price was provided in 1973 by the Yom Kippur War between Israel and its Middle Eastern neighbours.[25] Arab nations resented Western support for Israel. Within weeks, ordinary motorists around the world faced steeply rising petrol prices.
This changed the balance of power between mainly Western oil companies and their mainly Middle Eastern suppliers just as Shell had suggested (and its directors had been alone in planning for).[26] This could not have been achieved through traditional methods of forecasting. As a result, it was better placed than other global oil companies to respond to the price increases, which allowed it to assume a leading role in the global oil business.[27]
Following the shock of events round the Yom Kippur War, and growing awareness of Shell’s pioneering approach, a significant number of Fortune 100 companies went on to adopt scenario-based planning.[28] A survey of 200 large corporations soon identified scenario-based planning as the most common strategic planning tool.[29]
A growing appreciation of the potential consequences of strategic errors in the Cold War context therefore forced some analysts to adopt a new approach to studying the future. This method caused them to dispense with the notion that there was one pre-ordained future, and that capable forecasters could accurately identify that future. Instead, they abandoned the certainty promised by traditional forecasting methods in favour of identifying a range of equally plausible futures for the same system or country. This is what later enabled Shell’s scenario planners to identify the possibility that Gorbachev could bring about major political and economic reforms in Russia.
Professor Duvenhage and I immediately understood that the strength, and later success, of this method could be ascribed to a single characteristic, namely its inherent ability to overcome the butterfly effect. For this reason scenario planning dovetailed very neatly with our work on complex systems and freed us from guessing at the future. In scenario planning we therefore found a methodology we could apply to our work on complex systems theory to determine the prospects for South Africa’s long-term stability. The final conclusions of that study are the focus of this book.
The failure of analysts and economists to anticipate events such as the Arab Spring and the global financial crisis therefore suggests two things. The first is that the butterfly effect means that the future of complex systems is plural rather than singular. The second is that scenario building is the only method that can overcome the consequences of the butterfly effect.
Therefore, in the case of South Africa, any serious attempt to gain greater insight into our future must be based on a series of equally plausible scenarios, plus – and this will become very important in this book – a method for navigating our way through time towards the scenario that will eventually materialise.
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
