Performance Analysis of Supply Chain Networks using Petri nets

In this paper, we investigate dynamic modeling tcchniques 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-tostock 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. 1 The Supply Chain Process: An Overview Manufacturing supply chain networks (SCNs) are formed when manufacturing and service providers such as the original equipment manufacturers, raw material and component vendors, logistics operators, warehouse managers, etc., enter into a strategic alliance with a view to deliver value to the customers. Modeling and analysis of such a complex network is crucial for performance evaluation and benchmarking. Supply chain performance depends on all the constituents of the supply chain. Long term issues in SCP involve location of production and inventory facilities, choice of alliance partners such as the suppliers and distributors, and the logistics chain. The long term decisions also include choosing between make to order and make to stock policies, degree of vertical integration, capacity decisions of various plants. amount of flexibility in each subsystem, etc. 1.1 Brief Literature Siirvey In this section, we brieliy survey various articles available on supply chains. The performance modeling of supply chain networks (SCN) follows the same methodology as othcr discrete event systems. Basically there are three anN. Vswanadham Dept. of Mechanical and Production Engg. National Univ. of Singapore, Singapore 119260 e-mail: [email protected] alytical methods: Markov chains, queuing networks and stochastic Petri nets. Discrete event simulation is another often used tool, while mathematical programming techniques are used mostly for strategic decision making. The stochastic models generally deal with tactical and operational level