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Book Contents

Ch. 9
Building Model-Driven Decision Support Systems

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Modeling Decision Situations

Mathematical and analytical models are the dominant component in a Model-Driven Decision Support System. When a model is needed to understand a situation, then a Model-Driven DSS can deliver the needed representation to managers. DSS Analysts can create a wide variety of Model-Driven DSS. So actually building an MDSS involves resolving a number of important design and development questions.

Models can help understand financial, marketing and many other business decisions. One major issue that must be resolved is the purpose of a proposed Model-Driven DSS. Is the purpose to assist in credit and lending decisions, budgeting or product demand forecasting? Each Model-Driven DSS should have a clearly stated and specific purpose. To accomplish the specific purpose of a system more than one type of model is sometimes used in building the Model-Driven DSS. So, a second issue is what models should be included in a specific MDSS.

The tasks involved in building Model-Driven DSS are complex enough that a model specialist is usually needed on a development team for a large-scale system. End users should only develop Model-Driven DSS for one-time and special purpose decision support needs. Therefore, managers must confront the issue of who should build a planned or contemplated DSS.

In many specific DSS, a model produces outputs displayed for users. Also, the decision variables of Model-Driven DSS are frequently manipulated directly by managers. As mentioned in Chapter 4, DSS builders must determine the future users of the model.

Model-Driven DSS have been built using statistical software packages, forecasting software, modeling packages and end-user tools like spreadsheets. In all of these development environments the goal is the same: to build a model that can be manipulated and tested. The values of key variables or parameters are changed, often repeatedly, to reflect changes and uncertainty in supply, production, the economy, sales, costs, or other environmental and internal business factors. This capability of a Model-Driven DSS is usually called "What if?" analysis or sensitivity analysis. The results from using the DSS are analyzed and evaluated by decision-makers; the model is not making the decision.

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