Optimizing Decision Making in the Apparel Supply Chain Using Artificial Intelligence (AI)

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

W. K. Wong is full professor at The Hong Kong Polytechnic University, Hong Kong and is currently with the endowed professorship title as Cheng Yik Hung Professor in Fashion. His areas of research range from computer vision to artificial intelligence with applications in the textile and fashion industries. He has published over hundred research articles in high-impact artificial intelligence related journals and serves as editorial board member of several journals. He also provides consultancy services to fashion and textile companies in the industry.S. Y. S. Leung is based at the Institute of Textiles and Clothing, The Hong Kong Polytechnic University, China.

Woodhead Publishing Series in TextilesPrefaceAcknowledgementsChapter 1: Understanding key decision points in the apparel supply chainAbstract:1.1 Introduction1.2 Selection of plant locations1.3 Production scheduling and assembly line balancing control1.4 Cutting room1.5 RetailingChapter 2: Fundamentals of artificial intelligence techniques for apparel management applicationsAbstract:2.1 Artificial intelligence (AI) techniques: a brief overview2.2 Rule-based expert systems2.3 Evolutionary optimization techniques2.4 Feedforward neural networks (FNNs)2.5 Fuzzy logic2.6 ConclusionsChapter 3: Selecting the location of apparel manufacturing plants using neural networksAbstract:3.1 Introduction3.2 Classification methods using artificial neural networks3.3 Classifying decision models for the location of clothing plants3.4 Classification using unsupervised artificial neural networks (ANN)3.5 Classification using supervised ANN3.6 Conclusion3.7 Acknowledgements3.9 Appendix: performance of back propagation (BP) and learning vector quantization (LVQ) with a different number of hidden neuronsChapter 4: Optimizing apparel production order planning scheduling using genetic algorithmsAbstract:4.1 Introduction4.2 Problem formulation4.3 Dealing with uncertain completion and start times4.4 Genetic algorithms for order scheduling4.5 Experimental results and discussion4.6 Conclusions4.7 AcknowledgementChapter 5: Optimizing cut order planning in apparel production using evolutionary strategiesAbstract:5.1 Introduction5.2 Formulation of the cut order planning (COP) decision-making model5.3 Genetic COP optimization5.4 An example of a genetic optimization model for COP5.5 Conclusions5.6 Acknowledgement5.8 Appendix: comparison between industrial practice and proposed COP decision-making modelChapter 6: Optimizing marker planning in apparel production using evolutionary strategies and neural networksAbstract:6.1 Introduction6.2 Packing method for optimized marker packing6.3 Evolutionary strategy (ES) for optimizing marker planning6.4 Experiments to evaluate performance6.5 ConclusionChapter 7: Optimizing fabric spreading and cutting schedules in apparel production using genetic algorithms and fuzzy set theoryAbstract:7.1 Introduction7.2 Problem formulation in fabric-cutting operations7.3 Genetic optimization of fabric scheduling7.4 Case studies using real production data7.5 Conclusions7.6 Acknowledgement7.8 Appendix: nomenclatureChapter 8: Optimizing apparel production systems using genetic algorithmsAbstract:8.1 Introduction8.2 Problem formulation in sewing operations8.3 Genetic optimization of production line balancing8.4 Experimental results8.5 Conclusions8.6 Acknowledgement8.8 Appendix: nomenclatureChapter 9: Intelligent sales forecasting for fashion retailing using harmony search algorithms and extreme learning machinesAbstract:9.1 Introduction9.2 Hybrid intelligent model for medium-term fashion sales forecasting9.3 Evaluating model performance with real sales data9.4 Experimental results and analysis9.5 Assessing forecasting performance9.6 Conclusions6.7 AcknowledgementChapter 10: Intelligent product cross-selling system in fashion retailing using radio frequency identification (RFID) technology, fuzzy logic and rule-based expert systemAbstract:10.1 Introduction10.2 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 IPCS10.5 Evaluation of the RFID-enabled SDS10.6 Assessing the use of RFID technology in fashion retailing10.7 Conclusions10.8 AcknowledgementIndex