Waste makes productivity dead in the water

 A post by author Charles Hugh Smith hits the nail on the head about the ‘productivity puzzle’ – rising waste in all sectors, hardly mentioned by the experts, is mostly to blame

Productivity in the U.S. has been declining since the early 2000s. This trend mystifies economists, as the tremendous investments in software, robotics, networks and mobile computing would be expected to boost productivity, as these tools enable every individual who knows how to use them to produce more value.

One theory holds that the workforce has not yet learned how to use these tools, an idea that arose in the 1980s to explain the decline in productivity even as personal computers, desktop publishing, etc. entered the mainstream.

A related explanation holds that institutions and corporations are not deploying the new technologies very effectively for a variety of reasons: the cost of integrating legacy systems, insufficient training of their workforce, and hasty, ill-planned investments in mobile platforms that don’t actually yield higher productivity.

Productivity matters because producing more value with every unit of energy, every tool and every hour of labor is the foundation of higher wages, profits, taxes and general prosperity.

I have four theories about the secular decline in productivity, and all are difficult to model and back up with data, as they are inherently ambiguous and hard to quantify:

1. Mobile telephony and social media distract workers so significantly and ubiquitously that the work being produced has declined per worker/per hour of paid labo

2. Public and private institutions have become grossly inefficient and ineffective, soaking up any gains in productivity via their wasteful processes and institutionalized incompetence.

3. Our institutions have substituted signaling and compliance for productivity

4. The financial elites at the top of our neo-feudal economy have optimised protecting their skims and scams above all else; their focus is rigging the system in their favor and so productivity is of no concern to them.

Other commentators have noted the drain on productivity as workers constantly check their mobile phones and social media accounts–up to 400 times a day is average for many people.

“Addicted” is a loaded word, so let’s simply note the enormous “able-to-focus-without-interruptions” gap between those who only answer phone calls and limit social media to a few minutes per day in the evening during off-work time, and those who are distracted hundreds of times throughout the day.

Some tasks can be interrupted without much loss of productivity, but most knowledge-worker type tasks are decimated by this sort of constant distraction – even though the distracted worker will naturally claim that their productivity is unharmed.

The list of public institutions that now demand absurd wait times for minimal or even defective service keeps growing:

  •  The California Dept. of Motor Vehicles (DMV) now soaks up to eight hours of waiting to complete mundane tasks. Employees have been caught napping for hours, and customers waiting for service note the lines finally start moving in the last half-hour of the day when the employees are motivated to process the people in line so they can go home.
  • Other public-sector systems are equally Kafkaesque – building permits that once took hours to process now take months
  • In the private sector, it’s becoming increasingly difficult to fix problems created by the corporations themselves – multiple phone calls, long wait times, etc.

The core dynamic is that public institutions and corporate cartels lack any mechanisms to enforce transparency and accountability

There is no competitive pressure on the DMV or courts, and essentially zero competitive pressure on monopolies such as Facebook and Google and cartels such as the big healthcare insurers.

The only possible output of this system is extortion as a way of life:

  • We make you wait
  • We make you pay more for a poor quality service
  • We make you comply with useless regulations
  • We make you use buggy, bloated software, and so on.

Quasi-monopolies like Microsoft and Apple force tens of millions of users to re-learn new versions of software, detracting from productivity rather than enhancing it, despite their claims.

Other types of planned obsolescence are equally destructive.

With no mechanisms in place to enforce accountability and efficiency, there is no accountability or efficiency – so these monopolies and cartels can be as wasteful, inefficient and unaccountable as they want.

Compliance is a productivity killer – doctors and nurses no longer have enough time to serve patients because compliance now soaks up so much of their time.

Signaling, like compliance, is a productivity killer – the entire trillion-dollar system of higher education doesn’t measure or reward learning or the acquisition of knowledge; the diploma / credential signals that the student dutifully navigated the bureaucracy and is ready to be a corporate/government drone in another bureaucracy. That they learned next to nothing is of no concern to the system. If learning was the goal, we’d accredit the student, not the institution.

