Abstract
Though traditional DSS provide strong data management, modelling and visualisation capabilities for the decision maker, they do not explicitly support scenario management appropriately. Systems that purport to support scenario planning are complex and difficult to use and do not fully support all phases of scenario management. This research presents a life cycle approach for scenario management. The proposed process helps the decision maker with idea generation, scenario planning, development, organization, analysis, execution, and the use of scenarios for decision making. This research introduces scenario as a DSS component and develops a domain independent, component-based, modular framework that supports the proposed scenario management process.
TopBackground
Herman Kahn, a military strategist at Rand Corporation, first applied the term scenario to planning in the 1950s (Schoemaker, 1993). Scenario analysis was initially an extension of traditional planning for forecasting or predicting future events. Currently, scenarios are constructed for discovering possibilities, leading to a projection of the most likely alternative. Scenarios explore the joint impact of various uncertainties, which stand side by side as equals. Usually sensitivity analysis examines the effect of a change in one variable, keeping all other variables constant. Moving one variable at a time makes sense for small changes. However, if the change is much larger, other variables do not stay constant. Schoemaker (1995) argues that scenario, on the other hand, changes several variables at a time, without keeping others constant. Decision makers have been using the concepts of scenarios for a long time, but due to its complexity, its use is still limited to strategic decision making tasks. Scenario planning varies widely from one decision maker to another mainly because of lack of generally accepted principles for scenario management. Albert (1983) proposes three approaches for scenario planning, namely, Expert scenario approach, Morphological approach and Cross-Impact approach. Ringland (1998) identifies three-step scenario planning – namely brainstorming, building scenarios, and decisions and action planning. Schoemaker (1995) outlines a ten-step scenario analysis process. Huss and Honton (1987) describe three categories of scenario planning.
Key Terms in this Chapter
Pipelining Scenarios: - One scenario is an input to another scenario in a hierarchical scenario structure. In this type of scenario, lower-level scenario can be tightly or loosely integrated with the higher-level scenario.
Scenario Life Cycle: is an iterative process of scenario idea generation, planning, organization, development, execution, what-if analysis, evaluation and decision support.
Simple Scenarios: - The simple scenario is not dependent on other scenarios but completely meaningful and usable.
Scenario: is a complex problem situation analogous to a model that is instantiated by data and tied to solver(s). A scenario can be presented dynamically using different visualizations. A scenario may contain other scenarios.
Scenario-driven DSS: is an interactive computer-based system, which integrates diverse data, models and solvers to explore decision scenarios for supporting the decision makers in solving problems.
Intelligence Density: is the useful ‘decision support information’ that a decision maker gets from using a system for a certain amount of time or alternately the amount of time taken to get the essence of the underlying data from the output.
Goal-seek analysis: accomplishes a particular task rather than analyzing the changing future. This goal seek analysis is just a reverse or feedback evaluation where the decision maker supplies the target output and gets the required input.
Aggregate Scenarios: - The structure of different scenarios or results from multiple scenarios are combined / aggregated together to develop a more complex scenario.
Decision Support Systems/Tools: in a wider sense can be defined as systems/tools that affect the way people make decisions. But in our present context it is defined as systems that increase the intelligence density of data and supports interactive decision analysis.
Sensitivity analysis: allows changing one or more parametric value(s) at a time and analyses the outcome for the change. It reveals the impact on itself as well as the impact on other related scenarios.