Intelligent Fashion Forecasting Systems: Models and Applications

Transcription

1 Intelligent Fashion Forecasting Systems: Models and Applications

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3 Tsan-Ming Choi Chi-Leung Hui Yong Yu Editors Intelligent Fashion Forecasting Systems: Models and Applications 123

4 Editors Tsan-Ming Choi Business Division Institute of Textiles and Clothing The Hong Kong Polytechnic University Hong Kong People s Republic of China Chi-Leung Hui Business Division Institute of Textiles and Clothing The Hong Kong Polytechnic University Hong Kong People s Republic of China Yong Yu Business Division Institute of Textiles and Clothing The Hong Kong Polytechnic University Hong Kong People s Republic of China ISBN ISBN (ebook) DOI / Springer Heidelberg New York Dordrecht London Library of Congress Control Number: Springer-Verlag Berlin Heidelberg 2014 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (

5 Preface Forecasting is a crucial function for companies in the fashion apparel industry. Despite the fact that there is no perfect forecast, forecasting for highly structured data (e.g., the time series with high seasonality or trend) is known to be easy because there are many well-established models which provide the needed analytical formulations. However, for many real-life forecasting applications in the fashion industry, the data patterns are notorious for being highly volatile, and it is very difficult, if not impossible, to analytically learn about the underlying pattern. As a result, many traditional methods (such as statistical models) will fail to make a sound prediction. Over the past decade, advances in artificial intelligence technologies have provided an alternative way of generating precise and accurate forecasting results for fashion (e.g., sales forecasting, color trend forecasting). Although being an important and timely topic, there is currently an absence of a comprehensive reference source that provides the state-of-the-art findings on both theoretical and applied research on the intelligent fashion forecasting systems. In view of the above, we have edited this Springer handbook which features several peer-refereed papers. To be specific, this handbook contains three parts that cover (i) introductory, review, and exploratory materials related to fashion forecasting; (ii) theoretical modeling research on fashion forecasting; and (iii) intelligent fashion forecasting applications and analysis. The specific topics covered include the following: Introduction to Intelligent Fashion Forecasting Sales Forecasting in Apparel and Fashion Industry: A Review Collaborative Planning Forecasting Replenishment Schemes in Apparel Supply Chain Systems: Cases and Research Opportunities Measuring Forecasting Accuracy: Problems and Recommendations Forecasting Demand for Fashion Goods: A Hierarchical Bayesian Approach Forecasting Fashion Store Reservations: Booking Horizon Forecasting with Dynamic Updating Fuzzy Forecast Combining for Apparel Demand Forecasting v

6 vi Preface Intelligent Fashion Colour Trend Forecasting Schemes: A Comparative Study Neural Networks Based Forecasting for Romanian Clothing Sector We are pleased to offer through this handbook new analytical and empirical results with valuable insights, which will contribute to the literature. To the best of our knowledge, this research handbook is the first one which specifically examines intelligent fashion forecasting. Before ending, we would like to take this opportunity to thank Niels Peter Thomas, Emmie Yang, and Michelle Feng of Springer for their kindest support and advice along the course of carrying out this book project. We are grateful to all the authors who have contributed their research to this handbook and the anonymous reviewers who have helped review the papers. We also acknowledge the editorial assistance of Na Liu and Hau-Ling Chan. Hong Kong, China Hong Kong, China Hong Kong, China Tsan-Ming Choi Chi-Leung Hui Yong Yu

7 Contents Part I Introduction, Review and Exploratory Studies 1 Introduction: Intelligent Fashion Forecasting... 3 Tsan-Ming Choi, Chi-Leung Hui, and Yong Yu 2 Sales Forecasting in Apparel and Fashion Industry: A Review... 9 Sébastien Thomassey 3 Collaborative Planning Forecasting Replenishment Schemes in Apparel Supply Chain Systems: Cases and Research Opportunities Daisy Ka-Yee Ho and Tsan-Ming Choi Part II Theoretical Modeling Research 4 Measuring Forecasting Accuracy: Problems and Recommendations (by the Example of SKU- Level Judgmental Adjustments) Andrey Davydenko and Robert Fildes 5 Forecasting Demand for Fashion Goods: A Hierarchical Bayesian Approach Phillip M. Yelland and Xiaojing Dong 6 Forecasting Fashion Store Reservations: Booking Horizon Forecasting with Dynamic Updating Alwin Haensel vii

8 viii Contents Part III Intelligent Fashion Forecasting: Applications and Analysis 7 Fuzzy Forecast Combining for Apparel Demand Forecasting Murat Kaya, Engin Yeşil, M. Furkan Dodurka, and Sarven Sıradağ 8 Intelligent Fashion Colour Trend Forecasting Schemes: A Comparative Study Yong Yu, Sau-Fun Ng, Chi-Leung Hui, Na Liu, and Tsan-Ming Choi 9 Neural Networks Based Forecasting for Romanian Clothing Sector Logica Banica, Daniela Pirvu, and Alina Hagiu