If we look at the economy as a whole, we find it is dominated by monopolies and cartels, public and private.

No wonder overall productivity is declining: there are no feedback loops or mechanisms to enforce transparency, accountability or pressures to improve efficiency and productivity gains on these neofeudal, extortionist structures.

For more, see ‘The Nearly Free University and the Emerging Economy’ ($2.99 Kindle, $15 print)

Misleading research metrics

In an article entitled ‘Capitalism is ruining science’, Meagan Day (for www.jacobinmag.com) points out that universities existed before capitalism and pursued not profit but truth and knowledge

But no longer

The modern university has become increasingly subservient to the imperatives of capitalism i.e. competition, profit maximisation and increasing labour productivity

In academia, this manifests itself as ‘publish or perish, funding or famine’

Without public investment, universities are compelled to play by private sector rules i.e. to operate like businesses, focus on their bottom line and constantly evaluate their inputs and outputs

Hence, according to researchers Marc Edwards and Siddhartha Roy, they have introduced new performance metrics which govern almost everything researchers do, including:

  • Publication counts
  • Citations
  • Journal impact factors
  • Total research dollars
  • Total patents

These metrics now dominate decision-making in faculty hiring, promotion and tenure, and awards – so academic scientists are increasingly driven to get their research published and cited – scientific output as measured by cited work has doubled every nine years since WW2, they say

But quantity does not equate to quality:

  • Rewards for publication volumes have resulted in scientific papers becoming shorter and less comprehensive, ‘boasting poor methods and an increase in false discovery rates’
  • The growing emphasis on work citations has resulted in reference lists becoming bloated to meet career needs due to peer reviewers requesting their own work be cited as a condition of publication

Meanwhile, because increased grant funding also includes more professional opportunities, scientists spend an outsize amount of time writing grant proposals and overselling the positive results of research to catch the attention of funders – and lose opportunities for careful contemplation and deep exploration, which are vital if they are to uncover complex truths

Sadly, the combination of perverse incentives and decreased funding increases pressures which can lead to unethical behaviour – and if a critical mass of scientists become untrustworthy, a tipping point may be reached where scientists are thought to be corrupt, and public trust is lost

Peter Higgs, the British theoretical physicist who, in 1964, predicted the existence of the Higgs boson particle, said he would never have been able to make his breakthrough in the current academic environment:

  • “It’s difficult to imagine how I would ever have enough peace and quiet in the present climate to do what I did in 1964
  • Today, I wouldn’t get an academic job – it’s as simple as that – I don’t think I would be regarded as productive enough
  • I became an embarrassment to the physics department at Edinburgh University when they did research assessment exercises – they would send around a message saying ‘please give us a list of your recent publications’
  • I would send back a statement – ‘None’
  • I was kept around, despite this, solely in the hope that I would win the Nobel Prize which would be a boon to the university

The noble purpose of any science academy is to provide the resources and encouragement for people to carry out rigorous experiments that will enhance collective knowledge about the world we live in

At present, those aspirations suffer when (US) austerity-minded administrations stem the tide of federal funding and institutions change their business models to suit

What to measure?

A sample of US managers’ views was recently published on performance measures they use

In essence, they said:

