Grenfell Tower – Elites on trial – Trust in bureaucracy revisited

Grenfell Tower fire (wider view).jpg

Grenfell Tower fire1

Nobody can react to the Grenfell Tower fire with anything other than horror, sadness, anger and resolve.

Much of that anger is, legitimately, directed at the elite professions who make the decisions on which individual safety turns. I am proud to have been a member of two elite professions during my lifetime: engineering and law. I wanted to say something about the nature of practice, of responsibility and of blame.

It is too early to be confident of causes, remedies or punishments. Those will have to await full investigation but professionals of all disciplines will need little encouragement to spend the coming weeks searching their own souls over their wider obligations to society. For, that is what membership of a profession entails.

The need for bureaucracy

“Bureaucracy” is a word most often used pejoratively, as a rebuke to a turgid rigidity that frustrates spontaneity, creativity, efficiency and expedition. It is that. But once society starts to enjoy systems of reasonable complexity, civil aviation, networked electricity supply, international transport of goods etc., much decision making is going to be reserved to a cadre of experts. Diane Vaughan’s analysis of the Space Shuttle Challenger disaster2 is a relevant and salutary account of engineering as a bureaucratic profession.

Of course, you can embed some of your bureaucracy in software but don’t expect that to improve spontaneity, creativity or flexibility. Efficiency and expedition, perhaps. Even putting a bunch of flowers on your dining table requires this.

I have often, on this blog, cited Robert Michels’ iron law of oligarchy. Michels contended that any team of bureaucrats soon realised the power they held in controlling the levers of policy. A willingness to pull those levers in the direction of their own self-interest, and a jealous protection of their professional status and expertise, soon followed. That sometimes put them at odds with the objectives they were supposed to be implementing on behalf of their principals. As one political scientist put it:3

Many governance dysfunctions arise because the agents have different agendas from the principals, and the problem of institutional design is related to incentivising the agents to do the principal’s bidding.

Max Weber had realised all this earlier in the nineteenth century. Weber was a child of that mother of all bureaucracies, the Prussian civil service. The self interest of managing elites bothered him and he sought to inculcate an ethic of responsibility whereby professionals thought hard about the wider consequences of their decisions.4 That is the ethic that modern professions seek to foster among their members. Engagement in a profession carries responsibilities.

Faith in regulation

So much marginally informed debate among journalists has been about “the building regulations”. I guess that they mean the Building Regulations 2010. You can examine the relevant parts here, for what they are worth. Scroll down to Part B. Of course the Regulations themselves are supported by the statutory guidance. Here that is. The guidance refers to a legion of British Standards. As I learned during my time in the railway industry, the scientific basis of the guidance is not always easy to trace. Expect to hear more of that as inquiries progress.

The fundamental truth of such regulations is that it is the building professionals themselves who write them. Who else? That does not mean that the professionals, even in conclave, are infallible. Nobel laureate psychologist Daniel Kahneman has written extensively about the bounded rationality that limits everybody’s individual, or group, vision beyond a limited range of experiences, values and prejudices. Experts are just as prone as anybody. You and me too.5 It is unlikely that software will do a better job. Expert systems will work, here I go again, in “an environment that is sufficiently regular to be predictable”. I heard Daniel Dennett speak in London recently. Software will provide us with tissues not colleagues.

All this feeds into the collateral phenomenon whereby businesses actively use their expert involvement in setting regulations as a strategy to capture market share, promote their own products and erect barriers against entry for would-be competitors. The extreme consequence here is regulator capture, where the regulator becomes so dependent upon the expertise of the regulated that she is glad to let them define the regulatory regime.

To some extent, the self validating nature of expertise is reinforced, at least in the UK, by the approach of the courts. In assessing the negligence of a professional an individual is judged against the standards of his profession. Only where no reasonable member of his profession would have acted as he did is he negligent.6, 7 But the courts have warned that, in some circumstances, they might call into question the standards of a whole profession if there were a failure of logic. That is something that the courts would never do lightly.8 It would be a spectacle indeed.

When it comes to professional responsibility, the courts refuse to be dazzled by statutory regulations or industry standards. Professionals are expected to exercise their judgment and not hide behind mere compliance.9 In 2003, giving judgment against a firm of architects for inadequate fire precautions in a food factory refurbishment, Judge Bowsher QC observed:10

I should add that I was not the slightest impressed by the submission that since the defendants had complied with their statutory requirements … they had fully performed their duties.

This is what a judge said in a different case concerning the safety of a flight of stairs.11

Looking at a photograph of the stairs, I myself would form the view that they are reasonably safe … But it is the fact that the stairs did not comply with the Building Regulations, or the relevant British Standard. That is evidence which we must certainly take into account. It represents the current professional opinion as to what is desirable in order that accidents should be avoided. But it is one thing to lay down regulations and standards, with that objective, and another to define what is reasonably safe in the circumstances of a particular case [emphasis added].

In any event, trying to manage a risk by statutory regulation is not so efficient a means as you might think. Regulations do not always ensure the best outcome for society.12

Trust in elites

That all leaves the elite professions with grave responsibilities. Let none of us deny that another salient feature of the professions is that they are businesses run to make a profit for the professionals. Members get the further reward of status in society. I know that we have all constructed narratives of our own expertise and that challenges, particularly from clients, are not always welcome. We think we know best and we don’t always want to waste the client’s time explaining what to us seems so obvious.

And when things go wrong, and they will, all that is thrown back at us. Quite justifiably. Trust in bureaucracy has been a recurrent theme on this blog. It is a complex matter. When it leads to herd immunity from disease it is good. When it leads to complicity in torture it is bad. The public trust we aspire to is not blind faith. It is collaboration. Blind faith leads to bad consequences, collaboration to an environment where professionals are able to explain and reassure. Reflecting on that, I think there are some things was all can do to improve that relationship of trust.

