Fashion Retail Supply Chain Management: A Systems Optimization Approach 978-0-203-76469-5, 0203764692, 978-1-138-00028-5

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COMMUNICATIONS IN CYBERNETICS, SYSTEMS SCIENCE AND ENGINEERING

This volume will be of interest not only to those involved in the fashion industry, but also to academics and praciioners in the wider fields of business, manufacturing engineering, systems engineering and supply chain management.

Communicaions in Cyberneics, Systems Science and Engineering disciplinary book series devoted to theoreical and applied research contribuions, that cater to a rapidly growing worldwide interest in a cyberneic and systemic methodology with an ever-increasing capacity to deal with new challenges in a way that tradiional science cannot. The series aims to become a comprehensive reference work on and guide to developments within the field and strategies required for beter implementaion of advances, with a view to environmental protecion and sustainable social and economic development. The CCSSE series targets all working in theoreical and applied fields of cyberneics, systems science and engineering, e.g. academics, researchers and consultants, computer and informaion scienists, development and systems engineers, mathemaicians, management cyberneicists and systemists, medical scienists, and intelligent and manufacturing engineers in industry, as well as leading decision- and policy-makers. SERIES EDITOR: JEFFREY ‘YI-LIN’ FORREST ISSN: 2164-9693

Fashion Retail Supply Chain Management

Fashion Retail Supply Chain Management: A Systems Optimization Approach is a comprehensive reference source that provides the state-of-the-art findings on many important emerging research issues related to retail supply chain management and opimizaion problems. The book takes an explicit systems approach, and discusses retailled fashion supply chain coordinaion mechanisms and consumer market informaiondriven fashion retail supply chain models, as well as suggesing future research avenues.

7

COMMUNICATIONS IN CYBERNETICS, SYSTEMS SCIENCE AND ENGINEERING

7

Tsan-Ming Choi

Fashion Retail Supply Chain Management A Systems Opimizaion Approach

Fashion Retail Supply Chain Management

Communications in Cybernetics, Systems Science and Engineering ISSN: 2164-9693

Book Series Editor:

Jeffrey Yi-Lin Forrest

International Institute for General Systems Studies, Grove City, USA Slippery Rock University, Slippery Rock, USA

Volume 7

Fashion Retail Supply Chain Management

A Systems Optimization Approach

Tsan-Ming Choi Business Division, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

CRC Press/Balkema is an imprint of the Taylor & Francis Group, an informa business © 2014 Taylor & Francis Group, London, UK Typeset by MPS Limited, Chennai, India Printed and bound by CPI Group (UK) Ltd, Croydon, CR0 4YY All rights reserved. No part of this publication or the information contained herein may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, by photocopying, recording or otherwise, without written prior permission from the publishers. Although all care is taken to ensure integrity and the quality of this publication and the information herein, no responsibility is assumed by the publishers nor the author for any damage to the property or persons as a result of operation or use of this publication and/or the information contained herein. Library of Congress Cataloging-in-Publication Data Choi,Tsan-Ming. Fashion retail supply chain management : a systems optimization approach / Tsan-Ming Choi. – 1 Edition. pages cm — (Communications in cybernetics, systems science and engineering ;Volume 7) Includes bibliographical references and index. ISBN 978-1-138-00028-5 (hardback) – ISBN 978-0-203-76469-5 (eBook PDF) 1. Business logistics. 2. Fashion merchandising. I. Title. HD38.5.C398 2014 746.9’20688–dc23 2014008069 Published by: CRC Press/Balkema P.O. Box 11320, 2301 EH Leiden,The Netherlands e-mail: [email protected] www.crcpress.com – www.taylorandfrancis.com ISBN: 978-1-138-00028-5 (Hbk) ISBN: 978-0-203-76469-5 (eBook PDF)

Table of contents

Editorial board Preface About the author

vii ix xi

1

Fashion retail supply chain management – An introduction 1.1 What is fashion retail supply chain management? 1.2 Overview of this book References

2

Customer service management in fashion retail supply chains 2.1 Introduction 2.2 Quantitative customer service performance measures 2.3 Retail service quality scale 2.3.1 Conceptual model and related studies 2.3.2 The RSQS based systems optimization model 2.3.3 Numerical example 2.3.4 Numerical sensitivity analysis 2.4 Conceptual framework: Deming’s Quality Management Framework 2.5 Conclusion References Appendix: Tables of the numerical analysis

24 28 28 29

Inventory models and coordination in fashion retail supply chains 3.1 Introduction 3.2 The EOQ model-based retail inventory problem 3.2.1 An illustrative example 3.2.2 Optimal inventory decisions and ordering frequency 3.2.3 Numerical example 3.3 The newsvendor model-based retail inventory problem 3.3.1 An illustrative fast fashion retailing example 3.3.2 Profit maximization model 3.3.3 A numerical example 3.3.4 Remarks 3.4 Coordination in fashion retail supply chain systems 3.4.1 EOQ model based inventory planning 3.4.2 Newsvendor model-based inventory planning

33 33 34 34 34 36 36 36 37 37 38 39 39 39

3

1 1 2 5 7 7 8 9 9 10 15 17

vi Table of contents

3.5

3.6

Sensitivity analysis 3.5.1 The EOQ-based model 3.5.2 The newsvendor problem-based model Conclusion References Appendix: Tables of the numerical analysis

41 41 43 46 46 47

4

Efficient consumer response in fashion retail supply chain systems 4.1 Introduction 4.2 Basic analytical model 4.3 Profit analysis for the fashion retailer 4.4 All-win situation 4.5 Supply chain coordination 4.6 Numerical analysis 4.7 Conclusion References Appendix: Tables of the numerical analysis

51 51 52 54 58 59 61 64 65 66

5

New product selection in fashion retail supply chains 5.1 Introduction 5.2 Basic model 5.3 Analysis: Scenario one 5.4 Analysis: Scenario two 5.5 Numerical analysis 5.5.1 Scenario one 5.5.2 Scenario two 5.6 Conclusion References Appendix: Tables of the numerical analysis

69 69 71 73 74 76 76 79 79 80 81

6

Mean-risk analysis for fashion retail supply chain information systems projects 6.1 Introduction 6.2 Problem formulation 6.3 Mean-variance analysis 6.3.1 Model details 6.3.2 Example 1 6.3.3 Example 2 6.4 Mean-semi-variance approach 6.5 Safety-first objective 6.6 Information system portfolio 6.7 Conclusion References

83 83 84 85 85 86 88 90 91 92 93 93

7

Fashion retail supply chain management – Concluding remarks 7.1 Managerial insights 7.2 Future research directions References

Subject index

95 95 98 101 105

Editorial board

Michael C. Jackson, University of Hull, UK Jerzy Jozefczyk, Wroclaw University of Technology, Poland Doncho Petkov, Eastern Connecticut State University, USA Vladimir Tsurkov, Russian Academy of Sciences, Russia Shouyang Wang, Chinese Academy of Sciences, P.R. China

ADVISORY EDITORIAL BOARD C.L. Philip Chen, University of Macau, P.R. China Zengru Di, Beijing Normal University, P.R. China Raul Espejo, Syncho Ltd. and World Organization of Systems and Cybernetics, UK Keith W. Hipel, University of Waterloo, Canada Baoding Liu, Tsinghua University, China Nagendra Nagarur, State University of New York at Binghamton, USA John Pourdehnad, University of Pennsylvania, USA Brian Howard Rudall, Institute of the World Organisation of Systems and Cybernetics & Bangor University, UK Rudolf Scheidl, Johannes Kepler University of Linz, Austria Markus Schwaninger, Institute of Management, University of St. Gallen, Switzerland

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Preface

Fashion retail supply chains are retailer-led dynamic multi-echelon systems in which fashion products, related information and funds flow between the point of origin to the point of consumption in both forward and reverse directions. Decisions in the fashion retail supply chain systems are highly consumer demand-driven. Over the past few years, more and more studies have been exploring the fashion retail supply chain management related problems though most of them are based on empirical analysis and exploratory case studies. Motivated by the importance of fashion retail supply chain management and the relatively few theoretical analyses being done in this context, I have authored this book and explored different important topics by an analytical systems optimization approach. I believe that this book will be a pioneering text focusing on this important topic. This book includes seven chapters. Chapter 1 introduces the topic and presents an overview of the book. Chapter 2 examines the customer service management challenge in fashion retail supply chains. Both analytical and conceptual tools are discussed. Supply chain service coordination is proposed and consumer welfare is also considered in the analysis. Chapter 3 explores two classical inventory models and the issue of coordination in the respective fashion retail supply chain systems. To achieve coordination incentive alignment schemes are discussed. Chapter 4 investigates the popular and commonly seen efficient consumer response program in fashion retail supply chains. The critical role played by the consumer welfare coefficient is examined. Chapter 5 discusses the new product selection problem in fashion retail supply chains with the consideration of market uncertainty. Chapter 6 presents a few analytical models for evaluating risky fashion retail supply chain information systems projects. Chapter 7 concludes the book and discusses various managerial insights and future research directions. As a remark, every chapter in this book can be taken as a self-contained article and the notation within each chapter is consistently employed. In terms of the potential audience, I believe that this book is suitable for both researchers and practitioners interested in fashion retail supply chain management, and retail operations management. It can also be a good reference book for undergraduate and postgraduate students for related subjects. As a matter of fact, many materials covered in this book are based on my own lecture notes used in my MBA/MA classes. Since this book targets a rather broad pool of potential readers, I intentionally reduce the complexity of the models and most of the analytical results can be understood easily by readers with a fundamental knowledge of calculus.

