Ch. 9
Building Model-Driven Decision Support Systems

Simulation involves setting up a model of a real system and conducting repetitive experiments on it. The methodology consists of a number of steps. The following is a brief discussion of the process:

Problem Definition. A problem is examined and defined. The analyst should specify why simulation is necessary. The system's boundaries and other such aspects of the problem should be stated.

Constructing the Simulation Model. This step involves gathering the necessary data. In many cases, a flowchart is used to describe the process. Then the model is programmed. Figure 9.6 shows a visual simulation model.

Testing and Validating the Model. The simulation model must accurately imitate the system under study. This involves the process of validation.

Design of the Experiments. Once the model has been validated, the experiment is designed. In this step the analyst determines how long to run the simulation. This step deals with two important and contradictory objectives, maximizing the accuracy of the model and minimizing the cost of developing the model.

Conducting the Experiment. Conducting the experiment involves issues such as how to generate random numbers, the number of trials or time period for the experiment, and the appropriate presentation of the results.

Evaluating the Results. The last step is the evaluation of the results. In addition to statistical tools, managers/analysts may conduct "What If" and sensitivity analyses.