Revolutionizing industrial efficiency through generative AI: Case studies and impacts on supply chain operations

College of Urban Rail Transit and Logistics, Beijing Union University, Beijing, 100101, China

^* Corresponding author: 2020260359003@buu.edu.cn

Abstract

With the advancement of Industry 4.0, the manufacturing industry is working to create a new smart industrial world through computerization, digitization and intelligence enhancement. Gen AI is primarily characterized by its ability to generate novel data patterns and solutions rather than merely analyzing predefined data inputs. This paper explores the transformative impact of Gen AI on supply chain efficiency in industrial engineering and logistics. Key applications include inventory optimization, predictive maintenance, fraud detection, risk management, logistics optimization, and demand forecasting. The study shows that Gen AI significantly improves operational efficiency and reduces stress for industrial workers by providing dynamic data-driven solutions. Through real-world case studies, including companies, this study demonstrates how Gen AI can revolutionize supply chain management and increase productivity. Despite its significant benefits, Gen AI still faces several challenges due to its cutting-edge nature. Further, in-depth research is needed in the future as the number of relevant cases and literature increases.