x

Preface

In terms of presentation, I use simple “layman’’ terms as much as possible, and also prepare a lot of numerical examples to illustrate the concepts and theories explored in the analytical studies. I believe that the book is easy to understand by business managers as well as undergraduate and postgraduate students. Before ending, I would like to take this opportunity to thank Jeffrey Yi Lin Forest, Alistair Bright and José van der Veer for their kindest help in the preparation and completion of this important book project. I am also indebted to my family, colleagues, friends, and students, who have been supporting me during the development of this book. In particular, my former and current PhD students have helped me with the proofreading of this book. This book is partially supported by the funding of the Research Grants Council of Hong Kong under grant number PolyU 5424/11H. Tsan-Ming Choi (PhD) The Hong Kong Polytechnic University December 2013

About the author

Tsan-Ming Choi (Jason) is currently teaching at The Hong Kong Polytechnic University. Over the past few years, he has actively participated in a variety of research projects on supply chain management and applied optimization. He has authored/edited ten research-oriented handbooks and guest-edited twenty special issues for various leading journals on related topics. He has published extensively in peer-refereed academic journals such as Annals of Operations Research, Automatica, Computers and Operations Research, Decision Support Systems, European Journal of Operational Research, IEEE Transactions on Automatic Control, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Industrial Informatics, IEEE Transactions on Systems, Man, and Cybernetics (Parts A, B, C; Systems), International Journal of Production Economics, International Journal of Production Research, Journal of Fashion Marketing and Management, Journal of the Operational Research Society, Journal of the Textile Institute, Omega, Production and Operations Management, Service Science (INFORMS Journal), Supply Chain Management: An International Journal, Textile Research Journal, Tourism Management, Transportation Research – Part E, etc. He is currently an area editor/associate editor/guest editor of Annals of Operations Research, Asia-Pacific Journal of Operational Research, Decision Sciences, Decision Support Systems, European Management Journal, IEEE Transactions on Industrial Informatics, IEEE Transactions on Systems, Man, and Cybernetics – Systems, Information Sciences, Journal of the Operational Research Society, Production and Operations Management, and various other operations management and information systems journals. He is also an executive committee member/officer of professional organizations such as IEEE-SMC (HK) and POMS (HK). He received the President’s Award for Excellent Achievement of The Hong Kong Polytechnic University in 2008. He was named distinguished alumnus of the Department of Systems Engineering and Engineering Management, Faculty of Engineering, The Chinese University of Hong Kong, during the Faculty’s 20th Anniversary in 2011. Most recently, he received the Best Associate Editor Award of IEEE SMC Society in 2013. Before joining his current department in fall 2004, he was an assistant professor at The Chinese University of Hong Kong. He is a member of various internationally renowned professional organizations and societies such as IEEE, INFORMS, ITAA, POMS, MSOM, and SMC.

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Chapter 1

Fashion retail supply chain management – An introduction

SUMMARY Fashion retail supply chain management (FRSCM) is a critically important topic. In this introductory chapter, we first discuss the meaning behind fashion retail supply chains and then give the formal definition of fashion retail supply chain management. After that, we present an overview of the book, introducing every chapter. Some related literature is also reviewed. Keywords Fashion retail supply chain management, customer service, inventory management, efficient consumer response, new product development, mean-risk analysis, information systems project management, systems engineering.

1.1 WHAT IS FASHION RETAIL SUPPLY CHAIN MANAGEMENT? In recent years, there is a growing interest among both academicians and practitioners in “retail supply chain management’’ (see Agrawal and Smith 2009). This trend is driven by the real world phenomenon that giant retailers, such as Walmart, emerge, and they rule the respective supply chains. Furthermore, more and more supply chain members (both upstream and downstream) realize that their operations are in fact significantly affected by the consumer preferences as observable in the retail market. Thus, even the traditionally strong manufacturers are nowadays willing to listen to the retailers in shaping their supply chain related operational strategies. In the fashion1 industry, fashion retail supply chains refer to the retailer-led dynamic multi-echelon systems in which fashion products, related information and fund flow between the point of origin to the point of consumption in both forward and reverse directions. The common characteristics of fashion retail supply chains include: (i) They are retail-led, (ii) they are usually stochastic systems with all kinds of inherent uncertainties (on demand, service, and value), (iii) the life cycle of their

1

The term fashion refers to many different kinds of products ranging from apparel and footwear to fashion accessories and fashion beauty, etc.

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Fashion Retail Supply Chain Management

products is short (Chiu et al. 2011, Choi 2011), (iv) decisions in the fashion retail supply chain systems are highly consumer demand-driven. As a result, seeking an optimal decision in the fashion retail supply chain systems is known to be very challenging. For the definition of fashion retail supply chain management, I make reference to and adapt from the definition offered by The Council of Supply Chain Management Professionals (CSCMP 2013) for supply chain management: “Fashion Retail Supply Chain Management encompasses the planning and management of all activities involved in sourcing and procurement, conversion, and all logistics management activities in the fashion retail supply chain. It includes coordination and collaboration with supply chain partners. In essence, Fashion Retail Supply Chain Management integrates supply and demand management within and across the fashion retail supply chain with a goal of satisfying the customer requirements under the leadership of the retailer.’’ Following the above definition for fashion retail supply chain management, in this book, an important emphasis is on the “systems optimization’’ perspective which relates to the optimization and coordination of the fashion retail supply chain systems.

1.2

OVERVIEW OF THIS BOOK

Over the past few years, there are more and more research studies investigating the fashion retail supply chain management related problems. However, most of them are based on empirical analysis and exploratory case studies. Motivated by the importance of fashion retail supply chain management in practice and the relatively few theoretical analyses being reported in the literature, this book has been prepared. In this book, various important and timely topics on fashion retail supply chain management are explored by an analytical systems optimization approach. The topics are organized into independent, separate chapters in which each chapter can be viewed as a self-contained article. For each chapter, the respective analytical models/frameworks are presented with a discussion on their applications or implications. Most include numerical examples to better illustrate the applicability of the models. Numerical sensitivity analyses are also present in many chapters to generate additional insights. In the following, I introduce each chapter of the rest of this book. As the first technical chapter, Chapter 2 is devoted to the analysis and discussion of customer service management. From the definition of fashion retail supply chain management, it is obvious to note that customer requirements are critically and fundamentally important. In fact prior studies have shown that a lot of consumers leave retail stores because they cannot find the products they want (Gruen et al. 2002). The reason can be a poor inventory and assortment planning, but can also be a poor execution of product availability management scheme (DeHoratius and Ton 2009) which relates to the company’s inventory service policy. As a result, how to establish and implement an optimal customer service plan is crucial. In Chapter 2, the customer service and service quality management in fashion retail supply chain systems are examined. The importance of customer service in fashion retailing is discussed. The quantitative performance measures for customer service, such as inventory service and on-time delivery, are proposed. After that, the very popularly used retail service

