Smart and Sustainable Supply Chain and Logistics — Challenges, Methods and Best Practices
Volume 2
Series: EcoProduction;
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66 563 Ft (63 393 Ft + 5% VAT)
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Product details:
- Edition number 1st ed. 2023
- Publisher Springer International Publishing
- Date of Publication 26 January 2024
- Number of Volumes 1 pieces, Book
- ISBN 9783031154140
- Binding Paperback
- See also 9783031154119
- No. of pages282 pages
- Size 235x155 mm
- Weight 450 g
- Language English
- Illustrations IX, 282 p. 81 illus., 50 illus. in color. Illustrations, black & white 523
Categories
Long description:
The application of artificial intelligence methods and the increasing digitalization of the processes in a supply chain contribute the more seamless flow of materials and information. The disturbances in global supply chain during pandemic put pressure on companies to improve the existing operations. The Sustainable Development Goals put pressure on local and global markets to reduce carbon dioxide emissions and to implement a more resource-efficient business model. Integration of physical and cyber systems is necessary to achieve more environmentally friendly, efficient logistics and supply chain operations. This book presents the contemporary issues of sustainability and integration of physical and information flow in supply chain. In the individual chapters, the authors discuss new qualitative and qualitative theoretic methods, models and present case studies from business practice. This book might be a valuable source of knowledge for the academics, PhD students and practitioners to deepen their knowledge in the field of logistics and SCM.
MoreTable of Contents:
Impact of Internet of Things on Food Supply Chains.- An Agent-Based Approach to Evaluate Freight Consolidation Center Strategy for Last Mile Deliveries in Jaipur City, India.- A Proposed Framework for Designing Blockchain Solutions for Logistics in Post-Covid Scenario and Future Pandemics.- Application of Big Data Analytics in Modern Logistics Solutions.- Case Study Validation of a Predictive Maintenance Implementation Framework.
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