Information Systems for the Fashion and Apparel Industry

Opis

Information Systems for the Fashion and Apparel Industry brings together trends and developments in fashion information systems, industrial case-studies, and insights from an international team of authors. The fashion and apparel industry is fast-growing and highly influential. Computerized information systems are essential to support fashion business operations and recent developments in social media, mobile commerce models, radio frequency identification (RFID) technologies, and ERP systems are all driving innovative business measures in the industry.

After an introductory chapter outlining key decision points and information requirements in fast fashion supply chains, Part One focuses on the principles of fashion information systems, with chapters covering how decision making in the apparel supply chains can be improved through the use of fuzzy logic, RFID technologies, evolutionary optimization techniques, and artificial neural networks. Part Two then reviews the range of applications for information systems in the fashion and apparel industry to improve customer choice, aid design, implement intelligent forecasting and procurement systems, and manage inventory and returns.

Provides systematic and comprehensive coverage of information systems for the fashion and apparel industry
Combines recent developments and industrial best-practices in apparel supply chain management in order to meet the needs of the fashion and apparel industry professionals and academics
Features input from a team of highly knowledgeable authors with a range of professional and academic experience, overseen by an editor who is a leading expert in the field
Reviews the range of applications for information systems in the fashion and apparel industry to improve customer choice, aid design, implement intelligent forecasting and procurement systems, and manage inventory and returns

Spis treści

Front Cover 2
Information Systems for the Fashion and Apparel Industry 3
The Textile Institute and Woodhead Publishing 4
- Related titles 4
Information Systems for the Fashion and Apparel Industry 5
Copyright 6
Contents 7
List of contributors 11
Woodhead Publishing Series in Textiles 13
Preface 21
1 - Introduction: key decision points and information requirements in fast fashion supply chains 23
- 1.1 Introduction 23
- 1.2 Key decision points 24
- 1.3 Information requirements 25
- 1.4 Concluding remarks 27

