This book considers new analytics and AI approaches in the areas of inventory control, logistics, and supply chain management. It provides valuable insights for the retailers and managers to improve business operations and make more realistic and better decisions. It also offers a number of smartly designed strategies related to inventory control and supply chain management for the optimal ordering and delivery policies. The book further uses detailed models and AI computing approaches for demand forecasting to planning optimization and digital execution tracking. One of its key features is use of real-life examples, case studies, practical models to ensure adoption of new solutions, data analytics, and AI-lead automation methodologies are included.The book can be utilized by retailers and managers to improve business operations and make more accurate and realistic decisions. The AI-based solution, agnostic assessment, and strategy will support the companies for betteralignment and inventory control and capabilities to create a strategic road map for supply chain and logistics. The book is also useful for postgraduate students, researchers, and corporate executives. It addresses novel solutions for inventory to real-world supply chain and logistics that retailers, practitioners, educators, and scholars will find useful. It provides the theoretical and applicable subject matters for the senior undergraduate and graduate students, researchers, practitioners, and professionals in the area of artificial intelligent computing and its applications in inventory and supply chain management, inventory control, and logistics.

Contains various inventory models and AI techniques used to tackle logistics and supply chain management problems Discusses recent developments in data analytics and AI approaches to manage an end-to-end inventory and supply chains Identifies gaps and encourages adoption of planning optimization, coordinated efforts, and digital-execution tracking

Autorentext

Dinesh K. Sharma is a professor of Quantitative Methods and Computer Applications in the Department of Business, Management and Accounting at the University of Maryland Eastern Shore, USA. He earned his M.S. in Mathematics, M.S. in Computer Science, Ph.D. in Operations Research, and a second Ph.D. in Management. Professor Sharma has over twenty-eight years of teaching experience and served in several committees to supervise Ph.D. students and also acts as an external Ph.D. thesis examiner for several universities in India. Dr. Sharma's research interests include mathematical programming, artificial intelligence and machine learning techniques, inventory and supply chain management, healthcare management, and portfolio management. He has published over 250 refereed journal articles and conference proceedings and has also won fourteen best paper awards. Professor Sharma has collaborated on a number of funded research grants. Professor Sharma is editor of the Journal of Global Information Technology and Review of Business and Technology Research and is on the editorial board of several journals and a paper reviewer for a number of additional journals and conferences. Additionally, he is a member of Decision Sciences (USA), a life member of the Operational Research Society of India, and served as a program chair and coordinator of several international conferences in many countries.

Madhu Jain is presently working as an associate professor in the Department of Mathematics, Indian Institute of Technology Roorkee, India. Before joining IIT Roorkee, she served as reader/associate professor/professor in the Department of Mathematics in Dr. B.R.A. University, Agra, for 10 years and as visiting faculty in Indian Institute of Technology, Delhi. She has published over 450 research papers in International Journals, over 100 papers in Conference Proceeding/edited books, and 20 books and 6 edited books. She was conferred Young Scientist Award and SERC visiting fellow of the Department of Science and Technology (DST), India, and Career Award of University Grant Commission (UGC), India. She is the recipient of and Vigyan Gaurav award and Distinguish Service Award of Vijnana Parisad of India for her outstanding contributions to Mathematics. She was visiting fellow of Isaac Newton Institute of Mathematical Sciences, Cambridge, UK, for during summer in 2010, 2011, and 2014. At present, Dr. Madhu Jain is holding the post of president of Global Society of Mathematical and Allied Sciences. She has participated over 150 International/National Conferences in India and Abroad and visited many reputed universities/Institutes in USA, Canada, Australia, UK, Germany, France, Holland, Belgium, Singapore, Mauritius, Taiwan, UAE. Her current research interest includes stochastic modeling, inventory and supply chain management, queueing theory, software and hardware reliability, bio-informatics, data analytics, soft computing, etc.

Klappentext

Markov Decision Processes of a Two-tier Supply Chain Inventory System.- Nature-Inspired Optimization for Inventory Models with Imperfect Production.- A Multi-Objective Mathematical Model for Socially Responsible Supply Chain Inventory Planning.- Artificial Intelligence Computing and Nature Inspired Optimization Techniques for Effective Supply Chain Management.- An EPQ Model for Imperfect Production System with Deteriorating Items, Price Dependent Demand, Rework and Lead Time under Markdown Policy.- Retrial Inventory-Queueing Model with Inspection Processes and Imperfect Production.- Inventory Model for Growing Items and Its Waste Management.- Pavement Cracks Inventory Survey with Machine Deep Learning Models.- Decarbonisation Through Production of Rhino Bricks From the Waste Plastics: EPQ Model.- Cost Analysis of Supply Chain Model for Deteriorating Inventory Items with Shortages in Fuzzy Environment.

Inhalt
Markov Decision Processes of a Two-tier Supply Chain Inventory System.- Nature-Inspired Optimization for Inventory Models with Imperfect Production.- A Multi-Objective Mathematical Model for Socially Responsible Supply Chain Inventory Planning.- Artificial Intelligence Computing and Nature Inspired Optimization Techniques for Effective Supply Chain Management.- An EPQ Model for Imperfect Production System with Deteriorating Items, Price Dependent Demand, Rework and Lead Time under Markdown Policy.- Retrial Inventory-Queueing Model with Inspection Processes and Imperfect Production.- Inventory Model for Growing Items and Its Waste Management.- Pavement Cracks Inventory Survey with Machine Deep Learning Models.- Decarbonisation Through Production of Rhino Bricks From the Waste Plastics: EPQ Model.- Cost Analysis of Supply Chain Model for Deteriorating Inventory Items with Shortages in Fuzzy Environment.