  • ‘App overload’ constantly disrupts work flows – they’re meant to streamline productivity and communications but do the opposite – most employees want a single platform for phone calls, chat, email and team messaging – so get rid of legacy solutions
  • Measure the average quantity of work on a given day or week – 5 hi-quality projects are usually better than 20 hastily written ones – so emphasise the value of quality over quantity to get a better idea of pace – then help them improve their efficiency to produce more without sacrificing quality
  • Identify where workflow bottlenecks are, plus track down causes of those slowdowns – collect KPIs like the number of client issues resolved or the amount of time employees spend training to use a particular piece of software
  • Have teams set meaningful goals and then plan meaningful actions each week that will take them closer towards those goals – productivity can then be measured by comparing our weekly accomplishments against our planned actions for the week e.g. quantity of phone calls, press releases, blog posts, bugs fixed, products delivered, candidates interviewed
  • Establish a baseline for the employee – then set clear and concise goals with the date and the exact expected result
  • Have employees log time for tasks they’ve completed, mark them as billable or not, and assign them to certain projects – then you see where time is being spent and whether it is being dedicated to customers or internal work (if they’re honest!)
  • Develop KPIs e.g. number of five star ratings or new client files opened for a given week, month or quarter
  • Set a reachable goal (with moderate effort), a reach goal (very high performance) and a stretch goal (extremely high performance) – they’re a great way to motivate team members to go beyond minimum expectations
  • Measure the team by:
    • Time spent on various tasks and completion rate
    • Quality of tasks performed
    • Attendance on training programs
    • Helping others achieve their goals
  • Break projects down into concise granular tasks – assign a deadline and accountable individual to each

Quick comments:

  • Customers are barely mentioned e.g. satisfaction levels, end-to-end waiting times from their standpoint, time spent dealing with demand that should never have occurred in the first place and brings in no extra revenue, only extra costs
  • The measures are all inward looking, to their internal team, resources and processes, when they should be outward focussed on their customers’ needs and wants
  • There’s no reference to measures of waste of resources or time are mentioned 
  • It’s always labour productivity – and never material, capital or knowledge productivity

Forget productivity growth in future?

The following are notes jotted down whilst reading a lecture (40 x A4 pages long) given by Adair Turner, Chair of INET (Institute for New Economic Thinking) in Washington DC in 2018

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Summary:

  • The lecture covers the possible long term impact of rapid technological progress – i.e. work automation and AI – on the nature of and need for work
  • What if all useful, versus zero-sum, human work could be automated, say 50 to 100 years on?
  • What are the implications for the distribution of income and wealth?
    • Incomes for useful work will fall to zero, but for zero-sum work will rise
    • We cannot rely on the market to determine acceptable income levels
    • Wealth will come more and more from land, brands and beauty
  • As technological advances accelerate, the national productivity growth rate will fall faster – so productivity should stop being a national priority
  • The current combination of rapid technological innovation and low measured productivity growth is exactly what we should expect

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Section 1 – When, not if, almost all economic activity is automated:

  • ICT progress means, in 50 years, we’ll be able to deploy unimaginably massive quantities of computing power, and automate almost all activities we call work and for which people receive income
  • Many jobs are repetitive/ predictable, others more complex/ thoughtful/ creative, and others a mix of the two
  • Automation reduces the time needed for the first and last categories, and so employment overall (if we continue to work 35 hour weeks)
  • Accommodation and food services are far more susceptible to automation than health and social services and education
  • ICT hardware costs keep on reducing – software originals cost a lot but marginal copies cost next to nothing
  • At some stage. combinations of hard and software will equal, and then far exceed, human intelligence
  • N.B.
    • The above merely extrapolates current known technological capabilities without any possibility given to whole new ideas/ sectors unexpectedly emerging for mopping up surplus labour and offering both new productivity improvement potential but also higher wages and jobs they want versus have to do – aka unknown unknowns – e.g. the internet, search engines and social media back in the 80s
    • Re humans being overtaken by machines, what if man implants IAs (Intelligent Assistants) to upgrade his grey cells – and keeps on upgrading by up/ downloading his brain contents for updates and extra capacity – and thus keeps well ahead of any machines alone so he’s not threatened by them?

