Volume 9, Issue 1 (2019)                   ORMR 2019, 9(1): 141-163 | Back to browse issues page

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zareie A A, Shakeri M, Azar A, maleki M. A Mathematical Model for Analyzing Medicine Supply Chains’ System Interactive Risks Using Bayesian Belief Networks. ORMR 2019; 9 (1) :141-163
URL: http://ormr.modares.ac.ir/article-28-29402-en.html
A Mathematical Model for Analyzing Medicine Supply Chains’ System Interactive Risks Using Bayesian Belief Networks
Azim allah Zareie 1, Mahdi Shakeri2 , Adel Azar3 , Morteza Maleki4
1- Associate professor of semnan university , a_zarei@semnan.ac.ir
2- Phd student of system management, Semnan university
3- Professor of Tarbiat Modares university of Tehran
4- Assistant professor of Semnan university
Abstract:   (6210 Views)
Risk and failure in the supply chain can have a significant and negative effect on the short-term and long-term performance of the participants in the chain. Therefore, this research has an innovative look for a mathematical model for analyzing the interactive risks of the supply chain system using Bayesian belief networks. The study is descriptive in terms of purpose and has descriptive nature. The research community is classified into the two categories of academic experts and operational experts. In this research, information on the drug supply chain of the Imam Reza Hospital of Mashhad was obtained and analyzed using the bayesian belief network modeling process. The findings of this study show that Bayesian belief networks are much better than traditional risk analysis methods, because it can analyze basic risk analysis, including risk ranking and scenario analysis, and other essentials. BBN can also display different uncertainties in the language of probabilities with an appropriate visual form and provide more comprehensive view of the supply chain conditions and its risks
Keywords: Interactive Risks of Supply Chain System, Mathematical Modeling, Bayesian Belief Networks.
Full-Text [PDF 472 kb]   (1925 Downloads)    
Article Type: Original Research | Subject: Strategy and Management
Received: 2019/01/14 | Accepted: 2019/07/6 | Published: 2019/10/2
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