Fashion retail supply chain management – An introduction 3

quality scale (RSQS) model is presented. The core dimensions under the RSQS model are studied. Based on the RSQS model, a formal analytical model is constructed and the optimal service levels for the fashion retailer and the whole fashion retail supply chain are derived. It is found that under the pure wholesale pricing contract, owing to the notorious double marginalization effect, the optimal service level for the fashion retailer (under a decentralized setting) is lower than the optimal service level for the whole fashion retail supply chain system. This directly means that the fashion retail supply chain system under a decentralized setting is not optimal. To optimize the fashion retail supply chain system (i.e. to coordinate it), a consignment contract which is based on a “wholesale pricing and revenue sharing’’ contract is proposed. A formal analytical proof has indicated that this proposed consignment contract can optimize the fashion retail supply chain system and achieve the all win situation in which the upstream supplier, the downstream fashion retailer, the consumers, and the whole fashion retail supply chain will all benefit from this consignment contract (as compared to the previous case with the use of the pure wholesale pricing contract). To better illustrate the contracting mechanism, a simple numerical example is prepared. Finally, in Chapter 2, the Deming’s Quality Management Framework is examined and its implications for the execution of customer service quality improvement programs in fashion retail supply chain systems are discussed. After exploring customer service management in Chapter 2, Chapter 3 focuses on exploring the inventory management and the related coordination challenges. Specifically, two fundamental inventory models, namely the EOQ model (Choi 2013) and the newsvendor model (Choi 2012), are reviewed and studied. How these models relate to fashion retailing inventory problems is illustrated by examples. The closed-form optimal inventory ordering quantity for each model is derived. Illustrative numerical examples are presented. After reviewing these two inventory models, we extend the analysis to the respective fashion retail supply chain systems. We prove that for both EOQ model-based and newsvendor problem-based fashion retail supply chain systems under a decentralized setting, the optimal ordering quantities for the fashion retailer and for the supply chain system are different. Thus, the supply chains are not optimal. To overcome this system inefficiency, we analytically examine the methods which can optimize the systems (which means achieving supply chain coordination) (Chen et al. 2010; Chiu et al. 2011). Numerical analyses are conducted and various managerial insights are derived. Fast fashion is an industrial trend (Bhardwaj and Fairhurst 2010; Caro and Gallien 2010; Choi 2014). To implement fast fashion strategies, the fashion retail supply chain must first implement the efficient consumer response system in which inventory lead time is reduced and the supply chain can react quickly to consumer demand changes (Choi and Sethi 2010; Cachon and Swinney 2011). In Chapter 4, we explore the impacts brought about by the efficient consumer response system in a fashion retail supply chain. Following the literature, we employ the Bayesian information updating model (Choi 2007) and construct a formal analytical model to conduct analysis. Different from the previous related studies, we include the consumer welfare in the analysis. From the analysis, we find that the efficient consumer response program is beneficial to the fashion retailer. However, it is interesting to reveal that the consumer welfare coefficient plays a critical role in determining whether the efficient consumer response program is harmful or beneficial to the consumers and the manufacturer. To be specific, we

4

Fashion Retail Supply Chain Management

reveal that if the consumer welfare coefficient is sufficiently big, the efficient consumer response program brings harm to the manufacturer and also reduces the consumer welfare. However, if the consumer welfare coefficient is sufficiently small, the efficient consumer response program brings benefits to the manufacturer and improves consumer welfare, which directly leads to an all-win situation (i.e., the fashion retailer, the manufacturer, the consumer and the whole fashion retail supply chain all benefit from the efficient consumer response program). Furthermore, we look into the fashion retail supply chain coordination challenge and demonstrate analytically how the markdown contract can be set to achieve coordination. Finally, we conduct numerical sensitivity analysis to reveal the impacts brought about by different important model parameters. In Chapter 5, we investigate the new product selection problem for a fashion retailer. Two different scenarios are examined. In Scenario One, we assume the new products’ demands follow two market states which depend on a common external factor (such as the economic situation). Thus, the chance for the economic situation to be good (high) or bad (low) is independent of the specific new product candidate and hence we assume the same probability of occurrence of high or low market demand distribution for all new product candidates even though their specific demand distribution parameters are different. In Scenario Two, we consider the situation when each product has exactly the same high and low demand distribution parameters. However, the chance of occurrence of each demand distribution is different for each new product candidate. We analytically prove that under both scenarios, the fashion retailer can identify the optimal new product to launch by finding the one with the highest expected mean demand. Interestingly, we show that the optimal new product choice decided by the fashion retailer will also be the best for the manufacturer and the whole fashion retail supply chain system. Thus, the fashion retail supply chain system is automatically coordinated with respect to the optimal new product selection decision. We present numerical sensitivity analysis and show that the whole fashion retail supply chain system will be benefited if the retail selling price increases, the market clearance sale price increases, the wholesale price decreases, and the product’s manufacturing cost decreases. In fashion retail supply chain management, the use of information systems is crucial. However, versatile fashion retail supply chain management information systems are expensive and the respective project is usually termed “highly risky’’. In Chapter 6, we review the use of the classical mean-risk models to evaluate the information systems projects for fashion retail supply chain management. To be specific, we propose two models for conducting benefit and risk analysis, namely the classical mean-variance model, and the revised mean-semi-variance model. Both of them originate from the Nobel prize-winning mean-variance portfolio theory (Markowitz 1959). We demonstrate the analytical details as well as the applicability of the models via numerical examples. After that, we discuss the probability based safety first objective approach (Roy 1952) and analytically explore its relationship with the mean-variance model. Last but not least, we propose how the information systems portfolio can be used to help the fashion retailers prioritize the information systems projects to enhance fashion retail supply chain management. Finally, in Chapter 7, we conclude the book with the presentation of some major managerial insights, and the discussion of several probable areas for future studies in fashion retail supply chain management.

Fashion retail supply chain management – An introduction 5

As introduced above, this book has covered several critical topics in fashion retail supply chain management. Analytical optimization models are explored and managerial implications are discussed. To the best of my knowledge, this text is the first book in the literature which explores fashion retail supply chain systems by a systems optimization approach, and hence it is the pioneering text on this timely and imperative topic.

REFERENCES Agrawal, N. & Smith, S.A. (2009) Retail supply chain management: Quantitative models and empirical Studies. International Series in Operations Research & Management Science, 122. Bhardwaj, V. & Fairhurst, A. (2010) Fast fashion: Response to changes in the fashion industry. The International Review of Retail, Distribution and Consumer Research, 20, 165–173. Cachon, G.P. & Swinney, R. (2011) The value of fast fashion: Quick response, enhanced design, and strategic consumer behavior. Management Science, 57, 778–795. Caro, F. & Gallien, J. (2010) Inventory management of a fast-fashion retail network. Operations Research, 58, 257–273. Chen, H., Chen, Y.H., Chiu, C.H., Choi, T.M. & Sethi, S. (2010) Coordination mechanism for supply chain with leadtime consideration and price-dependent demand. European Journal of Operational Research, 203, 70–80. Chiu, C.H., Choi, T.M. & Tang, C.S. (2011) Price, rebate, and returns supply contracts for coordinating supply chains with price dependent demand. Production and Operations Management, 20, 81–91. Choi, T.M. (ed.) (2014) Fast Fashion Systems: Theories and Applications. CRC Press. Choi, T.M. (ed.) (2013) Handbook of EOQ inventory problems: Stochastic and deterministic models and applications. International Series in Operations Research & Management Science, 197. Choi, T.M. (ed.) (2012) Handbook of newsvendor problems: Models, extensions and applications. International Series in Operations Research & Management Science, 176. Choi, T.M. (2007) Pre-season stocking and pricing decisions for fashion retailers with multiple information updating. International Journal of Production Economics, 106, 146–170. Choi, T.M. & Sethi. S. (2010) Innovative quick response programmes: A review. International Journal of Production Economics, 127, 1–12. DeHoratius, N. & Ton, Z. (2009) The role of execution in managing product availability. In: Agrawal & Smith (eds.) Retail Supply Chain Management: Quantitative Models and Empirical Studies, Chapter 4, Springer. Gruen, T.W., Corsten, D.S. & Bharadwaj, S. (2002) Retail Out-of-Stocks: A Worldwide Examination of Extent, Causes and Consumer Responses. Grocery Manufacturers, of America, The Food Marketing Institute, and CIES. Roy, A.D. (1952) Safety first and the holding of assets. Econometrica, 20(3), 431–449. Markowitz, H.M. (1959) Portfolio Selection: Efficient Diversification of Investment, John Wiley & Sons, New York. The Council of Supply Chain Management Professionals (CSCMP 2013): Definition of Supply Chain Management (Accessed 12 December 2013). Available from: http://cscmp.org/aboutus/supply-chain-management-definitions

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Chapter 2

Customer service management in fashion retail supply chains

SUMMARY In this chapter, we investigate customer service and service quality management in fashion retail supply chains. First, we discuss the importance of customer service in fashion retail supply chain management. Second, we propose different quantitative performance measures for customer service management. Third, we review the conceptual retail service quality scale (RSQS) model for fashion retailing. Fourth, based on the RSQS model, we build a formal analytical model and derive the optimal service level for the fashion retailer and the whole fashion retail supply chain. We then prove that under the pure wholesale pricing contract in a decentralized fashion retail supply chain, the optimal retail service level is lower than the optimal service level for the fashion retail supply chain system. Thus, the decentralized fashion retail supply chain with the pure wholesale pricing contract is not optimal. As a result, we propose a “wholesale pricing and revenue sharing scheme’’ based consignment contract to coordinate the fashion retail supply chain system and achieve the all-win situation in which the upstream supplier, the downstream fashion retailer and the consumers will all benefit. We also present an illustrative numerical example. Finally, we examine the Deming’s Quality Management Framework with Deming’s famous fourteen points. For each of Deming’s points, we further discuss the implications for service quality management in fashion retail supply chain systems. Keywords Customer service, RSQS model, optimization, service coordination, Deming’s fourteen points.