Front Cover 2
Information Systems for the Fashion and Apparel Industry 3
The Textile Institute and Woodhead Publishing 4
- Related titles 4
Information Systems for the Fashion and Apparel Industry 5
Copyright 6
Contents 7
List of contributors 11
Woodhead Publishing Series in Textiles 13
Preface 21
1 - Introduction: key decision points and information requirements in fast fashion supply chains 23
- 1.1 Introduction 23
- 1.2 Key decision points 24
- 1.3 Information requirements 25
- 1.4 Concluding remarks 27
- References 29
2 - The use of fuzzy logic techniques to improve decision making in apparel supply chains 31
- 2.1 Introduction and background 31
  - 2.1.1 Fashion mass customization 31
  - 2.1.2 Human perception for human-centered design 32
  - 2.1.3 Sensory evaluation for acquiring human perception 32
  - 2.1.4 Decision making with the uncertainty related to human perception in the fashion design 33
- 2.2 Fuzzy logic techniques 34
  - 2.2.1 Fuzzy sets theory 34
  - 2.2.2 Fuzzy decision tree 38
  - 2.2.3 Fuzzy cognitive map 40
- 2.3 The target market selection in apparel supply chain using fuzzy decision making 42
  - 2.3.1 Perception, evaluation, and formalization 43
  - 2.3.2 Modeling 45
  - 2.3.3 Computation of the relevancy degrees for the perception of naked body shapes 48
    - 2.3.3.1 Computation of the relevancy degree REL(D, Y) 48
    - 2.3.3.2 Computation of the relevancy degree REL(ti, D) 51
    - 2.3.3.3 Computation of the relevancy degree REL(ti, BRY) 52
  - 2.3.4 Computation of the relevancy degrees for the perception of body shapes with a garment 52
    - 2.3.4.2 Computation of the relevancy degree REL(D, sk) 52
    - 2.3.4.1 Computation of the relevancy degree REL(dev, S) 52
    - 2.3.4.3 Computation of the relevancy degree REL(ti, BRCA170∨dev) 53
    - 2.3.4.4 Computation of the relevancy degree REL(ti, BRY∨dev) 54
  - 2.3.5 Comparison of the two relevancy degrees 54
  - 2.3.6 Application and validation 56
- 2.4 Conclusion 58
- References 60
3 - Using radiofrequency identification (RFID) technologies to improve decision-making in apparel supply chains 63
- 3.1 Introduction 63
- 3.2 Literature review 64
  - 3.2.1 Descriptive case of RFID applications 65
  - 3.2.2 Examination of RFID investments and returns 69
  - 3.2.3 Exploration of the motivation and impact of RFID system adoption 69
  - 3.2.4 RFID-enabled systems development 70
- 3.3 Case studies 71
  - 3.3.1 Zara1 71
  - 3.3.2 Marks and Spencer2 73
  - 3.3.3 American Apparel3 74
  - 3.3.4 Discussion 75
- 3.4 Conclusions and future research directions 77
- References 79
4 - Using big data analytics to improve decision-making in apparel supply chains 85
- 4.1 Introduction 85
- 4.2 Literature review 87
  - 4.2.1 A brief insight into big data in business 87
  - 4.2.2 Leveraging big data on cloud computing 89
  - 4.2.3 Big data–driven fashion supply chains 90
- 4.3 Romanian clothing and fashion industry 91
  - 4.3.1 An overview of the actual stage 91
  - 4.3.2 Facing the global market: Romanian products and their competitive advantage 96
- 4.4 Community-influenced decision-making: the answer is in the social cloud 102
  - 4.4.1 Big data tools and techniques 104
  - 4.4.2 Big data on clouds for fashion 106
  - 4.4.3 Points of view and discussions about the influence of big data in a fashion supply chain 109
- 4.5 Conclusions 110
- Appendix A: The evolution of the Romanian investments during 2008–2012 111
- Appendix B: The evolution of Romanian exports and imports 112
- Appendix C: The evolution of clothing sector exports during 2008–2012 113
- Appendix D: The strategy to promote the Romanian exports 114
- References 114
5 - Using artificial neural networks to improve decision making in apparel supply chain systems 119
- 5.1 Introduction 119
- 5.2 Decision process involved in the apparel supply chain 120
- 5.3 Applications of ANN in apparel supply chain to improve their decision 122
  - 5.3.1 Data mining of customer's buying preference 122
  - 5.3.2 Sales forecasting 123
  - 5.3.3 Prediction of stock level 124
  - 5.3.4 Optimization of marker planning in apparel production 125
- 5.4 Conclusion and limitations of using ANNs in apparel supply chain systems 126
- References 127