Listen The most useful person on a project is often the person who knows nothing about it. She can ask the dumb question. Physicists told Gulgielmo Marconi he would not be able to transmit a radio signal across the Atlantic. But he did, not because he knew something the physicists didn’t but because sometimes it takes an unashamed maverick to test an orthodoxy.13 There are sundry examples of rumours and folk tales that have sparked scientific curiosity and discovery. Sometimes data is the plural of anecdote. It’s not even all about testing scientific theories. People sometimes need confidence and reassurance in unfamiliar situations. They need to be told, in language they understand, what is happening and why you think this is a good idea. Their questions and reservations need to be taken seriously.

A signal is a signal One of the key skills for any professional is being able to distinguish signal from noise. Where there is a signal, a surprise, that suggests an established orthodoxy has stopped working then you must immediately take action to protect those at risk. The “regular environment” you relied on is blown. Don’t wait to see if it happens again. Don’t dismiss it as a “one off” or, heaven forbid, the most useless word in the English language, an “outlier”. It is the signals that contain all the information. Don’t relax when the signal isn’t repeated immediately. That is just regression to the mean. It’s what signals do. Something that you didn’t expect has happened. Pierce the veil of bounded rationality. Protect the client, investigate and look to update your practice.

Noise is noise The corollary to taking signals seriously is not mistaking noise for signal. When that happens we start looking for causes specific of an individual outcome when the true causes were generic to all outcomes. Professionals also need to know when they are embedded in a “stable system of trouble”. That brings its own challenges, not least of which is the cost and effort of perpetually protecting the client.

Humility Professionals don’t always get it right. There are individual errors. There are systemic failures of practice. If you have to start hiding behind the shield that you are beyond challenge and that dissenting views are outlawed then you are probably dismissing the best hope you have for avoiding problems.

Continual improvement We have to keep listening to counsels of despair from politicians about productivity. It is down to us. Continual improvement is not just for our individual domain expertise, it’s also about getting better at listening, distinguishing signal from noise and practising humility. It’s about getting better at improving too.

Professionals have bodies to kick and souls to damn

Over two hundred years ago, English judge Edward Thurlow famously observed that corporations have neither bodies to kick nor souls to damn. I am always baffled by calls, in cases like that of Grenfell Tower, for prosecutions for corporate manslaughter. The calls seem to reflect a mistaken sentiment that corporate manslaughter is some sort of aggravated form of manslaughter. This isn’t just manslaughter, it’s corporate manslaughter. But why would anybody want to relieve individuals of responsibility and impose it on a faceless abstraction?

Part of the deal when seeking certification as a professional is that you assume a responsibility to society. That’s where you get your status from. When you fail you will be held to account. There are always voices calling for an end to blame culture. But have no doubt, it is a professional’s duty to act within the standards she has adopted. If she falls below those standards then reparation is expected, to the extent that it remains possible. Anybody who causes death when they fall sufficiently far below standard can expect to be indicted for manslaughter and, on conviction, punished and shamed. There is a principle in criminal law called fair labelling. The name of a crime must reflect the offence. Manslaughter is a fair label in such cases.

There has been an increasing tendency in the UK for legislators to take power to order reparation away from the civil courts and to attempt to regulate with criminal sanctions. I am not persuaded that is always the right approach.

Trust in elites, bureaucrats, experts, call them what you will, is important. South African statesman Paul Kruger once remarked:

When I look at history I’m a pessimist. When I look at pre-history I’m an optimist.

If you live in the UK then, on any measure you can dream up, life is getting safer and better. That is the triumph of elite engineers, planners, security professionals, physicians … I could go on. If people at large lose faith in professionals then it will be to our common ruin. Only the professionals can work on building the trust we need. Politicians won’t do it.

What did you do today?


  1. Wikimedia Commons contributors, “File:Grenfell Tower fire (wider view).jpg,” Wikimedia Commons, the free media repository, title=File:Grenfell_Tower_fire_(wider_view).jpg&oldid=248417865 (accessed June 25, 2017)
  2. Vaughan, D (1996) The Challenger Launch Decision: Risky Technology, Culture, and Deviance at NASA, University Of Chicago Press
  3. Fukuyama, F (2012) The Origins of Political Order: From Prehuman Times to the French Revolution, Profile Books, p207
  4. Kim, Sung Ho, “Max Weber”, The Stanford Encyclopedia of Philosophy (Fall 2012 Edition), Edward N. Zalta (ed.)
  5. Kahneman, D (2011) Thinking, Fast and Slow, Allen Lane, pp199-254
  6. Bolam v Friern Hospital Management Committee [1957] 1 WLR 582
  7. Pantelli Associates Ltd v Corporate City Developments Number Two Ltd [2010] EWHC 3189 (TCC)
  8. Bolitho v City and Hackney Health Authority [1996] 4 All ER 771
  9. Charlesworth & Percy on Negligence, 12th ed., 2010 and supplements, 7-46
  10. Sahib Foods Ltd & Ors v Paskin Kyriakides Sands (A Firm) [2003] EWHC 142 (TCC) at [43]
  11. Green v Building Scene Limited [1994] PIQR P259, CA at 269
  12. Coase, R H (1960) “The problem of social cost” Journal of Law and Economics 3, 1-44
  13. Raboy, M (2016) Marconi: The Man Who Networked the World, Oxford, p176

Building targets, constructing behaviour

Recently, the press reported that UK construction company Bovis Homes Group PLC have run into trouble for encouraging new homeowners to move into unfinished homes and have therefore faced a barrage of complaints about construction defects. It turns out that these practices were motivated by a desire to hit ambitious growth targets. Yet it has all had a substantial impact on trading position and mark downs for Bovis shares.1

I have blogged about targets before. It is worth repeating what I said there about the thoughts of John Pullinger, head of the UK Statistics Authority. He gave a trenchant warning about the “unsophisticated” use of targets. He cautioned:2

Anywhere we have had targets, there is a danger that they become an end in themselves and people lose sight of what they’re trying to achieve. We have numbers everywhere but haven’t been well enough schooled on how to use them and that’s where problems occur.