2.1

INTRODUCTION

In fashion retail supply chains, consumers are a central part. Satisfying consumer needs is critically important. Traditionally, customer service can be interpreted as a means to achieve customer satisfaction. In fashion retail supply chains, achieving a high customer service means providing the needed fashion products to the consumers in the right place, at the right time, and with the right quality and price. For example, in fast fashion companies such as H&M and Zara, features of the specific product

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Fashion Retail Supply Chain Management

offering (e.g., style, color and size, etc.) are all driven by consumer preference. For fashion retailers offering a make-to-measure kind of mass customization service, such as Lands’ End and Brooks Brothers, their mass customization business strategy is exactly the one with a strong focus on customer service (Liu et al. 2012; Choi 2013). In a fashion retail supply chain, in order to achieve a high customer service, the efficient consumer response strategy1 (also known as quick response) is widely implemented. For example, as a pioneer of the efficient consumer response strategy, the Canadian retail giant Hudson’s Bay forms a strategic alliance with the manufacturers. Under their strategic alliance scheme, the manufacturers are obligated to install computerized information systems (including EDI) so as to work with Hudson’s Bay. In addition, the manufacturers have to take up extra responsibility. For instance, Hudson’s Bay requires the manufacturers to prepare ready-to-sell merchandise with the right labels, barcodes and price tags before shipment. The idea behind the whole scheme is to shorten lead time which means Hudson’s Bay can quickly respond to the market needs and advise the manufacturers as to the exact destination for the shipment with respect to its own latest consumer demand and preference information. Walmart, another international retail giant, has also been highly successful in achieving high inventory customer service. To be specific, Walmart works closely with its suppliers, and it requests the suppliers to offer information related to shipping status, delivery schedules, quantities, billing, etc. in a timely manner. At the same time, Walmart contributes its sales information to many suppliers so as to implement the vendor managed inventory (VMI) scheme in which the inventory replenishment decision and arrangement is mainly the responsibility of the suppliers, with reference to the consumer demand, the retail inventory, the target retail inventory service level, and other related information contributed by Walmart. With this scheme, Walmart can save inventory holding cost, reduce the number of markdowns and improve customer service (by reducing stockout) because the matching between demand and supply is improved. Undoubtedly, customer service is crucially important in fashion retail supply chains2 . In this chapter, we discuss both qualitative and quantitative measures for establishing customer service strategies for fashion retail supply chain systems. The rest of this chapter is organized as follows. Section 2.2 discusses various commonly used quantitative performance measures for customer service. Section 2.3 explores the retail service quality scale model. Section 2.4 presents the Deming’s fourteen points based quality management framework. Section 2.5 concludes this chapter. 2.2

QUANTITATIVE CUSTOMER SERVICE PERFORMANCE MEASURES

In fashion retail supply chain management, it is imperative to have a quantitative performance measure for customer service. One major reason is: Without quantitative measures, it is tremendously difficult to examine (i) how good or bad the current 1

We will discuss the efficient consumer response system in Chapter 4. It is widely noticed that service can be a means for retailers to gain competitive advantages (Hall and Portues 2000; Kurata and Nam 2010).

2

Customer service management in fashion retail supply chains

9

customer service system is, and (ii) the significance of different proposed measures. Thus, in order to be able to establish the customer service strategy to yield a sound customer service level, we first have to measure it. In fashion retail supply chain management, the following measures are widely adopted: 1

2

3

4

2.3

Inventory service level: There are different detailed classifications and definitions for inventory service level. In the simplest term, a high inventory service in a fashion retailer means a low chance of stock out of the desirable fashion product that customers look for. For example, a 95% inventory service is usually achieved by fashion retailers after adopting some modern inventory practice such as efficient consumer response system (Iyer and Bergen 1997). On-time delivery: Transportation is an imperative area of fashion retail supply chain management. Imagine that a consumer orders a pair of mass customized shoes from NIKEiD.com. The company promises on the web that the lead time is 3 weeks (the time from ordering to receipt). The probability of achieving this 3 weeks service promise is certainly an important customer service. We hence should have a customer service measure for this kind of on-time delivery service. Percentage of defective products: In fashion, it is common to note that there are defective products. The defects can come from poor quality management scheme during manufacturing or distribution. They can also come from mistakes such as incorrect labeling (a large sized shirt is labeled as medium size). Since fashion retailing sells fashion products, the percentage of defective items is hence a performance measure to reflect product quality as well as the related customer service. Number of consumer complaints: Fashion retailing operations have many areas. Whenever one area is not doing well or satisfactorily, customers will complain. Thus, the number of complaints or the percentage of customers (or VIP members) complaining can reflect the customer service from the retail operations perspective. It is hence also an important performance measure of customer service.

RETAIL SERVICE QUALITY SCALE

2.3.1 Conceptual model and related studies Customer expectations and perceptions of service quality in fashion retail stores have been examined in the literature (Parasuraman et al. 1985; Gagliano and Hathcote 1994; Kim and Jin 2002). Relatively recently, in customer service management for retailing, a well-established model called the retail service quality scale (RSQS) model has been developed (Dabholkar et al. 1996). In fashion retailing, from Choi et al. (2013), we know that the retail service under the RSQS model can be examined in various dimensions as shown below: 1

Physical aspects: This dimension refers mainly to the functional elements associated with the fashion retailer. For example, store layouts, visual merchandising, convenience of physical facilities, etc. are all related to this aspect. Product quality,

10

2

3

4

Fashion Retail Supply Chain Management

color and style are also included. Basically, this service dimension is fundamental and is critically important for all fashion retailers. Reliability: This refers to whether the retailers can keep promises to serve the customers, provide the needed service, offer the desired products, and implement measures which protect customers in the fashion retailing operations. Personal interaction: This dimension includes how the staff members of the fashion retailers help to answer queries, provide advice and fashion tips, offer appropriate service, attitude in response to customer requests, etc. This dimension is especially critical for fashion retailing as it is a kind of retail business that highly involves personal interaction. Problem solving: This dimension covers issues like solving technical problems (e.g., product features and functions), handling consumer returns, product exchanges, and other complaints.

Note that the above dimensions are all customer-oriented, and hence the RSQS is a consumer-driven model. In the literature, the RSQS model for fashion retailing has been examined in a number of studies. For example, Leen and Ramayah (2011) study the retail service in fashion specialty stores. They empirically find that all the dimensions of RSQS are suitable for measuring retail service quality. They further propose that retail service quality for fashion specialty stores associates with various dimensions of consumer behavior such as frequency of store visit, consumer purchasing intention, and personal recommendation. Choi et al. (2013) study the fashion boutique service operations. They empirically show that the RSQS model is a statistically valid construct. They reveal via a gap analysis that among all the dimensions under the RSQD model, the problem-solving dimension has the largest service gap between consumer expectation and perception. After that, they conduct an optimization model based analytical study to determine the optimal service decision for the fashion boutiques with the focus on the problem-solving dimension under a game-theoretical competitive setting. Their analysis proves that retail competition between homogeneous boutiques will lead to a smaller optimal problem-solving service gap if a critical parameter known as the relative problem-solving service gap dependent demand sensitivity (RPDDS)is sufficiently large or small. They also analytically reveal that RPDDS determines how the optimal problem-solving service gap varies.

2.3.2 The RSQS based systems optimization model We consider a particular fashion retail supply chain which includes an upstream supplier (e.g., a national fashion brand) and a downstream fashion retailer (e.g., a retail store). Following the mainstream analysis in supply chain management, we focus on a single fashion product and the respective profitability analysis. Suppose that the fashion retailer is considering optimizing its service quality in a particular dimension under the RSQS model (because that dimension is most critical). We represent the service level of this dimension by s. Since a higher service level means a higher customer utility, it leads to a higher demand. We thus have the following linear service dependent demand function: D(s) = a + bs.

Customer service management in fashion retail supply chains

11

Following Xiao et al. (2012) and Choi et al. (2013), we assume that there is a quadratic cost associated with service offering. To be specific, to achieve a service level of s, the cost incurred is: 1 K(s) = ks2 . 2 For the fashion product under consideration, the unit retail selling price is r3 , the unit wholesale price is w, and the unit product cost at the supplier side is m. Thus, we can express the fashion retailer’s profit function as follows: πretail (s) = (r − w)D(s) − K(s) 1 = (r − w)(a + bs) − ks2 . 2 Taking the first and second order derivatives of πretail (s) with respect to s gives the following: dπretail (s) = (r − w)b − ks, ds d 2 πretail (s) = −k < 0. ds2 From the second order derivative, it is obvious that πretail (s) is a strictly concave function. Solving the first order condition gives the optimal service level for the fashion retailer (denoted by s∗retail ): (r − w)b dπretail (s) =0⇒s= . ds k Thus, s∗retail = (r −kw)b . From the analytical expression of s∗retail , it is straightforward to observe that the optimal service level for the fashion retailer is increasing in retail selling price r and the coefficient of service-demand sensitivity b, and decreasing in the wholesale price w and the service cost coefficient k. As such, a higher retail selling price, a larger coefficient of service-demand sensitivity, a lower wholesale price, and a smaller service cost coefficient lead to a higher optimal fashion retail service level. Now we turn our attention to the fashion retail supply chain system. Since the product cost is m, the profit of the whole fashion retail supply chain, πSC (s), is given below: πSC (s) = (r − m)D(s) − K(s) 1 = (r − m)(a + bs) − ks2 . 2 3

Note that we assume price is exogenously given in the following even though many studies would consider both pricing and service level together (e.g., So 2000).