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Section 2 – Explaining the Solow paradox i.e. “Computers are everywhere but in the productivity statistics”:

  • Why is measured productivity growth slowing down?
  • Because an acceleration in technological progress, which enables dramatic productivity improvement in some sectors, can be accompanied by displaced labour having no choice but to move to low-wage sectors, resulting in a decline in total measured productivity
  • How come?
    • Proliferation of low-productivity jobs – for those displaced by increasingly automated sectors:
      • In the past, labour displaced were able to move to new sectors which also had potential for productivity improvement e.g. agriculture to manufacturing, then on to some services
      • But once those with money have all they ‘must have’ plus ‘like to have’, they don’t provide the demand for more – they have ‘enough’ of what is currently on offer 
      • However, they might like a domestic servant or two, on very low wages – plus maybe the occasional painter or singer to entertain them
      • So aggregating all incomes from all (currently known) sectors arithmetically reduces GDP and national productivity because of the rapidly growing low-wage, high employment sectors
      • Thus, rapid productivity growth in one sector combined with low productivity in others results in lower overall productivity growth
      • Total productivity growth is as much driven by the productivity growth potential of the sectors into which workers move as those where they are automated away from – it’s simple arithmetic
      • The logic is that, eventually, all jobs will be automated, so all humans will be displaced to lower and lower productivity jobs – until there’s no productivity growth at all
      • Then we’ll need to ‘find something for them all to do’, albeit at lower rates of pay
    • Rise of zero-sum activities as nations get richer:
      • Zero-sum activities are those where more and more human talent can be applied (and higher and higher incomes paid) but not produce more GDP or value to humans
      • Examples include:
        • Criminals v Police – they balance each other out – they don’t add to the total sum of goods or services for increasing human welfare
        • Cyber criminals v cyber experts defending people and firms against them
        • Legal services – if divorce lawyers improve their quality and so results, the other side does the same, so soon one is back to square one
        • Corporate and IP lawyers – they secure new ventures or protect valuable IP rights, so benefit others
        • Tax accountants for minimising tax versus HMG tax officers for maximising tax-take
        • Marketing and advertising executives, and communications consultants – who seek to convince us that product A is better than B
        • Financial traders and asset managers – most add no value versus index investing
        • Financial regulators and compliance officers
        • Corporate financiers who organise M&As which rarely enhance shareholder value
        • Political campaigners and lobbyists who seek to influence votes one way or the other
      • But more education is good for all
      • And fashion design is a creative artistic process which adds to the variety and enjoyment of life
      • However, productivity improvement leads to the creation of more and more zero-sum jobs, many of which are not counted by GDP and cancel each other out anyway – another reason national productivity is seen to fall
      • If you apply AI to zero-sum jobs, you simply increase the intensity parties on both sides work at – it’s not their efficiency that matters but their effectiveness – did the lawyer win the case, or not?
    • Growth of low-cost/ free goods and services which enhance lives:
      • After automating so much and dispelling the need to work, why do we get a proliferation of low-paid jobs and zero-sum activities but no better human welfare – or do we underestimate the benefits to human welfare?
      • We only need a few very clever people plus AI to do wondrous things – e.g. to invent super drugs so we all live to 100, or forever evenand disease free – so the rest are mostly surplus to overall requirements
      • GDP accounting conventions – the methods, estimates and assumptions used – are flawed for the future viz:
        • GDP clocks salaries of the above clever inventors plus their sales – but when their patents expire, their sales revenue drops to very little yet their products and benefits continue
        • ‘GDP deflators’ used to cover price changes are suspect – all sorts of shenanigans are possible here
      • The knowledge of how to produce something can cost a lot – but the marginal cost of actually producing it can be peanuts
      • Professor Martin Feldstein -“Government statisticians are almost bound to underestimate the scale of productivity improvement – as low growth estimates fail to reflect the innovations in everything from healthcare to internet services to video entertainment that have made life better during these years”
      • Turner questions whether all innovations make life better
      • As unit prices collapse with technical advances, so do sales revenue and apparent benefits clocked by GDP – thus, GDP undercounts technological progress
      • And, as computers get more powerful, do our kids get happier/ less stressed – so is human welfare also not measured well?
      • Thus “real productivity growth fails to account for some of the most dramatic increases in productivity”
      • Note “the apparent paradox of expanding opportunities for automation combined with mediocre and declining productivity growth”
      • “With limitless potential to automate jobs, it is almost inevitable that we will observe a slowdown in measured productivity growth”
  • N.B.
    • What if we reduce average weekly work input hours to 15 (a la J M Keynes) for no loss of GDP? – productivity would gallop ahead
    • Can we really expect no more new hi-wage/ hi-employment/ hi-productivity sectors, as Turner seems to assume – just ones which have no automatable potential?
    • Where’s the huge benefit clocked of less work hours and more leisure hours – of more doing what we ‘want to do’ , not what we ‘have to do’ to earn money to pay for stuff we ‘must have’
    • Surely, the ultimate human aim is NOT to have to work at all, and instead be able to choose whether to potter in our gardens, mow our own lawns, launder our own clothes, sail boats or play golf with chums – this would not only fill available time enjoyably but allow us to develop what talents we may have enthusiastically
    • Over time, all sectors tend to peak as all potential to improve is addressed, first in quantum leaps then increasingly via marginal gains – but new sectors are new, and offer huge potential for clever people to address productivity issues therein