6 - Smart systems for improved customer choice in fashion retail outlets 131
- 6.1 Context overview 131
  - 6.1.2 The unequal fashion system relationships between brand/retailers, manufacturers, and consumers 131
  - 6.1.1 The rising consumer awareness, knowledge, and access to fashion information 131
  - 6.1.3 Social, economic, and environmental issues facing the contemporary fashion retail consumption model 132
- 6.2 Research parameter 134
  - 6.2.1 Fashion industry and market supply chain as a system 134
  - 6.2.2 Customer choice in conventional fashion retail offerings 134
  - 6.2.3 Mass customization retail strategies increase actual customer choice 135
- 6.3 Model proposition 136
  - 6.3.1 Fashion system framework reconfiguration 136
    - 6.3.1.1 Principles of modularization 136
    - 6.3.1.2 Architecture 137
    - 6.3.1.3 Modules 137
    - 6.3.1.4 Interface 138
  - 6.4 Deploying smart systems compilation of customers' choice through modular customization model 138
  - 6.5 Conclusion 138
    - 6.5.1 Redefining customer choice in customized fashion design and retailing 138
    - 6.5.3 Toward a new target for resource efficiency, consumption sustainability, and customer satisfaction 139
    - 6.5.2 Reorienting producers' push supply chain to customers' pull demand chain 139
  - References 141
- 7 - Intelligent procurement systems to support fast fashion supply chains in the apparel industry 143
  - 7.1 Introduction 143
  - 7.2 Two-period models with reordering during the selling season 146
    - 7.2.1 Multiple products with heterogeneous Poisson demands 148
    - 7.2.2 Price-sensitive demand 150
    - 7.2.3 Flexible timing of the order placed after demand forecast update 150
    - 7.2.4 Supply chain coordination 151
    - 7.2.5 More than one demand forecast revision 151
    - 7.2.6 Mid-season replenishment without forecast updating 152
  - 7.3 Multiple-order models with all orders placed before the selling season 152
    - 7.3.1 Flexible timing of the purchase orders 154
    - 7.3.3 Purchase cost uncertainty 156
    - 7.3.2 Price-sensitive demand 156
    - 7.3.5 Order cancellation flexibility 157
    - 7.3.6 Purchase budget constraint 157
    - 7.3.4 Limited supply at time 2 157
    - 7.3.8 Coordinating the supply chain 158
    - 7.3.7 Multiproduct problem 158
    - 7.3.9 Loss-averse retailer's problem 159
    - 7.3.10 Order policy for product components 159
    - 7.3.11 Order policy with consideration of carbon footprint 160
    - 7.3.12 Demand learning by precommitted orders 160
    - 7.3.13 Two-period problem with exogenous market information update 161
    - 7.3.14 Optimal policy under a service level constraint 161
    - 7.3.16 Use of demand signals in a multiperiod problem with service level constraint 162
    - 7.3.15 Second order based on perfect demand information 162
  - 7.4 Conclusion 162
  - References 163
- 8 - Intelligent demand forecasting systems for fast fashion 167
  - 8.1 Introduction 167
  - 8.2 Fashion and fast fashion sales forecasting 168
  - 8.3 Sales forecasting methods for fast fashion retailing 170
  - 8.4 Intelligent system based on sales forecasting and replenishment modules 172
    - 8.4.1 Long-term forecasting model 172
    - 8.4.2 Short-term forecasting model 173
    - 8.4.3 Store replenishment model 174
    - 8.4.4 Experiment and results 175
      - 8.4.4.2 Models used for comparison 175
      - 8.4.4.3 Long-term forecasting module 175
      - 8.4.4.1 Data 175
      - 8.4.4.4 Short-term forecasting module 176
      - 8.4.4.5 Replenishment module 178
    - 8.5 Conclusion 180
    - References 181
  - 9 - Fashion design using evolutionary algorithms and fuzzy set theory – a case to realize skirt design customizations 185
    - 9.1 Introduction 185
      - 9.1.1 Sketch design and its related work 185
      - 9.1.2 Pattern design and its related work 187
      - 9.1.3 Cutting-edge CAD developments 188
      - 9.1.4 A new approach to customized fashion product design 189
    - 9.2 Style classification and style feature database 191
    - 9.3 Sketch design using fuzzy numbers and IGA 191
      - 9.3.1 Sketch representation and composing method (SRCM) 191
      - 9.3.2 Design knowledge model by fuzzy numbers 199
      - 9.3.3 Sketch design by interactive genetic algorithm 202
        
