Artificial Intelligence for Fashion Industry in the Big Data Era

Preface

Contents

Introduction: Artificial Intelligence for Fashion Industry in the Big Data Era

References

Part I AI for Fashion Sales Forecasting

AI-Based Fashion Sales Forecasting Methods in Big Data Era

1 Introduction

2 AI-Based Fashion Sales Forecasting Methods

2.1 ANN and ELM-Based Methods

2.2 Fuzzy Logic-Based Methods

2.3 Support Vector Machines (SVMs)

3 Application of Big Data in Fashion Industry

4 AI-Based Fashion Sales Forecasting Methods in Big Data Era

4.1 Data Filtering

4.2 Feature Extraction

4.3 Data Training

4.4 Forecast Output

5 Conclusion

References

Enhanced Predictive Models for Purchasing in the Fashion Field by Applying Regression Trees Equipped with Ordinal Logistic Regression

1 Introduction

2 Related Work

3 Ordinal Logistic Regression (OLR)

3.1 Evaluation

4 Regression Trees

5 Algorithm

6 Experiments

6.1 Datasets

6.2 Experimental Setup and Evaluation

6.3 Results

6.4 Tree Illustration

7 Concluding Remarks

References

A Data Mining-Based Framework for Multi-item Markdown Optimization

1 Introduction

2 Grouping-Related Items

2.1 Associated Group Heuristic

2.2 k-Means Clustering

2.3 Constrained Clustering

3 Multiple Forecasts in Retail

4 Deterministic Dynamic Pricing Model

5 Empirical Study

5.1 Finding-Related Item Groups

5.2 Conducting Multivariate Regression Analysis Within Item Groups

5.3 Implementing Deterministic Multi-item Markdown Optimization Model

6 Concluding Remarks

References

Social Media Analytics for Decision Support in Fashion Buying Processes

1 Introduction

2 Theoretical Background

2.1 Social Media

2.2 Text Mining

3 Research Approach: Topic Detection and Tracking in Fashion Blogs

4 Results on Experimental Analyses of Fashion Blogs

4.1 Topic Detection—Single Colour Occurrences

4.2 Topic Detection—Co-occurred Colour Occurrences

4.3 Topic Tracking of Fashion Topics

5 Buyers Perspective—Discussion

6 Conclusion and Outlook

References

Part II AI for Textile Apparel Manufacturing and Supply Chain

Review of Artificial Intelligence Applications in Garment Manufacturing

1 Introduction

2 Applications of AI to Production Planning, Control, and Scheduling

2.1 Production Order Scheduling

2.2 Cut-Order Planning

2.3 Marker Making

2.4 Fabric Spreading and Cutting Schedules

2.5 Assembly-Line Balancing

2.6 Machine Layout Design

3 Garment Quality Control and Inspection

3.1 Seam and Fabric Sewing Performance

3.2 Sewing Automation Equipment

3.3 Assessing Seam Pucker

3.4 Detecting and Classifying Garments Defects

3.5 Dimensional Change Issue

4 Garment Quality Evaluation

4.1 Clothing Sensory Comfort

4.2 Clothing Thermal Properties

4.3 Garment Appearance Quality

5 Challenges Facing Adoption of AI Techniques in Clothing Industry

6 Conclusion

References

AI for Apparel Manufacturing in Big Data Era: A Focus on Cutting and Sewing

1 Introduction

2 Apparel Manufacturing Process

2.1 Cutting

2.2 Sewing

2.3 Finishing and Packing

3 Applications of the AI-Related Approaches

3.1 Literature Review Analysis

3.2 AI-Related Approaches Analysis

3.3 Conclusion

4 New Perspectives

References

A Discrete Event Simulation Model with Genetic Algorithm Optimisation for Customised Textile Production Scheduling

1 Introduction

2 State of the Art

2.1 Simulation in Manufacturing and Textile Production

2.2 Scheduling and Optimisation by Genetic Algorithm

2.3 Hybrid Model Integrating a Discrete Event Simulation Model with an Optimisation Model

3 Methodology

3.1 Description of the Manufacturing Unit

3.2 Production Parameters, Constraints and Simulation Logic

4 Experimentation and Results

4.1 Results Obtained from Before Optimisation

4.2 GA Hybrid Model Optimisation Results

4.3 Results Obtained from the Best Sequence by GA Hybrid Model

4.4 Discussion

5 Conclusion and Scope

References

An Intelligent Fashion Replenishment System Based on Data Analytics and Expert Judgment

1 Introduction

2 Literature Review

3 Methodology and Implementation

3.1 Notation

3.2 Extra Features of the Proposal

3.3 Internal Marketplace

3.4 Optimal Allocation

4 Pilot Study and Results

4.1 Test Impact Evaluation

5 Conclusions

References

Blockchain-Based Secured Traceability System for Textile and Clothing Supply Chain

1 Introduction

2 Understanding T&C Supply Chain

3 Traceability

4 What Is Blockchain and How It Differs from Regular Digital Ledger?

5 Traceability in the T&C Supply Chain and Blockchain

6 Use Case Example

7 Limitations of Blockchain-Based Traceability System

8 Conclusions

References

Part III AI for Garment Design and Comfort

Artificial Intelligence Applied to Multisensory Studies of Textile Products

1 Novel Sensory Methodologies for Fabric Hand Study

2 Prediction of Emotional Preference from Fabric Tactile Properties Based on a Fuzzy-Genetic Model

2.1 Sensory Experiments on Suiting Fabrics

2.2 Predictive Model Based on a Fuzzy-Genetic Algorithm

3 Visuo-haptic Perception of Fabric Tactile Properties Based on a Fuzzy Inclusion Approach

3.1 Consistency Between Visual and Haptic Perception of Fabric Tactile Properties

3.2 Visual Interpretation of Fabric Tactile Properties

4 General Conclusion

References

Evaluation of Fashion Design Using Artificial Intelligence Tools

1 Introduction

2 Experimental Work

2.1 Experiment I Production Pattern Design and 3D Virtual Try-on

2.2 Experiment II Evaluation and Adjustment of the 3D Try-on Perception

3 Results and Discussion

4 Conclusions

Bibliography

Garment Wearing Comfort Analysis Using Data Mining Technology

1 Introduction

2 Method

2.1 Action Design for Measuring Clothing Pressures

2.2 Measurement of Clothing Pressures

3 Results and Discussion

3.1 Data Preprocessing and Analysis

3.2 Factor Analysis

3.3 Wearing Comfort Analysis on Different Human Body Parts

3.4 Limitation

4 Conclusions and Prospects

References

Garment Fit Evaluation Using Machine Learning Technology

1 Introduction

2 General Principle and Formalization

2.1 General Principle

2.2 Formalization of the Concepts and Data

3 Learning Data Acquisition

3.1 Preparation Work for Experiments

3.2 Experiment I: Acquisition of the Data on Garment Fit

3.3 Experiment II: Acquisition of the Data on Digital Clothing Pressures

4 Modeling the Relation Between Clothing Pressures and Garment Fit Level

5 Model Validation

6 Discussion

6.1 Influence of the Difference Between Real and Digital Pressures on the Prediction Results

6.2 Application Prospect

6.3 Limitation and Future Research

7 Conclusion

References

15 Erratum to: Artificial Intelligence for Fashion Industry in the Big Data Era

Erratum to:S. Thomassey and X. Zeng (eds.), Artificial Intelligence for Fashion Industry in the Big Data Era, Springer Series in Fashion Business, https://doi.org/10.1007/978-981-13-0080-6