Tag Archives: non-predictive strategy

Crafting Non Predictive Strategy, Part III: Acknowledge the Nature of the Problem

Despite formidable developments in business strategy over the last fifty years, organizations keep being disrupted by events they should have seen coming, but didn’t, or by events they saw coming but were unable to avoid or take advantage of. In 1971, NCR was surprised by the rapid rise of electronic cash registers and lost its leadership of the market. In 2007, Nokia was unable to react to the launch of the iPhone, an event the Finnish firm dismissed as minor, and is now struggling to survive. In 2011, the Arab uprising came as a complete surprise to everybody, not just business and governments but the people involved as well. And the list goes on:  if strategy is about addressing the key challenges an organization face, then the general lack of preparedness (if not prevention of) the economic and political crises that the world has been facing since 2008 is a massive failure of strategy. Hence it’s no surprise that in a survey conducted in 2011 by consulting firm Booz, fully 53% of senior executives did not think their company’s strategy would be successful. Houston, we have a problem…with strategy. Continue reading

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Crafting Non-Predictive Strategy, Part II: Start with who you are

In the first part of this series, Milo and I examined the complexity of nonlinear environments and tried to show how, when confronted with such an environment, energy spent on a deep understanding of the present beats attempts at predicting the future.  Hence our call for a non-predictive approach to strategy.

Nonlinear systems can be found in nature, but they are particularly common and problematic when they involve human issues.  While such human nonlinear systems can display regularities over long time periods, most major political, economic and business issues are essentially nonlinear and permeated by social facts.  What such human-centered, nonlinear systems have in common but which is often overlooked is that one cannot deal with them as if they were natural science problems.  For one thing, and as we have argued in a recent Forbes article with the example of Usama bin Ladin, how you define the issue you’re dealing with depends on who you are.  This is also the reason that “genius” fails.

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Crafting Non-Predictive Strategy, Part I: Deep Understanding Beats Prediction

As Milo and I have argued before, the environments and issues businesses deal with are more complex than traditional strategy models admit.   Business issues today display high levels of uncertainty, they can behave non-linearly, and they can be vulnerable to “Black swans”, i.e. low-probability but high impact events that disrupt even the best formulated strategies.  The added difficulty for strategists and managers is that nonlinear environments often appear linear for an extended time period (think US house prices).  As a result, some conclude that what seems to be an essentially linear pattern (prices fluctuate a bit around a ‘long term trend’ but always rise), are linear in reality – before a radical change occurs that completely disrupts previously assumed patterns (e.g. prices fall dramatically).  In short, people often assume an environment is linear and predictable when in fact the continuity we observe is only a particular case of limited duration.  To make matters worse, with many nonlinear systems change is not nicely spread over the years:  most of the cumulative change occurs in one, single – often dramatic – occurrence.  In the language of engineering, some things don’t “fail gracefully” (e.g. a bridge that breaks suddenly instead of bending slowly).

Not a “graceful failure”.

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Making strategic decisions under uncertainty: The case for non-predictive strategy

The goal of strategy is to decide what to do in a given situation to achieve a given objective.  Basically, strategic decisions comes down to the question “what to do next?”. In environments characterized by uncertainty (defined as objective lack of information), this is no simple question, and several approaches are possible to address it.  Two dimensions characterize these possible approaches: prediction and control.

Prediction asks  to what extent does my approach rely on a forecast of the future environment. Strong prediction corresponds to either a planning-type approach – I create a detailed prediction of the future before initiating action – or a vision type:  I imagine the future and I strive to make this vision a reality.  Low prediction corresponds to a more adaptive approach:  I do not try to predict the future environment, but instead I move on and I adapt to changes along the way.
Control asks how I can control the evolution of my environment.  The over-arching assumption of classic strategy is that the firm has little influence on its environment, which is for the most part given (or “exogenous”).  All a firm can do is to find a place in this environment (planning /positioning) or adapt when it changes (adaptation).  Hence the importance of the notion of “fit” that the field insists upon (e.g. Michael Porter in 1996).  On the opposite side of the spectrum, the field of entrepreneurship observes that a firm can change its environment in profound ways, sometimes from an ex ante defined vision, or through the logic of future-agnostic gradual transformation of the environment.  There are many examples of entrepreneurs starting with odds apparently stacked against them and completely transforming their environments:  Michael Dell, Richard Branson, Sam Walton, to name just a few.

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Welcome to Extremistan! Why some things cannot be predicted and what that means for your strategy

In an earlier post about forecasting, I mentioned the work by Nassim Taleb on the concept of black swan. In his remarkable book, “The Black Swan”, Taleb describes at length the characteristics of environments that can be subject to black swans (unforeseeable, high-impact events).

When we make a forecast, we usually explicitly or implicitly base it on an assumption of continuity in a statistical series. For example, a company building its sales forecast for next year considers past sales, estimates a trend based on these sales, makes some adjustments based on current circumstances and then generates a sales forecast.  The hypothesis (or rather assumption, as it is rarely explicit) in this process is that each additional year is not fundamentally different from the previous years. In other words, the distribution of possible values for next year’s sales is Gaussian (or “normal”): the probability that sales are the same is very high; the probability of an extreme variation (doubling or dropping to zero) is very low. In fact, the higher the envisaged variation, the lower the probability that such variation will occur.  As a result, it is reasonable to discard extreme values in the forecasts:  no marketing director is working on an assumption of sales dropping to zero.

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The Fragility of the Future (and Your Strategy)

Today I was reminded of the perils of forecasting while reviewing  a Department of Defense document, the Joint Operating Environment 2010.

“JOE 2010” as it’s called, is designed to provide the various branches of the US Armed Forces a joint perspective on likely global trends, possible shocks and their future operating environment.  If you’re interested in geopolitics and strategy, I recommend that you take a look.

Apart from its inherent interest, JOE 2010 opens with a defense planning timeline that business and financial strategy practitioners – and anyone who consumes their work  – would do well to bear in mind.  I have reproduced it verbatim here:

1900 If you are a strategic analyst for the world’s leading power, you are British, looking warily at Britain’s Age-old enemy, France.

1910 You are now allied with France, and the enemy is now Germany.

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Gresham’s Law of Strategy: Why Bad Advice Drives out Good Advice

Near the end of a seminal essay on strategic surprise, Richard Betts writes, “The intelligence officer may perform most usefully by not offering the answers sought by authorities, but by offering questions, acting as a Socratic agnostic, nagging decision makers into awareness of the full range of uncertainty, and making authorities’ calculations harder rather than easier.”  I believe that the same should be true for corporate strategy consultants:  often their job is to make long-range calculations harder rather than easier.

Why then, is the opposite so often true?  In a world in which surprise, disruption and the unanticipated are rife, why do strategists who promise to make calculations easier rather than harder often succeed?  I think a phenomenon that I call of “Gresham’s Law of Strategic Advice” is at work.

E pluribus unum

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