Like the clown wanting to play Hamlet, management is a messy, imprecise craft that aspires to be a clean-cut, hard-eyed science and many of its ills stem from the effort to be something it isn’t. Management’s besetting sin shows up most clearly in its seemingly incurable urge to reduce all the variables it has to deal with to numbers.
As the strategy guru Igor Ansoff put it ruefully, “Corporate managers start off trying to manage what they want, and finish up wanting what they can measure.”
The slippage is understandable. Thinking and making judgements on the basis of incomplete information – the most important part of the manager’s job – is hard work with little certainty about the outcome. Doing business across borders and opening up new markets raise the stakes higher.
So anything that appears to make decision-making easier, or less uncertain, is likely to be seized on with gratitude. Then (over)simplification is also a principal feature of the academic thinking on which current governance and performance management practices are based, in particular the assumptions about economic rationality and individual self-interest without which the underlying equations about shareholder value don’t work.
The result, both practically and theoretically, is to turn today’s management into a technology of control that attempts to minimise rather than capitalise on the pesky human element.
But in management it pays to be careful what you wish for. “The only problems that have simple solutions are simple problems,” warned the systems thinker Russ Ackoff. “Problems that arise in organisations are almost always the product of interactions of parts, never the action of a simple part. Treating a single part destabilises the whole and demands more fruitless management intervention; management becomes a consumer of energy, rather than a creator.”
A good example of this is the near-ubiquitous practice of management by numerical target. Targets seem plausible. Yet the reality is that performance in any but the simplest tasks is multifaceted. So singling out one or two dimensions to focus on leaves out others of equal importance that are harder to measure.
Problems that arise in organisations are almost always the product of interactions of parts, never the action of a simple part
What’s measured does indeed get managed, so as with any incentive, effort is displaced on to meeting the numbers rather than meeting the purposes – the target becomes the purpose. All too often the target is hit, but the point missed. This is the reason that targets proliferate: the broader the target, the cruder the unintended result and the greater the pressure to add new targets to compensate.
Thus, in the UK, the nonsense of a target for compassion in the NHS and lessons that engage students in schools, respectively to balance pressures to meet financial priorities in hospitals and to drive students through exams to safeguard a school’s place in the league table.
Anyone who has seen The Big Short, a film account of the sub-prime housing bubble that eventually culminated in the great financial crash of 2008, will have seen how, even in finance, supposedly the domain of hard numbers, in fact especially in finance, treating a single part – in this case the incentives of mortgage salesmen and bond and derivative traders – can have catastrophic results for the system as a whole, eventually consuming all the management energy of not only Wall Street firms, but of governments, too.
Failing to take account of the effects of the incentives, the banks’ risk models were disastrously wrong. Giving evidence to a house oversight committee later that year, former Fed chairman Alan Greenspan lamented: “I made a mistake in presuming that the self-interests of organisations, specifically banks and others, were such that they were best capable of protecting their own shareholders and their equity in the firms… I discovered a flaw in the model that I perceived as the critical functioning structure that defines how the world works.”
None of this to deny the importance of science and data in making and managing decisions. Of course, they are crucial. The tricky question is, what kind and when to use them?
MIT’s Don Sull put it well. Business education in the UK and US, he said, has been construed more and more narrowly as applied science, on the fundamental assumption that there are universal laws that can be discovered and acted on. But other than in finance, there aren’t.
“There are useful generalisations,” Sull said, “but in management, context, timing, personality and history are everything. The challenge lies in developing judgement, knowing which tool to use rather than reaching for the hammer every time.”
It’s striking that many global industries are characterised by the presence of a ‘positive deviant’ – a company that is notably successful although (or because of) adopting a completely different management model from the rest of its sector. Think Apple in high tech, the Swedish bank Handelsbanken, Berkshire Hathaway in investment (and management in general), all of which have prospered by knowing ‘which tool to use’ for their purposes, and how.
Perhaps the paradigmatic example is motor manufacturer Toyota. The Toyota Production System (TPS) is a wonder of the management world, almost single-handedly destroying the notion of a necessary trade-off between cost and quality, volume and variety.
[Image:treasurertoolkit_bi.jpg class=”left right20” alt=”Atlas of data sources that apply to various countries or regions”]
[NB, this is the graphic from the top of Page 26 headed “The treasurer’s emerging-market toolkit comprises a range of data sources]
No one individual knows exactly how the TPS works. It is a complex adaptive system that has been evolving for 60 years, and its chief architect, Taiichi Ohno, resisted all attempts to codify it in rules, on the grounds that to do so would stop managers thinking about the problems they faced.
