Using MCDM Methods to Optimise Machine Learning Decisions for Supply Chain Delay Prediction: A Stakeholder-centric Approach

Mateusz Wyrembek, George Baryannis

Research output: Contribution to journalArticlepeer-review


Background: This study addresses challenges faced by supply chain stakeholders who lack expert knowledge in making decisions related to Machine Learning. It introduces a novel use of Multi-Criteria Decision-Making as an evaluation mechanism for different classifiers, aiding stakeholders in selecting appropriate Machine Learning models for predicting supply chain delays.

Methods: The proposed methodology involves applying classifiers (Decision Tree, Bagging, AdaBoost, Random Forest) and evaluating them using quantitative and qualitative metrics. MCDM methods (TOPSIS, MARCOS, COCOSO, MABAC) rank these Machine Learning models, facilitating accessible decision-making for stakeholders. A pharmaceutical industry case study is employed to validate the approach, utilizing Python for analysis.

Results: Case study results confirm the effectiveness of the proposed approach, combining Multi-Criteria Decision-Making with Machine Learning in order to facilitate stakeholder decisions on suitable algorithms for predicting supply chain delays. The Random Forest classifier is identified as the most balanced option in the context of the case study and clear rationale can be drawn in support or against each option through metrics comparison, validating the approachs practical applicability and effectiveness.

Conclusions: The combination of Multi-Criteria Decision-Making with Machine Learning provides a significant advancement in empowering stakeholders in supply chain management, particularly those lacking in-depth Machine Learning expertise. This approach enhances decision-making in model selection, with the potential of improving supply chain efficiency as a result.
Original languageEnglish
Publication statusAccepted/In press - 23 Mar 2024

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