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.
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