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Section 3 – Meaningless measures:

  • GDP measures have always been imperfect but, as we get richer, they become even more so – especially with new ICT collapsing hardware costs and enjoying zero cost software replication
  • GDP fails to reflect the pace of technical progress which enables us to deliver more with less
  • There’s a limit to how many cars or washing machines we want to buy, and as we reach those limits, labour must inevitably shift to activities which cannot be automated
  • Past measures were perhaps ‘good enough’ for policy-making to reflect technological advances and GDP/ productivity growth – and each generation feeling better off than the last one
  • But no longer, when GDP counts many activities which cannot possibly improve human welfare (e.g. social networks and always-on devices) and does not count many others that do (e.g. healthcare) – and where productivity growth is rapid in some areas but more than offset by low wage/ low productivity jobs elsewhere
  • What’s the difference between consumption (tangible?) and welfare (intangible?) for different goods and services? – GDP per capita is already suspect as a measure of human welfare
  • Maximising real GDP growth can no longer be the prime objective of economic policy if it’s losing its meaning
  • Standard GDP measures are already less meaningful and less useful – they will be worse in future

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THE FUTURE?

  • All work will be automated – so if people still need an adequate income, employment will be dominated by low-wage jobs, many still existing because the rich like being served by people, not robots, even though those jobs could be automated
  • GDP will be dominated by property values and various forms of rent (of property and IP i.e. stuff people compete for) as all other goods and services are produced at ever falling prices so most income people have will pay for what remains either limited or is distinctly different e.g. high fashion, pop heroes, top footballers, highly talented people – or zero-sum activities attracting the very best to outdo others re winning elections, court cases, cyber defence efforts
  • According to Thomas Piketty, almost all developed economy wealth over the last 50 years has been explained by rising property values, and almost all that explained by rising land values, which is not limitless
  • Employment will be dominated by low-wage face-to-face services
  • Inequality between the rich few and the poor many will widen further
  • Measured productivity growth will be very slow

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Section 4 – Average is over – Income and wealth inequality is inevitable:

