wheel effect.

<Lesley>So another option is to actively swim towards the middle of the Vortex – the end would at least be quick! But that is giving up and adopting the Hopelessness attitude of burned out Victim.  That would be the equivalent of taking voluntary redundancy or early retirement. It is not my style!

<Bob>Yes. It does not solve the problem either. The Vortex is always hoovering up new Victims. It is insatiable.

<Lesley> And another option would be to swim with the flow to avoid being “got” from behind. That would be seem sensible and is possible; and at least I would feel better for doing something. I might even escape if I swim fast enough!

<Bob>That is indeed what some try. The movers and shakers. The pace setters. The optimists. The extrovert leaders. The problem is that it makes the Vortex spin even faster.  It actually makes the Vortex bigger,  more chaotic and more dangerous than before.

<Lesley>Yes – I can see that.  So my other option is to swim against the flow in an attempt to slow the Vortex down. Would that work?

<Bob>If everyone did that at the same time it might but that is unlikely to happen spontaneously. If you could achieve that degree of action alignment you would not have a Victim Vortex in the first place. Trying to do it alone is ineffective – you tire very quickly, the other Victims bash into you, you slow them down, and then you all get sucked down the Plughole of Despair.

<Lesley>And I suppose a small group of like-minded champions who try to swim-against the flow might last longer if they stick together but even then eventually they would get bashed up and broken up too. I have seen that happen.  And that is probably where our team are heading at the moment. I am out of options. Is it impossible to escape the Victim Vortex?

<Bob>There is one more direction you can swim.

<Lesley>Um? You mean across the flow heading directly away from the center?

<Bob>Exactly. Consider that option.

<Lesley>Well, it would still be hard work and I would still be going around with the Vortex and I would still need to watch out for flotsam but every stroke I make would take me further from the center. The chaos would get gradually less and eventually I would be in clear water and out of danger.  I could escape the Victim Vortex!

<Bob>Yes. And what would happen if others saw you do that and did the same?

<Lesley>The Victim Vortex would dissipate!

<Bob>Yes. So that is your best strategy. It is a win-win-win strategy too. You can lead others out of the Victim Vortex.

<Lesley>Wow! That is so cool!  So how would I apply that metaphor to the Information System niggle?

<Bob>I will leave you to ponder on that.  Think about it as a design assignment. The design of the system that generates IT solutions that are fit-for-purpose.

<Lesley> Somehow I knew you were going to say that! I have my squared-paper and sharpened pencil at the ready.  Yes – an improvement-by-design assignment. Thank you once again Bob. This ISP course is the business!

stick_figure_open_cupboard_150_wht_8038Improvement implies change.

Change requires motivation.

And there are two flavours of motivation juice – Fear and Fuel

Fear is the emotion that comes from anticipated loss in the future.  Loss means some form of damage. Physical, psychological or social harm.  We fear loss of peer-esteem and we fear loss of self-esteem … almost more than we fear physical harm.

Our fear of anticipated loss may be based on reality. Our experience of actual loss in the past.  We remember the emotional pain and we learn from past pain to fear future loss.

Our fear of anticipated loss may also be fueled by rhetoric.  The doom-mongering of the Shroud-Wavers, the Nay-Sayers, the Skeptics and the Cynics.

And there are examples where the rhetorical fear is deliberately generated to drive the fear-of-reality to “the solution” – which of course we have to pay dearly for. This is Machiavellian mass manipulation for commercial gain.

“Fear of germs, fear of fatness, fear of the invisible enemies outside and inside”.

Generating and ameliorating fear is big business. It is a Burn-and-Scrape design.

What we are seeing here is the Drama Triangle operating on a massive scale. The Persecutors create the fear, the Victims run away and the Persecutors then switch role to Rescuers and offer to sell the terrified-and-now-compliant Victims “the  solution” to their fear.  The Victims do not learn.  That is not the purpose – because that would end the Game and derail the Gravy Train.

So fear is not an effective way to motivate for sustained improvement,  and we have ample evidence to support that statement!  It might get us started, but it won’t keep us going.

The Burn-and-Scrape design that we see everywhere is a fear-driven-design.

Any improvements are transitory and usually only achieved at the emotional expense of a passionate idealist. When they get too tired to push any more the toast gets burnt again because the toaster is perfectly designed to burn toast.  Not intentionally designed to burn the toast but perfectly designed to nevertheless.

The use of Delusional Ratios and Arbitrary Targets (DRATs) is a fear-based-design-strategy. It ensures the Fear Game and Gravy Train continue.

And fear has a frightening cost. The cost of checking-and-correcting. The cost of the defensive-bureaucracy that may catch errors before too much local harm results but which itself creates unmeasurable global harm in a different way – by hoovering up the priceless human resource of life-time – like an emotional black hole.

The cost of errors. The cost of queues. The list of fear-based-design costs is long.

A fear-based-design for delivering improvement is a poor design.

So we need a better design.

And a better one is based on a positive-attractive-emotional force pulling us forwards into the future. The anticipation of gains for all. A win-win-win design.

Win-win-win design starts with the Common Purpose: the outcomes that everyone wants; and the outcomes that no-one wants.  We need both.  This balance creates alignment of effort on getting the NiceIfs (the wants) while avoiding the NoNos (the do not wants).

Then we ask the simple question: “What is preventing us having our win-win-win outcome now?

The blockers are the parts of our current design that we need to change: our errors of omission and our errors of commission.  Our gaps and our gaffes.

And to change them we need to be clear what they are; where they are and how they came to be there … and that requires a diagnostic skill that is one of our errors of omission. We have never learned how to diagnose our process design flaws.

Another common blocker is that we believe that a win-win-win outcome is impossible. This is a learned belief. And it is a self-fulfilling prophesy.

We may also believe that all swans are white because we have never seen a black swan – even though we know, in principle, that a black swan could be possible.

Rhetoric and Reality are not the same thing.  Feeling it could be possible and knowing that it actually is possible are different emotions. We need real evidence to challenge our life-limiting rhetoric.

Weary and wary skeptics crave real evidence not rhetorical exhortation.

So when that evidence is presented – and the Impossibility Hypothesis is disproved – then an emotional shock is inevitable.  We are now on the emotional roller-coaster called the Nerve Curve.  And the deeper our skepticism the bigger the shock.

After the shock we characteristically do one of three things:

1. We discount the evidence and go into denial.  We refuse to challenge our own rhetoric. Blissful ignorance is attractive.  The gap between intent and impact is scary.

2. We go quiet because we are now stuck in the the painful awareness of the transition zone between the past and the future. The feelings associated with the transition are anxiety and depression. We don’t want to go back and we don’t know how to go forwards.

3. We sit up, we take notice, we listen harder, we rub our chins, our minds race as we become more and more excited. The feelings associated with the stage of resolution are curiosity, excitement and hope.

It is actually a sequence and it is completely normal.

And those who reach Stage 3 of the Nerve Curve say things like “We have food for thought;  we feel inspired; our passion is re-ignited; we now have a beacon of hope for the future.

That is the flavour of motivation-juice that is needed to fuel the improvement-by-design engine and to deliver win-win-win designs that are both surprising and self-sustaining.

And what actually changes our belief of what is possible is when we learn to do it for ourselves. For real.

That is Improvement Science in action. It is a pragmatic science.

[Bing Bong]  The sound bite heralded Leslie joining the regular Improvement Science mentoring session with Bob.  They were now using web-technology to run virtual meetings because it allows a richer conversation and saves a lot of time. It is a big improvement.

<Bob> Hi Lesley, how are you today?

<Leslie> OK thank you Bob.  I have a thorny issue to ask you about today. It has been niggling me even since we started to share the experience we are gaining from our current improvement-by-design project.

<Bob> OK. That sounds interesting. Can you paint the picture for me?

<Leslie> Better than that – I can show you the picture, I will share my screen with you.

DRAT_01 <Bob> OK. I can see that RAG table. Can you give me a bit more context?

<Leslie> Yes. This is how our performance management team have been asked to produce their 4-weekly reports for the monthly performance committee meetings.

<Bob> OK. I assume the “Period” means sequential four week periods … so what is Count, Fail and Fail%?

<Leslie> Count is the number of discharges in that 4 week period, Fail is the number whose length of stay is longer than the target, and Fail% is the ratio of Fail/Count for each 4 week period.

<Bob> It looks odd that the counts are all 28.  Is there some form of admission slot carve-out policy?

<Leslie> Yes. There is one admission slot per day for this particular stream – that has been worked out from the average historical activity.

<Bob> Ah! And the Red, Amber, Green indicates what?

<Leslie> That is depends where the Fail% falls in a set of predefined target ranges; less than 5% is green, 5-10% is Amber and more than 10% is red.

<Bob> OK. So what is the niggle?

<Leslie>Each month when we are in the green we get no feedback – a deafening silence. Each month we are in amber we get a warning email.  Each month we are in the red we have to “go and explain ourselves” and provide a “back-on-track” plan.

<Bob> Let me guess – this feedback design is not helping much.

<Leslie> It is worse than that – it creates a perpetual sense of fear. The risk of breaching the target is distorting people’s priorities and their behaviour.

<Bob> Do you have any evidence of that?

<Leslie> Yes – but it is anecdotal.  There is a daily operational meeting and the highest priority topic is “Which patients are closest to the target length of stay and therefore need to have their  discharge expedited?“.

<Bob> Ah yes.  The “target tail wagging the quality dog” problem. So what is your question?

<Leslie> How do we focus on the cause of the problem rather than the symptoms?  We want to be rid of the “fear of the stick”.

<Bob> OK. What you have hear is a very common system design flaw. It is called a DRAT.

<Leslie> DRAT?

<Bob> “Delusional Ratio and Arbitrary Target”.

<Leslie> Ha! That sounds spot on!  “DRAT” is what we say every time we miss the target!

<Bob> Indeed.  So first plot this yield data as a time series chart.

<Leslie> Here we go.

DRAT_02<Bob>Good. I see you have added the cut-off thresholds for the RAG chart. These 5% and 10% thresholds are arbitrary and the data shows your current system is unable to meet them. Your design looks incapable.

<Leslie>Yes – and it also shows that the % expressed to one decimal place is meaningless because there are limited possibilities for the value.

<Bob> Yes. These are two reasons that this is a Delusional Ratio; there are quite a few more.

