Intended learning outcomes: Describe scenario forecasts and scenario planning along a time axis. Present the procedure for scenario planning.
Scenario forecasts (or Scenario-based forecasts) are plans for how an organization will respond to anticipated future situations ([ASCM22]).
Scenario planning is a planning process that identifies critical events before they occur and uses this knowledge to determine effective alternatives ([ASCM22]).
A scenario driver is a key factor or key parameter in determining how the future environment that the organization works in will look.
Scenario planning and the scenario forecasts that the planning leads to can be used as tools for use when dealing with situations where the long-term mechanisms of action are either unknown or not fully understood. This applies in particular where various influencing factors could play a role in a company's surrounding systems — especially ones that might not even be considered at first glance. In this respect, scenario planning consciously assumes that the the past is not necessarily an accurate predictor for the future. Scenario planning forms part of analyzing the macro environment, which means it is part of the first stage of the strategic process of designing the supply chain in Figure 2.1.0.1.
Several alternative scenarios explore the way that social, technical, (macro) economic, environmental or political trends may develop over time, and identify their drivers. There may be several drivers in each scenario, and each driver can also influence other drivers. For scientific discussion about scenario planning, see for example [Scho93]. Figure 10.4.3.1 shows the principle of scenario planning along a time axis.
Fig. 10.4.3.1 Scenario planning along a time axis
Si represents the scenario i, i ≥ 1. The scenario drivers originate from a specific context in one or more of the company's surrounding systems. Individual drivers may also feature in several different scenarios. Scenarios may overlap for this reason, and also for other reasons. A funnel-shaped representation for each scenario shows its postulated development along the time axis. Si’, Si", … represent a revision of scenario Si at time point t’, t", …. This revision is based on a reassessment of the effective development of the scenario between the previous assessment and the assessment at the time of the revision. New scenarios can be added at any time (e.g. S4 at time point t’, S5 at time point t"). At any stage, it may be decided that scenarios are no longer applicable (e.g. S1 at time point t", indicated by Ω).
One well-known example of a company that uses scenario planning is Royal Dutch/Shell. See also for example [Corn05]. In the late 1960s they developed various scenarios that could potentially affect their two most important planning variables, namely the demand for energy and the price of crude oil. These two initial and linearly independent planning variables, which also influence each other, largely determine the other planning variables used by Shell. In a world that had until then been characterized by solid economic growth, one scenario considered a disruptive increase in the price of oil. In 1974, this became a reality when Arab oil-producing countries imposed an oil embargo following the Yom Kippur war.
The core components and processes used in scenario planning have a lot in common with systems engineering (see chapter 19.1). Figure 10.4.3.2 shows a possible approach.
| 1. | Determining the context and environment in which the scenario takes place, and identifying stakeholders with an interest in the scenario. |
| 2. | Identifying likely underlying trends and uncertainties, and their drivers. Suitable methods can be utilized for each trend, such as experiments, the Delphi method, other interviews and questionnaires or brainstorming. |
| 3. | Developing the scenario. One possibility would be to use workshops to illustrate the possible development whilst maintaining the current business strategy. All drivers and elements that have a positive effect can be used to develop a very extreme positive scenario, and all that have a negative effect can be used to develop to a very extreme negative scenario. |
| 4. | Plausibility and consistency checks: Are the assumed values for the variables that depict the drivers actually realistic? Are there any contradictions between drivers that influence each other and values for variables that describe other drivers? Could some of the trends or uncertainties be described more clearly? |
| 5. | Reviewing the completed scenario: Can the scenario be explained to the various stakeholders in a way that matches their cognitive perception? Is the scenario perceived as being inherently consistent and effective? Does it describe the different future that it portrays in comparison with other scenarios? Will it remain valid for a period of time? |
Fig. 10.4.3.2 Procedure for scenario planning
Only after the completion or (possibly rolling) revision of the scenario planning should possible scenario forecasts for the microeconomic variables of interest be developed within the framework of the corporate strategy, e.g. long-term planning of demand for products or the long-term progression of procurement costs for raw materials. For scenario forecasts, intuitive procedures such as those described in 10.4.1 and 10.4.2 can be used.
The described procedure is complex. Scenario forecasting is therefore more suitable where there are fewer planning variables, and where there are also greater financial consequences if the forecasts are not accurate. This was the case, for example, with their use by Shell as described above. Developing the scenarios, on the other hand, provides extensive knowledge, which can be held “in stock”. That in turn offers time savings if sudden and radically changed peripheral systems impact on the planning variables.
Course section 10.4: Subsections and their intended learning outcomes
10.4 Future-Oriented Techniques
Intended learning outcomes: Explain the trend extrapolation forecast and the Delphi method. Describe scenario forecasts.
10.4.1 Trend Extrapolation Forecast
Intended learning outcomes: Identify base demand B0(k) for period k>0 known at time t=0. Explain the calculation of the quotient “actual demand in period t+k” divided by “base demand known for period t+k at the end of period t”, k>1. Describe smoothing of quotient means for extrapolation leading to extrapolated forecast values for forecast distance k.
10.4.2 The Delphi Method
Intended learning outcomes: Explain the Delphi forecasting method as a series of successive surveys that increase consensus.
10.4.3 Scenario Forecasts and Scenario Planning
Intended learning outcomes: Describe scenario forecasts and scenario planning along a time axis. Present the procedure for scenario planning.