He went on.

The whole point of all these things is to change behaviour. The trick is to have a sophisticated understanding of what will happen when you put these things out.

That message was clearly one that Bovis didn’t get. They legitimately adopted an ambitious growth target but they forgot a couple of things. They forgot that targets, if not properly risk assessed, can create perverse incentives to distort the system. They forgot to think about how manager behaviour might be influenced. Leaders need to be able to harness insights from behavioural economics. Further, a mature system of goal deployment imposes a range of metrics across a business, each of which has to contribute to the global organisational plan. It is no use only measuring sales if measures of customer satisfaction and input measures about quality are neglected or even deliberately subverted. An organisation needs a rich dashboard and needs to know how to use it.

Critically, it is a matter of discipline. Employees must be left in no doubt that lack of care in maintaining the integrity of the organisational system and pursuing customer excellence will not be excused by mere adherence to a target, no matter how heroic. Bovis was clearly a culture where attention to customer requirements was not thought important by the staff. That is inevitably a failure of leadership.

Compare and contrast

Bovis are an interesting contrast with supermarket chain Sainsbury’s who featured in a law report in the same issue of The Times.3 Bovis and Sainsbury’s clearly have very different approaches as to how they communicate to their managers what is important.

Sainsbury’s operated a rigorous system of surveying staff engagement which aimed to embrace all employees. It was “deeply engrained in Sainsbury’s culture and was a critical part of Sainsbury’s strategy”. An HR manager sent an email to five store managers suggesting that the rigour could be relaxed. Not all employees needed to be engaged, he said, and participation could be restricted to the most enthusiastic. That would have been a clear distortion of the process.

Mr Colin Adesokan was a senior manager who subsequently learned of the email. He asked the HR manager to explain what he had meant but received no response and the email was recirculated. Adesokan did nothing. When his inaction came to the attention of the chief executive, Adesokan was dismissed summarily for gross misconduct.

He sued his employer and the matter ended up in the Court of Appeal, Adesokan arguing that such mere inaction over a colleague’s behaviour was incapable of constituting gross misconduct. The Court of Appeal did not agree. They found that, given the significance placed by Sainsbury’s on the engagement process, the trial judge had been entitled to find that Adesokan had been seriously in dereliction of his duty. That failing constituted gross misconduct because it had the effect of undermining the trust and confidence in the employment relationship. Adesokan seemed to have been indifferent to what, in Sainsbury’s eyes, was a very serious breach of an important procedure. Sainsbury’s had been entitled to dismiss him summarily for gross misconduct.

That is process discipline. That is how to manage it.

Display constancy of purpose in communicating what is important. Do not turn a blind eye to breaches. Do not tolerate those who would turn the blind eye. When you combine that with mature goal deployment and sophistication as to how to interpret variation in metrics then you are beginning to master, at least some parts of, how to run a business.


  1. “Share price plunges as Bovis tries to rebuild customers’ trust” (paywall), The Times (London), 20 February 2017
  2. “Targets could be skewing the truth, statistics chief warns” (paywall), The Times (London), 26 May 2014
  3. Adesokan v Sainsbury’s Supermarkets Ltd [2017] EWCA Civ 22, The Times, 21 February 2017 (paywall)

UK railway suicides – 2016 update

The latest UK rail safety statistics were published in September 2016, again absent much of the press press fanfare we had seen in the past. Apologies for the long delay but the day job has been busy. Regular readers of this blog will know that I have followed the suicide data series, and the press response, closely in 20152014, 2013 and 2012. Again, I “Cast a cold eye/ On life, on death.” Again I have re-plotted the data myself on a Shewhart chart.


Readers should note the following about the chart.

  • Many thanks to Tom Leveson Gower at the Office of Rail and Road who confirmed that the figures are for the year up to the end of March.
  • Some of the numbers for earlier years have been updated by the statistical authority.
  • I have recalculated natural process limits (NPLs) as there are still no more than 20 annual observations, and because the historical data has been updated. The NPLs have therefore changed in that the 2014 total is no longer above the upper NPL.
  • The observation above the upper NPL in 2015 has not persisted. The latest total is within the NPLs. We have to think about how to interpret this.

The current chart shows two signals, an observation above the upper NPL in 2015 and a run of 8 below the centre line from 2002 to 2009. As I always remark, the Terry Weight rule says that a signal gives us license to interpret the ups and downs on the chart. So I shall have a go at doing that. Last year I was coming to the conclusion that the data increasingly looked like a gradual upward trend. Has the 2016 data changed that?

The Samaritans posted on their website, “Rail suicides fall by 12%,” and went on to say:

Suicide prevention measures put in place as part of the partnership between Samaritans, Network Rail and the wider rail industry are saving more lives on the railways.

In fairness, the Samaritans qualified their headline with the following footnote.

We must be mindful that suicide data is best understood by looking at trends over longer periods of time, and year-on-year fluctuations may not be indicative of longer term trends. It is however very encouraging to see such a decrease which we would hope to see continuing in future years.

