Modeling and Performance Analysis Of a Closed Loop Supply Chain using First Order Hybrid Petri Nets
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Green or closed loop supply chain (CLSC) had been the focus of many manufacturers during the last decade. The application of closed loop supply chain in today’s manufacturing is not only due to growing environmental concerns and the recognition of its benefits in reducing greenhouse gas emissions, energy consumption and meeting a more strict environmental regulations, it also offers economic competitive advantages if appropriately managed. First order hybrid petri nets (FOHPN) represent a powerful graphical and mathematical formalism to map and analyze the dynamics of complex systems such as CLSC networks. The paper aims at illustrating the use of FOHPN to model a CLSC network and evaluate its operational, financial and environmental performance measures under different management policies. Actual data from auto manufacturer in US is used to validate network’s performance under both tactical and strategic decision making namely, Tactical decision - Production policies: Increase of recovered vs. new components, Strategic decision - CLSCN structure: Manufacturer internal recovery process or recovery process done by a third party collection and recovery center. The modularity property of FOHPN has been used in the modeling process and the simulation and analysis of the modeled network are done in Matlab® environment. Unlike other researches on modeling SC networks that focus on evaluating individually cost, operational or environmental aspects, this work shows how FOHPN can be extended to assess simultaneously operational, financial and environmental network’s performance measures at different managerial decision making levels in a CLSC. The results may particularly be compelling for researchers and industrial practitioners who can use the same methodology in evaluating their network’s performance and making educated management decisions based on the performance results and the impact of their selected supply chain and manufacturing strategies.
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