This chapter first defines the concepts of supply chain risks and supply chain resilience. A trend in the semiconductor industry—semiconductor supply chain localization is owing to pandemic risks, war risks, and political risks. Facing such risks, supply chain participants are taking various actions to mitigate the impact of these risks, so as to maintain their competitiveness and pursue long-term sustainability. To this end, a lot of artificial intelligence (AI) applications can be seen. In particular, there are numerous reports in the literature on the applications of fuzzy inference systems (FIS) in supply chain risk and resilience management. Therefore, the basic concepts of fuzzy numbers and linguistic terms are introduced. The procedure for building an FIS is also detailed. However, although linguistic terms and fuzzy inference rules (FIRs) can be easily comprehended, it is still difficult to infer the output of an FIR from the inputs, making the FIS a black box. To address this issue, first, traditional techniques for explaining FISs are reviewed, such as system diagrams and response surface method (RSM). Subsequently, explainable artificial intelligence (XAI) techniques and tools are introduced, including partial dependence plot (PDP), Shapley value (SHAP) analysis, and local interpretable model-agnostic explanations (LIME).