12

Fashion Retail Supply Chain Management

Checking the second order derivative of πSC (s) with respect to s shows that it is a strictly concave function: dπSC (s) = (r − m)b − ks, ds d 2 πSC (s) = −k < 0. ds2 As a result, the optimal service level for the fashion retail supply chain system, s∗SC , can be found by solving the respective first order condition: dπSC (s) (r − m)b = 0 ⇒ s∗SC = . ds k Since in most traditional wholesaling business, the wholesale price w is larger than the product cost m, i.e. w > m, we have Proposition 2.1: Proposition 2.1. Under the pure wholesale pricing scheme with w > m, the optimal service level of the fashion retail supply chain system is larger than the optimal service level of the fashion retailer, i.e. s∗SC > s∗retail . Proof of Proposition 2.1: By a direct comparison between s∗SC and s∗retail , we have: w > m ⇒=

(r − m)b (r − w)b > ⇒ s∗SC > s∗retail k k

(Q.E.D.)

Proposition 2.1 indicates that the optimal service level for the fashion retail supply chain system is different from the optimal service level that the fashion retailer will offer to the market. As such, the fashion retail supply chain system is not optimal (we call it “uncoordinated’’) and it is not most efficient. This situation actually appears naturally owing to the presence of double marginalization effect in which there are two profit margins in the fashion retail supply chain, namely the fashion retailer’s profit margin and the fashion retail supply chain system’s profit margin. The optimal service level decisions with respect to these two different profit margins are hence different. In order to achieve the best fashion retail supply chain, the fashion retailer and the supplier can consider changing the supply contracting scheme from a traditional pure wholesale pricing scheme to a scheme close to consignment (Wang et al. 2004; Li et al. 2009; Zhang et al. 2010; Sarker 2013) in which the supplier offers the product to the fashion retailer at cost, i.e., setting w = m, and then the supplier shares the revenue with the fashion retailer for every item sold. By doing so, the double marginalization effect is no longer present and the fashion retail supply chain becomes optimized. We summarize the result in Proposition 2.2. Proposition 2.2. The fashion retail supply chain can be coordinated with respect to the service level decision by a consignment contract in which the product is supplied

Customer service management in fashion retail supply chains

13

at cost (w = m) and there is a revenue sharing scheme between the supplier and the fashion retailer. Proof of Proposition 2.2: By a direct comparison between s∗SC and s∗retail , we have: w = m ⇔=

(r − m)b (r − w)b = ⇔ s∗SC = s∗retail . (Q.E.D.) k k

(2.1)

Notice that since the profit of the fashion retail supply chain is equal to the sum of the profits of the supplier and the fashion retailer, when the fashion retail supply chain system is optimized (i.e., when the service level is equal to s∗SC ), the respective profit is also the largest. As a consequence, the piece of “profit cake’’ in the coordinated fashion retail supply chain system is maximized and there will be a profit surplus compared to the case when the fashion retail supply chain system is uncoordinated (under the pure wholesale pricing scheme). The supplier and the fashion retailer can then bargain and share this surplus which will create a win-win situation in the fashion retail supply chain system. This argument provides support for the fashion retailer and the supplier to implement the supply contract as proposed in Proposition 2.2. As a remark, suppose that under the proposed consignment scheme, the fashion retailer will enjoy the product supplied at cost from the supplier and share λ of its revenue to the supplier. In order to achieve a win-win situation and supply chain coordination at the same time, the revenue sharing ratio must be bounded. We first define two parameters and then present Proposition 2.3 to summarize this important result. w0 = the unit wholesale price before the use of the consignment contract, s∗retail (w0 ) =

(r − w0 )b , k

λ=

(w0 − m)(ak + (r − w0 )b2 ) , (r − m)(ak + (r − m)b2 )

λ¯ =

(w0 − m)(ak + 21 (2r − w0 − m)b2 ) , (r − m)(ak + (r − m)b2 )

λ = λ¯ − λ. Proposition 2.3. The fashion retail supply chain can be win-win coordinated (i.e., coordinated with a win-win situation being established between the fashion retailer and the supplier) via a consignment contract in which the product is supplied at cost (w = m) and the fashion retailer shares λ of its revenue to the supplier if and only if ¯ λ < λ < λ. Proof of Proposition 2.3: Notice that when w = m, coordination in the fashion retail supply chain is achieved. To attain the win-win situation between the fashion retailer and the supplier, we need to ensure that under the proposed consignment contract, they both will get a profit larger than before (i.e., under the pure wholesale pricing contract with the unit wholesale price equal to w0 . Thus, for the supplier, it will get a

14

Fashion Retail Supply Chain Management

larger profit with the consignment contract if and only if its profit is larger than the one with the pure wholesale pricing contract: λ(r − m)(a + bs∗SC ) > (w0 − m)(a + bs∗retail (w0 )) ⇔λ>

(w0 − m)(a + bs∗retail (w0 )) (r − m)(a + bs∗SC )

⇔λ>

(w0 − m)(a + b[(r − w0 )b/k]) (r − m)(a + b[(r − m)b/k])

⇔λ>

(w0 − m)(ak + (r − w0 )b2 ) (r − m)(ak + (r − m)b2 )

⇔ λ > λ. Similarly, for the fashion retailer, it will get a larger profit with the consignment contract if and only if the following is true: (1 − λ)(r − m)(a + bs∗SC ) −

1 ∗2 1 ksSC > (r − w0 )(a + bs∗retail (w0 )) − ks∗retail (w0 )2 2 2

⇔ (1 − λ)(r − m)(a + bs∗SC ) > (r − w0 )(a + bs∗retail (w0 )) −

⇔ (1 − λ) >

1 ∗ 1 ks (w0 )2 + ks∗2 2 retail 2 SC

1 1 ∗ ksretail (w0 )2 + ks∗2 2 2 SC (r − m)(a + bs∗SC )

(r − w0 )(a + bs∗retail (w0 )) −

⎛

⎜ ⇔λ 472 ⇒ (1 − λ)(924) > 472 + 162

⇒ (1 − λ)(924) > 472 + 162

⇒ λ < 31.3853%.

For the supplier to also benefit from this coordination, we need the supplier’s profit under the coordinated fashion retail supply chain to be larger than its profit under the uncoordinated case, i.e.: λ(r − m)(a + bs∗SC ) > 272 λ(924) > 272

⇒ λ > 29.4372%. Thus, the consignment contract which can coordinate the fashion retail supply chain and achieve win-win situation is: (i) the unit wholesale price = 4, and (ii) the revenue sharing parameter λ is bounded as follows: 29.4372% < λ < 31.3853%.

2.3.4 Numerical sensitivity analysis To have a better understanding on how different parameters affect the range of the revenue sharing parameter λ which can achieve win-win situation and coordination at the same time, we conduct a sensitivity analysis (with the numerical parameters as used in Section 2.3.3 as the base). Figure 2.1 to Figure 2.6 show the results (the detailed numbers are shown in Table 2.3 to Table 2.8, in Appendix of this Chapter). From Figure 2.1 to Figure 2.6, we can identify the trend and construct Table 2.1. Notice that a larger λ means a wider range of λ which can achieve the win-win coordination. It means there is a larger negotiation space for setting the contract parameter between the fashion retailer and the manufacturer to achieve the win-win situation in the coordinated supply chain system. From Table 2.1, we can see that the conditions which lead to a wider range of λ are: A smaller base demand parameter a, a larger service sensitivity parameter b, a smaller service cost coefficient k, a smaller unit product revenue r, a larger unit wholesale price (under the pure wholesale pricing contract before the implementation of the consignment contract) w0 , and a smaller unit product manufacturing cost m. These conditions hence indicate the situations under which there is a higher flexibility for the fashion retailer and the manufacturing in setting win-win coordinating consignment contract.

18

Fashion Retail Supply Chain Management

¯ Figure 2.1a The effect of changing a on λ and λ.

Figure 2.1b The effect of changing a on λ.

Customer service management in fashion retail supply chains

¯ Figure 2.2a The effect of changing b on λ and λ.

Figure 2.2b The effect of changing b on λ.

19

20

Fashion Retail Supply Chain Management

¯ Figure 2.3a The effect of changing k on λ and λ.

Figure 2.3b The effect of changing k on λ.

Customer service management in fashion retail supply chains

¯ Figure 2.4a The effect of changing r on λ and λ.

Figure 2.4b The effect of changing r on λ.

21

22

Fashion Retail Supply Chain Management

¯ Figure 2.5a The effect of changing w 0 on λ and λ.

Figure 2.5b The effect of changing w 0 on λ.

Customer service management in fashion retail supply chains

¯ Figure 2.6a The effect of changing m on λ and λ.

Figure 2.6b The effect of changing m on λ.