        9.3.3.1 Individual representation and its decoding 202
        
        9.3.3.2 Other design decisions and genetic operators 204
        
        9.3.3.3 Evolution process 205
      - 9.4 Intelligent pattern designs 205
        
        9.4.1 Mathematical relationship between sketches and patterns 209
        
        9.4.2 Patternmaking knowledge base 210
      - 9.5 Results and discussions 211
        
        9.5.1 Experimental results 211
        
        9.5.2 System characteristics 211
      - Acknowledgments 217
      - References 217
      - 9.6 Conclusions and future research 217
    - 10 - Intelligent systems for managing returns in apparel supply chains 221
      - 10.1 Introduction 221
      - 10.2 Literature review 222
        
        10.2.1 Returns management in the apparel supply chain 222
        
        10.2.2 Intelligent systems for managing returns in the textile and apparel supply chain 223
      - 10.3 Critical factors of returns management in apparel supply chains 224
        
        10.3.1 Common types of product returns in the apparel industry 224
        
        10.3.2 Characteristics of product returns in the apparel industry 225
        
        10.3.3 Strategies of returns management in the apparel industry 226
        
        10.3.4 Identifying return-inclined consumers in the apparel industry 227
      - 10.4 Quantity model for managing returns in apparel supply chains 227
        
        10.4.1 Optimal returns policy for retailers in apparel supply chains 227
        
        10.4.2 Optimal returns policy for consumers in apparel supply chains 228
        
        10.4.2.1 Advanced selling 229
        
        Case I: AS with AFP 230
        
        Case II: AS with APP 230
        
        10.4.2.2 Mass customization 230
        
        Case 1: risk-neutral MC fashion service provider 231
        
        Case 2: risk-averse MC fashion service provider 231
        
        10.4.3 Summary 232
        
        10.5 Intelligent system implementation for managing returns in apparel supply chains 233
        
        10.5.1 Expert system for forecasting returns quantity in apparel supply chains 233
        
        10.5.2 DSS for returns management in apparel supply chains 234
        
        10.6 Conclusions and future direction 238
        
        Acknowledgments 238
        
        References 239
      - 11 - Vendor-managed inventory systems in the apparel industry 243
        
        11.1 Introduction 243
        
        11.2 Vendor-managed inventory research 244
        
        11.3 Research design 246
        
        11.4 Case data 247
        
        11.5 Discussion 252
        
        References 255
        
        11.6 Conclusion and future research 255
      - 12 - Enterprise resource planning systems for use in apparel supply chains 257
        
        12.1 Introduction 257
        
        12.2 Enterprise resource planning systems in the apparel industry: review 258
        
        12.2.1 Need for an ERP system 258
        
        12.2.2 Benefits and efficiency of ERP systems 260
        
        12.2.3 SWOT analysis 263
        
        12.2.3.2 Weaknesses 263
        
        12.2.3.1 Strengths 263
        
        12.2.3.3 Opportunities 264
        
        12.2.3.4 Threats 265
        
        12.2.4 ERP systems in the apparel market: a brief summary 265
        
        12.2.4.1 Sync 265
        
        12.2.4.2 Ysoft 266
        
        12.2.4.3 VisualGEMS 268
        
        12.2.4.4 e-Smartx 269
        
        12.2.4.5 ATOM 270
        
        12.2.5 ERP system modules for the apparel sector 270
        
        12.2.6 Implementation 272
        
        12.2.6.1 Project planning 273
        
        12.2.6.2 Software selection 273
        
        12.2.6.3 Vendor selection 274
        
        12.2.6.4 Customization 274
        
        12.2.6.5 Architectural design 274
        
        12.2.6.8 Preparing for the venture 275
        
        12.2.6.6 Cost of the software 275
        
        12.2.6.7 Data management 275
        
        12.3 Case studies 276
        
        12.3.1 Success 276
        
        12.3.1.1 Need for ERP systems at Pantaloons 277
        
        12.3.1.2 The solution 277
        
        12.3.1.3 The implementation 277
        
        12.3.1.4 Postimplementation 278
        
        12.3.2 Failures 279
        
        12.3.2.1 Failure 280
        
        12.3.2.2 The reason 280
        
        12.3.2.3 Reasons behind failure of new system 280
        
        12.3.2.4 The result 281
        
        12.4 Conclusion 282
        
        References 282
        
        13 - Intelligent demand forecasting supported risk management systems for fast fashion inventory management 285
        
        13.1 Introduction and background 285
        
        13.2 Demand forecasting supported inventory control 286
        
        13.3 Inventory models with risk considerations 288
        
        13.4 An intelligent fast fashion demand forecasting supported risk minimization inventory control model 289
        
        13.5 Concluding remarks and future research 290
        
        Acknowledgment 291
        
        References 291
        
        Index 295
        
        A 295
        
        B 296
        
        C 296
        
        D 297
        
        E 298
        
        F 298
        
        H 300
        
        I 300
        
        G 300
        
        K 301
        
        L 301
        
        M 301
        
        N 302
        
        O 302
        
        P 302
        
        Q 303
        
        R 303
        
        S 304
        
        T 305
        
        V 306
        
        U 306
        
        Z 307
        
        W 307
        
        Y 307
        
        Back Cover 309

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