Recent advancements in artificial intelligence have significantly increased its application in logistics and supply chain management. Its predictive capabilities have improved efficiency by anticipating potential delays and proposing alternative routes before disruptions occur. Furthermore, it takes quality improvement to a whole new level by guaranteeing reliability, consistency, and performance of products and services. However, these enhancements often come to the detriment of model transparency, resulting in AI systems that function as “black boxes.” This ambiguity blocks the adoption of machine learning in various fields, including logistics and supply chain management, as understanding the rationale behind AI decisions is crucial for trust among nonspecialist users. Consequently, explainable artificial intelligence (XAI) has gained importance for reviewing and improving model performance, aiding stakeholders in comprehending AI behavior. This chapter provides a comprehensive overview of the latest XAI methods proposed for logistics and supply chain management, with a particular focus on SHAP (Shapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). The objective of this chapter is to categorize research studies that have employed SHAP and LIME to improve AI model’s transparency to explain when solving real-life problems in logistics and supply chain management including inventory management, transport optimization, and quality improvement.