23

24

Fashion Retail Supply Chain Management

Table 2.1 Sensitivity results on the lower bound, the upper bound and the range of the revenue sharing proportion to achieve win-win coordination in the supply chain. Parameters

λ

λ¯

λ

a↑ b↑ k↑ r↑ w0↑ m↑

↑ ↓ ↑ ↓ ↑ ↓

↑ ↓ ↑ ↓ ↑ ↓

↓ ↑ ↓ ↓ ↑ ↓

(↑ = increases; ↓ = decreases; − = no change)

Table 2.2 Deming’s Quality Management Framework for customer service quality management (based on Deming’s fourteen points). Point 1. Be committed and create constancy of purpose towards customer service quality improvement. Point 2. Adopt a new philosophy towards customer service quality management. Point 3. Cease awarding business on price only and consider establishing long-term seller-buyer relationship. Point 4. Stop mass inspection and checking for customer service quality issues. Point 5. Enhance communication and remove barriers between departments. Point 6. Eliminate customer service quality related slogans. Point 7. Constantly improve the customer service quality system. Point 8. Improve leadership on customer service management. Point 9. Drive out fear for reporting customer service quality related issue. Point 10. Remove barriers to rob workers of their right to be proud in customer service management. Point 11. Eliminate work standards on customer service. Point 12. Institute training on the job for customer service improvement. Point 13. Institute education and self-improvement. Point 14. Everybody works together for customer service quality improvement.

2.4

CONCEPTUAL FRAMEWORK: DEMING’S QUALITY MANAGEMENT FRAMEWORK

Quality of customer service is an important issue in the fashion retail supply chain system. Following the classical literature on quality management, we discuss in this section the applicability and implications of Deming’s Quality Management Framework for enhancing fashion retail customer service4 . First of all, we present the fourteen points under Deming’s Quality Management Framework in Table 2.2 (adapted from Deming 1986, pp. 18–96; Foster 2004, pp. 37). In the following, we discuss the implications of these fourteen points for customer service quality management in fashion retail supply chain systems. 4

This framework, also known as Deming’s fourteen points, is widely adopted for quality management. It is especially influential in the Japanese and American industries. Note that Deming’s Quality Management Framework is conceptually in line with the “systems optimization’’ approach.

Customer service management in fashion retail supply chains

1

2

3

4

25

Be committed and create constancy of purpose towards customer service quality improvement: It is important to have long term commitment as well as setting achievable and practical milestones in customer service enhancement programs. Adopt a new philosophy towards customer service quality management: The fashion world is ever-changing and customer expectation on service is also dynamic. It is hence critically important for fashion retailers to adopt innovative and new philosophy when they want to improve customer service. For example, Nordstrom, a retailer famous for its customer service in fashion retailing, adopts a bottom up management practice in which frontline staff members (such as sales people in the stores) are given decision making power on various issues, including processing and accepting unconditional returns of merchandise by customers (even without receipt). It is also reported that when there is a stockout of a particular item in Nordstrom, Nordstrom’s sales associate may even go to a competitor’s store, buy the product at the price offered by the competitor, bring it to Nordstrom for the customer and charge the usual Nordstrom price (Levy and Weitz 2007). This kind of excellent customer service quality is achievable because Nordstrom possesses a new philosophy towards customer service quality. Cease awarding business on price only and consider establishing long-term sellerbuyer relationship: Traditionally, in order to achieve a low supply cost, fashion retailers tend to choose the supplier who offers the most attractive contract in terms of wholesale price. As a result, it happens that many fashion retailers have rather frequent changes in terms of the supplier base for the manufacturing of their products. However, this mechanism of granting business via price only consideration creates quality problems. In fashion retail supply chain management, problems such as delayed delivery, product quality issues, and lack of flexibility in supply are commonly seen if the fashion retailer mainly focuses on “price’’ when it selects suppliers. In fact, in Hong Kong, a well-established fashion retailer implemented the e-procurement scheme for suppliers to bid for contracts on “price’’ a few years ago. After running this e-procurement scheme for a short while, this fashion retailer realized that it created a lot of problems, including product quality. Finally, this fashion retailer gave up the e-procurement scheme. Moreover, in the whole fashion retail supply chain perspective, in order to achieve the systems optimality by having a coordinated supply chain with respect to customer service, some incentive alignment schemes and strategic alliance measures should be implemented. However, all these measures require the long-term relationship between the supplier and the fashion retailer. Thus, it is in fact crucially important for the fashion retailer to cease granting business by price only. Stop mass inspection and checking for customer service quality issues: Mass inspection for quality issue has been a widely implemented practice. It is applied for product quality inspection as well as customer service checking. However, mass inspection has the drawback that during the inspection, if quality related problems are identified, it is actually “too late’’ as the problems have existed for some time. For instance, a fashion retailer may conduct a periodic service quality survey to its customers two times a year. However, even if some quality problems are identified, the problems will have been there for some time prior to the survey. Thus, it is not the most efficient and effective service quality improvement scheme. In fact, Deming proposes to improve quality by building quality into the product or

26

5

6

7

8

9

Fashion Retail Supply Chain Management

service in the first place. For quality checking, it should also be conducted at the source in a continuous manner. Enhance communication and remove barriers between departments: In a fashion retail supply chain, there are different members and teams of people who work together towards achieving high customer services. For example, in order to attain high customer service levels, people in the supply side who are responsible for materials research, and product design, as well as people in the retail side who are responsible for sales and marketing, must work together as a team to foresee quality problems of products or services associated with the product that they produce and sell. It is hence imperative for the people working in different teams and departments to have seamless communications and discussions. Barriers between them should be removed or else it is impossible to achieve service excellence in the fashion retail supply chain system. Eliminate customer service quality related slogans: In many Asian countries, slogans are very popular and common. For example, we could easily find many banners with written slogans such as “achieving perfect service quality’’, “zero defect’’, “perfect customer service’’, etc. in many fashion manufacturers and retailers in Asia. However, many of these slogans do more harm than good because they are something unrealistic. After some time when the staff members realize that these slogans are empty words or “missions impossible’’, they will be ignored. For some Asian countries when the seniors have super power over the junior frontline staff, many of these slogans will even turn out to exert pressure on the juniors. This is harmful for service improvement. Constantly improve the customer service quality system: It is wise to gradually improve customer service quality in a step-by-step manner. For example, a fashion retailer wishes to achieve an excellent inventory service level of 99% and its current inventory service level is just 85%. To do so, the fashion retailer may set multiple targets of 90%, 95%, 98%, 99% and move towards these milestones one at a time. Constant measurements and fine-tuning of the respective inventory measures will be needed in order to achieve the goal. Improve leadership on customer service management: In order to enhance customer service quality, fashion retailers have to appoint the right people to act as leaders. These leaders are not only responsible for implementing the new philosophy on service quality improvement, but they also help other workers to do a better job. Furthermore, it is also critically important to have a strong leader who will direct and shape the strategy on service quality improvement for the fashion retailing company as well as the whole fashion retail supply chain system. Drive out fear for reporting customer service quality related issues: In a company, bad news regarding service goes up slowly. For example, a store manager may have detected that there is a customer service quality problem in the sales floor. However, the store manager usually will not report to the seniors promptly because of the fear being classified as incapable or trouble-maker. As a result, many customer service quality related problems will propagate up the company very slowly. In many cases, when the senior management notices about the service problem, it is too late and the company has suffered big losses already. Thus, it is important to drive out fear for reporting customer service quality related problems in the company.

Customer service management in fashion retail supply chains

10

11

12

13

14

27

Remove barriers to rob workers of their right to be proud in customer service management: There is a golden rule indicating that happy workers are more committed to the company, and workers who feel more respected in the company and “more proud of themselves’’ are happier. As such, in a fashion retailer, the staff members should be given the chance to show off their talents, be respected and rewarded. It will be a huge mistake to rob the staff members’ right to be proud if they have done something positive towards customer service quality improvement. Eliminate work standards: This point is a bit controversial but it does carry a very good meaning. For example, in a particular fashion retailer store, the company may have imposed a sales target for “jackets’’ to be 100 units everyday. With this target in mind, on a bad day when demand looks low, the sales people will tend to hard-sell and may even tell lies in order to lift the sales up. This results in a poor customer service. On the contrary, on a good day when demand during the first half of the day already hits the target, the sales people may become lazy and do not work hard to properly serve customers and promote the products for the day. This also results in a bad customer service. Similar situations arise whenever there is a “work standard’’. Based on this example, from service quality perspective, it does make sense to eliminate work standards of this kind. Institute training on the job for customer service improvement: Service quality management involves many new mindsets and action plans. Conventional wisdom alone is insufficient.Thus, in order to ensure the company’s service quality improvement program will be faithfully implemented by the staff members, formal training must be provided. As a remark, training here refers to the specific and necessary skills needed in order for the staff members in the fashion retail supply chains to help with the customer service quality improvement. Institute education and self-improvement: This point is in line with the former point on training. However, different from training, education and selfimprovement refers to those generic competency and unnecessary skills which may not be closely related to the customer service quality enhancement program. For example, for a fashion retailer, an education program on staff members’ proper reaction to stress and pressure can be offered. The spirit behind this point is: Having more knowledgeable and competent staff members will benefit the customer service quality improvement of the company. Everybody works together for customer service quality improvement: In a fashion retail supply chain, in order to achieve an optimal customer service, the staff members of both the upstream supplier and the downstream fashion retailer have to work hard. For example, in order to achieve the just-in-time delivery of products from the supplier to the retailer which helps to better match supply and demand (and hence improve inventory service at the retail level), the supplier has to work closely with the fashion retailer and they will each contribute important information so as to support this program. Another example is shown by Proposition 2.2 in which the supplier and the fashion retailer have to work together to design the parameters of the supply contract so that the optimal service level in the fashion retail supply chain can be achieved and an all-win situation can be attained.