  • “Where will the new jobs come from, especially the new incomes?”
  • It’s no good just blindly saying ‘give everyone better skills’
  • In the past, new ideas led to new sectors offering stuff more and more people soon found they wanted – this led to many more new jobs, often better paid
  • Productivity improvements across the board also led to more pay and so more disposable income for more people to buy other goods and services – thus did economies grow
  • If people have to work to gain income, and if there’s no minimum wage rates, then jobs will always be created to induce demand for some new service provision – an employment equilibrium will result albeit, in future, accompanied by ever-rising inequality:
    • A relative few digital companies and their clever top guys will be the big winners
    • Losers will be the rest of us, including those who previously were doing quite well but, now outplaced, are forced to accept low-wage jobs to scratch a living
  • But even if average incomes fall, that would fail to reflect everything from healthcare to internet services to video entertainment that have made most lives much better over the last few decades
  • But Turner asks: “Have always-on mobile phones, computer games and social networks made lives better?”
  • Most people will not be able to afford to live in the best areas/ cities so will migrate to where there is plentiful and cheap land with few planning restrictions or properties left unwanted and so cheap by the rich – this will enable them to live worthwhile/ acceptable lives – so social turmoil, as in Ned Ludd’s day, is unlikely
  • Meantime, over the long term, attempts to increase the productivity growth rate of developed countries are likely to be both unnecessary and ineffective:
    • We only need a few highly talented ICT experts to keep advances going in those sectors which can be automated
    • And, as some sectors get better, others less productive attract more of the outplaced labour which more than counteracts any productivity gains made so, on balance, productivity will continue to dip
  • The most important choices facing advanced rich societies in the future will be how we spend the fruits of increasing productivity and how to distribute it:
    • Forget ‘better skills’ – education is good for all, but not essential for productivity improvement – we only need a few very good IT bods for AI and super intelligence
    • Pay everyone a UBI (Universal Basic Income) to ensure they receive at least a basic minimum for a reasonable standard of living (varying only by location, property and land prices) plus enjoy high-quality public services e.g. health, education and public transport plus shared public spaces like countryside and beaches – however, UBI ignores the psychological benefit that work delivers a sense of status and self-worth
    • More people are likely to find satisfaction in becoming skilled gardeners, artists, cooks, brewers, organic farmers and beekeepers rather than software developers
    • Nations facing an ageing population problem need worry no longer – too few workers to support too many oldies will no longer be a threat
  • RCS:
    • Turner repeats many of his pearls, and not necessarily for emphasis
    • Why not push for more and more leisure and far fewer hours at work – all big benefits to ‘human welfare’?
    • With more education for all, people will be better able to decide what they want to do with their lives
    • Technical advances already mean many more low-wage people get to enjoy acceptable (to them) living standards, including cheap fun and education 

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Section 5 – The old ladder destroyed – Rapid economic catch-up is no longer possible:

  • Radical automation potential combined with rapid population growth could create almost insurmountable barriers to economic catch-up:
    • Note the USA and W Europe gap with the RoW (Rest of World) over period 1800 to 1950
    • A few other nations have achieved catch-up since, partially via their manufacturer exporters mopping up surplus agricultural workers, and higher incomes from manufacturing increasing savings and investments in plant and machinery – e.g. Japan, Korea, Taiwan
    • But much of manufacturing is, or soon will be, automatable at attractive cost so, if wages keep on rising there, automation will soon take over
    • India, with a growth rate of 5% p.a., needs to create 10 to 12 million new jobs every year just to keep unemployment and underemployment stable – but they’re failing as companies start to apply state-of-the-art technology despite labour available at very low cost
    • Ditto China, with a growth rate of 7% p.a.
    • Africa has a far bigger problem – average growth rate 4.6% p.a. versus 2.7% population growth rate
  • Soon the rich world will not need cheap emerging economy labour to provide low-priced footwear, apparel and other goods – automation at home will do the jobs
  • So the key is to boost sectors unlikely to be prone to automation e.g. tourism and construction
  • And boost the quality of education for all to equalise opportunity

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Section 6 – Implications for economic theory:

  • We now live in a world where:
    • Productivity improvement can be delivered with little capital investment
    • Most wealth resides in locationally desirable property/ land, IP rights and brands
    • Most wealth creation derives either from changes in the relative price of already existing assets, or from the creation of IP, brand and externality (?) effects
    • The problems of production will become unimportant
  • Hence, productivity improvement will no longer be about how to get more from less but how to resolve, in a fair and sustainable way, disputes about the distribution of those goods, services and assets, both created and natural, which automation does not make available at ever falling and close to zero prices
  • It will become a balancing act, between individual freedom versus fairness
  • According to Peter Orszag, in an article for Bloomberg Opinion commenting on Turner’s lecture:
    • The impact of new technology on total productivity growth depends crucially on who accrues the income from the new inventions – what additional consumption they choose to enjoy with that income – and the nature of productivity advances in the sectors that workers are shifted into as a result
    • And any new sectors are not expected to lend themselves to automation or significant productivity improvement
    • And, as the rich get richer, they may well choose services offered only by low-wage sectors e.g. personal care aides, cooks and servers, registered nurses and home health aides – i.e. person-to-person interactions that are, for now, difficult to automate
  • So no more big productivity gains on the horizon?
  • None foreseeable, for now