The Huffington Post, no, not sure I really think of them as part of the MSM, were less cautious in banking the 12% by stating, “It is the first time the number has dropped in three years.” True, but #executivetimeseries!

Signal or noise?

What shall we make of the decrease, a decrease to  “back within” the NPLs? First, the mere fact that there are fewer suicides is good news. That is a “better” outcome. The question still remains as to whether we are making progress in reducing the frequency of suicides. Has there been a change to the underlying cause system that drives the suicide numbers? We might just be observing noise unrelated to an underlying signal or trend. Remember that extremely high measurements are usually followed by lower ones because of the principle of regression to the mean.1 Such a decrease is no evidence of an underlying improvement but merely a deceptive characteristic of common cause variation.

One thing that I can do is to try to fit a trend line through the data and to ask which narrative best fits what I observe, a continuing increasing trend or a trend that has plateaued or even reversed. As you know, I am very critical of the uncritical casting of regression lines on data plots. However, this time I have a definite purpose in mind. Here is the data with a fitted linear regression line.


What I wanted to do was to split the data into two parts:

  • A trend (linear simply for the sake of exploratory data analysis (EDA); and
  • The residual variation about the trend.

The question I want to ask is whether the residual variation is stable, just plain noise, or whether there is a signal there that might give me a clue that a linear trend does not hold. The way that I do that is to plot the residuals on a Shewhart chart.


That shows a stable pattern of residuals. If I try to interpret the chart as a linear trend plus exchangeable noise then nothing in the data contradicts that. The original chart invites an interpretation, because of the signals. I adopt the interpretation of an increasing trend. Nothing in the data contradicts that. I can put the pictures together to show this model.


My opinion is that, when I plot the data that way, I have a compelling picture of a growing trend about which there is some stable common cause variation. Had there been an observation below the lower NPL on the last chart then that could have been evidence that the trend was slowing or even reversing. But not here.

I note that there’s also a report here from Anna Taylor and her colleagues at the University of Bristol. They too find an increasing trend with no signal of amelioration. They have used a different approach from mine and the fact that we have both got to the same broad result should reinforce confidence in out common conclusion.

Measurement Systems Analysis

Of course, we should not draw any conclusions from the data without thinking about the measurement system. In this case there is a legal issue. It concerns the standard of proof that the law requires coroners to apply before finding suicide as the cause of death. Findings of fact in inquests in England and Wales are generally made if they satisfy the civil standard of proof, the balance of probabilities. However, a finding of suicide can only be returned if such a conclusion satisfies the higher standard of beyond reasonable doubt, the typical criminal standard.2 There have long been suggestions that that leads to under reporting of suicides.3 The Matthew Elvidge Trust is currently campaigning for the general civil standard of balance of probabilities to be adopted.4

Next steps

Previously I noted proposals to repeat a strategy from Japan of bathing railway platforms with blue light. In the UK, I understand that such lights were installed at Gatwick in summer 2014 but I have not seen any data or heard anything more about it.

A huge amount of sincere endeavour has gone into this issue but further efforts have to be against the background that there is an escalating and unexplained problem.


  1. Kahneman, D (2011) Thinking, Fast and Slow, Allen Lane, pp175-184
  2. Jervis on Coroners 13th ed. 13-70
  3. Chambers, D R (1989) “The coroner, the inquest and the verdict of suicide”, Medicine, Science and the Law 29, 181
  4. Trust responds to Coroner’s Consultation“, Mathew Elvidge Trust, retrieved 4/1/17

Plan B, gut feel and Shewhart charts

Elizabeth Holmes 2014 (cropped).jpgI honestly had the idea for this blog and started drafting it six months ago when I first saw this, now quite infamous, quote being shared around the internet.

The minute you have a back-up plan, you’ve admitted you’re not going to succeed.

Elizabeth Holmes

Good advice? I think not! Let’s review some science.

Confidence and trustworthiness

As far back as the 1970s, psychologists carried out a series of experiments on individual confidence.1 They took a sample of people and set each of them a series of general knowledge questions. The participants were to work independently of each other. The questions were things like What is the capital city of France? The respondents had, not only to do their best to answer the question, but also then to state the probability that they had answered correctly.

As a headline to their results the researchers found that, of all those answers in the aggregate about which people said they were 100% sure that they had answered correctly, more than 20% were answered incorrectly.

Now, we know that people who go around assigning 100% probabilities to things that happen only 80% of the time are setting themselves up for inevitable financial loss.2 Yet, this sort of over confidence in the quality and reliability of our individual, internal cognitive processes has been identified and repeated over multiple experiments and sundry real life situations.

There is even a theory that the only people whose probabilities are reliably calibrated against frequencies are those suffering from clinically diagnosed depression. The theory of depressive realism remains, however, controversial.

Psychologists like Daniel Kahneman have emphasised that human reasoning is limited by a bounded rationality. All our cognitive processes are built on individual experience, knowledge, cultural assumptions, habits for interpreting data (good, bad and indifferent) … everything. All those things are aggregated imperfectly, incompletely and partially. Nobody can can take the quality of their own judgments for granted.

Kahneman points out that, in particular, wherever individuals engage sophisticated techniques of analysis and rationalisation, and especially those tools that require long experience, education and training to acquire, there is over confidence in outcomes.3 Kahneman calls this the illusion of validity. The more thoroughly we construct an internally consistent narrative for ourselves, the more we are seduced by it. And it is instinctive for humans to seek such cogent models for experience and aspiration. Kahneman says:4

Confidence is a feeling, which reflects the coherence of the information and the cognitive ease of processing it. It is wise to take admissions of uncertainty seriously, but declarations of high confidence mainly tell you that an individual has constructed a coherent story in their mind, not necessarily that the story is true.

