Generative AI's Impact on Supply Chain Decision‐Making

This chapter discusses recent progress in applying generative artificial intelligence (Gen-AI) to supply chain decision-making. Studies from manufacturing, retail, pharmaceuticals, e-commerce and green supply chains point to the increasing role of technologies such as large language models, generative adversarial networks, retrieval-augmented generation, reinforcement learning and digital twins. Gen-AI facilitates risk management, scenario planning and sustainability programs through which organizations are able to respond more dynamically to disruptions. There are hurdles to overcome, especially regarding data quality, ethical and privacy threats, algorithmic prejudice, integration with legacy systems and the requirement for skilled professionals. Through facilitating improved demand forecasting, disruption management, resource optimization, and real-time scenario simulation, Gen-AI offers decision-makers more adaptive and robust strategies. One of the biggest limitations is the ethical and legal aspects of data utilization. Supply chains deal with sensitive information such as supplier agreements, buyer orders and even financial or identification data.