 

 

 

Effective change management

The following is a punchy article by journalist Simon Caulkin describing the best way, by far, to improve customer service whilst minimising costs – it’s counter-intuitive, and ignored by big consultancies – however, it works well, and puts their approaches to shame

Google ‘change management’ and you get half a billion hits. ‘Change management models’ gets 17m. Yet perhaps never in management has so much been sought by so many to such little effect. Almost all of the models referenced have one unwanted trait in common. They don’t work.

70% of all large-scale change initiatives fail, according to the Harvard Business Review. When they involve IT, the failure rate, in whole or in part, is 90%.

Why?

Well, not coincidentally, there’s something else conventional models have in common: a starting assumption that when you launch change, or more fashionably ‘transformation’, you know where you’re going. Of course you do: what leader would admit she didn’t? So change is a matter of planning how to get to the appointed destination, with a schedule of carefully orchestrated quick wins, deliverables, milestones and communication campaigns to keep programme and people on track.

But there’s a snag.

In any body composed of interdependent moving parts, change happens not mechanically but through a series of interactions and feedback loops between the parts, which ripple out and alter the whole. The behaviour of the ensemble can’t be predicted in advance from that of the components, and vice versa. In other words, change is emergent – a result, not a cause.

This changes everything.

The result is not just a different ‘change model’ – it’s a different way of thinking:

  • Conventional change models come straight out of the command-and-control (aka central planning) playbook, decreed from above and cascaded down through the organisation.
  • In a systems view, change is better seen as discovery, proceeding not by way of an abstract plan, plotted to an arbitrarily fixed destination, but by open-ended investigation and iterative experiment leading to deliver ever-improving outcomes.

In this latter version of the process, change starts by establishing not where you’re going but where you are now. Like it or not, you start from here, facing forward. And the only way to start the process of discovery is to go and see for yourself.

Professor John Seddon, founder of Vanguard Consultants, recounts how a brilliant and mercurial mentor noticed on one assignment how little front-line service agents could actually do for clients calling in with a problem – ‘what if we equipped them to deal with the calls that they are likely to get?’

It was a pivotal moment.

To work out how to do that:

  • The first step was to listen to customers’ calls live – a revelation in itself, since the most striking thing was how many were complaints about something not done, or not done properly, on the first contact (since known as failure demand).
  • Next, they had to turn that thought round and ask themselves what should have been done that would have made the follow-up call unnecessary – that is, what was the purpose of the service, from the customer’s point of view?
  • Finally, they needed to know what kind of customer needs were predictable and which only arose from time to time. Only then could they proceed to train operators in a way that would reliably improve performance.

‘Go and see for yourself’ turned out to be critical in other ways:

  • The root problem to be addressed, and hence the nature of the subsequent change, was never the one managers thought it was:
    • The functional measures they were using – number of calls per shift, speed of response of the different functions – told them nothing about the experience of the customers, who naturally took an end-to-end view. As a result they were always surprised, and often dismayed, to discover that service that was excellent according to their (or regulators’) measures got a vigorous thumbs down from recipients.
    • Conversely, the eventual benefits often went far beyond the incremental gains required by the plan e.g. huge increases in capacity by cutting unnecessary work and failure demand and steadily shrinking costs as customer service improved.
  • The truth about the operational reality was so unpalatable to managers brought up on conventional methods, and who had so much invested in them, that unless they saw it with their own eyes they refused to believe it.
  • It’s not that a systems view of work or organisation is harder to grasp than a conventional one; it’s that the two are so different that there’s no intellectual route map between them. They are parallel tracks with no connection. In other words, it’s impossible to convince a conventional manager to cross from one track to the other by rational explanation. They have to see it with their own eyes – the corollary being, once they have ‘got’ it, they have crossed a Rubicon: there is no going back.

