A Structural Treatise on the Effects of Explainable AI and Big Data Technologies on Supply Chain Management
Venkata Naga Siva Kumar Challa, Kiran Kumar Thodeti, Shaikh Mahaboob Syed, P. Padmalatha · 2025
This chapter presents a comprehensive analysis of the integration of explainable artificial intelligence (XAI) and big data technologies in supply chain management (SCM). XAI, combined with big data, offers unprecedented transparency in decision-making processes, enhancing trust and accountability in SCM operations such as demand forecasting, inventory management, and logistics. By leveraging these technologies, businesses can optimize their operations through real-time insights, improved decision-making, and more efficient management of complex supply chains. This chapter explores the current trends and applications of XAI and big data in SCM, particularly in addressing challenges such as demand fluctuations, environmental sustainability, and resource optimization. This chapter highlights the importance of explainability in AI-driven systems, especially in industries where data transparency and trust are crucial for operational success. This chapter examines the impact of these technologies on green supply chain management (GSCM), demonstrating how data-driven insights can improve environmental performance and encourage sustainable practices. Through a structured approach, this chapter reviews existing literature on AI and big data in SCM and introduces new methodologies for enhancing predictive accuracy and decision-making efficiency. This chapter also discusses the role of federated learning and other advanced AI techniques in managing data across multiple levels of the supply chain.