DRAT_03<Leslie> OK  and if I plot this as an Individuals charts I can see that this variation is not exceptional.

<Bob> Careful Leslie. It can be dangerous to do this: an Individuals chart of aggregate yield becomes quite insensitive with aggregated counts of relatively rare events, a small number of levels that go down to zero, and a limited number of points.  The SPC zealots are compounding the problem and plotting this data as a C-chart or a P-chart makes no difference.

This is all the effect of the common practice of applying  an arbitrary performance target then counting the failures and using that as means of control.

It is poor feedback loop design – but a depressingly common one.

<Leslie> So what do we do? What is a better design?

<Bob> First ask what the purpose of the feedback is?

<Leslie> To reduce the number of beds and save money by forcing down the length of stay so that the bed-day load is reduced and so we can do the same activity with fewer beds and at the same time avoid cancellations.

<Bob> OK. That sounds reasonable from the perspective of a tax-payer and a patient. It would also be a more productive design.

<Leslie> I agree but it seems to be having the opposite effect.  We are focusing on avoiding breaches so much that other patients get delayed who could have gone home sooner and we end up with more patients to expedite. It is like a vicious circle.  And every time we fail we get whacked with the RAG stick again. It is very demoralizing and it generates a lot of resentment and conflict. That is not good for anyone – least of all the patients.

<Bob>Yes.  That is the usual effect of a DRAT design. Remember that senior managers have not been trained in process improvement-by-design either so blaming them is also counter-productive.  We need to go back to the raw data. Can you plot actual LOS by patient in order of discharge as a run chart.


<Bob> OK – is the maximum LOS target 8 days?

<Leslie> Yes – and this shows  we are meeting it most of the time.  But it is only with a huge amount of effort.

<Bob> Do you know where 8 days came from?

<Leslie> I think it was the historical average divided by 85% – someone read in a book somewhere that 85%  average occupancy was optimum and put 2 and 2 together.

<Bob> Oh dear! The “85% Occupancy is Best” myth combined with the “Flaw of Averages” trap. Never mind – let me explain the reasons why it is invalid to do this.

<Leslie> Yes please!

<Bob> First plot the data as a run chart and  as a histogram – do not plot the natural process limits yet as you have done. We need to do some validity checks first.


<Leslie> Here you go.

<Bob> What do you see?

<Leslie> The histogram  has more than one peak – and there is a big one sitting just under the target.

<Bob>Yes. This is called the “Horned Gaussian” and is the characteristic pattern of an arbitrary lead-time target that is distorting the behaviour of the system.  Just as you have described subjectively. There is a smaller peak with a mode of 4 days and are a few very long length of stay outliers.  This multi-modal pattern means that the mean and standard deviation of this data are meaningless numbers as are any numbers derived from them. It is like having a bag of mixed fruit and then setting a maximum allowable size for an unspecified piece of fruit. Meaningless.

<Leslie> And the cases causing the breaches are completely different and could never realistically achieve that target! So we are effectively being randomly beaten with a stick. That is certainly how it feels.

<Bob> They are certainly different but you cannot yet assume that their longer LOS is inevitable. This chart just says – “go and have a look at these specific cases for a possible cause for the difference“.

<Leslie> OK … so if they are from a different system and I exclude them from the analysis what happens?

<Bob> It will not change reality.  The current design of  this process may not be capable of delivering an 8 day upper limit for the LOS.  Imposing  a DRAT does not help – it actually makes the design worse! As you can see. Only removing the DRAT will remove the distortion and reveal the underlying process behaviour.

<Leslie> So what do we do? There is no way that will happen in the current chaos!

<Bob> Apply the 6M Design® method. Map, Measure and Model it. Understand how it is behaving as it is then design out all the causes of longer LOS and that way deliver with a shorter and less variable LOS. Your chart shows that your process is stable.  That means you have enough flow capacity – so look at the policies. Draw on all your FISH training. That way you achieve your common purpose, and the big nasty stick goes away, and everyone feels better. And in the process you will demonstrate that there is a better feedback design than DRATs and RAGs. A win-win-win design.

<Leslie> OK. That makes complete sense. Thanks Bob!  But what you have described is not part of the FISH course.

<Bob> You are right. It is part of the ISP training that comes after FISH. Improvement Science Practitioner.

<Leslie> I think we will need to get a few more people trained in the theory, techniques and tools of Improvement Science.

<Bob> That would appear to be the case. They will need a real example to see what is possible.

<Leslie> OK. I am on the case!

stick_figures_pulling_door_150_wht_6913It is surprising how competitive most people are. We are constantly comparing ourselves with others and using what we find to decide what to do next. Groan or Gloat.  Chase or Cruise.

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

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

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

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

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

So we need a better one.

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

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

We flip the “battle” into a “race”.

green_leader_running_the_race_150_wht_3444To do that we need:

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

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

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

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

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

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

figure_juggling_balls_150_wht_4301Improvement Science is like three-ball juggling.

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

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

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

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

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

The answer is surprising.

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

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

Here is how …

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

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

And you need something else.

You need someone to teach you.

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

juggling_at_Keele_June_2013Those are the necessary-and-sufficient requirements to learn to juggle in 10 minutes.

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

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

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

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

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

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

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

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

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

Anyone with much experience of  change will testify that one of the hardest parts is sustaining the hard won improvement.

The typical story is all too familiar – a big push for improvement, a dramatic improvement, congratulations and presentations then six months later it is back where it was before but worse. The cynics are feeding on the corpse of the dead change effort.

The cause of this recurrent nightmare is a simple error of omission.

Failure to complete the change sequence. Missing out the last and most important step. Step 6 – Maintain.

Regular readers may remember the story of the pharmacy project – where a skeptical department were surprised and delighted to discover that zero-cost improvement was achievable and that a win-win-win outcome was not an impossible dream.

Enough time has now passed to ask the question: “Was the improvement sustained?”

TTO_Yield_Nov12_Jun13The BaseLine© chart above shows their daily performance data on their 2-hour turnaround target for to-take-out prescriptions . The weekends are excluded because the weekend system is different from the weekday system. The first split in the data in Jan 2013 is when the improvement-by-design change was made. Step 4 on the 6M Design® sequence – Modify.

There was an immediate and dramatic improvement in performance that was sustained for about six weeks – then it started to drift back. Bit by Bit.  The time-series chart flags it clearly.

So what happened next?

The 12-week review happened next – and it was done by the change leader – in this case the Inspector/Designer/Educator.  The review data plotted as a time-series chart revealed instability and that justified an investigation of the root cause: which was that the final and critical step had not been completed as recommended. The inner feedback loop was missing. Step 6 – Maintain was not in place.

The outer feedback loop had not been omitted. That was the responsibility of the experienced change leader.

And the effect of closing the outer-loop is clearly shown by the third segment: a restoration of stability and improved capability. The system is again delivering the improvement it was designed to deliver.

What does this lesson teach us?

The message here is that the sponsors of improvement have essential parts to play in the initiation and the maintenance of change and improvement. If they fail in their responsibility then the outcome is inevitable and predictable. Mediocrity and cynicism.

Part 1: Setting the clarity and constancy of common purpose.

Without a clear purpose then alignment, focus and effectiveness are thwarted. Purpose that changes frequently is not a purpose – it is reactive knee-jerk politics.  Constancy of purpose is required because improvement takes time. There is always a lag so moving the target while the arrow is in flight is both dangerous and leads to disengagement. Establishing common ground is essential to avoiding the time-wasting discussion and negotiation that is inevitable when opinions differ – which they always do.

Part 2: Respectful challenge.

Effective change leadership requires an ability to challenge from a position of mutual respect.  Telling people what to do is not leadership – it is dictatorship.  Dodging the difficult conversations and passing the buck to others is not leadership – it is ineffective delegation. Asking people what they want to do is not leadership – it is abdication of responsibility.  People need their leaders to challenge them and to respect them at the same time.  It is not a contradiction.  It is possible to do both.

And one way that a leader of change can challenge with respect is to expose the need for change; to create the context for change; and then to commit to holding those charged with change to account. And to make it clear at the start what their expectation is as a leader – and what the consequences of disappointment are.

It is a delight to see individuals,  teams, departments and organisations blossom and grow when the context of change is conducive; at it is disappointing to see them wither and shrink when the context of change is laced with cynicide – the toxic product of cynicism.

So what is the next step?

What could an aspirant change leader do to get this for themselves and their organisations?

One option is to become a Student of Improvementology® – and they can do that here.

line_figure_phone_400_wht_9858[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.

one_on_one_challenge_150_wht_8069Improvement 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.

plant_growing_anim_150_wht_9902And 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!

growing_blue_vine_dissolve_150_wht_244So 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.

press_on_screen_anim_150_wht_7028Today is an important day.

The Robert Francis QC Report and recommendations from the Mid-Staffordshire Hospital Crisis has been published – and it is a sobering read.  The emotions that just the executive summary evoked in me were sadness, shame and anger.  Sadness for the patients, relatives, and staff who have been irreversibly damaged; shame that the clinical professionals turned a blind-eye; and anger that the root cause has still not been exposed to public scrutiny.

Click here to get a copy of the RFQC Report Executive Summary.

Click here to see the video of RFQC describing his findings. 

The root cause is ignorance at all levels of the NHS.  Not stupidity. Not malevolence. Just ignorance.

Ignorance of what is possible and ignorance of how to achieve it.

RFQC rightly focusses his recommendations on putting patients at the centre of healthcare and on making those paid to deliver care accountable for the outcomes.  Disappointingly, the report is notably thin on the financial dimension other than saying that financial targets took priority over safety and quality.  He is correct. They did. But the report does not say that this is unnecessary – it just says “in future put safety before finance” and in so doing he does not challenge the belief that we are playing a zero-sum-game. The  assumotion that higher-quality-always-costs-more.

This assumption is wrong and can easily be disproved.

A system that has been designed to deliver safety-and-quality-on-time-first-time-and-every-time costs less. And it costs less because the cost of errors, checking, rework, queues, investigation, compensation, inspectors, correctors, fixers, chasers, and all the other expensive-high-level-hot-air-generation-machinery that overburdens the NHS and that RFQC has pointed squarely at is unnecessary.  He says “simplify” which is a step in the right direction. The goal is to render it irrelevent.