If illusion is the spectre of confidence then having a Plan B seems like a good idea. Of course, Holmes is correct that having a Plan B will tempt you to use it. When disappointments accumulate, in escalating costs, stagnating revenues or emerging political risks, it is very tempting to seek the repose of a lesser ambition or even a managed mitigation of residual losses.

But to proscribe a Plan B in order to motivate success is to display the risk appetite of a Kamikaze pilot. Sometimes reality tells you that your business plan is predicated on a false prospectus. Given the science of over confidence and the narrative of bounded rationality, we know that it will happen a lot of the time.

GenericPBCHolmes is also correct that disappointment is, in itself, no reason to change plan. What she neglects is that there is a phenomenon that does legitimately invite change: a surprise. It is a surprise that alerts us to an inconsistency between the real world and our design. A surprise ought to make us go back to our working business plan and examine the assumptions against the real world data. A switch to Plan B is not inevitable. There may be other means of mitigation: Act, Adapt or Abandon. The surprise could even be an opportunity to be grasped. The Plan B doesn’t have to be negative.

How then are we to tell a surprise from a disappointment? With a Shewhart chart of course. The chart has the benefits that:

  • Narrative building is shared not personal.
  • Narratives are challenged with data and context.
  • Surprise and disappointment are distinguished.
  • Predictive power is tested.

Analysis versus “gut feel”

I suppose that what lies behind Holmes’ quote is the theory that commitment and belief can, in themselves, overcome opposing forces, and that a commitment borne of emotion and instinctive confidence is all the more potent. Here is an old Linkedin post that caught my eye a while ago celebrating the virtues of “gut feel”.

The author believed that gut feel came from experience and individuals of long exposure to a complex world should be able to trump data with their intuition. Intuition forms part of what Kahneman called System 1 thinking which he contrasted with the System 2 thinking that we engage in when we perform careful and lengthy data analysis (we hope).5 System 1 thinking can be valuable. Philip Tetlock, a psychologist who researched the science of forecasting, noted this.6

Whether intuition generates delusion or insight depends on whether you work in a world full of valid cues you can unconsciously register for future use.

In fact, whether the world is full of the sorts of valid clues that support useful predictions is exactly the question that Shewhart charts are designed to answer. Whether we make decisions on data or on gut feel, either can mislead us with the illusion of validity.

Again, what the chart supports is the continual testing of the reliability and utility of intuitions. Gut feel is not forbidden but be sure that the successive predictions and revisions will be recorded and subjected to the scrutiny of the Shewhart chart. Impressive records of forecasting will form the armature of a continually developing shared narrative of organisational excellence. Unimpressive forecasters will have to yield ground.


  1. Lichtenstein, S et al. (1982) “Calibration of probabilities: The state of the art to 1980” in Kahneman, D et al. Judgment Under Uncertainty: Heuristics and Biases, Cambridge University Press
  2. De Finetti, B (1974) Theory of Probability: A Critical Introductory Treatment, Vol.1, trans. Machi, A & Smith, A; Wiley, p113
  3. Kahneman, D (2011) Thinking, Fast and Slow, Allen Lane, p217
  4. p212
  5. pp19-24
  6. Tetlock, P (2015) Superforecasting: The Art and Science of Prediction, Crown Publishing, Kindle loc 1031

Productivity and how to improve it: II – Profit = Customer value – Cost

I said I would be posting on this topic way back here. Perhaps that says something about my personal productivity but I have been being productive on other things. I have a day job.

I wanted to start off addressing customer value and waste. Here are a couple of revealing stories from the press.

Blue dollars and green dollars


This story appeared on the BBC website about a pizza restaurant transferring the task of slicing lemons from the waiters to the kitchen staff. As you know I am rarely impressed by standards of data journalism at the state owned BBC. This item makes one of the gravest errors of attempted business improvement. It had been the practice that waiters, as their first job in the morning, would chop lemons for the day’s anticipated drinks orders. A pizza chef commented that chopping was one of the chefs’ trade skills. Lemon chopping should be transferred to the chefs. That would, purportedly, save the waiters from having to “take a break from their usual tasks, wash their hands, clear a space and then clean up after themselves.” The item goes on:

“Just by changing who chops the lemons, we were able to make a significant saving in hours which translates into a significant financial saving,” says Richard Hodgson, Pizza Express’ chief executive.

This looks, to the uncritical eye, like a saving. But it is a saving in what we call blue dollars (or pounds or euros). It appears in blue ink on an executive summary or monthly report. Did Pizza Express actually save any cash, as we call it green dollars (or …)? Did the initiative put a ding in the profit and loss account?

Perhaps it did but perhaps not. It is, actually, very easy to eliminate, or perhaps hide or redeploy, tasks or purchases and claim a saving in blue dollars. Demonstrating that this then mapped into a saving in green dollars requires committed analytics and the trenchant criticism of historical data. The blue dollars will turn into green dollars if Pizza Express can achieve a time saving that allows:

  • A reduction in payroll; or
  • Redeployment of time into an activity that creates greater value for the customer.

That is assuming that the initiative did result in a time saving. What it certainly lost was a team building opportunity between waiters and chefs and a signal for waiters to wash their hands.

The jury is out as to whether Pizza Express improved productivity. Translation of blue dollars into green dollars is not easy. It is certainly not automatic. Turning blue dollars into green dollars is the really tricky bit in improvement. The bit that requires all the skill and know-how. It turns on the Nolan and Provost question: How will you know when a change is an improvement? More work is needed here to persuade anybody of anything. More work is certainly needed by the BBC in improving their journalism.

