This chapter begins by introducing the basic concepts of explainable artificial intelligence (XAI) and focuses on its application in supply chain management (SCM). First, it defines XAI and highlights some research and development directions in XAI technologies. Then, this chapter elaborates on the implementation process of XAI applications and reviews some important applications in recent literature and reports to illustrate this process. Next, this chapter summarizes the most common XAI applications in SCM, including supply chain product quality management, supply chain resilience, supply chain demand forecasting, supply chain supplier selection, supply chain logistics management, and supply chain performance evaluation. Furthermore, the most commonly used XAI techniques include Shapley value (SHAP) analysis and locally interpretable model-agnostic interpretations (LIME). Next, this chapter discusses the difficulties encountered in applying XAI in SCM. Finally, this chapter introduces some basic XAI techniques and tools, including LIME, SHAP analysis, local foil tree method, random forest-based incremental interpretation (RFII), and branch-and-bound (B&B)-based decision rules.