The ignorance is ignorance of how to design a healthcare system that works right-first-time. The fact that the Francis Report even exists and is pointing its uncomfortable fingers-of-evidence at every level of the NHS from ward to government is tangible proof of this collective ignorance of system design.

And the good news is that this collective ignorance is also unnecessary … because the knowledge of how to design safe-and-affordable systems already exists. We just have to learn how. I call it 6M Design® – but  the label is irrelevent – the knowledge exists and the evidence that it works exists.

So here are some of the RFQC recommendations viewed though a 6M Design® lens:       

1.131 Compliance with the fundamental standards should be policed by reference to developing the CQC’s outcomes into a specification of indicators and metrics by which it intends to monitor compliance. These indicators should, where possible, be produced by the National Institute for Health and Clinical Excellence (NICE) in the form of evidence-based procedures and practice which provide a practical means of compliance and of measuring compliance with fundamental standards.

This is the safety-and-quality outcome specification for a healthcare system design – the required outcome presented as a relevent metric in time-series format and qualified by context.  Only a stable outcome can be compared with a reference standard to assess the system capability. An unstable outcome metric requires inquiry to understand the root cause and an appropriate action to restore stability. A stable but incapable outcome performance requires redesign to achieve both stability and capability. And if  the terms used above are unfamiliar then that is further evidence of system-design-ignorance.   
1.132 The procedures and metrics produced by NICE should include evidence-based tools for establishing the staffing needs of each service. These measures need to be readily understood and accepted by the public and healthcare professionals.

This is the capacity-and-cost specification of any healthcare system design – the financial envelope within which the system must operate. The system capacity design works backwards from this constraint in the manner of “We have this much resource – what design of our system is capable of delivering the required safety and quality outcome with this capacity?”  The essence of this challenge is to identify the components of poor (i.e. wasteful) design in the existing systems and remove or replace them with less wasteful designs that achieve the same or better quality outcomes. This is not impossible but it does require system diagnostic and design capability. If the NHS had enough of those skills then the Francis Report would not exist.

1.133 Adoption of these practices, or at least their equivalent, is likely to help ensure patients’ safety. Where NICE is unable to produce relevant procedures, metrics or guidance, assistance could be sought and commissioned from the Royal Colleges or other third-party organisations, as felt appropriate by the CQC, in establishing these procedures and practices to assist compliance with the fundamental standards.

How to implement evidence-based research in the messy real world is the Elephant in the Room. It is possible but it requires techniques and tools that fall outside the traditional research and audit framework – or rather that sit between research and audit. This is where Improvement Science sits. The fact that the Report only mentions evidence-based practice and audit implies that the NHS is still ignorant of this gap and what fills it – and so it appears is RFQC.   

1.136 Information needs to be used effectively by regulators and other stakeholders in the system wherever possible by use of shared databases. Regulators should ensure that they use the valuable information contained in complaints and many other sources. The CQC’s quality risk profile is a valuable tool, but it is not a substitute for active regulatory oversight by inspectors, and is not intended to be.

Databases store data. Sharing databases will share data. Data is not information. Information requires data and the context for that data.  Furthermore having been informed does not imply either knowledge or understanding. So in addition to sharing information, the capability to convert information-into-decision is also required. And the decisions we want are called “wise decisions” which are those that result in actions and inactions that lead inevitably to the intended outcome.  The knowledge of how to do this exists but the NHS seems ignorant of it. So the challenge is one of education not of yet more investigation.

1.137 Inspection should remain the central method for monitoring compliance with fundamental standards. A specialist cadre of hospital inspectors should be established, and consideration needs to be given to collaborative inspections with other agencies and a greater exploitation of peer review techniques.

This is audit. This is the sixth stage of a 6M Design® – the Maintain step.  Inspectors need to know what they are looking for, the errors of commission and the errors of omission;  and to know what those errors imply and what to do to identify and correct the root cause of these errors when discovered. The first cadre of inspectors will need to be fully trained in healthcare systems design and healthcare systems improvement – in short – they need to be Healthcare Improvementologists. And they too will need to be subject to the same framework of accreditation, and accountability as those who work in the system they are inspecting.  This will be one of the greatest of the challenges. The fact that the Francis report exists implies that we do not have such a cadre. Who will train, accredit and inspect the inspectors? Who has proven themselves competent in reality (not rhetorically)?

1.163 Responsibility for driving improvement in the quality of service should therefore rest with the commissioners through their commissioning arrangements. Commissioners should promote improvement by requiring compliance with enhanced standards that demand more of the provider than the fundamental standards.

This means that commissioners will need to understand what improvement requires and to include that expectation in their commissioning contracts. This challenge is even geater that the creation of a “cadre of inspectors”. What is required is a “generation of competent commissioners” who are also experienced and who have demonstrated competence in healthcare system design. The Commissioners-of-the-Future will need to be experienced healthcare improvementologists.

The NHS is sick – very sick. The medicine it needs to restore its health and vitality does exist – and it will not taste very nice – but to withold an effective treatment for an serious illness on that basis is clinical negligence.

It is time for the NHS to look in the mirror and take the strong medicine. The effect is quick – it will start to feel better almost immediately. 

To deliver safety and quality and quickly and affordably is possible – and if you do not believe that then you will need to muster the humility to ask to have the how demonstrated.



no_smoking_400_wht_6805It is not easy to kick a habit. We all know that. And for some reason the ‘bad’ habits are harder to kick than the ‘good’ ones. So what is bad about a ‘bad habit’ and why is it harder to give up? Surely if it was really bad it would be easier to give up?

Improvement is all about giving up old ‘bad’ habits and replacing them with new ‘good’ habits – ones that will sustain the improvement. But there is an invisible barrier that resists us changing any habit – good or bad. And it is that barrier to habit-breaking that we need to understand to succeed. Luck is not a reliable ally.

What does that habit-breaking barrier look like?

The problem is that it is invisible – or rather it is emotional – or to be precise it is chemical.

Our emotions are the output of a fantastically complex chemical system – our brains. And influencing the chemical balance of our brains can have a profound effect on our emotions.  That is how anti-depressants work – they very slightly adjust the chemical balance of every part of our brains. The cumulative effect is that we feel happier.  Nicotine has a similar effect.

And we can achieve the same effect without resorting to drugs or fags – and we can do that by consciously practising some new mental habits until they become ingrained and unconscious. We literally overwrite the old mental habit.

So how do we do this?

First we need to make the mental barrier visible – and then we can focus our attention on eroding it. To do that we need to remove the psychological filter that we all use to exclude our emotions. It is rather like taking off our psychological sunglasses.

When we do that the invisible barrier jumps into view: illuminated by the glare of three negative emotions.  Sadness, fear, and anxiety.  So whenever we feel any of these we know there is a barrier to improvement hiding  the emotional smoke. This is the first stage: tune in to our emotions.

The next step is counter-intuitive. Instead of running away from the negative feeling we consciously flip into a different way of thinking.  We actively engage with our negative feelings – and in a very specific way. We engage in a detached, unemotional, logical, rational, analytical  ‘What caused that negative feeling?’ way.

We then focus on the causes of the negative emotions. And when we have the root causes of our Niggles we design around them, under them, and over them.  We literally design them out of our heads.

The effect is like magic.

And this week I witnessed a real example of this principle in action.

figure_pressing_power_button_150_wht_10080One team I am working with experienced the Power of Improvementology. They saw the effect with their own eyes.  There were no computers in the way, no delays, no distortion and no deletion of data to cloud the issue. They saw the performance of their process jump dramatically – from a success rate of 60% to 96%!  And not just the first day, the second day too.  “Surprised and delighted” sums up their reaction.

So how did we achieve this miracle?

We just looked at the process through a different lens – one not clouded and misshapen by old assumptions and blackened by ignorance of what is possible.  We used the 6M Design® lens – and with the clarity of insight it brings the barriers to improvement became obvious. And they were dissolved. In seconds.

Success then flowed as the Dam of Disbelief crumbled and was washed away.

figure_check_mark_celebrate_anim_150_wht_3617The chaos has gone. The interruptions have gone. The expediting has gone. The firefighting has gone. The complaining has gone.  These chronic Niggles have have been replaced by the Nuggets of calm efficiency, new hope and visible excitement.

And we know that others have noticed the knock-on effect because we got an email from our senior executive that said simply “No one has moaned about TTOs for two days … something has changed.”    

That is Improvementology-in-Action.


pin_marker_lighting_up_150_wht_6683Last 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.figure_juggling_time_150_wht_4437

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.

single_file_line_PA_150_wht_3113But 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!

stick_figure_sitting_confused_150_wht_2587So 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.

stick_figure_run_clock_150_wht_7094Now 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.

CarveOut_00We 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.

time_moving_fast_150_wht_10108On 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.

CarveOut_01This 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.

CarveOut_02This 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.

CarveOut_03And 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.

CarveOut_04And 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.  

CarveOut_07And 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? 

puzzle_lightbulb_build_PA_150_wht_4587And 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. 


stick_figure_shovel_snow_anim_150_wht_9579It does not seem to take much to bring a real system to an almost standstill.  Six inches of snow falling between 10 AM and 2 PM in a Friday in January seems to be enough!

It was not so much the amount of snow – it was the timing.  The decision to close many schools was not made until after the pupils had arrived – and it created a logistical nightmare for parents. 

Many people suddenly needed to get home before they expected which created an early rush hour and gridlocked the road system.

The same number of people travelled the same distance in the same way as they would normally – it just took them a lot longer.  And the queues created more problems as people tried to find work-arounds to bypass the traffic jams.

How many thousands of hours of life-time was wasted sitting in near-stationary queues of cars? How many millions of poundsworth of productivity was lost? How much will the catchup cost? 

And yet while we grumble we shrug our shoulders and say “It is just one of those things. We cannot control the weather. We just have to grin and bear it.”  

Actually we do not have to. And we do not need a weather machine to control the weather. Mother Nature is what it is.

Exactly the same behaviour happens in many systems – and our conclusion is the same.  We assume the chaos and queues are inevitable.

They are not.

They are symptoms of the system design – and specifically they are the inevitable outcomes of the time-design.

