Classification and Allocation of Suppliers to Customers in Resilince Supply Chains Using Machine Learning

Document Type : Original Article

Authors

1 Ph.D Student, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabatabai University.

2 Professor, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabatabai University.

3 Associate Professor, Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabatabai University.

Abstract

Selection and allocation in the resilient supply chain, when disruption threatens the supply chain, has become a strategic decision and the focus of many researches; increase in the applications of machine learning in supply chain studies has led to the emergence of faster and reliable decision-making methods, however, in few studies, machine learning has been used to deal with the problem of selecting and assigning suppliers to customers in resilient mode. The purpose of this research is to take a step towards solving this gap by using machine learning algorithms on real world data from the automotive supply chain in Iran. the performance data of 441 suppliers and 7 customers in 1401 was used. In this research, two clustering algorithms have been used to generate labels based on the concept of resilience capacity; Then, since the interpretability of the results was a priority, based on the labeling of the clusters by the experts, the decision tree was used to classify the suppliers based on their performance. The results showed the K-means tree performs better than the DBSCAN tree and criteria such as on-time delivery, capacity, production line stoppage, quality alert, logistics performance and quality performance are effective on suppliers' resilience.

Keywords

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References

  1. Adobor, H., & McMullen, R. S. (2018). Supply chain resilience: a dynamic and multidimensional approach. International Journal of Logistics Management, 29(4), 1451–1471.
  2. Ali, Md. R., Nipu, S. Md. A., & Khan, S. A. (2023). A decision support system for classifying supplier selection criteria using machine learning and random forest approach. Decision Analytics Journal, 7,
  3. Alpaydin, E. (2020). Introduction to machine learning. MIT press.
  4. Atalay, M., & Çelik, E. (2017). Büyük veri analizinde yapay zekâ ve makine öğrenmesi uygulamalari-artificial intelligence and machine learning applications in big data analysis. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 9(22), 155–172.
  5. Bai, C., Rezaei, J., & Sarkis, J. (2017). Multicriteria green supplier segmentation. IEEE Transactions on Engineering Management, 64(4), 515–528.
  6. Baryannis, G., Dani, S., & Antoniou, G. (2019). Predicting supply chain risks using machine learning: The trade-off between performance and interpretability. Future Generation Computer Systems, 101, 993–1004.
  7. Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179–2202.
  8. Brusset, X., & Teller, C. (2017). Supply chain capabilities, risks, and resilience. International Journal of Production Economics, 184, 59–68.
  9. Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86–97.
  10. Echefaj, K., Charkaoui, A., Cherrafi, A., Garza-Reyes, J. A., Khan, S. A. R., & Chaouni Benabdellah, A. (2023). Sustainable and resilient supplier selection in the context of circular economy: an ontology-based model. Management of Environmental Quality: An International Journal, 34(5), 1461–1489.
  11. Eyika Gaida, I. W., Mittal, M., & Yadav, A. S. (2022). Optimal Strategy for Supplier Selection in a Global Supply Chain Using Machine Learning Technique. International Journal of Decision Support System Technology, 14(1), 1–13.
  12. Geetha, T. V, & Sendhilkumar, S. (2023). Machine Learning: Concepts, Techniques and Applications. CRC Press.
  13. Glock, C. H., Grosse, E. H., & Ries, J. M. (2017). Reprint of “Decision support models for supplier development: Systematic literature review and research agenda.” International Journal of Production Economics, 194, 246–260.
  14. Guo, X., Yuan, Z., & Tian, B. (2009). Supplier selection based on hierarchical potential support vector machine. Expert Systems with Applications, 36(3), 6978–6985.
  15. Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1), 16–24.
  16. Hosseini, S., & Khaled, A. Al. (2019). A hybrid ensemble and AHP approach for resilient supplier selection. Journal of Intelligent Manufacturing, 30, 207–228.
  17. Hosseini, S., Morshedlou, N., Ivanov, D., Sarder, M. D., Barker, K., & Al Khaled, A. (2019). Resilient supplier selection and optimal order allocation under disruption risks. International Journal of Production Economics, 213, 124–137.
