A Combined Knowledge-Driven and Data-Driven Approach to Selecting Resilient Suppliers Based on Heterogeneous Information (Case Study: Steel Industry)

Authors

  • Sayedeh Saeideh Azarmina Department Industrial Engineering, Systems Optimization, Yazd University, Yazd, Iran.
  • Mohammad Saber Fallahnejad * Department of Industrial Engineering, Yazd University, Yazd, Iran. https://orcid.org/0000-0003-3343-2769
  • Hassan Khademi Zare Department of Industrial Engineering, Yazd University, Yazd, Iran.

https://doi.org/10.48313/scodm.v2i4.44

Abstract

Intoday's world, competition among firms has evolved into competition among supply chains. Supply chains must be resilient to maintain operational continuity in highly risky, turbulent environments. Suppliers, as the first and outermost layer of the supply chain, are the most vulnerable to risks, and disruptions in supplier performance can affect the entire supply chain. Therefore, incorporating resilience into supplier selection is essential. This study proposes an integrated approach combining Multi-Criteria Decision-Making (MCDM) and machine learning to select resilient suppliers, leveraging both quantitative and qualitative data. First, resilience criteria were identified through a comprehensive literature review, and then localized in collaboration with experts from a domestic steel company. Ultimately, 15 resilience criteria and 17 financial indicators were determined as the final evaluation criteria. To assess suppliers, Shannon entropy and the Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) method were used to weight and calculate resilience scores. These scores, together with financial data, were then used in a machine learning framework comprising Principal Component Analysis (PCA) and K-means clustering. By reducing the data to four principal components, the 24 main suppliers of the case study company were clustered into five groups. Model validity was examined by comparing supplier rankings obtained from the MARCOS and TOPSIS methods, yielding a Spearman correlation coefficient of 0.87, which indicates a strong relationship. In addition, the elbow method and silhouette index confirmed that five clusters were appropriate. By integrating data-driven and knowledge-driven approaches, this research provides a practical step toward improving decision-making in resilient supplier selection.

Keywords:

Resilient supplier selection, Heterogeneous information, Knowledge-driven approach, Measurement alternatives and ranking according to compromise solution, Machine learning

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Published

2025-12-08

Issue

Vol. 2 No. 4 (2025): Supply Chain and Operations Decision Making

Section

Articles

How to Cite

Azarmina, S. S. ., Fallahnejad, M. S. ., & Khademi Zare, H. . (2025). A Combined Knowledge-Driven and Data-Driven Approach to Selecting Resilient Suppliers Based on Heterogeneous Information (Case Study: Steel Industry). Supply Chain and Operations Decision Making, 2(4), 201-219. https://doi.org/10.48313/scodm.v2i4.44

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