But it is tricky to visualise the time-design of a system.  We can see the manifestations of the poor time-design, the queues and chaos, but we do not so easily perceive the causes. So the poor time-design persists. We are not completely useless though; there are lots of obvious things we can do. We can devise ingenious ways to manage the queues; we can build warehouses to hold the queues; we can track the jobs in the queues using sophisticated and expensive information technology; we can identify the hot spots; we can recruit and deploy expediters, problem-solvers and fire-fighters to facilitate the flow through the hottest of them; and we can pump capacity and money into defences, drains and dramatics. And our efforts seem to work so we congratulate ourselves and conclude that these actions are the only ones that work.  And we keep clamouring for more and more resources. More capacity, MORE capacity, MORE CAPACITY.

Until we run out of money!

And then we have to stop asking for more. And then we start rationing. And then we start cost-cutting. And then the chaos and queues get worse. 

And all the time we are not aware that our initial assumptions were wrong.

The chaos and queues are not inevitable. They are a sign of the time-design of our system. So we do have other options.  We can improve the time-design of our system. We do not need to change the safety-design; nor the quality-design; nor the money-design.  Just improving the time-design will be enough. For now.

So the $64,000,000 question is “How?”

Before we explore that we need to demonstrate What is possible. How big is the prize?

The class of system design problem that cause particular angst are called mixed-priority mixed-complexity crossed-stream designs.  We encounter dozens of them in our daily life and we are not aware of it.  One of particular interest to many is called a hospital. The mixed-priority dimension is the need to manage some patients as emergencies, some as urgent and some as routine. The mixed-complexity dimension is that some patients are easy and some are complex. The crossed-stream dimension is the aggregation of specialised resources into departments. Expensive equipment and specific expertise.  We then attempt to push patients with different priorites long different paths through these different departments . And it is a management nightmare! 

BlueprintOur usual and “obvious” response to this challenge is called a carve-out design. And that means we chop up our available resource capacity into chunks.  And we do that in two ways: chunks of time and chunks of space.  We try to simplify the problem by dissecting it into bits that we can understand. We separate the emergency departments from the  planned-care facilities. We separate outpatients from inpatients. We separate medicine from surgery – and we then intellectually dissect our patients into organ systems: brains, lungs, hearts, guts, bones, skin, and so on – and we create separate departments for each one. Neurology, Respiratory, Cardiology, Gastroenterology, Orthopaedics, Dermatology to list just a few. And then we become locked into the carve-out design silos like prisoners in cages of our own making.

And so it is within the departments that are sub-systems of the bigger system. Simplification, dissection and separation. Ad absurdam.

The major drawback with our carve-up design strategy is that it actually makes the system more complicated.  The number of necessary links between the separate parts grows exponentially.  And each link can hold a small queue of waiting tasks – just as each side road can hold a queue of waiting cars. The collective complexity is incomprehensible. The cumulative queue is enormous. The opportunity for confusion and error grows exponentially. Safety and quality fall and cost rises. Carve-out is an inferior time-design.

But our goal is correct: we do need to simplify the system so that means simplifying the time-design.

To illustrate the potential of this ‘simplify the time-design’ approach we need a real example.

One way to do this is to create a real system with lots of carve-out time-design built into it and then we can observe how it behaves – in reality. A carefully designed Table Top Game is one way to do this – one where the players have defined Roles and by following the Rules they collectively create a real system that we can map, measure and modify. With our Table Top Team trained and ready to go we then pump realistic tasks into our realistic system and measure how long they take in reality to appear out of the other side. And we then use the real data to plot some real time-series charts. Not theoretical general ones – real specific ones. And then we use the actual charts to diagnose the actual causes of the actual queues and actual chaos.

TimeDesign_BeforeThis is the time-series chart of a real Time-Design Game that has been designed using an actual hospital department and real observation data.  Which department it was is not of importance because it could have been one of many. Carve-out is everywhere.

During one run of the Game the Team processed 186 tasks and the chart shows how long each task took from arriving to leaving (the game was designed to do the work in seconds when in the real department it took minutes – and this was done so that one working day could be condensed from 8 hours into 8 minutes!)

There was a mix of priority: some tasks were more urgent than others. There was a mix of complexity: some tasks required more steps that others. The paths crossed at separate steps where different people did defined work using different skills and special equipment.  There were handoffs between all of the steps on all of the streams. There were  lots of links. There were many queues. There were ample opportunities for confusion and errors.

But the design of the real process was such that the work was delivered to a high quality – there were very few output errors. The yield was very high. The design was effective. The resources required to achieve this quality were represented by the hours of people-time availability – the capacity. The cost. And the work was stressful, chaotic, pressured, and important – so it got done. Everyone was busy. Everyone pulled together. They helped each other out. They were not idle. They were a good team. The design was efficient.

The thin blue line on the time-series chart is the “time target” set by the Organisation.  But the effective and efficient system design only achieved it 77% of the time.  So the “obvious” solution was to clamour for more people and for more space and for more equipment so that the work can be done more quickly to deliver more jobs on-time.  Unfortunately the Rules of the Time-Design Game do not allow this more-money option. There is no more money.

To succeed at the Time-Design Game the team must find a way to improve their delivery time performance with the capacity they have and also to deliver the same quality.  But this is impossible! If it were possible then the solution would be obvious and they would be doing it already. No one can succeed on the Time-Design Game. 

Wrong. It is possible.  And the assumption that the solution is obvious is incorrect. The solution is not obvious – at least to the untrained eye.

To the trained eye the time-series chart shows the characteristic signals of a carve-out time-design. The high task-to-task variation is highly suggestive as is the pattern of some of the earlier arrivals having a longer lead time. An experienced system designer can diagnose a carve-out time-design from a set of time-series charts of a process just as a doctor can diagnose the disease from the vital signs chart for a patient.  And when the diagnosis is confirmed with a verification test then the time-Redesign phase can start. 

TimeDesign_AfterPhase1This chart shows what happened after the time-design of the system was changed – after some of the carve-out design was modified. The Y-axis scale is the same as before – and the delivery time improvement is dramatic. The Time-ReDesigned system is now delivering 98% achievement of the “on time target”.

The important thing to be aware of is that exactly the same work was done, using exactly the same steps, and exactly the same resources. No one had to be retrained, released or recruited.  The quality was not impaired. And the cost was actually less because less overtime was needed to mop up the spillover of work at the end of the day.

And the Time-ReDesigned system feels better to work in. It is not chaotic; flow is much smoother; and it is busy yet relaxed and even fun.  The same activity is achieved by the same people doing the same work in the same sequence. Only the Time-Design has changed. A change that delivered a win for the workers!

What was the impact of this cost-saving improvement on the customers of this service? They can now be 98% confident that they will get their task completed correctly in less than 120 minutes.  Before the Time-Redesign the 98% confidence limit was 470 minutes! So this is a win for the customers too!

And the Time-ReDesigned system is less expensive so it is a win for whoever is paying.

Same safety and quality, quicker with less variation, and at lower cost. Win-Win-Win.

And the usual reaction to playing the Time-ReDesign Game is incredulous disbelief.  Some describe it as a “light bulb” moment when they see how the diagnosis of the carve-out time-design is made and and how the Time-ReDesign is done. They say “If I had not seen it with my own eyes I would not have believed it.” And they say “The solutions are simple but not obvious!” And they say “I wish I had learned this years ago!”  And thay apologise for being so skeptical before.

And there are those who are too complacent, too careful or too cynical to play the Time-ReDesign Game (which is about 80% of people actually) – and who deny themselves the opportunity of a win-win-win outcome. And that is their choice. They can continue to grin and bear it – for a while longer.     

And for the 20% who want to learn how to do Time ReDesign for real in their actual systems there is now a Ray Of Hope.

And the Ray of Hope is illuminating a signpost on which is written “This Way to Improvementology“. 

Before we explore this question we need to establish something. If the issue is Safety then that always goes First – and by safety we mean “a risk of harm that everyone agrees is unacceptable”.

figure_running_hamster_wheel_150_wht_4308Many Improvement Zealots state dogmatically that the only way reach the Nirvanah of “Right Thing – On Time – On Budget” is to focus on Quality First.

This is incorrect.  And what makes it incorrect is the word only.

Experience teaches us that it is impossible to divert people to focus on quality when everyone is too busy just keeping afloat. If they stop to do something else then they will drown. And they know it.

The critical word here is busy.

‘Busy’ means that everyone is spending all their time doing stuff – important stuff – the work, the checking, the correcting, the expediting, the problem solving, and the fire-fighting. They are all busy all of the time.

So when a Quality Zealot breezes in and proclaims ‘You should always focus on quality first … that will solve all the problems’ then the reaction they get is predictable. The weary workers listen with their arms-crossed, roll-their eyes, exchange knowing glances, sigh, shrug, shake their heads, grit their teeth, and trudge back to fire-fighting. Their scepticism and cynicism has been cut a notch deeper. And the weary workers get labelled as ‘Not Interested In Quality’ and ‘Resisting Change’  and ‘Laggards’ by the Quality Zealot who has spent more time studying and regurgitating rhetoric than investing time in observing and understanding reality.

The problem here is the seemingly innocuous word ‘always’. It is too absolute. Too black-and-white. Too dogmatic. Too simple.

Sometimes focussing on Quality First is a wise decision. And that situation is when there is low-quality and idle-time. There is some spare capacity to re-invest in understanding the root causes of the quality issues,  in designing them out of the process, and in implementing the design changes.

But when everyone is busy – when there is no idle-time – then focussing on quality first is not a wise decision because it can actually make the problem worse!

[The Quality Zealots will now be turning a strange red colour, steam will be erupting from their ears and sparks will be coming from their finger-tips as they reach for their keyboards to silence the heretical anti-quality lunatic. “Burn, burn, burn” they rant]. 

When everyone is busy then the first thing to focus on is Time.

And because everyone is busy then the person doing the Focus-on-Time stuff must be someone else. Someone like an Improvementologist.  The Quality Zealot is a liability at this stage – but they become an asset later when the chaos has calmed.

And what our Improvementologist is looking for are queues – also known as Work-in-Progress or WIP.

Why WIP?  Why not where the work is happening? Why not focus on resource utilisation? Isn’t that a time metric?

Yes, resource utilisation is a time-related metric but because everyone is busy then resource utilisation will be high. So looking at utilisation will only confirm what we already know.  And everyone is busy doing important stuff – they are not stupid – they are busy and they are doing their best given the constraints of their process design.        

