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<p>This study proposes a hybrid machine learning framework to predict supply chain resilience by integrating principal component analysis, K-Means clustering, and ensemble learning models. The approach captures firm-level heterogeneity, enabling context-specific resilience prediction and interpretability using SHAP values. The findings demonstrate that ensemble models, particularly XGBoost, outperform traditional regression models, and reveal distinct resilience drivers across operational clusters. The framework offers actionable insights for improving resilience strategies and contributes a scalable, explainable approach for data-driven supply chain risk management.</p>

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A statistically guided hybrid machine learning framework for predicting supply chain resilience in complex operational environments

  • 期刊论文

  • 发表日期：

    2025年10月25日

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  • G. V. Radhakrishnan,
  • Kamal Upreti,
  • Pravin R. Kshirsagar,
  • Sivaneasan Bala Krishnan,
  • Uma Shankar,
  • Rituraj Jain,
  • Akhilesh Tiwari

摘要

This study proposes a hybrid machine learning framework to predict supply chain resilience by integrating principal component analysis, K-Means clustering, and ensemble learning models. The approach captures firm-level heterogeneity, enabling context-specific resilience prediction and interpretability using SHAP values. The findings demonstrate that ensemble models, particularly XGBoost, outperform traditional regression models, and reveal distinct resilience drivers across operational clusters. The framework offers actionable insights for improving resilience strategies and contributes a scalable, explainable approach for data-driven supply chain risk management.