Explainable Artificial Intelligence in Supply Chain Management: Methodology, System Architecture, and Applications

This book systematically reviews XAI techniques and introduces how these XAI techniques can be systematically applied to SCM, including methodology, system architecture, and applications. This book systematically reviews XAI techniques and introduces how these XAI techniques can be systematically applied to SCM, including methodology, system architecture, and applications. Relevant references, examples, or cases are also used as supporting evidence. So far, artificial intelligence (AI) technologies have been widely used in the field of supply chain management (SCM) for supply chain design, production and transportation planning, demand and sales forecasting, cell manufacturing, just-in-time (JIT) control, etc. Some applications of AI technologies in SCM are not easy to understand or communicate, especially for supply chain stakeholders with insufficient background knowledge of AI, which undoubtedly limits the practicality and credibility of these applications. To solve this problem, explainable artificial intelligence (XAI) is considered as a feasible strategy. However, most of the relevant research results are scattered in various journals or conference proceedings, and there is an urgent need to systematically integrate these results. In addition, although there have been many reviews on the possible applications of XAI in SCM, there are few systematic introductions, including methodology, system architecture, and case studies. This book answers this need.