The queue is where an Improvementologist will direct attention first.  And the specific focus of their attention is the cause of the queue.

This is because there is only one cause of a queue: a mismatch-over-time between demand and activity.

So, the critical first step to diagnosing the cause of a queue is to make the flow visible – to plot the time-series charts of demand, activity and WIP.  Until that is done then no progress will be made with understanding what is happening and it wil be impossible to decide what to do. We need a diagnosis before we can treat. And to get a diagnosis we need data from an examination of our process; and we need data on the history of how it has developed. And we need to know how to convert that data into information, and then into understanding, and then into design options, and then into a wise decision, and then into action, and then into improvement.

And we now know how to spot an experienced Improvementologist because the first thing they will look for are the Queues not the Quality.

But why bother with the flow and the queues at all? Customers are not interested in them! If time is the focus then surely it is turnaround times and waiting times that we need to measure! Then we can compare our performance with our ‘target’ and if it is out of range we can then apply the necessary ‘pressure’!

This is indeed what we observe. So let us explore the pros and cons of this approach with an example.

We are the manager of a support department that receives requests, processes them and delivers the output back to the sender. We could be one of many support departments in an organisation:  human resources, procurement, supplies, finance, IT, estates and so on. We are the Backroom Brigade. We are the unsung heros and heroines.

The requests for our service come in different flavours – some are easy to deal with, others are more complex.  They also come with different priorities – urgent, soon and routine. And they arrive as a mixture of dribbles and deluges.  Our job is to deliver high quality work (i.e. no errors) within the delivery time expected by the originator of the request (i.e. on time). If  we do that then we do not get complaints (but we do not get compliments either).

From the outside things look mostly OK.  We deliver mostly on quality and mostly on time. But on the inside our department is in chaos! Every day brings a new fire to fight. Everyone is busy and the pressure and chaos are relentless. We are keeping our head above water – but only just.  We do not enjoy our work-life. It is not fun. Our people are miserable too. Some leave – others complain – others just come to work, do stuff, take the money and go home – like Zombies. They comply.

three_wins_agreementOnce in the past we were were seduced by the sweet talk of a Quality Zealot. We were promised Nirvanah. We were advised to look at the quality of the requests that we get. And this suggestion resonated with us because we were very aware that the requests were of variable quality. Our people had to spend time checking-and-correcting them before we could process them.  The extra checking had improved the quality of what we deliver – but it had increased our costs too. So the Quality Zealot told us we should work more closely with our customers and to ‘swim upstream’ to prevent the quality problems getting to us in the first place. So we sent some of our most experienced and most expensive Inspectors to paddle upstream. But our customers were also very busy and, much as they would have liked, they did not have time to focus on quality either. So our Inspectors started doing the checking-and-correcting for our customers. Our people are now working for our customers but we still pay their wages. And we do not have enough Inspectors to check-and-correct all the requests at source so we still need to keep a skeleton crew of Inspectors in the department. And these stay-at-home Inspectors  are stretched too thin and their job is too pressured and too stressful. So no one wants to do it.And given the choice they would all rather paddle out to the customers first thing in the morning to give them as much time as possible to check-and-correct the requests so the days work can be completed on time.  It all sounds perfectly logical and rational – but it does not seem to have worked as promised. The stay-at-home Inspectors can only keep up with the more urgent work,  delivery of the less urgent work suffers and the chronic chaos and fire-fighting are now aggravated by a stream of interruptions from customers asking when their ‘non-urgent’ requests will be completed.

figure_talk_giant_phone_anim_150_wht_6767The Quality Zealot insisted we should always answer the phone to our customers – so we take the calls – we expedite the requests – we solve the problems – and we fight-the-fire.  Day, after day, after day.

We now know what Purgatory means. Retirement with a pension or voluntary redundancy with a package are looking more attractive – if only we can keep going long enough.

And the last thing we need is more external inspection, more targets, and more expensive Quality Zealots telling us what to do! 

And when we go and look we see a workplace that appears just as chaotic and stressful and angry as we feel. There are heaps of work in progress everywhere – the phone is always ringing – and our people are running around like headless chickens, expediting, fire-fighting and getting burned-out: physically and emotionally. And we feel powerless to stop it. So we hide.

Does this fictional fiasco feel familiar? It is called the Miserable Job Purgatory Vortex.

Now we know the characteristic pattern of symptoms and signs:  constant pressure of work, ever present threat of quality failure, everyone busy, just managing to cope, target-stick-and-carrot management, a miserable job, and demotivated people.

The issue here is that the queues are causing some of the low quality. It is not always low quality that causes all of the queues.

figure_juggling_time_150_wht_4437Queues create delays, which generate interruptions, which force investigation, which generates expediting, which takes time from doing the work, which consumes required capacity, which reduces activity, which increases the demand-activity mismatch, which increases the queue, which increases the delay – and so on. It is a vicious circle. And interruptions are a fertile source of internally generated errors which generates even more checking and correcting which uses up even more required capacity which makes the queues grow even faster and longer. Round and round.  The cries for ‘we need more capacity’ get louder. It is all hands to the pump – but even then eventually there is a crisis. A big mistake happens. Then Senior Management get named-blamed-and shamed,  money magically appears and is thrown at the problem, capacity increases,  the symptoms settle, the cries for more capacity go quiet – but productivity has dropped another notch. Eventually the financial crunch arrives.    

One symptom of this ‘reactive fire-fight design’ is that people get used to working late to catch up at the end of the day so that the next day they can start the whole rollercoaster ride again. And again. And again. At least that is a form of stability. We can expect tomorrow to be just a s miserable as today and yesterday and the day before that. But TOIL (Time Off In Lieu) costs money.

The way out of the Miserable Job Purgatory Vortex is to diagnose what is causing the queue – and to treat that first.

And that means focussing on Time first – and that means Focussing on Flow first.  And by doing that we will improve delivery, improve quality and improve cost because chaotic systems generate errors which need checking and correcting which costs more. Time first is a win-win-win strategy too.

And we already have everything we need to start. We can easily count what comes in and when and what goes out and when.

The first step is to plot the inflow over time (the demand), the outflow over time (the activity), and from that we work out and plot the Work-in-Progress over time. With these three charts we can start the diagnostic process and by that path we can calm the chaos.

And then we can set to work on the Quality Improvement.  

13/01/2013Newspapers report that 17 hospitals are “dangerously understaffed”  Sound familiar?

Next week we will explore how to diagnose the root cause of a queue using Time charts.

For an example to explore please play the SystemFlow Game by clicking here


Stop Press: For those who prefer cartoons to books please skip to the end to watch the Who Moved My Cheese video first.

ThomasKuhnIn 1962 – that is half a century ago – a controversial book was published. The title was “The Structure of Scientific Revolutions” and the author was Thomas S Kuhn (1922-1996) a physicist and historian at Harvard University.  The book ushered in the concept of a ‘paradigm shift’ and it upset a lot a people.

In particular it upset a lot of scientists because it suggested that the growth of knowledge and understanding is not smooth – it is jerky. And Kuhn showed that the scientists were causing the jerking.

Kuhn described the process of scientific progress as having three phases: pre-science, normal science and revolutionary science.  Most of the work scientists do is normal science which means exploring, consolidating, and applying the current paradigm. The current conceptual model of how things work.  Anyone who argues against the paradigm is regarded as ‘mistaken’ because the paradigm represents the ‘truth’.  Kuhn draws on the history of science for his evidence, quoting  examples of how innovators such as Galileo, Copernicus, Newton, Einstein and Hawking radically changed the way that we now view the Universe. But their different models were not accepted immediately and ethusiastically because they challenged the status quo. Galileo was under house arrest for much of his life because his ‘heretical’ writings challenged the Church.  

Each revolution in thinking was both disruptive and at the same time constructive because it opened a door to allow rapid expansion of knowledge and understanding. And that foundation of knowledge that has been built over the centuries is one that we all take for granted.  It is a fragile foundation though. It could be all lost and forgotten in one generation because none of us are born with this knowledge and understanding. It is not obvious. We all have to learn it.  Even scientists.

Kuhn’s book was controversial because it suggested that scientists spend most of their time blocking change. This is not necessarily a bad thing. Stability for a while is very useful and the output of normal science is mostly positive. For example the revolution in thinking introduced by Isaac Newton (1643-1727) led directly to the Industrial Revolution and to far-reaching advances in every sphere of human knowledge. Most of modern engineering is built on Newtonian mechanics and it is only at the scales of the very large, the very small and the very quick that it falls over. Relativistic and quantum physics are more recent and very profound shifts in thinking and they have given us the digital computer and the information revolution. This blog is a manifestation of the quantum paradigm.

Kuhn concluded that the progess of change is jerky because scientists create resistance to change to create stability while doing normal science experiments.  But these same experiments produce evidence that suggest that the current paradigm is flawed. Over time the pressure of conflicting evidence accumulates, disharmony builds, conflict is inevitable and intellectual battle lines are drawn.  The deeper and more fundamental the flaw the more bitter the battle.

In contrast, newcomers seek harmony in the cacophony and propose new theories that explain both the old and the new. New paradigms. The stage is now set for a drama and the public watch bemused as the academic heavyweights slug it out. Eventually a tipping point is reached and one of the new paradigms becomes dominant. Often the transition is triggered by one crucial experiment.

There is a sudden release of the tension and a painful and disruptive conceptual  lurch – a paradigm shift. Then the whole process starts over again. The creators of the new paradigm become the consolidators and in time the defenders and eventually the dogmatics!  And it can take decades and even generations for the transition to be completed.

It is said that Albert Einstein (1879-1955) never fully accepted quantum physics even though his work planted the seeds for it and experience showed that it explained the experimental observations better. [For more about Einstein click here].              

The message that some take from Kuhn’s book is that paradigm shifts are the only way that knowledge  can advance.  With this assumption getting change to happen requires creating a crisis – a burning platform. Unfortunatelty this is an error of logic – it is a unverified generalisation from an observed specific. The evidence is growing that this we-always-need-a-burning-platform assumption is incorrect.  It appears that the growth of  knowledge and understanding can be smoother, less damaging and more effective without creating a crisis.

So what is the evidence that this is possible?