28

2.5

Fashion Retail Supply Chain Management

CONCLUSION

In this chapter, we have discussed customer service and service quality management in fashion retail supply chain systems. We have examined the importance of customer service in fashion retailing. We have explored the quantitative performance measures for customer service and studied the conceptual RSQS model. Based on the RSQS model, we have built an analytical model and derived the optimal service level for the fashion retailer and the whole fashion retail supply chain. We have proven that under the pure wholesale pricing contract, owing to the double marginalization effect, the optimal retail service level is lower than the optimal service level for the fashion retail supply chain system. As a result, the fashion retail supply chain under a decentralized setting will not be optimal. In order to achieve the optimal fashion retail supply chain, we have proposed the use of a consignment contract which is based on a wholesale pricing and revenue sharing contract in which the wholesale price is set as the product cost, and the revenue sharing scheme is properly designed to achieve the all-win situation in which the upstream supplier, the downstream fashion retailer, the consumers, and the whole fashion retail supply chain will all be benefited by this consignment contract (as compared to before with the use of the pure wholesale pricing contract). A numerical example has been presented to illustrate the respective optimal service level decisions and the design of the supply chain coordinating consignment contract. Finally, we have discussed the Deming’s Quality Management Framework with the famous Deming’s fourteen points. For each point, the implications for service quality management in fashion retail supply chains are discussed. Undoubtedly, these fourteen points provide a nice qualitative framework for service quality improvement in fashion retail supply chains.

REFERENCES Choi, T.M. (2013) Optimal return service charging policy for fashion mass customization program. Service Science, 5(1), 56–68. Choi, T.M., Chow, P.S., Shen, B. & Wan, M.L. (2013) Service quality of fashion boutique operations: An empirical and analytical study. Working paper, The Hong Kong Polytechnic University. Dabholkar, P., Thorpe, D. & Rentz, J.O. (1996) A measure of service quality for retail stores: scale development and validation. Journal of the Academy of Marketing Science, 24(1), 3–16. Deming, W.E. (1986) Out of the Crisis. Boston: MIT/CAES. Foster, S.T. (2004) Managing Quality: An Integrative Approach. Prentice-Hall, 2nd Edition. Gagliano, K.B. & Hathcote, J. (1994) Customer expectation and perceptions of service quality in retail apparel specialty stores. Journal of Service Marketing, 8(1), 60–69. Hall, J. & Porteus, E. (2000) Customer service competition in capacitated systems. Manufacturing and Service Operations Management, 2(2), 144–165. Kim, S. & Jin, B. (2002) Validating the retail service quality scale for US and Korean customers of discount stores: An exploratory study. Journal of Services Marketing, 16(3), 223–237. Kurata, H. & Nam, S.H. (2010) After-sales service competition in a supply chain: Optimization of customer satisfaction level or profit or both? International Journal of Production Economics, 127(1), 136–146. Leen, J.Y.A. & Ramayah, T. (2011) Validation of the RSQS in apparel specialty stores. Measure Business Excellence, 15(3), 16–33.

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Levy, M. & Weitz, B.A. (2007) Customer service and relationship management at Nordstrom. Retail Management, McGraw-Hill Irwin, 580–581. Li, S., Zhu, Z. & Huang, L. (2009) Supply chain coordination and decision making under consignment contract with revenue sharing. International Journal of Production Economics, 120(1), 88–99. Liu, N., Choi, T.M., Yuen, M. & Ng, F. (2012) Optimal pricing, modularity and return policy under mass customization. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 42, 604–614. Parasuraman, A., Zeithaml, V. A. & Berry, L. L. (1985) A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), 41–50. Sarker, B.R. (2013) Consignment stocking policy models for supply chain systems: A critical review and comparative perspectives. International Journal of Production Economics, http://dx.doi.org/10.1016/j.ijpe.2013.11.005. So, K.C. (2000) Price and time competition for service delivery. Manufacturing & Service Operations Management, 2(4), 392–409. Wang, Y., Jiang, L. & Shen, Z.J. (2004) Channel performance under consignment contract with revenue sharing. Management Science, 50(1), 34–47. Xiao, T., Choi, T.M., Yang, D. & Cheng, T.C.E. (2012) Service commitment strategy and pricing decisions in retail supply chains with risk-averse players. Service Science, 4(3), 236–252. Zhang, D., de Mattab, R. & Lowe, T.J. (2010) Channel coordination in a consignment contract. European Journal of Operational Research, 207(2), 897–905.

APPENDIX: TABLES OF THE NUMERICAL ANALYSIS ¯ Table 2.3 The effect of changing a on λ and λ. a

λ

λ¯

λ

90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110

29.1667% 29.1954% 29.2237% 29.2517% 29.2793% 29.3065% 29.3333% 29.3598% 29.3860% 29.4118% 29.4372% 29.4624% 29.4872% 29.5117% 29.5359% 29.5597% 29.5833% 29.6066% 29.6296% 29.6524% 29.6748%

31.2500% 31.2644% 31.2785% 31.2925% 31.3063% 31.3199% 31.3333% 31.3466% 31.3596% 31.3725% 31.3853% 31.3978% 31.4103% 31.4225% 31.4346% 31.4465% 31.4583% 31.4700% 31.4815% 31.4928% 31.5041%

2.0833% 2.0690% 2.0548% 2.0408% 2.0270% 2.0134% 2.0000% 1.9868% 1.9737% 1.9608% 1.9481% 1.9355% 1.9231% 1.9108% 1.8987% 1.8868% 1.8750% 1.8634% 1.8519% 1.8405% 1.8293%

30

Fashion Retail Supply Chain Management ¯ Table 2.4 The effect of changing b on λ and λ. b

λ

λ¯

λ

20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

31.1828% 31.0085% 30.8328% 30.6563% 30.4796% 30.3030% 30.1271% 29.9522% 29.7787% 29.6070% 29.4372% 29.2697% 29.1047% 28.9424% 28.7829% 28.6263% 28.4728% 28.3225% 28.1755% 28.0316% 27.8912%

32.2581% 32.1709% 32.0831% 31.9948% 31.9065% 31.8182% 31.7302% 31.6428% 31.5560% 31.4702% 31.3853% 31.3015% 31.2190% 31.1379% 31.0581% 30.9798% 30.9031% 30.8279% 30.7544% 30.6825% 30.6122%

1.0753% 1.1624% 1.2503% 1.3385% 1.4269% 1.5152% 1.6031% 1.6906% 1.7773% 1.8632% 1.9481% 2.0318% 2.1143% 2.1955% 2.2752% 2.3535% 2.4302% 2.5054% 2.5789% 2.6508% 2.7211%

¯ Table 2.5 The effect of changing k on λ and λ. k

λ

λ¯

λ

90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110

29.1667% 29.1954% 29.2237% 29.2517% 29.2793% 29.3065% 29.3333% 29.3598% 29.3860% 29.4118% 29.4372% 29.4624% 29.4872% 29.5117% 29.5359% 29.5597% 29.5833% 29.6066% 29.6296% 29.6524% 29.6748%

31.2500% 31.2644% 31.2785% 31.2925% 31.3063% 31.3199% 31.3333% 31.3466% 31.3596% 31.3725% 31.3853% 31.3978% 31.4103% 31.4225% 31.4346% 31.4465% 31.4583% 31.4700% 31.4815% 31.4928% 31.5041%

2.0833% 2.0690% 2.0548% 2.0408% 2.0270% 2.0134% 2.0000% 1.9868% 1.9737% 1.9608% 1.9481% 1.9355% 1.9231% 1.9108% 1.8987% 1.8868% 1.8750% 1.8634% 1.8519% 1.8405% 1.8293%

Customer service management in fashion retail supply chains ¯ Table 2.6 The effect of changing r on λ and λ. r

λ

λ¯

λ

9 9.1 9.2 9.3 9.4 9.5 9.6 9.7 9.8 9.9 10 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 11

35.0345% 34.3776% 33.7455% 33.1370% 32.5507% 31.9854% 31.4400% 30.9134% 30.4046% 29.9129% 29.4372% 28.9769% 28.5312% 28.0994% 27.6808% 27.2749% 26.8811% 26.4988% 26.1276% 25.7669% 25.4163%