Politicians don’t get it

I asked above if the freed time could be translated into an activity that creates greater value for the customer. The value of a thing is what somebody is willing to pay for it. When we say that an activity creates value we mean that it increases the price at which we can sell output. The importance of price is that it captures a revealed preference rather than just a casual attitude for which the subject will never have to give an account. Any activity that does not create value for the customer is waste. The Japanese word muda has become fashionable. It is at the core of achieving operational excellence that unrelenting, gradual and progressive elimination of waste is a daily activity for everybody in the organisation. Waste, everything that does not create value for the customer. Everything that does not make the customer willing to pay more. If the customer will not pay more there is no value for them.

John Redwood was a middle ranking official in John Major’s government of the 1990s though he had frustrated ambitions for higher office. He offered us his personal thoughts on productivity here. I think he illustrates how poorly politicians understand what productivity is. Redwood thinks that we are over simplifying things when we say that productivity is:


or, a better definition:


Redwood thinks that, in the service sector, “labour intensity is often seen as better service rather than as worse productivity”. It may be true but only in so far as the customer sees it as such and is willing to pay proportionately for the staffing. Where the customer will not pay then productivity is reduced and insisting that labour intensity is an inherent virtue is a delusion. I think this is the basis of what Redwood is trying to say about purchasing coffee from a store. The test is that the customer is willing to pay for the experience.

However imperfect the statistics, they do seek to capture what the customers have been willing to pay. The spend at the coffee stand should show up on the aggregated statistics for “customer value created” and so the retail coffee phenomenon will not manifest itself as a decrease in productivity. Redwood has completely misunderstood.

Of course there are measurement issues and they are serious ones. There is nothing though that suggests that the concept or its definition are at fault.

What is worrying is that Redwood’s background is in banking though I certainly know bankers who are less out of touch with the real world. Redwood needs to get that the fundamental theorem of business is that:

profit = price – cost

— and that price is set by the market. There are only two things to do to improve.

  • Develop products that enhance customer value.
  • Eliminate costs that do not contribute to customer value.

UK figures

I could not find a long-term productivity time series on the UK Office for National Statistics website (“ONS”). I think that is shameful. You know that I am always suspicious of politicians’ unwillingness to encourage sharing long term statistical series. I managed to find what I was looking for here at Click on the “MAX” tab on the chart.

That chart gave me a suspicion. The ONS website does have the data from 2008. There is a link to this data after Figure 3 of the ONS publication Labour Productivity: Oct to Dec 2015. However, all the charts in that publication are fairly hideous and lacking in graphical excellence. Here is the 2008 to 2015 data replotted.


I am satisfied that, following the steep drop in UK productivity coinciding with the world financial crisis of 2007/08, there has been a (fairly) steady rise in productivity to the region of pre-crash levels. Confirming that with a Shewhart chart is left as an exercise for the reader. Of course, there is common cause variation around the upward trend. And, I suspect, some special causes too. However, I think that inferences of gloom following the Quarter 4 2015 figures, the last observation plotted, are premature. A bad case of #executivetimeseries.

I think that makes me less gloomy about UK productivity than the press and politicians. I have a suspicion that growth since 2008 has been slower than historically but I do not want to take that too far here.

Coming next: Productivity and how to improve it III – Signal and noise



Why would a lawyer blog about statistics?

Brandeis and Taylor… is a question I often get asked. I blog here about statistics, data, quality, data quality, productivity, management and leadership. And evidence. I do it from my perspective as a practising lawyer and some people find that odd. Yet it turns out that the collaboration between law and quantitative management science is a venerable one.

The grandfather of scientific management is surely Frederick Winslow Taylor (1856-1915). Taylor introduced the idea of scientific study of work tasks, using data and quantitative methods to redesign and control business processes.

Yet one of Taylorism’s most effective champions was a lawyer, Louis Brandeis (1856-1941). In fact, it was Brandeis who coined the term scientific management.


Taylor was a production engineer who advocated a four stage strategy for productivity improvement.

  1. Replace rule-of-thumb work methods with methods based on a scientific study of the tasks.
  2. Scientifically select, train, and develop each employee rather than passively leaving them to train themselves.
  3. Provide “Detailed instruction and supervision of each worker in the performance of that worker’s discrete task”.1
  4. Divide work nearly equally between managers and workers, so that the managers apply scientific management principles to planning the work and the workers actually perform the tasks.

Points (3) and (4) tend to jar with millennial attitudes towards engagement and collaborative work. Conservative political scientist Francis Fukuyama criticised Taylor’s approach as “[epitomising] the carrying of the low-trust, rule based factory system to its logical conclusion”.2 I have blogged many times on here about the importance of trust.

However, (1) and (2) provided the catalyst for pretty much all subsequent management science from W Edwards Deming, Elton Mayo, and Taiichi Ohno through to Six Sigma and Lean. Subsequent thinking has centred around creating trust in the workplace as inseparable from (1) and (2). Peter Drucker called Taylor the “Isaac Newton (or perhaps the Archimedes) of the science of work”.

Taylor claimed substantial successes with his redesign of work processes based on the evidence he had gathered, avant la lettre, in the gemba. His most cogent lesson was to exhort managers to direct their attention to where value was created rather than to confine their horizons to monthly accounts and executive summaries.

Of course, Taylor was long dead before modern business analytics began with Walter Shewhart in 1924. There is more than a whiff of the #executivetimeseries about some of Taylor’s work. Once management had Measurement System Analysis and the Shewhart chart there would no longer be any hiding place for groundless claims to non-existent improvements.