Well, what pattern would you look for to illustrate that it is possible to improve smoothly and continually? A smooth growth curve of some sort? Yes – but it is more than that.  It is a smooth curve that is steeper than anyone else’s and one that is growing steeper over time.  Evidence that someone is learning to improve faster than their peers – and learning painlessly and continuously without crises; not painfully and intermittently using crises.

Two examples are Toyota and Apple.

ToyotaLogoToyota is a Japanese car manufacturer that has out-performed other car manufacturers consistently for 40 years – despite the global economic boom-bust cycles. What is their secret formula for their success?

WorldOilPriceChartWe need a bit of history. In the 1980’s a crisis-of-confidence hit the US economy. It was suddenly threatened by higher-quality and lower-cost imported Japanese products – for example cars.

The switch to buying Japanese cars had been triggered by the Oil Crisis of 1973 when the cost of crude oil quadrupled almost overnight – triggering a rush for smaller, less fuel hungry vehicles.  This is exactly what Toyota was offering.

This crisis was also a rude awakening for the US to the existence of a significant economic threat from their former adversary.  It was even more shocking to learn that W Edwards Deming, an American statistician, had sown the seed of Japan’s success thirty years earlier and that Toyota had taken much of its inspiration from Henry Ford.  The knee-jerk reaction of the automotive industry academics was to copy how Toyota was doing it, the Toyota Production System (TPS) and from that the school of Lean Tinkering was born.

This knowledge transplant has been both slow and painful and although learning to use the Lean Toolbox has improved Western manufacturing productivity and given us all more reliable, cheaper-to-run cars – no other company has been able to match the continued success of Japan.  And the reason is that the automotive industry academics did not copy the paradigm – the intangible, subjective, unspoken mental model that created the context for success.  They just copied the tangible manifestation of that paradigm.  The tools. That is just cynically copying information and knowledge to gain a competitive advantage – it is not respecfully growing understanding and wisdom to reach a collaborative vision.

AppleLogoApple is now one of the largest companies in the world and it has become so because Steve Jobs (1955-2011), its Californian, technophilic, Zen Bhuddist, entrepreneurial co-founder, had a very clear vision: To design products for people.  And to do that they continually challenged their own and their customers paradigms. Design is a logical-rational exercise. It is the deliberate use of explicit knowledge to create something that delivers what is needed but in a different way. Higher quality and lower cost. It is normal science.

Continually challenging our current paradigm is not normal science. It is revolutionary science. It is deliberately disruptive innovation. But continually challenging the current paradigm is uncomfortable for many and, by all accounts, Steve Jobs was not an easy person to work for because he was future-looking and demanded perfection in the present. But the success of this paradigm is a matter of fact: 

“In its fiscal year ending in September 2011, Apple Inc. hit new heights financially with $108 billion in revenues (increased significantly from $65 billion in 2010) and nearly $82 billion in cash reserves. Apple achieved these results while losing market share in certain product categories. On August 20, 2012 Apple closed at a record share price of $665.15 with 936,596,000 outstanding shares it had a market capitalization of $622.98 billion. This is the highest nominal market capitalization ever reached by a publicly traded company and surpasses a record set by Microsoft in 1999.”

And remember – Apple almost went bust. Steve Jobs had been ousted from the company he co-founded in a boardroom coup in 1985.  After he left Apple floundered and Steve Jobs proved it was his paradigm that was the essential ingredient by setting up NeXT computers and then Pixar. Apple’s fortunes only recovered after 1998 when Steve Jobs was invited back. The rest is history so click to see and hear Steve Jobs describing the Apple paradigm.

So the evidence states that Toyota and Apple are doing something very different from the rest of the pack and it is not just very good product design. They are continually updating their knowledge and understanding – and they are doing this using a very different paradigm.  They are continually challenging themselves to learn. To illustrate how they do it – here is a list of the five principles that underpin Toyota’s approach:

  • Challenge
  • Improvement
  • Go and see
  • Teamwork
  • Respect

This is Win-Win-Win thinking. This is the Science of Improvement. This is Improvementology®.

So what is the reason that this proven paradigm seems so difficult to replicate? It sounds easy enough in theory! Why is it not so simple to put into practice?

The requirements are clearly listed: Respect for people (challenge). Respect for learning (improvement). Respect for reality (go and see). Respect for systems (teamwork).

In a word – Respect.

Respect is a big challenge for the individualist mindset which is fundamentally disrespectful of others. The individualist mindset underpins the I-Win-You-Lose Paradigm; the Zero-Sum -Game Paradigm; the Either-Or Paradigm; the Linear-Thinking Paradigm; the Whole-Is-The-Sum-Of-The-Parts Paradigm; the Optimise-The-Parts-To-Optimise-The-Whole Paradigm.

Unfortunately these are the current management paradigms in much of the private and public worlds and the evidence is accumulating that this paradigm is failing. It may have been adequate when times were better, but it is inadequate for our current needs and inappropriate for our future needs. 

So how can we avoid having to set fire to the current failing management paradigm to force a leap into the cold and uninviting reality of impending global economic failure?  How can we harness our burning desire for survival, security and stability? How can we evolve our paradigm pro-actively and safely rather than re-actively and dangerously?

all_in_the_same_boat_150_wht_9404We need something tangible to hold on to that will keep us from drowning while the old I-am-OK-You-are-Not-OK Paradigm is dissolved and re-designed. Like the body of the caterpillar that is dissolved and re-assembled inside the pupa as the body of a completely different thing – a butterfly.

We need a robust  and resilient structure that will keep us safe in the transition from old to new and we also need something stable that we can steer to a secure haven on a distant shore.

We need a conceptual lifeboat. Not just some driftwood,  a bag of second-hand tools and no instructions! And we need that lifeboat now.

But why the urgency?

UK_PopulationThe answer is basic economics.

The UK population is growing and the proportion of people over 65 years old is growing faster.  Advances in healthcare means that more of us survive age-related illnesses such as cancer and heart disease. We live longer and with better quality of life – which is great.

But this silver-lining hides a darker cloud.

The proportion of elderly and very elderly will increase over the next 20 years as the post WWII baby-boom reaches retirement age. The number of people who are living on pensions is increasing and the demands on health and social services is increasing.  Pensions and public services are not paid out of past savings  they are paid out of current earnings.  So the country will need to earn more to pay the bills. The UK economy will need to grow.

UK_GDP_GrowthBut the UK economy is not growing.  Our Gross Domestic Product (GDP) is currently about £380 billion and flat as a pancake. This sounds like a lot of dosh – but when shared out across the population of 56 million it gives a more modest figure of just over £100 per person per week.  And the time-series chart for the last 20 years shows that the past growth of about 1% per quarter took a big dive in 2008 and went negative! That means serious recession. It recovered briefly but is now sagging towards zero.

So we are heading for a big economic crunch and hiding our heads in the sand and hoping for the best is not a rational strategy. The only way to survive is to cut public services or for tax-funded services to become more productive. And more productive means increasing the volume of goods and services for the same cost. These are the services that we will need to support the growing population of  dependents but without increasing the cost to the country – which means the taxpayer.

The success of Toyota and Apple stemmed from learning how to do just that: how to design and deliver what is needed; and how to eliminate what is not; and how to wisely re-invest the released cash. The difference can translate into higher profit, or into growth, or into more productivity. It just depends on the context.  Toyota and Apple went for profit and growth. Tax-funded public services will need to opt for productivity. 

And the learning-productivity-improvement-by-design paradigm will be a critical-to-survival factor in tax-payer funded public services such as the NHS and Social Care.  We do not have a choice if we want to maintain what we take for granted now.  We have to proactively evolve our out-of-date public sector management paradigm. We have to evolve it into one that can support dramatic growth in productivity without sacrificing quality and safety.

We cannot use the burning platform approach. And we have to act with urgency.

We need a lifeboat!

Our current public sector management paradigm is sinking fast and is being defended and propped up by the old school managers who were brought up in it.  Unfortunately the evidence of 500 years of change says that the old school cannot unlearn. Their mental models go too deep.  The captains and their crews will go down with their ships.  [Remember the Titanic the unsinkable ship that sank in 1912 on the maiden voyage. That was a victory of reality over rhetoric.]

Those of us who want to survive are the ‘rats’. We know when it is time to leave the sinking ship.  We know we need lifeboats because it could be a long swim! We do not want to freeze and drown during the transition to the new paradigm.

So where are the lifeboats?

One possibility is an unfamiliar looking boat called “6M Design”. This boat looks odd when viewed through the lens of the conventional management paradigm because it combines three apparently contradictiry things: the rational-logical elements of system design;  the respect-for-people and learning-through-challenge principles embodied by Toyota and Apple; and the counter-intuitive technique of systems thinking.

Another reason it feel odd is because “6M Design” is not a solution; it is a meta-solution. 6M Design is a way of creating a good-enough-for-now solution by changing the current paradigm a bit at a time. It is a-how-to-design framework; it is not the-what-to-do solution. 6M Design is a paradigm shaper – not a paradigm shaker or a paradigm shifter.

And there is yet another reason why 6M Design does not float the current management boat.  It does not need to be controlled by self-appointed experts.  Business schools and management consultants, who have a vested interest in defending the current management paradigm, cannot make a quick buck from it because they are irrelevant. 6M Design is intended to be used by anyone and everyone as a common language for collectively engaging in respectful challenge and lifelong learning. Anyone can learn to use it. Anyone.

We do not need a crisis to change. But without changing we will get the crisis we do not want. If we choose to change then we can choose a safer and smoother path of change.

The choice seems clear.  Do you want to go down with the ship or stay afloat aboard an innovation boat?

And we will need something to help us navigate our boat.

If you are a reflective, conceptual learner then you might ike to read a synopsis of Thomas Kuhn’s book.  You can download a copy here. [There is also a 50 year anniversary edition of the original that was published this year].

And if you prefer learning from stories then there is an excellent one called “Who Moved My Cheese” that describes the same challenge of change. And with the power of the digital paradigm you can watch the video here.

line_figure_phone_400_wht_9858<Ring Ring><Ring Ring>

♦ 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.

campfire_burning_150_wht_174♦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.

employee_diciplined_400_wht_5635Ouch! 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?

emotion_head_mad_400_wht_7632The 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?

ready_to_launch_PA_150_wht_5052Um – 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?

emotion_head_sad_frown_400_wht_7644Yes. 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?


And each discount requires a different – um – tactic to defuse?


