OPENING VIGNETTE: DUPONT SIMULATES RAIL TRANSPORTATION SYSTEM AND AVOIDS COSTLY

4.1 OPENING VIGNETTE: DUPONT SIMULATES RAIL TRANSPORTATION SYSTEM AND AVOIDS COSTLY

CAPITAL EXPENSEl DuPont used simulation to avoid costly capital expenditures for rail car fleets as customer

demands changed. Demand changes could involve rail car purchases, better management of the existing fleet, or possibly fleet size reduction. The old analysis method, past experience, and conventional wisdom led managers to feel that the fleet size should be increased. The real problem was that DuPont was not using its specialized rail cars efficiently or effectively, not that there were not enough of them. There was immense variability in production output and transit cycle time, maintenance scheduling, and order sequencing. This made it difficult, if not impossible, to handle all the factors in a cohesive and useful manner leading to a good decision.

The fleets of specialized rail cars are used to transport bulk chemicals from DuPont to manufacturers. The cost of a rail car can vary from $80,000 for a standard tank car to more than $250,000 for a specialized tanker. Because of the high capital expense, effective and efficient use of the existing fleet is a must.

Instead of simply purchasing more rail cars, DuPont developed a ProModel simulation model (ProModel Corporation, Orem, Utah, www.promodel.com) that represented the firm's entire transportation system. It accurately modeled the variability inherent in chemical production, tank car availability, transportation time, loading and unloading time, and customer demand. A simulation model can provide a virtual environment in which experimentation with various policies that affect the physical transportation system can be performed before real changes are made. Changes can be made quickly and inexpensively in a simulated world because relationships among the components of the system are represented mathematically. It is not necessary to purchase expensive rail cars to determine the effect.

PART II DECISION SUPPORT SYSTEMS

ProModel allowed the company to construct simulation models easily and quickly (the first one took just two weeks to develop) and to conduct what-if analyses. It also included extensive graphics and animation capabilities. The simulation involved the entire rail transportation system. Many scenarios were developed, and experiments were run. DuPont experimented with a number of conditions and scheduling policies. Development of the simulation model helped the decision-making team understand the entire problem (see Banks et al.; 2001; Evans and Olson, 2002; Harrell et al., 2000; Law et al., 2000; Ross, 2003; Seila, Tadikamalla, and Ceric, 2003). The ProModel simulation accurately represented the variability associated with production, availability of tank cars, transportation times, and unloading at the customer site.

With the model, the entire national distribution system can be displayed graphically (visual simulation) under a variety of conditions-especially the current ones and forecasted customer demand. The simulation model helped decision-makers identify bottlenecks and other problems in the real system. By experimenting with the simulation model, the real issues were easily identified. The results convinced decisionmakers that a capital expense was unjustified. In fact, the needed customer deliveries could still be made after downsizing the fleet. Simulation drove this point home hard. After only two weeks of analysis, DuPont saved $500,000 in capital investment that year.

Following the proven success of this simulation model, DuPont has started performing logistics modeling on a variety of product lines, crossing division boundaries and political domains. Simulation dramatically improved DuPont's logistics. The next step focused on international logistics and logistics support for new market development. Savings in these areas can be substantially higher.

.:. QUESTIONS FOR THE OPENING VIGNETTE

1. Why did the decision-makers initially feel that fleet expansion was the right deci- sion?

2. How do you think the decision-makers learned about the real system through model development? As a consequence, were they able tofocus better on the structure of the real system? Do you think their involvement in model building helped them in accepting the results? Why or why not?

3, Explain how simulation was used to evaluate the operation of the rail system

before the changes were actually made.

4. How could the time compression capability of simulation help in this situation?

5. Simulation does not necessarily guarantee that an analyst will find the best solution. Comment on what this might mean to DuPont.

6. Once the system indicated that downsizing was a viable alternative, why do you think the managers bought into the system? Do you think that this is why the

- development team continues to work on other logistics problems? Explain.