Supply Chain Applications Simulation Software Case Study

Siemens, DuPont, IBM, and Case Corporation were amongst those companies presenting supply chain case studies at the ProModel users conference, August 1997.

These are just some of the ProModel user companies whose customers

• expect greater responsiveness in fulfilling their orders and /or
• require highly customized products made or configured to order.

To stay competitive, the performance of the supply chain that includes both demand satisfied from inventories and demand for specially configured products becomes more critical. Companies are therefore using ProModel to evaluate strategies for reducing response time to customers and for shrinking the finished goods inventory along the supply chain.

Case studies show that ProModel can consider the entire supply chain beginning with the supplier, extending through the production and storage areas, and ending with the customers at several distribution locations. Based on those elements, ProModel is able to demonstrate the effects caused by changes on the demand patterns, the logistics control system, the level of safety stocks, the reordering algorithms, or even the structure of the supply chain itself.

For example, a supply chain may function on a pull or a push principle, work with low or high safety stocks, have different levels of distribution. A company may make some goods, and buy others. A company may be considering a relocation of a manufacturing site.

Among other capabilities, ProModel captures the random variations in sales, transportation lead times, supplier issues for specially configured orders, and inventory levels. By modelling the plant operations in sufficient detail in terms of major operations, product mix, and line scheduling, ProModel also provides a tool to balance supply and demand at an aggregate level.

Reported benefits of supply chain simulation using ProModel are significant and include:

• several $million dollars removed from an authorised project
• reduced operating costs due to fewer truck rentals
• customers have fewer incidents of running low on stocks
• cut inventory by 30% without reducing customer service levels
• capital cost avoidance of 3.5 million dollars.

The user group case studies also demonstrated ProModel's ability to model the transport fleet. In most real-world situations, this problem is highly dynamic (affected by planned shut-downs of producing and consuming units, seasonality of demand, changing products and customers, etc.) and stochastic (affected by random outages, variable rail transit and dwell times, variable rail car maintenance times, etc.). Hence, the fleets are usually sized conservatively (too large), to compensate for the many uncertainties. ProModel is an ideal tool for this problem because of its ability to handle the complexity, dynamics, and randomness. Some fleets have been dramatically down-sized as a result, with no loss of service.