Explainable AI in Supply Chain Management: Opportunities and Research Challenges

Abstract

The rapid adoption of Artificial Intelligence (AI) in Supply Chain Management (SCM) has transformed traditional decision-making processes by enabling predictive analytics, demand forecasting, inventory optimization, supplier evaluation, logistics planning, and risk management. Despite the significant performance improvements achieved through AI-driven systems, the increasing reliance on complex machine learning and deep learning models has raised concerns regarding transparency, trustworthiness, accountability, and regulatory compliance. Many advanced AI models operate as “black boxes,” making it difficult for supply chain managers and stakeholders to understand how decisions are generated. Consequently, Explainable Artificial Intelligence (XAI) has emerged as a promising solution to enhance transparency and interpretability while maintaining the predictive power of AI systems. This paper presents a comprehensive review and conceptual analysis of Explainable AI in Supply Chain Management, examining its opportunities, applications, challenges, and future research directions. The study synthesizes findings from recent literature published between 2018 and 2026 and critically evaluates the role of XAI across major supply chain functions, including demand forecasting, procurement, supplier selection, inventory management, transportation, sustainability monitoring, and risk mitigation. The paper further identifies key barriers to XAI implementation, including data quality issues, model complexity, scalability concerns, organizational resistance, and the lack of standardized evaluation frameworks. A conceptual framework is proposed to illustrate how explainability can improve decision quality, stakeholder trust, operational resilience, and sustainable supply chain performance. The findings indicate that organizations integrating explainable AI mechanisms into supply chain analytics are better positioned to achieve transparency, regulatory compliance, and collaborative decision-making. The paper contributes to both theory and practice by establishing a research agenda that addresses emerging technological, managerial, and sustainability-related challenges associated with Explainable AI adoption in modern supply chains. Keywords: Explainable Artificial Intelligence (XAI), Supply Chain Management, Machine Learning, Decision Support Systems, Supply Chain Analytics, Transparency, Sustainable Supply Chains