MSS MODELING

4.2 MSS MODELING

The opening vignette illustrates a complex decision-making problem for which con- ventional wisdom dictated an inferior decision alternative. By accurately modeling the rail transportation system, decision-makers were able to experiment with different policies and alternatives quickly and inexpensively. Simulation was the modeling

C H A P T E R 4 MODELING AND ANALYSIS

ware, which is typical. The simulation approach saves DuPont a substantial amount of money annually. Instead of investing in expensive rail cars and then experimenting with how best to use them (also quite expensive), all the work was performed on a computer, initially in two weeks. Before the first flight to the moon, the National Aeronautics and Space Administration (NASA) performed countless simulations. NASA still simulates space shuttle missions. General Motors now simulates all aspects of new car development and testing (see Gallagher, 2002; Gareiss, 2002; Witzerman, 2001). And Pratt & Whitney uses a simulated (virtual reality) environment in designing and testing engines for jet fighters (Marchant, 2002). It is extremely easy to change a model of a physical system's operation with computer simulation.

The DuPont simulation model was used to learn about the problem at hand, not necessarily to derive new alternative solutions. The alternative solutions were known, but were untested until the simulation model was developed and tested. Some other examples of simulation are given by Van der Heijden et al. (2002) and Rossetti and Selandar (2001). Van der Heijden et al. (2002) used an object-oriented simulation to

design an automated underground freight transportation system at Schiphol Airport (Amsterdam). Rossetti and Selandar (2001) developed a simulation model that com-

pared using human couriers to robots in a university hospital. The simulation showed that the hospital could save over $200,000 annually by using the robots. Simulation models can enhance an organization's decision-making process and enable it to see the impact of its future choices. For example, Fiat saves $1 million annually in manufactur- ing costs through simulation. The 2002 Winter Olympics (Salt Lake City, Utah) used simulation to design security systems and bus transportation for most of the venues. The predictive technology enabled the Salt Lake Organizing Committee to model and test a variety of scenarios, including security operations, weather, and transportation- system design. Its their highly variable and complex vehicle-distribution network. Savings were over $20 million per year. Benefits included lower costs and improved customer service. (See promodel.com for details.)

Modeling is a key element in most DSS/business intelligence (also business analyt- ics) and a necessity in a model-based DSS. There are many classes of models, and there are often many specialized techniques for solving each one. Simulation is a common modeling approach, but there are several others. For example, consider the optimiza- tion approach taken by Procter and Gamble (P&G) in redesigning its distribution sys- tem (Web Chapter). P&G's DSS for its North America supply chain redesign includes several models:

• A generating model (based on an algorithm) to make transportation cost esti- mates. This model is programmed directly in the DSS. • A demand forecasting model (statistically based). • A distribution center location model. This model uses aggregated data (a special

modeling technique) and is solved with a standard linear/integer optimization package.