Brandeis practised as a lawyer in the US from 1878 until he was appointed a Justice of the Supreme Court in 1916. Brandeis’ principles as a commercial lawyer were, “first, that he would never have to deal with intermediaries, but only with the person in charge…[and] second, that he must be permitted to offer advice on any and all aspects of the firm’s affairs”. Brandies was trenchant about the benefits of a coherent commitment to business quality. He also believed that these things were achieved, not by chance, but by the application of policy deployment.

Errors are prevented instead of being corrected. The terrible waste of delays and accidents is avoided. Calculation is substituted for guess; demonstration for opinion.

Brandeis clearly had a healthy distaste for muda.3 Moreover, he was making a land grab for the disputed high ground that these days often earns the vague and fluffy label strategy.

The Eastern Rate Case

The worlds of Taylor and Brandeis embraced in the Eastern Rate Case of 1910. The Eastern Railroad Company had applied to the Interstate Commerce Commission (“the ICC”) arguing that their cost base had inflated and that an increase in their carriage rates was necessary to sustain the business. The ICC was the then regulator of those utilities that had a monopoly element. Brandeis by this time had taken on the role of the People’s Lawyer, acting pro bono in whatever he deemed to be the public interest.

Brandeis opposed the rate increase arguing that the escalation in Eastern’s cost base was the result of management failure, not an inevitable consequence of market conditions. The cost of a monopoly’s ineffective governance should, he submitted, not be born by the public, nor yet by the workers. In court Brandeis was asked what Eastern should do and he advocated scientific management. That is where and when the term was coined.4


The insight that profit cannot simply be wished into being by the fiat of cost plus, a fortiori of the hourly rate, is the Milvian bridge to lean.

But everyone wants to occupy the commanding heights of an integrated policy nurturing quality, product development, regulatory compliance, organisational development and the economic exploitation of customer value. What’s so special about lawyers in the mix? I think we ought to remind ourselves that if lawyers know about anything then we know about evidence. And we just might know as much about it as the statisticians, the engineers and the enforcers. Here’s a tale that illustrates our value.

Thereza Imanishi-Kari was a postdoctoral researcher in molecular biology at the Massachusetts Institute of Technology. In 1986 a co-worker raised inconsistencies in Imanishi-Kari’s earlier published work that escalated into allegations that she had fabricated results to validate publicly funded research. Over the following decade, the allegations grew in seriousness, involving the US Congress, the Office of Scientific Integrity and the FBI. Imanishi-Kari was ultimately exonerated by a departmental appeal board constituted of an eminent molecular biologist and two lawyers. The board heard cross-examination of the relevant experts including those in statistics and document examination. It was that cross-examination that exposed the allegations as without foundation.5

Lawyers can make a real contribution to discovering how a business can be run successfully. But we have to live the change we want to be. The first objective is to bring management science to our own business.

The black-letter man may be the man of the present but the man of the future is the man of statistics and the master of economics.

Oliver Wendell Holmes, 1897


  1. Montgomery, D (1989) The Fall of the House of Labor: The Workplace, the State, and American Labor Activism, 1865-1925, Cambridge University Press, p250
  2. Fukuyama, F (1995) Trust: The Social Virtues and the Creation of Prosperity, Free Press, p226
  3. Kraines, O (1960) “Brandeis’ philosophy of scientific management” The Western Political Quarterly 13(1), 201
  4. Freedman, L (2013) Strategy: A History, Oxford University Press, pp464-465
  5. Kevles, D J (1998) The Baltimore Case: A Trial of Politics, Science and Character, Norton

Regression done right: Part 3: Forecasts to believe in

There are three Sources of Uncertainty in a forecast.

  1. Whether the forecast is of “an environment that is sufficiently regular to be predictable”.1
  2. Uncertainty arising from the unexplained (residual) system variation.
  3. Technical statistical sampling error in the regression calculation.

Source of Uncertainty (3) is the one that fascinates statistical theorists. Sources (1) and (2) are the ones that obsess the rest of us. I looked at the first in Part 1 of this blog and, the second in Part 2. Now I want to look at the third Source of Uncertainty and try to put everything together.

If you are really most interested in (1) and (2), read “Prediction intervals” then skip forwards to “The fundamental theorem of forecasting”.

Prediction intervals

A prediction interval2 captures the range in which a future observation is expected to fall. Bafflingly, not all statistical software generates prediction intervals automatically so it is necessary, I fear, to know how to calculate them from first principles. However, understanding the calculation is, in itself, instructive.

But I emphasise that prediction intervals rely on a presumption that what is being forecast is “an environment that is sufficiently regular to be predictable”, that the (residual) business process data is exchangeable. If that presumption fails then all bets are off and we have to rely on a Cardinal Newman analysis. Of course, when I say that “all bets are off”, they aren’t. You will still be held to your existing contractual commitments even though your confidence in achieving them is now devastated. More on that another time.

Sources of variation in predictions

In the particular case of linear regression we need further to break down the third Source of Uncertainty.

  1. Uncertainty arising from the unexplained (residual) variation.
  2. Technical statistical sampling error in the regression calculation.
    1. Sampling error of the mean.
    2. Sampling error of the slope

Remember that we are, for the time being, assuming Source of Uncertainty (1) above can be disregarded. Let’s look at the other Sources of Uncertainty in turn: (2), (3A) and (3B).

Source of Variation (2) – Residual variation

We start with the Source of Uncertainty arising from the residual variation. This is the uncertainty because of all the things we don’t know. We talked about this a lot in Part 2. We are content, for the moment, that they are sufficiently stable to form a basis for prediction. We call this common cause variation. This variation has variance s2, where s is the residual standard deviation that will be output by your regression software.


Source of Variation (3A) – Sampling error in mean

To understand the next Source of Variation we need to know a little bit about how the regression is calculated. The calculations start off with the respective means of the X values ( X̄ ) and of the Y values ( Ȳ ). Uncertainty in estimating the mean of the Y , is the next contribution to the global prediction uncertainty.

