Book description
Practitioners in apparel manufacturing and retailing enterprises in the fashion industry, ranging from senior to front line management, constantly face complex and critical decisions. There has been growing interest in the use of artificial intelligence (AI) techniques to enhance this process, and a number of AI techniques have already been successfully applied to apparel production and retailing. Optimizing decision making in the apparel supply chain using artificial intelligence (AI): From production to retail provides detailed coverage of these techniques, outlining how they are used to assist decision makers in tackling key supply chain problems. Key decision points in the apparel supply chain and the fundamentals of artificial intelligence techniques are the focus of the opening chapters, before the book proceeds to discuss the use of neural networks, genetic algorithms, fuzzy set theory and extreme learning machines for intelligent sales forecasting and intelligent product cross-selling systems.- Helps the reader gain an understanding of the key decision points in the apparel supply chain
- Discusses the fundamentals of artificial intelligence techniques for apparel management techniques
- Considers the use of neural networks in selecting the location of apparel manufacturing plants
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Woodhead Publishing Series in Textiles
- Preface
- Acknowledgements
- Chapter 1: Understanding key decision points in the apparel supply chain
- Chapter 2: Fundamentals of artificial intelligence techniques for apparel management applications
-
Chapter 3: Selecting the location of apparel manufacturing plants using neural networks
- Abstract:
- 3.1 Introduction
- 3.2 Classification methods using artificial neural networks
- 3.3 Classifying decision models for the location of clothing plants
- 3.4 Classification using unsupervised artificial neural networks (ANN)
- 3.5 Classification using supervised ANN
- 3.6 Conclusion
- 3.7 Acknowledgements
- 3.9 Appendix: performance of back propagation (BP) and learning vector quantization (LVQ) with a different number of hidden neurons
- Chapter 4: Optimizing apparel production order planning scheduling using genetic algorithms
-
Chapter 5: Optimizing cut order planning in apparel production using evolutionary strategies
- Abstract:
- 5.1 Introduction
- 5.2 Formulation of the cut order planning (COP) decision-making model
- 5.3 Genetic COP optimization
- 5.4 An example of a genetic optimization model for COP
- 5.5 Conclusions
- 5.6 Acknowledgement
- 5.8 Appendix: comparison between industrial practice and proposed COP decision-making model
- Chapter 6: Optimizing marker planning in apparel production using evolutionary strategies and neural networks
- Chapter 7: Optimizing fabric spreading and cutting schedules in apparel production using genetic algorithms and fuzzy set theory
- Chapter 8: Optimizing apparel production systems using genetic algorithms
- Chapter 9: Intelligent sales forecasting for fashion retailing using harmony search algorithms and extreme learning machines
-
Chapter 10: Intelligent product cross-selling system in fashion retailing using radio frequency identification (RFID) technology, fuzzy logic and rule-based expert system
- Abstract:
- 10.1 Introduction
- 10.2 Radio frequency identification (RFID)-enabled smart dressing system (SDS)
- 10.3 Intelligent product cross-selling system (IPCS)
- 10.4 Implementation of the RFID-enabled SDS and IPCS
- 10.5 Evaluation of the RFID-enabled SDS
- 10.6 Assessing the use of RFID technology in fashion retailing
- 10.7 Conclusions
- 10.8 Acknowledgement
- Index
Product information
- Title: Optimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI)
- Author(s):
- Release date: January 2013
- Publisher(s): Woodhead Publishing
- ISBN: 9780857097842
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