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Information systems for the fashion and apparel industry /
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Duxford, UK :
Woodhead Publishing is an imprint of Elsevier,
2016.
|
| Colección: | Woodhead publishing in textiles ;
no. 179. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Information Systems for the Fashion and Apparel Industry; The Textile Institute and Woodhead Publishing; Related titles; Information Systems for the Fashion and Apparel Industry; Copyright; Contents; List of contributors; Woodhead Publishing Series in Textiles; Preface; 1
- Introduction: key decision points and information requirements in fast fashion supply chains; 1.1 Introduction; 1.2 Key decision points; 1.3 Information requirements; 1.4 Concluding remarks; References; 2
- The use of fuzzy logic techniques to improve decision making in apparel supply chains.
- 2.1 Introduction and background2.1.1 Fashion mass customization; 2.1.2 Human perception for human-centered design; 2.1.3 Sensory evaluation for acquiring human perception; 2.1.4 Decision making with the uncertainty related to human perception in the fashion design; 2.2 Fuzzy logic techniques; 2.2.1 Fuzzy sets theory; 2.2.2 Fuzzy decision tree; 2.2.3 Fuzzy cognitive map; 2.3 The target market selection in apparel supply chain using fuzzy decision making; 2.3.1 Perception, evaluation, and formalization; 2.3.2 Modeling.
- 2.3.3 Computation of the relevancy degrees for the perception of naked body shapes2.3.3.1 Computation of the relevancy degree REL(D, Y); 2.3.3.2 Computation of the relevancy degree REL(ti, D); 2.3.3.3 Computation of the relevancy degree REL(ti, BRY); 2.3.4 Computation of the relevancy degrees for the perception of body shapes with a garment; 2.3.4.1 Computation of the relevancy degree REL(dev, S); 2.3.4.2 Computation of the relevancy degree REL(D, sk); 2.3.4.3 Computation of the relevancy degree REL(ti, BRCA170("ev); 2.3.4.4 Computation of the relevancy degree REL(ti, BRY("ev).
- 2.3.5 Comparison of the two relevancy degrees2.3.6 Application and validation; 2.4 Conclusion; References; 3
- Using radiofrequency identification (RFID) technologies to improve decision-making in apparel supply chains; 3.1 Introduction; 3.2 Literature review; 3.2.1 Descriptive case of RFID applications; 3.2.2 Examination of RFID investments and returns; 3.2.3 Exploration of the motivation and impact of RFID system adoption; 3.2.4 RFID-enabled systems development; 3.3 Case studies; 3.3.1 Zara1; 3.3.2 Marks and Spencer2; 3.3.3 American Apparel3; 3.3.4 Discussion.
- 3.4 Conclusions and future research directionsReferences; 4
- Using big data analytics to improve decision-making in apparel supply chains; 4.1 Introduction; 4.2 Literature review; 4.2.1 A brief insight into big data in business; 4.2.2 Leveraging big data on cloud computing; 4.2.3 Big data-driven fashion supply chains; 4.3 Romanian clothing and fashion industry; 4.3.1 An overview of the actual stage; 4.3.2 Facing the global market: Romanian products and their competitive advantage; 4.4 Community-influenced decision-making: the answer is in the social cloud; 4.4.1 Big data tools and techniques.