Explainable AI in Supply Chain Decision-Making
Sunil Sharma, Pankaj Kumar Vaishnav, Ravi Teli, Sandip Das, Bhupendra Kumar Soni · 2025
This chapter explores the role of explainable artificial intelligence (XAI) in revolutionizing decision-making processes within supply chain management (SCM). The chapter begins by introducing the core concepts of XAI, focusing on its capacity to simplify AI-driven decisions, thereby enabling transparent and reliable supply chains. This chapter discusses the limitations of traditional AI models in SCM, which, despite their predictive accuracy, lack explainability, leading to skepticism among stakeholders. Through real-world case studies, this chapter illustrates how XAI has been applied in various industries such as retail, manufacturing, and healthcare to improve operational efficiency and stakeholder confidence. The chapter examines various XAI techniques, including feature importance, saliency maps, and counterfactual explanations, each tailored to different supply chain scenarios. These techniques allow managers and decision-makers to better understand AI recommendations, enhancing trust and enabling more informed strategic decisions. This chapter highlights the growing importance of XAI in aligning AI systems with ethical standards and regulatory compliance, ensuring that AI decisions are transparent, justifiable, and auditable. This chapter explores emerging trends and potential research directions, emphasizing the increasing relevance of XAI as supply chains become more complex and data-driven.