An important part of calculating the regression line is to calculate the mean of the Ys. That mean is subject to sampling error. The variance of the sampling error is the familiar result from the statistics service course.


— where n is the number of pairs of X and Y. Obviously, as we collect more and more data this term gets more and more negligible.


Source of Variation (3B) – Sampling error in slope

This is a bit more complicated. Skip forwards if you are already confused. Let me first give you the equation for the variance of predictions referable to sampling error in the slope.


This has now introduced the mysterious sum of squaresSXX. However, before we learn exactly what this is, we immediately notice two things.

  1. As we move away from the centre of the training data the variance gets larger.3
  2. As SXX gets larger the variance gets smaller.

The reason for the increasing sampling error as we move from the mean of X is obvious from thinking about how variation in slope works. The regression line pivots on the mean. Travelling further from the mean amplifies any disturbance in the slope.


Let’s look at where SXX comes from. The sum of squares is calculated from the Xs alone without considering the Ys. It is a characteristic of the sampling frame that we used to train the model. We take the difference of each X value from the mean of X, and then square that distance. To get the sum of squares we then add up all those individual squares. Note that this is a sum of the individual squares, not their average.


Two things then become obvious (if you think about it).

  1. As we get more and more data, SXX gets larger.
  2. As the individual Xs spread out over a greater range of XSXX gets larger.

What that (3B) term does emphasise is that even sampling error escalates as we exploit the edge of the original training data. As we extrapolate clear of the original sampling frame, the pure sampling error can quickly exceed even the residual variation.

Yet it is only a lower bound on the uncertainty in extrapolation. As we move away from the original range of Xs then, however happy we were previously with Source of Uncertainty (1), that the data was from “an environment that is sufficiently regular to be predictable”, then the question barges back in. We are now remote from our experience base in time and boundary. Nothing outside the original X-range will ever be a candidate for a comfort zone.

The fundamental theorem of prediction

Variances, generally, add up so we can sum the three Sources of Variation (2), (3A) and (3B). That gives the variance of an individual prediction, spred2. By an individual prediction I mean that somebody gives me an X and I use the regression formula to give them the (as yet unknown) corresponding Ypred.


It is immediately obvious that s2 is common to all three terms. However, the second and third terms, the sampling errors, can be made as small as we like by collecting more and more data. Collecting more and more data will have no impact on the first term. That arises from the residual variation. The stuff we don’t yet understand. It has variance s2, where s is the residual standard deviation that will be output by your regression software.

This, I say, is the fundamental theorem of prediction. The unexplained variation provides a hard limit on the precision of forecasts.

It is then a very simple step to convert the variance into a standard deviation, spred. This is the standard error of the prediction.4,5


Now, in general, where we have a measurement or prediction that has an uncertainty that can be characterised by a standard error u, there is an old trick for putting an interval round it. Remember that u is a measure of the variation in z. We can therefore put an interval around z as a number of standard errors, z±ku. Here, k is a constant of your choice. A prediction interval for the regression that generates prediction Ypred then becomes:


Choosing k=3 is very popular, conservative and robust.6,7 Other choices of k are available on the advice of a specialist mathematician.

It was Shewhart himself who took this all a bit further and defined tolerance intervals which contain a given proportion of future observations with a given probability.8 They are very much for the specialist.

Source of Variation (1) – Special causes

But all that assumes that we are sampling from “an environment that is sufficiently regular to be predictable”, that the residual variation is solely common cause. We checked that out on our original training data but the price of predictability is eternal vigilance. It can never be taken for granted. At any time fresh causes of variation may infiltrate the environment, or become newly salient because of some sensitising event or exotic interaction.

The real trouble with this world of ours is not that it is an unreasonable world, nor even that it is a reasonable one. The commonest kind of trouble is that it is nearly reasonable, but not quite. Life is not an illogicality; yet it is a trap for logicians. It looks just a little more mathematical and regular than it is; its exactitude is obvious, but its inexactitude is hidden; its wildness lies in wait.

G K Chesterton

The remedy for this risk is to continue plotting the residuals, the differences between the observed value and, now, the prediction. This is mandatory.


Whenever we observe a signal of a potential special cause it puts us on notice to protect the forecast-user because our ability to predict the future has been exposed as deficient and fallible. But it also presents an opportunity. With timely investigation, a signal of a possible special cause may provide deeper insight into the variation of the cause-system. That in itself may lead to identifying further factors to build into the regression and a consequential reduction in s2.

It is reducing s2, by progressively accumulating understanding of the cause-system and developing the model, that leads to more precise, and more reliable, predictions.


  1. Kahneman, D (2011) Thinking, Fast and Slow, Allen Lane, p240
  2. Hahn, G J & Meeker, W Q (1991) Statistical Intervals: A Guide for Practitioners, Wiley, p31
  3. In fact s2/SXX is the sampling variance of the slope. The standard error of the slope is, notoriously, s/√SXX. A useful result sometimes. It is then obvious from the figure how variation is slope is amplified as we travel father from the centre of the Xs.
  4. Draper, N R & Smith, H (1998) Applied Regression Analysis, 3rd ed., Wiley, pp81-83
  5. Hahn & Meeker (1991) p232
  6. Wheeler, D J (2000) Normality and the Process Behaviour Chart, SPC Press, Chapter 6
  7. Vysochanskij, D F & Petunin, Y I (1980) “Justification of the 3σ rule for unimodal distributions”, Theory of Probability and Mathematical Statistics 21: 25–36
  8. Hahn & Meeker (1991) p231