There’s a rigorous discipline to ‘study’, but broadly speaking once customers have put them right about where they are, managers and front-line workers can jointly start to figure out what to do to meet the purpose of the service without recipients having to make follow-up calls to remind them. It’s only when the hypothesis has been tested in action and adjusted accordingly that it is possible to envisage what the redesigned process will actually look like.

This empirical approach to change brings two enormous benefits, one negative, the other positive:

  • It prevents managers wasting large amounts of money and effort on top-down change programmes that are doomed to fail.
  • It can eventually lead to the kind of gains that no one would have dared to put in a plan.

Both of these are well illustrated by the case of IT.

IT is usually presented as the ‘driver’ or ‘enabler’ of large-scale change, as in the ill-fated Universal Credit project in the public sector, and countless ‘digital transformations’ in the private sector. The assumption is that the IT system comes first and operations will automatically be more efficient if digitised. But this is diametrically the wrong way round. When managers start by learning how their system works, they usually find, again to their surprise, that a giant, all-singing, all-dancing IT system not only does nothing to solve the real problems – by locking in the old system, it is a constraint rather than an enabler.

This is not to denigrate or downplay the importance of technology – provided it is kept in its proper place, which is last, and always as an aid to rather than replacement for human intelligence.

As for any change project, the order is:

  • First, study the system – get knowledge
  • Second, improve the service to the customer – redesign
  • Third, ‘pull’ the IT that you need – so you use it all and don’t buy bells and whistles you don’t need.

This goes for heavily IT-dependent services such as banking and insurance just as much as for customer helplines or emergency services.

If that sounds unlikely, consider the stories put forward by senior financial executives at a recent event put on by Vanguard where bank CIOs said:

  • Changing rules of the game meant an urgent need to experiment with the customer journey without having a full plan, representing ‘a profoundly new world, mindset and model for banking,’
  • ‘If you think of the solution as a technology thing or opportunity, you’ll solve the wrong thing or make matters worse.’
  • ‘We forgot that banking is not about current accounts, it’s about accessing money and buying a home,’
  • ‘It was a cost-related, industrialised approach. We had a lot to unlearn.’
  • ‘Now, no one can touch anything unless they can show they understand how the system works and have experienced how the service is consumed.’
  • ‘Don’t digitise what you don’t need to. Our problems weren’t caused by technology, so how can it solve them?’

Another leader in banking confessed that having joined the bandwagon to ‘go digital’ and invested heavily in new digital services, managers discovered through studying that it led to increases in failure demand into its service centres. Calling a halt to the costly dysfunction, they set about doing what should have been the starting-place – studying customer demand, studying how well the bank serviced those demands (not very well), improving the way the demands were serviced and, finally, on the basis of thorough knowledge, ‘pulling’ IT into the designs.

‘Innovation isn’t about technology. It’s about solving customer problems, and using tech to do it where necessary,’ said a South African insurance CEO who, after much heart-searching, had cancelled a big IT systems investment because she could see it was simply a modernisation of the old architecture that would do nothing to attract new customers. The breakthrough moment was a ‘what if’ question that emerged from studying the system: ‘What if we thought of our business not as picking up the pieces when things get broken but stopping bad things happening in the first place?’ Out of that came a clever initiative to use advanced technology to monitor customers’ heating boilers, triggering instant alert and repair in case of failure. ‘Insurance at the touch of a button! But it’s critical that the IT architecture supports the right measures.’

Change of this kind, as all the participants emphasised, isn’t a one-off event but a never-ending journey

What emerges is a service design that absorbs the variety of customer demand using new and fundamentally different controls which facilitate a constant focus on perfection.

Effective change starts with ‘study’, not plan.

The consequence of gaining knowledge is that change is guaranteed to work, and deliver results far beyond what might have been considered possible in a plan.

NB We have no connection with Vanguard Consultants but have always applauded their approach