Ch. 9
Building Model-Driven Decision Support Systems

Recently, more DSS have been built using simulation models. The increased use of this approach can be attributed to a number of factors. First, simulation theory is relatively easy to understand. A simulation model is a collection of many elementary relationships and interdependencies. Second, simulation allows the manager to ask "What-If" type questions. Third, DSS analysts work directly with managers because an accurate simulation model requires an intimate knowledge of the problem. The model is built from the manager's perspective using his or her conceptual model of the system.

Fourth, a simulation model is built for one particular problem and, typically, will not solve any other problem. Thus, no generalized understanding of a problem is required of the manager; every component in the model corresponds one to one with a part of the real-life model. Fifth, simulation can handle an extremely wide variation in problem types such as inventory and staffing, as well as long-range planning decisions. Sixth, managers can use simulation to experiment with different variables to determine which are important, and with different alternatives to determine which is best. Seventh, new software packages and tools like Java and C++ make simulations easier to build.

Finally, simulation allows for the inclusion of the real-life complexities of problems; simplifications are not necessary. Due to the nature of simulation, a great amount of time compression can be attained, giving the manager some information about the long-term effects of various policies. Also, with a simulation it is easy to include a wide variety of performance measures.

There are three primary disadvantages of simulation. First, an optimal or best solution cannot be guaranteed. Second, constructing a simulation model is frequently a slow and costly process. Third, solutions and inferences from a specific simulation study are usually not transferable to other problems.