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TEN STEPS TO BUILDING SCENARIOS

A number of scenario planners have written that scenarios are very powerful instruments in that they provide their users with a sense of standing in the future and looking back at the present. When they are well written, this sensation is so intense that it often persuades people to change their current behaviour to avoid unfavourable futures, and to realise more favourable ones. For that reason, often the point of developing and disseminating scenarios is to change the present behaviour of governments, as well as business and political leaders.

What are scenarios really? How are they built, and what are they meant to accomplish? Scenario theorists and planners often use analogies to illuminate the nature and function of scenarios, and it is useful to start by recounting some of these.

My friend and colleague Louis van der Merwe of the Centre for Innovative Leadership, a South African scenario consultancy, uses a brilliant illustration involving the weather. According to him, most people probably have some confidence in weather forecasts for the following day. If the forecaster says it will rain, many will take umbrellas to work, and, if the forecaster says it will be cold, they will dress warmly. A smaller number of people will have the same degree of confidence in a two-day weather forecast, and an even smaller number in a three-day, four-day or five-day forecast. Eventually all people will reach their ‘predictable horizon’ – the point at which they begin to lose confidence in the accuracy of a given forecast. The level of uncertainty around forecasts grows as they are projected further and further into the future.

In this context, consider a family leaving on a ten-day holiday. Among other things, they pack an umbrella, and perhaps some warm clothes. They do not do this because of a weather forecast – ten days into the future is too far beyond their predictable horizon. The items are not packed because they know it will rain, or even because they think it might rain. Rather, the family knows it could be hot or cold, wet or dry, and therefore they weigh up which combination of these factors is most plausible. They then decide to take an umbrella because it could rain, and some warm clothes, because it could be cold. Unwittingly, this family has done some scenario planning.

Weather scenarios are relatively straightforward because the weather can only be hot, cold, wet or dry, or a combination of these factors. Scenarios involving South Africa’s political future are infinitely more complex because a myriad of social, economic, and political factors are at play. However, the principles applied by the family going on holiday remain valid, and it remains a good example. It also illustrates the difference between scenario planning and forecasting, as practised by the weather forecasters on the evening news.

The evening weather forecast is an exercise in predicting a single future at a single point in time. However, as we noted in the previous chapter, the butterfly effect means that the future of any complex system is in fact plural and this explains why economists and political analysts often struggle to accurately predict future trends.

Ringland compares scenario planning to wind tunnels.[1] Wind tunnels seek to anticipate the real-world behaviour of aircraft and motor vehicles, among others. Just as a wind tunnel allows an aircraft designer to establish the likely future behaviour of his or her aircraft, scenario planning allows analysts to anticipate how the systems they study may behave in the future. Just as the aircraft designers can suspend their model aircraft in a wind tunnel to see how they react to certain air flows, long before they will actually be built, we can hang our political or economic systems in a metaphorical wind tunnel and expose them to different sets of variables to see how they will fly.

The weather and wind tunnel analogies reveal that scenario planning is very different from forecasting. Three such differences are commonly identified by scenario planners and they represent the principles upon which most scenario-planning methods rest.

The first is that scenarios are not forecasts or predictions of what will happen in the future.[2] Forecasters predict a single state at a single point in the future, which they argue will come to pass. By contrast, scenarios describe a set or series of alternative futures, all of which are more or less equally plausible.[3]

Second, forecasts often vary around a ‘midpoint base case’.[4] This means they tend to vary around a single common theme, such as what the ANC government may or may not do with respect to South Africa’s economic policy. By contrast, scenarios take account of a far broader range of variables. For instance, a well-developed set of scenarios would not just consider the future of economic policy under the ANC but also force analysts to think about what could happen with economic policy should the ANC no longer be around. In this way, scenarios expose analysts and end users to a broader range of variables, and reduce their vulnerability to unexpected external events.

Third, forecasts are typically ‘snapshots’ of the future at a single point in time.[5] By contrast, scenarios draw pathways from the present to several different outcomes, thus providing analysts with the advantage of considering a full series of events between the present and the future.

Scenarios and forecasts therefore find themselves at the opposite ends of the spectrum of methods for studying the future. To rely on forecasting means that you will be left with only one possible future to consider. If that future does not materialise, you will have no other plan or strategy to fall back on. When applied to a complex system, such as South Africa’s economy, a forecast therefore creates a misleading sense of accuracy and simplicity. By contrast, if you rely on scenario planning you will have several plausible futures to consider – which is consistent with the implications of the butterfly effect.

The desire for certainty

If scenarios are not forecasts or predictions, and produce a series of equally plausible futures instead of a single one, this raises obvious questions about their utility. How, for example, must an investor decide on whether to build a multi-million dollar plant in a country that may or may not be unstable in ten years’ time? Surely, only a concrete forecast could help a board of directors take such key long-term strategic decisions.

One response is that, due to the butterfly effect, long-term predictions of the futures of complex systems are bound to be incorrect in any case. This is because those futures still need to evolve over a certain period, during which time relatively small changes in present circumstances, or relatively small actions by any one of a wide range of actors, may cause significant changes.

A second response, cited by a number of scenario planners, is that, since scenarios are not forecasts, their utility does not hinge on whether any of them will turn out to be entirely accurate. Rather, by developing a series of plausible futures, and spelling out the pathways from the present to those futures, they present scenario planners and strategists with a series of route maps to the future. By studying unfolding events at any point ahead of the scenario horizon, users of a scenario can gain a sense of which scenario they are moving towards and adjust their planning and strategy accordingly.

