Ch. 9
Building Model-Driven Decision Support Systems

Assumptions are untested beliefs or predictions. We use them in building many models because we are projecting or anticipating results. We have to test assumptions through "what if" testing or sensitivity analysis before accepting the results of the model. DSS analysts and managers need to make assumptions about the time and risk dimensions for a situation. Model-Driven DSS can be designed assuming either a static or dynamic analysis. Making either assumption about changes in a decision situation has advantages and disadvantages.

Static analysis is based on a "single snapshot" of a situation. Everything occurs in a single interval, which can be a short or long duration. A decision about whether a company should make or buy a product can be considered static in nature. A quarterly or annual income statement is static. During a static analysis it is assumed that there is stability in the decision situation.

Dynamic analysis is used for situations that change over time. A simple example would be a five-year profit projection, where the input data, such as costs, prices, and quantities change from year to year. Dynamic models are also time dependent. For example, in determining how many cash registers should be open in a supermarket, it is necessary to consider the time of day. This time dependence occurs because in most supermarkets there are changes in the number of people that arrive at the market at different hours of the day.

Dynamic models are important because they show trends and patterns over time. Also, they can be used to calculate averages per period or moving averages, and to prepare comparative analyses. A comparative analysis might examine profit this quarter versus profit in the same quarter of last year. Dynamic analysis can provide an understanding of the changes occurring within a business enterprise. The analyses may identify possible solutions to specific business challenges and may facilitate the development of business plans, strategies and tactics.

DSS analysts and managers also must examine whether it is appropriate to assume certainty, uncertainty, or risk in a decision situation. When we build models the following types of situations need to be considered and an appropriate assumption needs to be made.

• Certainty. Do we have adequate information to assume certainty about relationships? Does X lead to Y? Models based on this assumption are easy to work with and can yield optimal solutions. Many financial models are constructed under assumed certainty.
• Uncertainty. Is information vague, unreliable and unpredictable? Is this a situation of high uncertainty? Analysts should attempt to avoid assuming uncertainty because it is very difficult to model that type of situation. Instead they should work with managers to acquire more information so that the problem can be modeled assuming a risk situation.
• Risk. Is some information missing or based on forecasts in the situation? Does our decision have some risk associated with outcomes? Most major business decisions are made with assumptions about risk. Several techniques can be used to deal with risk analysis. "What if" analysis is the primary means of considering risk. As previously noted, "what if" analysis is the capability of "asking" or manipulating a Model-Driven DSS to determine what the effect will be of changing some of the input data or independent variables.

The assumptions of DSS analysts and managers limit or constrain the types of models that can be used to build a DSS for the situation. Most of the rest of this chapter discusses various types of models.