Modelling supply chain disruption analytics under insufficient data: A decision support system based on Bayesian hierarchical approach
Recent disruptions in global and local supply chains (SCs) due to manmade and natural disasters have drawn a lot of attention to academics and practitioners. Such disruptions are often characterized by a lack of insufficient data. To tackle such a data scarce supply chain ecosystem, this article exa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096822000647 |
| _version_ | 1811321155990061056 |
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| author | Syed Mithun Ali A. B. M. Mainul Bari Abid Ali Moghul Rifat Majed Alharbi Sangita Choudhary Sunil Luthra |
| author_facet | Syed Mithun Ali A. B. M. Mainul Bari Abid Ali Moghul Rifat Majed Alharbi Sangita Choudhary Sunil Luthra |
| author_sort | Syed Mithun Ali |
| collection | DOAJ |
| description | Recent disruptions in global and local supply chains (SCs) due to manmade and natural disasters have drawn a lot of attention to academics and practitioners. Such disruptions are often characterized by a lack of insufficient data. To tackle such a data scarce supply chain ecosystem, this article examines the potential disruption risks in SCs under insufficient input data. For this, a decision support system (DSS) based on the Bayesian hierarchical approach and value at risk (VaR) reduction analysis is proposed to assess a supplier's disruption risk events probability as well as the supplier revenue impact on a company of interest. Empirical data are used to examine the DSS. The findings show that the DSS is effective in generating the suppliers’ risk profiles. The proposed DSS can be utilized by supply chain managers and practitioners to manage SC disruption risks in a more efficient manner. |
| first_indexed | 2024-04-13T13:12:11Z |
| format | Article |
| id | doaj.art-98d41f5b92d745f9a7565cdf898e0617 |
| institution | Directory Open Access Journal |
| issn | 2667-0968 |
| language | English |
| last_indexed | 2024-04-13T13:12:11Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj.art-98d41f5b92d745f9a7565cdf898e06172022-12-22T02:45:35ZengElsevierInternational Journal of Information Management Data Insights2667-09682022-11-0122100121Modelling supply chain disruption analytics under insufficient data: A decision support system based on Bayesian hierarchical approachSyed Mithun Ali0A. B. M. Mainul Bari1Abid Ali Moghul Rifat2Majed Alharbi3Sangita Choudhary4Sunil Luthra5Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh; Corresponding author.Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, BangladeshDepartment of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, BangladeshDepartment of Industrial Engineering, King Abdulaziz University, Jeddah, Saudi ArabiaSchool of Management, BML Munjal University, Gurgaon, IndiaDepartment of Mechanical Engineering, Ch. Ranbir Singh State Institute of Engineering and Technology, Jhajjar, Haryana, IndiaRecent disruptions in global and local supply chains (SCs) due to manmade and natural disasters have drawn a lot of attention to academics and practitioners. Such disruptions are often characterized by a lack of insufficient data. To tackle such a data scarce supply chain ecosystem, this article examines the potential disruption risks in SCs under insufficient input data. For this, a decision support system (DSS) based on the Bayesian hierarchical approach and value at risk (VaR) reduction analysis is proposed to assess a supplier's disruption risk events probability as well as the supplier revenue impact on a company of interest. Empirical data are used to examine the DSS. The findings show that the DSS is effective in generating the suppliers’ risk profiles. The proposed DSS can be utilized by supply chain managers and practitioners to manage SC disruption risks in a more efficient manner.http://www.sciencedirect.com/science/article/pii/S2667096822000647Bayesian hierarchical modelDecision support systemDisruption analyticsSupply chain disruptionsValue at Risk |
| spellingShingle | Syed Mithun Ali A. B. M. Mainul Bari Abid Ali Moghul Rifat Majed Alharbi Sangita Choudhary Sunil Luthra Modelling supply chain disruption analytics under insufficient data: A decision support system based on Bayesian hierarchical approach International Journal of Information Management Data Insights Bayesian hierarchical model Decision support system Disruption analytics Supply chain disruptions Value at Risk |
| title | Modelling supply chain disruption analytics under insufficient data: A decision support system based on Bayesian hierarchical approach |
| title_full | Modelling supply chain disruption analytics under insufficient data: A decision support system based on Bayesian hierarchical approach |
| title_fullStr | Modelling supply chain disruption analytics under insufficient data: A decision support system based on Bayesian hierarchical approach |
| title_full_unstemmed | Modelling supply chain disruption analytics under insufficient data: A decision support system based on Bayesian hierarchical approach |
| title_short | Modelling supply chain disruption analytics under insufficient data: A decision support system based on Bayesian hierarchical approach |
| title_sort | modelling supply chain disruption analytics under insufficient data a decision support system based on bayesian hierarchical approach |
| topic | Bayesian hierarchical model Decision support system Disruption analytics Supply chain disruptions Value at Risk |
| url | http://www.sciencedirect.com/science/article/pii/S2667096822000647 |
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