Green Supply Chain Risk Network Management and Performance Analysis: Bayesian Belief Network Modeling

Document Type: Research Article

Authors

1 Faculty of Economics and Management, Semnan University, Semnan, Iran

2 Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran

Abstract

With the increase in environmental awareness, competitions and government policies, implementation of green supply chain management activities to sustain production and conserve resources is becoming more necessary for different organizations. However, it is difficult to successfully implement green supply chain (GSC) activities because of the risks involved. These risks alongside their resources disrupt the normal functioning of the GSC and affect its environmental and economic performance. The pharmaceutical industry in particular, is crucial to providing life-saving products and services to the society. The products and services provided in this industry, have several impacts on the environment in different ways. These include expired or unused medicines, inappropriate distribution by pharmacies or drug companies, disposal of surplus medicines in household sewage and improper disposal of pills or capsules by patients. This study represents a GSC risk network model that considers the interrelationships between risks in order to achieve an optimal level of performance measures defined in the supply chain by Bayesian Belief Networks (BBN). The model is empirically implemented through a case study conducted in Imam Reza hospital of Mashhad medicine supply chain involving structured and semi-structured interviews and workshop sessions with experts. This work uses a literature review and a causal map BBN approach in finalizing the risks and also uses the BBN inference system and scenario analysis for prioritization and analysis of the risks through the network under probability conditions. According to the findings, inefficient logistics network design, supplier quality issues and green raw material supply disruption are highly prioritized.

Keywords


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