Performance analysis of supply chain networks using Petri nets

We investigate dynamic modeling techniques for analyzing supply chain networks using generalized stochastic Petri nets (GSPN). The customer order arrival process is assumed to be Poisson and the service processes at the various facilities of the supply chain are assumed to be exponential. Our modeling method accounts for both the logistics process as well as the interface processes that exist between any two members of the supply chain. We compare two production planning and control policies, the make-to-stock and the assemble-to-order systems and discuss their merits. Locating the decoupling point in the supply chain is a crucial decision. We formulate the problem as a total cost minimization problem with the total cost comprising the inventory carrying cost and delay costs. We use the framework of integrated GSPN-queueing network modeling, with the GSPN at the higher level and a generalized queueing network at the lower level