  18. Hurwitz, J., & Kirsch, D. (2018). Machine learning for dummies. IBM Limited Edition, 75.
  19. Islam, S., Amin, S. H., & Wardley, L. J. (2024). A supplier selection & order allocation planning framework by integrating deep learning, principal component analysis, and optimization techniques. Expert Systems with Applications, 235,
  20. Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915.
  21. Jafarnezhad Chaghooshi, A., Kazemi, A., & Arab, A. (2016). Identification and Prioritization of Supplier’s Resiliency Evaluation Criteria Based on BWM. The Journal of Industrial Management Perspective, 6(3), 159-186. (In Persian)
  22. Jiang, W., & Liu, J. (2018). Inventory financing with overconfident supplier based on supply chain contract. Mathematical Problems in Engineering, 2018.
  23. Kamalahmadi, M., & Parast, M. M. (2017). An assessment of supply chain disruption mitigation strategies. International Journal of Production Economics, 184, 210–230.
  24. Khan, M. M., Bashar, I., Minhaj, G. M., Wasi, A. I., & Hossain, N. U. I. (2023). Resilient and sustainable supplier selection: an integration of SCOR 4.0 and machine learning approach. Sustainable and Resilient Infrastructure, 8(5), 453–469
  25. Kumar, S., Dixit, A. K., & Akarte, M. (2023). Machine Learning Based Decision Support System for Resilient Supplier Selection. In R. Misra, N. Kesswani, M. Rajarajan, B. Veeravalli, I. Brigui, A. Patel, & T. N. Singh (Eds.), Advances in Data Science and Artificial Intelligence (pp. 33–43). Springer International Publishing.
  26. Kιran, M. S., Eșme, E., Torğul, B., & Paksoy, T. (2020). Supplier Selection with Machine Learning Algorithms. In Logistics 4.0 (pp. 103–125). CRC Press.
  27. Lin, J., & Lanng, C. (2020). Here’s how global supply chains will change after COVID-19. World Economic Forum .https://www.weforum.org/agenda/2020/05/ this-is-what-global-supply-chains-will-look-like-after-covid-19/
  28. Mirkouei, A., & Haapala, K. R. (2014). Integration of machine learning and mathematical programming methods into the biomass feedstock supplier selection process.
  29. Mueller, J. P., & Massaron, L. (2021). Machine learning for dummies. John Wiley & Sons.
  30. Pentakalos, O. (2019). Introduction to machine learning. Proc. C. Impact
  31. Rabieh, M., Azar, A., Modarres Yazdi, M., & Fetanat Fard Haghighi, M. (2011). Designing a Multi-Objective Resource-Based Mathematical Modeling: An Approach to Supply Chain Risk Reduction (Case Study: Iran Khodro Supply Chain). The Journal of Industrial Management Perspective, 1(1), 57-77. (In Persian)
  32. Rajesh, R., & Ravi, V. (2015). Supplier selection in resilient supply chains: a grey relational analysis approach. Journal of Cleaner Production, 86, 343–359.
  33. Ravanestan, K., Aghajani, H., Safaei Ghdikolaei, A., & Yahyazadefar, M. (2017). Determining and Weighting Resilience Strategies in the Iran Khodro Supply Chain. The Journal of Industrial Management Perspective, 7(1), 145-172. (In Persian)
  34. Ribeiro, J. P., & Barbosa-Povoa, A. (2018). Supply Chain Resilience: Definitions and quantitative modelling approaches–A literature review. Computers & Industrial Engineering, 115, 109–122.
  35. Roberta Pereira, C., Christopher, M., & Lago Da Silva, A. (2014). Achieving supply chain resilience: the role of procurement. Supply Chain Management: An International Journal, 19(5/6), 626–642.
  36. Shashi, Centobelli, P., Cerchione, R., & Ertz, M. (2020). Managing supply chain resilience to pursue business and environmental strategies. Business Strategy and the Environment, 29(3), 1215–1246.
  37. Simchi-Levi, D., Schmidt, W., Wei, Y., Zhang, P. Y., Combs, K., Ge, Y., Gusikhin, O., Sanders, M., & Zhang, D. (2015). Identifying risks and mitigating disruptions in the automotive supply chain. Interfaces, 45(5), 375–390.
  38. Tavana, M., Fallahpour, A., Di Caprio, D., & Santos-Arteaga, F. J. (2016). A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection. Expert Systems with Applications, 61, 129–144.
  39. Tirkolaee, E. B., Sadeghi, S., Mooseloo, F. M., Vandchali, H. R., & Aeini, S. (2021). Application of Machine Learning in Supply Chain Management: A Comprehensive Overview of the Main Areas. Mathematical Problems in Engineering, 2021,
  40. Torabi, S. A., Baghersad, M., & Mansouri, S. A. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 79, 22–48.
  41. Valluri, A., & Croson, D. C. (2005). Agent learning in supplier selection models. Decision Support Systems, 39(2), 219–240
  42. Zhao, L., Qi, W., & Zhu, M. (2021). A Study of Supplier Selection Method Based on SVM for Weighting Expert Evaluation. Discrete Dynamics in Nature and Society.

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