| Dewey Class |
658.7 |
| Title |
Data Analytics and Artificial Intelligence for Inventory and Supply Chain Management ( EBook/) / edited by Dinesh K. Sharma, Madhu Jain. |
| Added Personal Name |
Sharma, Dinesh K. |
| Jain, Madhu |
| Other name(s) |
SpringerLink (Online service) |
| Edition statement |
1st ed. 2022. |
| Publication |
Singapore : : Springer Nature Singapore : : Imprint: Springer, , 2022. |
| Physical Details |
XX, 284 p. 95 illus., 77 illus. in color. : online resource. |
| Series |
Inventory Optimization 2730-9355 |
| ISBN |
9789811963377 |
| Summary Note |
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 better alignment 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. .: |
| Contents note |
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. |
| Mode of acces to digital resource |
Digital reproduction.- |
| Cham : |
| Springer International Publishing, |
| 2022. - |
| Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format. |
| System details note |
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). |
| Internet Site |
https://doi.org/10.1007/978-981-19-6337-7 |
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