AI-Enabled Forecasting and Performance Optimization in Sustainable Supply Chains

Prévision basée sur l’intelligence artificielle et optimisation des performances dans les chaînes d’approvisionnement durables: une approche par réseaux de neurones utilisant le modèle numérique SCOR®

Mariem Mrad and Younes Boujelbene
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Mariem Mrad: Faculty of Economics and Management of SFAX, Tunisia.
Younes Boujelbene: Faculty of Economics and Management of SFAX, Tunisia.

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Abstract: This chapter presents a computational framework for forecasting supply chain performance using augmented SCOR®4.0 metrics and Multilayer Perceptron (MLP) neural networks. The authors operationalize an eight-module SCOR® architecture, incorporating digital, cost, working capital, cash cycle, responsiveness, reliability, and risk dimensions. Each MLP model is optimized through systematic topology selection, normalized input processing, and rigorous cross-validation. Empirical results demonstrate high predictive accuracy, with correlation coefficients exceeding 0.997 and statistical tests confirming model reliability. Comparative analysis indicates that MLP-based forecasting significantly outperforms traditional linear methods, capturing non-linear interactions among operational, financial, and sustainability indicators. The chapter highlights the practical utility of AI-driven models for decision support in complex, cross-border supply chains.

Date: 2026-03-27
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Published in IGI GLOBAL, 2026, Advances in Computational Intelligence and Robotics, pp.85-116. ⟨10.4018/979-8-3373-7847-3.ch004⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05570711

DOI: 10.4018/979-8-3373-7847-3.ch004

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