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.

two_stickmen_shaking_hands_puzzle_150_wht_5229♦ Thank you Leslie.

Defusing Trust Eroders – Part I

Defusing Trust Eroders – Part III

<Ring Ring><Ring Ring>

♦Hello, you are through to the Improvement Science Helpline. How can we help?

This is Leslie, one of your FISH apprentices.  Could I speak to Bob – my ISP coach?

♦Yes, Bob is free. I will connect you now.

<Ring Ring><Ring Ring>

♦Hello Leslie, Bob here. How can I help?

Hi Bob, I have a problem that I do not feel my Foundation training has equipped me to solve. Can I talk it through with you?

♦Of course. Can you outline the context for me?

Yes. The context is a department that is delivering an acceptable quality-of-service and is delivering on-time but is failing financially. As you know we are all being forced to adopt austerity measures and I am concerned that if their budget is cut then they will fail on delivery and may start cutting corners and then fail on quality too.  We need a win-win-win outcome and I do not know where to start with this one.

♦OK – are you using the 6M Design method?

Yes – of course!

♦OK – have you done The 4N Chart for the customer of their service?

Yes – it was their customers who asked me if I could help and that is what I used to get the context.

♦OK – have you done The 4N Chart for the department?

Yes. And that is where my major concerns come from. They feel under extreme pressure; they feel they are working flat out just to maintain the current level of quality and on-time delivery; they feel undervalued and frustrated that their requests for more resources are refused; they feel demoralized; demotivated and scared that their service may be ‘outsourced’. On the positive side they feel that they work well as a team and are willing to learn. I do not know what to do next.

♦OK. Do not panic. This sounds like a very common and treatable system illness.  It is a stream design problem which may be the reason your Foundation training feels insufficient. Would you like to see how a Practitioner would approach this?

Yes please!

♦OK. Have you mapped their internal process?

Yes. It is a six-step process for each job. Each step has different requirements and are done by different people with different skills. In the past they had a problem with poor service quality so extra safety and quality checks were imposed by the Governance department.  Now the quality of each step is measured on a 1-6 scale and the quality of the whole process is the sum of the individual steps so is measured on a scale of 6 to 36. They now have been given a minimum quality target of 21 to achieve for every job. How they achieve that is not specified – it was left up to them.

♦OK – do they record their quality measurement data?

Yes – I have their report.

♦OK – how is the information presented?

As an average for the previous month which is reported up to the Quality Performance Committee.

♦OK – what was the average for last month?

Their results were 24 – so they do not have an issue delivering the required quality. The problem is the costs they are incurring and they are being labelled by others as ‘inefficient’. Especially the departments who are in budget and are annoyed that this department keeps getting ‘bailed out’.

♦OK. One issue here is the quality reporting process is not alerting you to the real issue. It sounds from what you say that you have fallen into the Flaw of Averages trap.

I don’t understand. What is the Flaw of Averages trap?

♦The answer to your question will become clear. The finance issue is a symptom – an effect – it is unlikely to be the cause. When did this finance issue appear?

Just after the Safety and Quality Review. They needed to employ more agency staff to do the extra work created by having to meet the new Minimum Quality target.

♦OK. I need to ask you a personal question. Do you believe that improving quality always costs more?

I have to say that I am coming to that conclusion. Our Governance and Finance departments are always arguing about it. Governance state ‘a minimum standard of safety and quality is not optional’ and finance say ‘but we are going out of business’. They are at loggerheads. The departments get caught in the cross-fire.

♦OK. We will need to use reality to demonstrate that this belief is incorrect. Rhetoric alone does not work. If it did then we would not be having this conversation. Do you have the raw data from which the averages are calculated?

Yes. We have the data. The quality inspectors are very thorough!

♦OK – can you plot the quality scores for the last fifty jobs as a BaseLine chart?

Yes – give me a second. The average is 24 as I said.

♦OK – is the process stable?

Yes – there is only one flag for the fifty. I know from my FISH training that is not a cause for alarm.

♦OK – what is the process capability?

I am sorry – I don’t know what you mean by that?

♦My apologies. I forgot that you have not completed the Practitioner training yet. The capability is the range between the red lines on the chart.

Um – the lower line is at 17 and the upper line is at 31.

♦OK – how many points lie below the target of 21.

None of course. They are meeting their Minimum Quality target. The issue is not quality – it is money.

There was a pause.  Leslie knew from experience that when Bob paused there was a surprise coming.

♦Can you email me your chart?

A cold-shiver went down Leslie’s back. What was the problem here? Bob had never asked to see the data before.

Sure. I will send it now.  The recent fifty is on the right, the data on the left is from after the quality inspectors went in and before the the Minimum Quality target was imposed. This is the chart that Governance has been using as evidence to justify their existence because they are claiming the credit for improving the quality.

♦OK – thanks. I have got it – let me see.  Oh dear.

Leslie was shocked. She had never heard Bob use language like ‘Oh dear’.

There was another pause.

♦Leslie, what is the context for this data? What does the X-axis represent?

Leslie looked at the chart again – more closely this time. Then she saw what Bob was getting at. There were fifty points in the first group, and about the same number in the second group. That was not the interesting part. In the first group the X-axis went up to 50 in regular steps of five; in the second group it went from 50 to just over 149 and was no longer regularly spaced. Eventually she replied.

Bob, that is a really good question. My guess it is that this is the quality of the completed work.

♦It is unwise to guess. It is better to go and see reality.

You are right. I knew that. It is drummed into us during the Foundation training! I will go and ask. Can I call you back?

♦Of course. I will email you my direct number.

Click here to read the rest of the story

<Ring Ring><Ring Ring>

♦Hello, Bob here.

Bob – it is Leslie. I am  so excited! I have discovered something amazing.

♦Hello Leslie. That is good to hear. Can you tell me what you have discovered?

I have discovered that better quality does not always cost more.

♦That is a good discovery. Can you prove it with data?

Yes I can!  I am emailing you the chart now.

♦OK – I am looking at your chart. Can you explain to me what you have discovered?

Yes. When I went to see for myself I saw that when a job failed the Minimum Quality check at the end then the whole job had to be re-done because there was no time to investigate and correct the causes of the failure.  The people doing the work said that they were helpless victims of errors that were made upstream of them – and they could not predict from one job to the next what the error would be. They said it felt like quality was a lottery and that they were just firefighting all the time. They knew that just repeating the work was not solving the problem but they had no other choice because they were under enormous pressure to deliver on-time as well. The only solution they could see is was to get more resources but their requests were being refused by Finance on the grounds that there is no more money. They felt completely trapped.

♦OK. Can you describe what you did?

Yes. I saw immediately that there were so many sources of errors that it would be impossible for me to tackle them all. So I used the tool that I had learned in the Foundation training: the Niggle-o-Gram. That focussed us and led to a surprisingly simple, quick, zero-cost process design change. We deliberately did not remove the Inspection-and-Correction policy because we needed to know what the impact of the change would be. Oh, and we did one other thing that challenged the current methods. We plotted both the successes and the failures on the BaseLine chart so we could see both the the quality and the work done on one chart.  And we updated the chart every day and posted it chart on the notice board so everyone in the department could see the effect of the change that they had designed. It worked like magic! They have already slashed their agency staff costs, the whole department feels calmer and they are still delivering on-time. And best of all they now feel that they have the energy and time to start looking at the next niggle. Thank you so much! Now I see how the tools and techniques I learned in FISH school are so powerful and now I understand better the reason we learned them first.

♦Well done Leslie. You have taken an important step to becoming a fully fledged Improvement Science Practitioner. There are many more but you have learned some critical lessons in this challenge.

This scenario is fictional but realistic.

And it has been designed so that it can be replicated easily using a simple game that requires only pencil, paper and some dice.

If you do not have some dice handy then you can use this little program that simulates rolling six dice.

The Six Digital Dice program (for PC only).

1. Prepare a piece of A4 squared paper with the Y-axis marked from zero to 40 and the X-axis from 1 to 80.
2. Roll six dice and record the score on each (or one die six times) – then calculate the total.
3. Plot the total on your graph. Left-to-right in time order. Link the dots with lines.
4. After 25 dots look at the chart. It should resemble the leftmost data in the charts above.
5. Now draw a horizontal line at 21. This is the Minimum Quality Target.
6. Keep rolling the dice – six per cycle, adding the totals to the right of your previous data.

But this time if the total is less than 21 then repeat the cycle of six dice rolls until the score is 21 or more. Record on your chart the output of all the cycles – not just the acceptable ones.

7. Keep going until you have 25 acceptable outcomes. As long as it takes.

Now count how many cycles you needed to complete in order to get 25 acceptable outcomes.  You should find that it is about twice as many as before you “imposed” the Inspect-and-Correct QI policy.

This illustrates the problem of an Inspection-and-Correction design for quality improvement.  It does improve the quality of the output – but at a higher cost.  We are treating the symptoms and ignoring the disease.

The internal design of the process is unchanged – and it is still generating mistakes.

How much quality improvement you get and how much it costs you is determined by the design of the underlying process – which has not changed. There is a Law of Diminishing returns here – and a risk.

The risk is that if quality improves as the result of applying a quality target then it encourages the Governance thumbscrews to be tightened further and forces the people further into cross-fire between Governance and Finance.

The other negative consequence of the Inspection-and-Correction approach is that it increases both the average and the variation in lead time which also fuels the calls for more targets, more sticks, calls for  more resources and pushes costs up even further.

The lesson from this simple reality check seems clear.

The better strategy for improving quality is to design the root causes of errors out of the processes  because then we will get improved quality and improved delivery and improved productivity and we will discover that we have improved safety as well.

The Six Dice Game is a simpler version of the famous Red Bead Game that W Edwards Deming used to explain why the arbitrary-target-driven-stick-and-carrot style of management creates more problems than it solves.

The illusion of short-term gain but the reality of long-term pain.

And if you would like to see and hear Deming talking about the science of improvement there is a video of him speaking in 1984. He is at the bottom of the page.  Click here.

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


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 …

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?”


♦“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?”


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.”

Most of us are realists. We have to solve problems in the real world so we prefer real examples and step-by-step how-to-do recipes.

A minority of us are theorists and are more comfortable with abstract models and solving rhetorical problems.