Its ‘secret’ is that it learns – in effect, it is the expression of Toyota’s accumulated human capital. The TPS is highly data and measurement driven, but the reason that it learns is that the measures it uses – quality, time to delivery, for example – relate to its customer purpose, so any improvement instantly feeds back to the benefit of both customer and company (near retirement, Ohno was asked what he was working on. His reply: “Shortening the time between receipt of the order and getting the money”).
By contrast, a surprising amount of the measurement companies undertake fails to provide this insight. Unless it does, the data will not only not foster learning, it will keep managers in the dark about the reality of their customer service – which is why it is often so bad. As long as this is the case, the much-touted promise of Big Data to improve decision-making and thus company performance overall is likely to be elusive.
On the other hand, when good judgement about measures and data is allied to strong purpose and flair, the positive effects can be dramatically amplified. Thus, Toyota’s strategic decision to develop hybrid engine plants was a judgement call whose success was by no means inevitable.
If the decision now gets plaudits, it is largely because of execution that made the proposition compelling despite a relatively high purchase price. Look at the consequences: although globally second in output to Volkswagen, Toyota outranks VW in market capitalisation and stands eighth in Forbes’ 2015 list of most valuable brands as compared with the German carmaker at 67.
Toyota’s long-term vision is of a ‘dream car’ whose emissions will be cleaner than the air it takes in. Would you bet against it?
Or in technology, compare decisions taken by Apple under Steve Jobs and those of Microsoft under Bill Gates. Gates was by far the more capable technically, but that didn’t prevent Microsoft being serially wrong-footed by Apple’s abrupt product sidesteps, going far beyond anything that market-research data would have sanctioned, leaving Gates baffled and stymied each time.
Under Jobs, Apple revolutionised (for once no exaggeration) not one, but four, industries: computers, music, phones and retail (Apple stores, widely derided as a vanity project, are now the most profitable retail real estate per square foot on the planet). Jobs had another quality that helped ace his rival’s technical and analytical smarts: an acute sense of human psychology and motivation. Jobs was often mocked for his ‘reality distortion field’, the ability to project a parallel universe where the apparently impossible becomes not only possible, but routine.
But the laugh was on the mockers. The distortion field ‘worked’, Apple employees within its pull performing feats of innovation and endurance that other companies could only dream about.
In their book on evidence-based management, or the lack of it, Hard Facts, Dangerous Half-Truths and Total Nonsense, Stanford professors Jeffrey Pfeffer and Robert Sutton suggest a way forward. Numerous surveys suggest that finance professionals have a healthy respect for the importance of gut feel and experience in decision-making, in some cases trusting them as much as hard data.
That’s as it should be. But on their own they are unreliable decision-making guides, being subject to many psychological quirks, just as the record shows that data alone can’t provide the will to take bold, but necessary decisions (just ask Kodak). What’s required instead is due diligence on the assumptions and background ideas brought to bear on important decisions.
If the best research shows, as it does, that most mergers damage the long-term financial performance of the acquirer, there is no traceable link between high CEO pay and firm performance (and incentives generally are more likely to demotivate than motivate). The benefits of reorganisations and IT implementation are always over- and their costs underestimated, so you’d better have good reasons why this time it will be different.
That doesn’t remove ambiguity or the need for judgement, but it does – slightly – shorten the odds against getting it right. Decision-making will never be easy, but that’s as it should be, too: if a computer could do your job, in the end it probably will.
A little background on some of the classic decision aids that treasurers often refer to…
The World Bank Group’s Ease of Doing Business Index was first published in 2003 and began by looking at five business indicators for 133 economies. Since then, its reach has grown to 11 indicators for 189 economies.
It assesses corruption levels and access to judicial process country by country. The ranking for each economy is posited on the regulatory business regulation environment for the largest one or two business cities for each economy.
The rankings assess the ease of starting a business, registering property, gaining credit, trading across borders and labour market regulation among other issues, but they don’t cover measures on macroeconomic stability or the state of the financial infrastructure within countries.
Ratings agencies have their origins in the 19th century when Standard & Poor’s arrived on the London banking scene in 1860. The AAA to D rating system goes back to 1924 when it was introduced by John Knowles Fitch. Treasurers will have their own stories with ratings agencies, but sovereign ratings are an important quick assessment of a country for companies with a footprint in emerging markets, taking in as they do, overall economic conditions, political stability and capital market transparency.
Transparency International is a non-governmental body that assesses corruption levels country by country. Before setting it up in 1993, Eigen worked for the World Bank in Africa and Latin America and has also worked for the Ford Foundation.
ACT Handbook country profiles draw together information on banking infrastructure, FX controls, foreign investment and ownership, plus legal and taxation frameworks, clearing and payment systems for 31 countries. The Handbook also provides links to other resources. Find the online version of the Handbook here.
Simon Caulkin is a freelance writer specialising in business management
Learn about how British American Tobacco uses data in Africa
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Find out how QIAGEN applies information to emerging markets
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