Fashion Supply Chain and Logistics Management

· Routledge
E‑kniha
138
Stránky
Vhodná
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Podrobnosti o e‑knize

The fashion industry has a dynamic, ever-changing landscape. The last decade has seen a shift in consumer expectations and a heightened dependence on efficient and effective supply chain management. These shifts in the consumer mentality have already forced apparel retailers to adapt, making changes throughout their organisations to maintain consumer loyalty. This new text provides an overview of the latest trends and advances in fashion supply chain management and logistics, including:
  • The fundamentals of fashion supply chain management
  • Strategic management of the fashion supply chain, including the planning aspect of management
  • Technology in fashion supply chain management
  • Radio-frequency identification (RFID) and interoperability

Drawing on the expertise of academics, researchers and industry experts, including a wealth of real-life international cases, this book is ideal for advanced undergraduate and postgraduate students and academics of fashion management, logistics and supply chain management, as well as practising professionals.

O autorovi

Dr Yi Wang obtained his PhD from the Manufacturing Engineering Centre, Cardiff University, UK, in 2008. He is a lecturer in business decision making in the Faculty of Business, University of Plymouth, UK. Previously he worked in the Department of Computer Science, Southampton University, at the Business School, Nottingham Trent University, and in the School of Materials, University of Manchester. He holds various visiting lectureships in several universities worldwide. Dr Wang has special research interests in supply chain management, logistics, operation management, culture management, information systems, game theory, data analysis, semantics and ontology analysis, and neuromarketing. Dr Wang has published 75 technical peer-reviewed papers in international journals and conferences. He has co-authored two books: Operations Management for Business and Data Mining for Zero-defect Manufacturing.

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