Overview

This book discusses how artificial intelligence, machine learning, and digital technology are being used to re-engineer the manufacturing industry. With a particular emphasis on small- and medium-sized enterprises (SMEs), this book debunks the myth that smart factories are expensive and out of reach. By highlighting key features of Industry 4.0 and Industry 5.0, it focuses on AI-driven production optimization, predictive maintenance, quality assurance, supply chain resilience, and green manufacturing. Featuring real-world case examples, data-driven insights, and practical implementation plans, readers gain a clear picture of how AI can enhance efficiency, reduce costs, and foster sustainability. Key features of the book include illustrations, tables, and simulation-based analyses designed to simplify complex AI applications. It serves as an authoritative guide for manufacturing professionals, supply chain managers, researchers, and policymakers, while also presenting a futuristic vision of human-AI partnerships and the role of AI in reducing carbon footprints through intelligent energy management. Whether you are an industry leader seeking AI integration solutions or a researcher interested in next-generation intelligent manufacturing, this book acts as a manual for building an affordable, AI-enabled, and sustainable industrial ecosystem.

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9783032214652

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Author Information

Akash Kadam is an experienced mechanical engineer and also an independent researcher in smart supply chain systems, advanced manufacturing, and design innovation. He is an M.E. in Mechanical Engineering from Texas A&M University–Kingsville and over a decade of professional experience. He has led influential, self-driven research summing up AI, IoT, blockchain, and sustainability in Industry 4.0 and 5.0 paradigms. Through multiple authored papers and strategic outlooks, he contributes operational resilience, digital transformation, and quality excellence. Through his work, the future of smart manufacturing and world dominance in supply chains is proved to encompass considerable original work.

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