Industrial engineering has long been centred on efficiency, optimisation, and problem-solving, but with the rise of artificial intelligence (AI) and machine learning (ML), its scope has expanded into entirely new dimensions. Al and ML. in Industrial Engineering: Concepts and Applications explores this transformation with clarity, combining foundational theory with practical applications that reflect the realities of modern industry. The book begins by outlining the principles of Al and ML, showing how these technologies connect with data science and decision-making in industrial contexts. It then revisits core concepts of operations research, production systems, and industrial management, preparing readers to see how advanced tools can enhance traditional methods. Key approaches-supervised, unsupervised, and reinforcement learning-are explained in detail, alongside neural networks and deep learning, offering readers a strong understanding of both classical techniques and cutting-edge developments. Emphasis is placed on the central role of data, from collection and cleaning to preparation for meaningful Al-driven insights. Real-world applications are illustrated through predictive maintenance, defect detection, demand forecasting, warehouse optimisation, and the creation of smart factories supported by IloT and cyber-physical systems. The final chapters broaden the scope, addressing ethical implications, privacy concerns, workforce transformation, and the skill sets required for the next generation of industrial engineers. This balanced approach makes the book both a guide for students and a resource for professionals seeking to apply Al and ML to the evolving challenges of industrial engineering.
