A Generalized Stochastic Petri Net Model for Supply Chain Management

A Supply Chain (SC) is a collection of independent companies possessing complementary skills and integrated with transportation and storage systems. This paper deals with the issues of modeling and managing the SCs at the operational level, in order to study the performance measures of the systems controlled by different management strategies. The system is modeled as a timed discrete event dynamical system, whose evolution depends on the interaction of discrete events such as the arrival of components at the facilities, the departure of the transporters from the suppliers or the manufacturers, the start of assembly operations at the manufacturers. More precisely, generalized stochastic Petri nets model the system in a modular way and describe its behavior. Moreover, two well known broad policies are considered to manage the SC: make-to-stock and make-to-order. In order to compare the two management strategies and to show the effectiveness of the modeling technique, a case study is presented.