Enhancing Supply Chain Management: A Comparative Study of Machine Learning Techniques with Cost–Accuracy and ESG-Based Evaluation for Forecasting and Risk Mitigation

Mian Usman Sattar (), Vishal Dattana, Raza Hasan (), Salman Mahmood, Hamza Wazir Khan and Saqib Hussain
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Mian Usman Sattar: College of Science and Engineering, University of Derby, Kedleston Road, Derby DE22 1GB, UK
Vishal Dattana: Department of Computer Science and Management Information System, Oman College of Management & Technology, P.O. Box 680, Barka 320, Oman
Raza Hasan: Department of Science and Engineering, Southampton Solent University, Southampton SO14 0YN, UK
Salman Mahmood: Department of Computer Science, Nazeer Hussain University, ST-2, Near Karimabad, Karachi 75950, Pakistan
Hamza Wazir Khan: Department of Business Studies, Namal University, Mianwali 42250, Pakistan
Saqib Hussain: Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8QH, UK

Sustainability, 2025, vol. 17, issue 13, 1-45

Abstract: In today’s volatile market environment, supply chain management (SCM) must address complex challenges such as fluctuating demand, fraud, and delivery delays. This study applies machine learning techniques—Extreme Gradient Boosting (XGBoost) and Recurrent Neural Networks (RNNs)—to optimize demand forecasting, inventory policies, and risk mitigation within a unified framework. XGBoost achieves high forecasting accuracy (MAE = 0.1571, MAPE = 0.48%), while RNNs excel at fraud detection and late delivery prediction (F1-score ≈ 98%). To evaluate models beyond accuracy, we introduce two novel metrics: Cost–Accuracy Efficiency (CAE) and CAE-ESG, which combine predictive performance with cost-efficiency and ESG alignment. These holistic measures support sustainable model selection aligned with the ISO 14001, GRI, and SASB benchmarks; they also demonstrate that, despite lower accuracy, Random Forest achieves the highest CAE-ESG score due to its low complexity and strong ESG profile. We also apply SHAP analysis to improve model interpretability and demonstrate business impact through enhanced Customer Lifetime Value (CLV) and reduced churn. This research offers a practical, interpretable, and sustainability-aware ML framework for supply chains, enabling more resilient, cost-effective, and responsible decision-making.

Keywords: demand forecasting; inventory optimization; machine learning; XGBoost; RNNs; risk mitigation; supply chain management; model interpretability; Cost–Accuracy Efficiency (CAE); ESG metrics; sustainable supply chains (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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