Integrating Machine Learning with Multi-Criteria Decision-Making Models for Sustainable Supplier Selection in Dynamic Supply Chains

Osheyor Joachim Gidiagba (), Lagouge Tartibu and Modestus Okwu
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Osheyor Joachim Gidiagba: Department of Mechanical and Industrial Engineering, University of Johannesburg, Johannesburg 2092, South Africa
Lagouge Tartibu: Department of Mechanical and Industrial Engineering, University of Johannesburg, Johannesburg 2092, South Africa
Modestus Okwu: Department of Mechanical and Industrial Engineering, University of Johannesburg, Johannesburg 2092, South Africa

Logistics, 2025, vol. 9, issue 4, 1-22

Abstract: Background : Supplier evaluation and selection are pivotal processes in supply chain management, profoundly influencing organisational efficiency and sustainability. This study addresses the limitations of traditional multi-criteria decision-making approaches, particularly the Technique for Order Preference by Similarity to an Ideal Solution, which often lacks dimensional reduction capability and assumes uniform weight distribution across criteria. Methods : To overcome these challenges, a hybrid model integrating non-negative matrix factorisation, random forest, and the Technique for Order Preference by Similarity to an Ideal Solution is developed for supplier evaluation in the pharmaceutical sector. The method first applies non-negative matrix factorisation to condense twenty-four evaluation criteria into eight core dimensions, enhancing analytical efficiency and reducing complexity. Random forest is then employed to derive data-driven weights for each criterion, ensuring accurate prioritisation. Finally, the Technique for Order Preference by Similarity to an Ideal Solution ranks suppliers and provides actionable insights for decision-makers. Results : Results from real-world pharmaceutical data validate the model’s effectiveness and demonstrate superior performance over conventional evaluation methods. Conclusions : The findings confirm that integrating machine learning techniques with established decision-making frameworks enhances precision, interpretability, and sustainability in supplier selection while requiring adequate data quality and computational resources for implementation.

Keywords: supplier evaluation; sustainable supplier selection; random forest; MCDM; machine learning (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2025
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