Blockchain, IoT, and AI Technologies for Supply Chain Management

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Examine the synergistic possibilities of combining blockchain, IoT, and AI technologies in supply chain management. This book will address the difficulties and possibilities of integrating these technologies and offer helpful implementation advice.The current state of supply chain management involves several challenges, including lack of transparency, limited visibility into product movements, inefficient inventory management, and difficulty in tracking and tracing products. Blockchain, IoT, and AI can potentially address some of these challenges and improve supply chain management.To help illustrate this, case studies and actual instances of businesses implementing or experimenting with blockchain, IoT, and AI technology in their supply chains are included in the book. You'll review helpful advice on implementation and highlight successful deployments, lessons learned, and the impact of these technologies on supply chain visibility, agility, sustainability, and customer experience. This book further elaborates the fundamental concepts of AI and ML algorithms and demonstrates how AI can process enormous volumes of supply chain data to produce actionable insights, improve inventory control, forecast demand, and increase forecasting precision.This collective work will serve as a comprehensive guide for supply chain professionals, researchers, and technology enthusiasts interested in understanding the potential of blockchain, IoT, and AI technologies in revolutionizing supply chain management practices.What You Will LearnDiscuss the evolving landscape of technology, potential advancements, and how Blockchain, IoT, and AI can shape the future of supply chainsGain Insight into upcoming developments and prepare for changing dynamics of the industryUnderstand intricate details of blockchain, IoT, and AI Technologies in real-life scenariosWho Is This Book ForIndustry Professionals working in the domain of supply chain management who would be interested in understanding how these technologies can enhance efficiency, transparency, and optimization in their supply chains. N° de ref. del artículo LU-9798868803147

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Dr. Veena Grover is a Professor in Department of Management at Noida Institute of Engineering & Technology, India. Her interests include Micro Finance, Sustainability, Macro Economic Variables, and Innovation Strategy. She has published multiple articles in various journals and presented papers in various conferences. At present, she is guiding four research scholars and serves as an Editor of International Journal of Advanced Trends in Technology, Management & Applied Science.

Dr. Balamurugan Balusamy is currently working as Associate Dean–Student Engagement in Shiv Nadar University, India. He has 15 years of experience in the field of computer science. His areas of interest are Internet of Things, Big data, Networking. He has published more than 100 papers and contributed multiple book chapters.

Dr. Mariofanna Milanova is a Professor in Computer Science at the University of Arkansas at Little Rock, USA. She has 30 years of experience in the field of artificial intelligence. Dr. Milanova has extensive academic experience at various academic and research organizations worldwide. Dr. Milanova is an IEEE Senior Member, Fulbright U.S. Scholar, and NVIDIA Deep Learning Institute University Ambassador. Dr. Milanova’s work is supported by NSF, NIH, DARPA, DoD, Homeland Security, and NATO as well as Nokia Bell Lab, NJ, USA and NOKIA, Finland. She has published more than 130 publications, over 53 journal papers, 35 book chapters, and numerous conference papers. She also has two patents.

Dr. A. Yovan Felix has mentored students in the project areas of Big Data, Deep Learning, and Data Mining. He has published technical papers in national and international journals. He has over 17 years of experience and expertise in data analytics, smart prediction systems, and deep learning algorithms. He also coordinated in organizing several events at both national and international levels.