Many of these Improvement Science blog articles debate abstract concepts – because I am a strong iNtuitor by nature. Most realists are Sensors – so by popular request here is a “how-to-do” recipe for a Productivity Improvement Exercise (PIE)

Step 1 – Define Productivity.

There are many definitions we could choose because productivity means the results delivered divided by the resources used.  We could use any of the three currencies – quality, time or money – but the easiest is money. And that is because it is easier to measure and we have well established department for doing it – Finance – the guardians of the money.  There are two other departments who may need to be involved – Governance (the guardians of the safety) and Operations (the guardians of the delivery).

So the definition we will use is productivity = revenue generated divided cost incurred.

Step 2 – Draw a map of the process we want to make more productive.

This means creating a picture of the parts and their relationships to each other – in particular what the steps in the process are; who does what, where and when; what is done in parallel and what is done in sequence; what feeds into what and what depends on what. The output of this step is a diagram with boxes and arrows and annotations – called a process map. It tells us at a glance how complex our process is – the number of boxes and the number of arrows.  The simpler the process the easier it is to demonstrate a productivity improvement quickly and unambiguously.

Step 3 – Decide the objective metrics that will tell us our productivity.

We have chosen a finanical measure of productivity so we need to measure revenue and cost over time – and our Finance department do that already so we do not need to do anything new. We just ask them for the data. It will probably come as a monthly report because that is how Finance processes are designed – the calendar month accounting cycle is not negotiable.

We will also need some internal process metrics (IPMs) that will link to the end of month productivity report values because we need to be observing our process more often than monthly. Weekly, daily or even task-by-task may be necessary – and our monthly finance reports will not meet that time-granularity requirement.

These internal process metrics will be time metrics.

Start with objective metrics and avoid the subjective ones at this stage. They are necessary but they come later.

Step 4 – Measure the process.

There are three essential measures we usually need for each step in the process: A measure of quality, a measure of time and a measure of cost.  For the purposes of this example we will simplify by making three assumptions. Quality is 100% (no mistakes) and Predictability is 100% (no variation) and Necessity is 100% (no worthless steps). This means that we are considering a simplified and theoretical situation but we are novices and we need to start with the wood and not get lost in the trees.

The 100% Quality means that we do not need to worry about Governance for the purposes of this basic recipe.

The 100% Predictability means that we can use averages – so long as we are careful.

The 100% Necessity means that we must have all the steps in there or the process will not work.

The best way to measure the process is to observe it and record the events as they happen. There is no place for rhetoric here. Only reality is acceptable. And avoid computers getting in the way of the measurement. The place for computers is to assist the analysis – and only later may they be used to assist the maintenance – after the improvement has been achieved.

Many attempts at productivity improvement fail at this point – because there is a strong belief that the more computers we add the better. Experience shows the opposite is usually the case – adding computers adds complexity, cost and the opportunity for errors – so beware.

Step 5 – Identify the Constraint Step.

The meaning of the term constraint in this context is very specific – it means the step that controls the flow in the whole process.  The critical word here is flow. We need to identify the current flow constraint.

A tap or valve on a pipe is a good example of a flow constraint – we adjust the tap to control the flow in the whole pipe. It makes no difference how long or fat the pipe is or where the tap is, begining, middle or end. (So long as the pipe is not too long or too narrow or the fluid too gloopy because if they are then the pipe will become the flow constraint and we do not want that).

The way to identify the constraint in the system is to look at the time measurements. The step that shows the same flow as the output is the constraint step. (And remember we are using the simplified example of no errors and no variation – in real life there is a bit more to identifying the constraint step).

Step 6 – Identify the ideal place for the Constraint Step.

This is the critical-to-success step in the PIE recipe. Get this wrong and it will not work.

This step requires two pieces of measurement data for each step – the time data and the cost data. So the Operational team and the Finance team will need to collaborate here. Tricky I know but if we want improved productivity then there is no alternative.

Lots of productivity improvement initiatives fall at the Sixth Fence – so beware.  If our Finance and Operations departments are at war then we should not consider even starting the race. It will only make the bad situation even worse!

If they are able to maintain an adult and respectful face-to-face conversation then we can proceed.

The time measure for each step we need is called the cycle time – which is the time interval from starting one task to being ready to start the next one. Please note this is a precise definition and it should be used exactly as defined.

The money measure for each step we need is the fully absorbed cost of time of providing the resource.  Your Finance department will understand that – they are Masters of FACTs!

The magic number we need to identify the Ideal Constraint is the product of the Cycle Time and the FACT – the step with the highest magic number should be the constraint step. It should control the flow in the whole process. (In reality there is a bit more to it than this but I am trying hard to stay out of the trees).

Step 7 – Design the capacity so that the Ideal Constraint is the Actual Constraint.

We are using a precise definition of the term capacity here – the amount of resource-time available – not just the number of resources available. Again this is a precise definition and should be used as defined.

The capacity design sequence  means adding and removing capacity to and from steps so that the constraint moves to where we want it.

The sequence  is:
7a) Set the capacity of the Ideal Constraint so it is capable of delivering the required activity and revenue.
7b) Increase the capacity of the all the other steps so that the Ideal Constraint actually controls the flow.
7c) Reduce the capacity of each step in turn, a click at a time until it becomes the constraint then back off one click.

Step 8 – Model your whole design to predict the expected productivity improvement.

This is critical because we are not interested in suck-it-and-see incremental improvement. We need to be able to decide if the expected benefit is worth the effort before we authorise and action any changes.  And we will be asked for a business case. That necessity is not negotiable either.

Lots of productivity improvement projects try to dodge this particularly thorny fence behind a smoke screen of a plausible looking business case that is more fiction than fact. This happens when any of Steps 2 to 7 are omitted or done incorrectly.  What we need here is a model and if we are not prepared to learn how to build one then we should not start. It may only need a simple model – but it will need one. Intuition is too unreliable.

A model is defined as a simplified representation of reality used for making predictions.

All models are approximations of reality. That is OK.

The art of modeling is to define the questions the model needs to be designed to answer (and the precision and accuracy needed) and then design, build and test the model so that it is just simple enough and no simpler. Adding unnecessary complexity is difficult, time consuming, error prone and expensive. Using a computer model when a simple pen-and-paper model would suffice is a good example of over-complicating the recipe!

Many productivity improvement projects that get this far still fall at this fence.  There is a belief that modeling can only be done by Marvins with brains the size of planets. This is incorrect.  There is also a belief that just using a spreadsheet or modelling software is all that is needed. This is incorrect too. Competent modelling requires tools and training – and experience because it is as much art as science.

Step 9 – Modify your system as per the tested design.

Once you have demonstrated how the proposed design will deliver a valuable increase in productivity then get on with it.

Not by imposing it as a fait accompli – but by sharing the story along with the rationale, real data, explanation and results. Ask for balanced, reasoned and respectful feedback. The question to ask is “Can you think of any reasons why this would not work?” Very often the reply is “It all looks OK in theory but I bet it won’t work in practice but I can’t explain why”. This is an emotional reaction which may have some basis in fact. It may also just be habitual skepticism/cynicism. Further debate is usually  worthless – the only way to know for sure is by doing the experiment. As an experiment – as a small-scale and time-limited pilot. Set the date and do it. Waiting and debating will add no value. The proof of the pie is in the eating.

Step 10 – Measure and maintain your system productivity.

Keep measuring the same metrics that you need to calculate productivity and in addition monitor the old constraint step and the new constraint steps like a hawk – capturing their time metrics for every task – and tracking what you see against what the model predicted you should see.

The correct tool to use here is a system behaviour chart for each constraint metric.  The before-the-change data is the baseline from which improvement is measured over time;  and with a dot plotted for each task in real time and made visible to all the stakeholders. This is the voice of the process (VoP).

A review after three months with a retrospective financial analysis will not be enough. The feedback needs to be immediate. The voice of the process will dictate if and when to celebrate. (There is a bit more to this step too and the trees are clamoring for attention but we must stay out of the wood a bit longer).

And after the charts-on-the-wall have revealed the expected improvement has actually happened; and after the skeptics have deleted their ‘we told you so’ emails; and after the cynics have slunk off to sulk; and after the celebration party is over; and after the fame and glory has been snatched by the non-participants – after all of that expected change management stuff has happened …. there is a bit more work to do.

And that is to establish the new higher productivity design as business-as-usual which means tearing up all the old policies and writing new ones: New Policies that capture the New Reality. Bin the out-of-date rubbish.

This is an essential step because culture changes slowly.  If this step is omitted then out-of-date beliefs, attitudes, habits and behaviours will start to diffuse back in, poison the pond, and undo all the good work.  The New Policies are the reference – but they alone will not ensure the improvement is maintained. What is also needed is a PFL – a performance feedback loop.

And we have already demonstrated what that needs to be – the tactical system behaviour charts for the Intended Constraint step.

The finanical productivity metric is the strategic output and is reported monthly – as a system behaviour chart! Just comparing this month with last month is meaningless.  The tactical SBCs for the constraint step must be maintained continuously by the people who own the constraint step – because they control the productivity of the whole process.  They are the guardians of the productivity improvement and their SBCs are the Early Warning System (EWS).

If the tactical SBCs set off an alarm then investigate the root cause immediately – and address it. If they do not then leave it alone and do not meddle.

This is the simplified version of the recipe. The essential framework.

Reality is messier. More complicated. More fun!

Reality throws in lots of rusty spanners so we do also need to understand how to manage the complexity; the unnecessary steps; the errors; the meddlers; and the inevitable variation.  It is possible (though not trivial) to design real systems to deliver much higher productivity by using the framework above and by mastering a number of other tools and techniques.  And for that to succeed the Governance, Operations and Finance functions need to collaborate closely with the People and the Process – initially with guidance from an experienced and competent Improvement Scientist. But only initially. This is a learnable skill. And it takes practice to master – so start with easy ones and work up.

If any of these bits are missing or are dysfunctional the recipe will not work. So that is the first nettle the Executive must grasp. Get everyone who is necessary on the same bus going in the same direction – and show the cynics the exit. Skeptics are OK – they will counter-balance the Optimists. Cynics add no value and are a liability.

What you may have noticed is that 8 of the 10 steps happen before any change is made. 80% of the effort is in the design – only 20% is in the doing.

If we get the design wrong the the doing will be an ineffective and inefficient waste of effort, time and money.

The best complement to real Improvement PIE is a FISH course.