Proposition of a modeling and an analysis methodology of integrated reverse logistics chain in the direct chain

Abstract

Purpose: Propose a modeling and analysis methodology based on the combination of Bayesian networks and Petri networks of the reverse logistics integrated the direct supply chain. Design/methodology/approach: Network modeling by combining Petri and Bayesian network Findings: Modeling with Bayesian network complimented with Petri network to break the cycle problem in the Bayesian network Research limitations/implications: Demands are independent from returns Practical implications: Model can only be used on nonperishable products Social implications: Legislation aspects: Recycling laws; Protection of environment; Client satisfaction via after sale service. Originality/value: Bayesian network with a cycle combined with the Petri Network.