Design and Implementation of Scenario Management Systems

Design and Implementation of Scenario Management Systems

M. Daud Ahmed (Manukau Institute of Technology, New Zealand) and David Sundaram (University of Auckland, New Zealand)
Copyright: © 2009 |Pages: 10
DOI: 10.4018/978-1-60566-026-4.ch164
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Scenarios have been defined in many ways, for example, a management tool for identifying a plausible future (Porter, 1985; Schwartz, 1991; Ringland, 1998; Tucker, 1999; Alter, 1983) and a process for forward-looking analysis. A scenario is a kind of story that is a focused description of a fundamentally different future (Schoemaker, 1993), that is plausibly based on analysis of the interaction of a number of environmental variables (Kloss, 1999), that improves cognition by organizing many different bits of information (De Geus, 1997; Wack, 1985; van der Heijden, 1996), and that is analogous to a “what if” story (Tucker, 1999). It can be a series of events that could lead the current situation to a possible or desirable future state. Scenarios are not forecasts (Schwartz, 1991), future plans (Epstein, 1998), trend analyses, or analyses of the past. Schoemaker (1993) also explains that scenarios are for strategy identification rather than strategy development. Fordham and Malafant (1997) observe that decision scenarios allow the policymaker to anticipate and understand risk, and to discover new options for action. Ritson (1997) agrees with Schoemaker (1995) and explains that scenario planning scenarios are situations planned against known facts and trends, but deliberately structured to enable a wide range of options and to track the key triggers that would precede a given situation or event within the scenario. In this article we propose an operational definition of scenarios that enables us to manage and support scenarios in a coherent fashion. This is then followed by an in-depth analysis of the management of scenarios at the conceptual level as well as at the framework level. The article goes on to discuss the realization of such a framework through a component-based layered architecture that is suitable for implementation as an n-tiered system. We end with a discussion on current and future trends.
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The basic structure and behavior of the scenario is similar to the decision support system (DSS) components model and solver respectively. In information systems literature, a use case instance irrespective of transaction or decision context is considered as a scenario. But scenarios are primarily related to complex business change management processes; they might address semi-structured and unstructured decision problems. Hence we define scenario as a complex decision situation analogous to a model that is instantiated by data and tied to solver(s). In its simplest form, scenario is a complex combination of data, model, and solver.

Decision makers have been using the concepts of scenarios for a long time, but due to their complexity, their use is still limited to strategic decision-making tasks. Scenario planning varies widely from decision maker to decision maker, mainly because of lack of a generally accepted principle for scenario management. Albert (1983) proposes three approaches for scenario planning: expert scenario approach, morphological approach, and cross-impact approach. Ringland (1998) describes three-step scenario planning: brainstorming, building scenarios, and decisions and action planning. Schoemaker (1995) outlines a 10-step scenario analysis process. Huss and Honton (1987) identify three categories of scenario planning: intuitive logics, trend-impact analysis, and cross-impact analysis. These planning processes are useful but they are not entirely supported by the available decision support systems frameworks. Either or both of the existing scenario planning processes and the DSS frameworks needs to be modified for planning scenarios within DSS.

Key Terms in this Chapter

Intelligence Density: 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.

Pipelining Scenarios: One scenario is an input to another scenario in a hierarchical scenario structure. In this type of scenario, the lower-level scenario can be tightly or loosely integrated with the higher-level scenario.

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.

Sensitivity analysis: Allows changing one or more parametric value(s) at a time and analyzes the outcome for the change. It reveals the impact on itself as well as the impact on other related scenarios.

Simple Scenarios: The simple scenario is not dependent on other scenarios, but completely meaningful and usable.

Scenario: 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.

Decision Support Systems/Tools: In a wider sense, can be defined as systems/tools that affect the way people make decisions. In our present context it is defined as systems that increase the intelligence density of data and support interactive decision analysis.

Aggregate Scenarios: The structure of different scenarios or results from multiple scenarios are combined/aggregated together to develop a more complex scenario.

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