Despite this, the craving for certainty still tempts some planners to reject the scenario approach in favour of forecasting. Many governments and businesses still want, and demand, a higher level of certainty than that offered by scenario planning. I know from my own work that this is true of many South African companies. It is very frustrating to see companies that have been burned over and over again by failed forecasts persisting with this method because they crave the simplicity and certainty promised by a single future. Many South African companies and other institutions routinely develop inaccurate forecasts of GDP growth and inflation, and then have to revise them over and over again. They could escape from this cycle if they understood that the forecasts will probably never be accurate – no matter how good the forecasters are.

For example, in 2005 a prominent group of economists forecast that South African GDP growth would average 4%-5% over the next five years. Due to the global financial crisis – a typical unforeseen event – GDP growth averaged less than 3% over this period, and even contracted by 2% in 2009. These economists are among the best in the country, and among the best in the world. The reason they were wrong is that they were trying to produce a single forecast for a very complex system.

Despite this, the desire for certainty persists. Consider my discussion with a strategic planner for a major furniture chain. He agreed with me that forecasting was futile, but said his board demanded one future, and he had been tasked with producing it. However, by reverting to the familiar and seemingly clear option of a single forecast, his board exposed itself to the consequences of the butterfly effect, and hence the danger of basing their business operations on a misleading picture of the future. Therefore, while the craving to gain certainty about the future is both understandable and tempting, it should be resisted when dealing with complex systems. Moreover, scenario consultants Ralston and Wilson warn that the growing uncertainty of global and regional environments is leading to a ‘corresponding decrease in the accuracy and utility of forecasting’.[6]

Probably the most powerful argument against forecasting is provided by Pierre Wack of Shell fame. He argued that forecasts are only accurate when known trends change very slowly. When the trends underpinning a given forecast change more rapidly, or are actually broken, the forecast necessarily fails. However, this is precisely the moment at which an accurate forecast would have been most useful.[7]

Again, a good example is that of the recent political upheavals in Egypt, Libya, and other parts of the Middle East and North Africa. Conventional forecasts continued to project a stable region, based on trends that pointed to well-entrenched regimes. When young people in those countries turned on their leaders, and overthrew them in the case of Egypt and Libya, it took the whole world by surprise. In exactly the same way that many oil companies in the 1970s were unprepared for the oil price shock, many governments and corporations were entirely unprepared for the rapid power shifts in North Africa and the Middle East.

In political science, no single university department or think-tank can claim that it had forecast both the nature and timing of recent events in these regions. The fundamental reason for this is that these events simply could not be forecast. Egypt today differs so greatly from Egypt 20 years ago that a single-point forecast of the overthrow of the Mubarak regime would not have been possible. However, a well-developed series of scenarios might well have anticipated growing youth dissent and a resultant major power shift in the region.

The same is true of South Africa. The trend of the ANC winning one election after the other with more than 60% of the vote is firmly established. When the Centre for Risk Analysis (CRA) first started producing scenarios in which the ANC had lost power, many people laughed. A professor of politics at a prominent South African university suggested that we did not understand how the world worked. Jacob Zuma, too, is fond of saying that his party will govern until the Second Coming. Well, just imagine what it must have been like for diplomats in North Africa and the Middle East to have to phone their foreign desks in Washington and London and explain that there seemed to be a revolution on the go. Within a year, the governments of Tunisia, Libya, and Egypt had collapsed. Or think how ridiculous it would have been to declare after PW Botha’s Rubicon speech in 1985 that within 20 years the last leader of the NP would be an ANC cabinet minister.

In South Africa’s case, growing service delivery protests may lead some forecasters to argue that by 2024 the ANC will have been overthrown. More and more people may agree with this view, given persistent inequality together with the growing infighting in the ANC and the ruling alliance. Therefore, should a planner reject scenarios in favour of forecasting, this is a forecast he may make. The problem, of course, is that the ANC may respond by introducing reforms that boost GDP growth, employment, and incomes. Ten years into the future, South Africa could be an increasingly prosperous middle-income economy led by an even stronger ANC.

If a company had chosen not to invest in South Africa based on the flawed forecast, it would have lost an opportunity to make a lot of money. That is the price it would have paid for the certainty it had sought. However, if it had been allowed to consider a range of plausible scenarios, it could have made an informed risk-versus-reward decision about building a new factory in South Africa.

Neither method would have provided it with complete certainty, but the forecast could easily have turned out to be entirely wrong, whereas the scenarios would have provided it with a broader choice around a limited number of options – and a vital route map of how to get to the future.

Take the example of the South African mining industry, which is currently in the doldrums, and faces the double onslaught of increasingly hostile government policy and trade union militancy. It is no secret that the extent of this onslaught has caught many in the industry by surprise. This could have been avoided if the industry had put a number of serious mining scenarios together some ten years ago. These would almost certainly have revealed the adverse circumstances South African mining companies confront today, and allowed them to plan accordingly.

Ten steps to building effective scenarios

Adopting a scenario approach to gain strategic insight into the future of a country or an economy therefore has significant advantages over a forecasting approach. So how would one go about building such a set of scenarios? When applied to a country’s political or economic future, as in our case, this process should follow the following ten steps:[8]