37.5172% 36.7966% 36.1035% 35.4364% 34.7939% 34.1745% 33.5771% 33.0005% 32.4437% 31.9056% 31.3853% 30.8819% 30.3946% 29.9227% 29.4654% 29.0221% 28.5921% 28.1748% 27.7697% 27.3762% 26.9939%

2.4828% 2.4191% 2.3580% 2.2994% 2.2432% 2.1891% 2.1372% 2.0872% 2.0391% 1.9927% 1.9481% 1.9050% 1.8634% 1.8233% 1.7846% 1.7471% 1.7110% 1.6760% 1.6421% 1.6093% 1.5776%

¯ Table 2.7 The effect of changing w 0 on λ and λ. w0

λ

λ¯

λ

5 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 7

15.6926% 17.1548% 18.5974% 20.0206% 21.4242% 22.8084% 24.1732% 25.5184% 26.8442% 28.1504% 29.4372% 30.7045% 31.9524% 33.1807% 34.3896% 35.5790% 36.7489% 37.8994% 39.0303% 40.1418% 41.2338%

16.1797% 17.7440% 19.2987% 20.8436% 22.3788% 23.9042% 25.4199% 26.9259% 28.4221% 29.9085% 31.3853% 32.8523% 34.3095% 35.7570% 37.1948% 38.6228% 40.0411% 41.4497% 42.8485% 44.2376% 45.6169%

0.4870% 0.5893% 0.7013% 0.8231% 0.9545% 1.0958% 1.2468% 1.4075% 1.5779% 1.7581% 1.9481% 2.1477% 2.3571% 2.5763% 2.8052% 3.0438% 3.2922% 3.5503% 3.8182% 4.0958% 4.3831%

31

32

Fashion Retail Supply Chain Management ¯ Table 2.8 The effect of changing m on λ and λ. m

λ

λ¯

λ

3 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 5

35.7581% 35.2618% 34.7395% 34.1896% 33.6109% 33.0017% 32.3604% 31.6853% 30.9744% 30.2257% 29.4372% 28.6065% 27.7312% 26.8086% 25.8359% 24.8100% 23.7276% 22.5853% 21.3792% 20.1051% 18.7586%

39.3076% 38.6454% 37.9580% 37.2441% 36.5024% 35.7316% 34.9302% 34.0966% 33.2291% 32.3260% 31.3853% 30.4050% 29.3828% 28.3166% 27.2036% 26.0413% 24.8268% 23.5568% 22.2280% 20.8369% 19.3793%

3.5495% 3.3836% 3.2185% 3.0544% 2.8915% 2.7299% 2.5698% 2.4113% 2.2548% 2.1002% 1.9481% 1.7984% 1.6516% 1.5080% 1.3678% 1.2314% 1.0991% 0.9715% 0.8489% 0.7318% 0.6207%

Chapter 3

Inventory models and coordination in fashion retail supply chains

SUMMARY In this chapter, we investigate two fundamental inventory models in fashion retail supply chains. To be specific, first we examine the EOQ model and its applications in fashion retailing. Second, we examine the newsvendor problem and discuss how it relates to fast fashion retailing. For both models, numerical illustrative examples are included to better present the ideas. After discussing these two classical models, we proceed to examine the coordination challenge in the fashion retail supply chain systems. We prove that owing to double marginalization effect, the fashion retail supply chains under both the EOQ-based model and the newsvendor problem -based model will not be coordinated by itself under a decentralized setting. We then discuss some measures to achieve coordination. In particular, we demonstrate with analytical proof how the commonly seen contracts can be applied to coordinate the fashion retail supply chains. Insights are discussed. Keywords EOQ model, newsvendor model, double marginalization effect, coordination, markdown contract 3.1

INTRODUCTION

In fashion retail supply chains, inventory management is the most fundamental and critical issue. As a matter of fact, the goal of fashion retail supply chain systems is to offer the right fashion product to the right place and at the right time for customers to purchase. In order to achieve this goal, most of the time, fashion retailers have to keep and plan inventory carefully. However, depending on the nature of the fashion products, the respective inventory planning problem and challenges are different. In this chapter, we examine two classical inventory models, namely the EOQ model (Choi 2014) and the newsvendor problem (Choi 2012) and present illustrative examples on how they can be applied to explore inventory planning problems in fashion retail supply chains. After that, we discuss the issue on supply chain coordination, i.e., how to achieve the systems optimality of the fashion retail supply chains. We propose different measures which can achieve coordination.

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This chapter is organized as follows. Section 3.2 presents the EOQ model-based inventory management model. Section 3.3 discusses the newsvendor problem-based inventory control model. Section 3.4 explores the coordination challenge in the fashion retail supply chain systems. Section 3.5 presents the sensitivity analysis. Section 3.6 concludes this chapter.

3.2 THE EOQ MODEL-BASED RETAIL INVENTORY PROBLEM

3.2.1 An illustrative example Suppose that there is a small fashion retail boutique which operates 7 days a week. In order to attract customers, it sells a product (say a t-shirt) at a special discount price for only X units every day. Customers love it and the X units will be sold very quickly every day. Assume that this boutique can get the replenishment very quickly from a supplier nearby. The following parameters apply for this product: a. b. c.

The fixed ordering cost (transportation, order processing): T per order. The inventory holding cost is estimated to be a% of the product’s value (annually) and the product value is equal to the unit wholesale price w. There is no quantity discount and the fashion retailer has enough budget to purchase the needed quantity. In addition, as a service promise, the X units of this special product must be offered everyday (and hence insufficient quantity is strictly not allowed).

The optimal decisions include the optimal ordering quantity for this special product, and the corresponding ordering frequency.

3.2.2 Optimal inventory decisions and ordering frequency In the illustrative example, imagine in a one year horizon, if the fashion retailer orders a larger quantity every time, it saves the fixed ordering cost. However, it holds more inventory and sells it for a longer period of time (before ordering again). As a result, the inventory holding cost is higher. On the contrary, if the fashion retailer orders in a smaller quantity, it saves inventory holding cost because the inventory is sold within a shorter period of time (before ordering again). However, the retailer orders more frequently and hence has to pay more fixed ordering cost such as transportation cost and order processing cost. With the inventory holding cost and the fixed ordering cost tradeoff in mind, we can develop the following cost functions. First, when the daily demand is X, the annual demand = 365X. For notational convenience, we define the annual demand as D, which is equal to 365X in the illustrative example. We represent the order quantity for each order by Q. So, the number of orders that the fashion retail will order for this product every year is equal to D/Q. The fixed ordering cost incurred for the ordering per year is hence given as follows: FOC(Q) = TD/Q.

Inventory models and coordination in fashion retail supply chains

35

Second, for the inventory holding cost, note that the average inventory level in the fashion retailer is Q/2. By definition, the cost of holding one unit of inventory for one year = aw. Thus, the annual inventory holding cost is expressed below: IHC(Q) = awQ/2. With the annual fixed ordering cost and the annual inventory holding cost derived above, we have the total cost function: TC(Q) = FOC(Q) + IHC(Q) = TD/Q + awQ/2.

It is straightforward to check that TC(Q) is a convex function by the second order condition. Thus, the optimal ordering quantity can be found by solving the first order condition below: 2TD ∗ dTC(Q)/dQ = 0 ⇒ Q = . aw With this optimal ordering quantity, the corresponding ordering frequency per year is found as follows: awD D ∗ . N = ∗= Q 2T

Note that Q∗ = 2TD is the classical economic order quantity (EOQ) formula (Choi aw 2014)1 . Despite being simple and being derived based on many assumptions, the EOQ formula gives very nice managerial implications to the fashion retailer: 1

It provides a good tool for inventory planning: The closed form expression of the EOQ formula gives the “square root relationship’’ between the optimal ordering quantity and the critical parameters in the problem. In particular, adapted from Chopra and Meindl (2013, pp. 291), we have the following analytical findings which can be proven by simple mathematics: a.

If the annual demand D increases by a√factor of k, then (i) the optimal ordering quantity will increase by a factor√of k, (ii) the number of ordering per year will also increase by a factor of k. b. In order to reduce the optimal ordering quantity by a factor of k by reducing the fixed ordering cost T, we can reduce T by a factor of k2 . 2 3

1

It is especially applicable to the products which have stable demands, e.g., men’s basic causal wear. It can be customized with the relaxation of some assumptions which make the model more applicable. Some of the extended models are even programmed into computer application software as a decision supporting tool.

Note that the EOQ model was first developed by Ford W. Harris a full century ago.

36

Fashion Retail Supply Chain Management

4

It forms the building block for studying more complex supply chain inventory management systems with multiple products and multiple echelons.

As another remark, it is interesting to observe that the optimal ordering quantity can actually be found as the quantity in which the inventory holding cost function and the fixed ordering cost function intersects: 2TD ∗ . FOC(Q) = IHC(Q) ⇒ TD/Q = awQ/2 ⇒ Q = aw This provides another way of understanding how the optimal ordering quantity in the EOQ model can be derived by very simple mathematics.

3.2.3 Numerical example We refer back to the illustrative example in Section 3.2.1. Suppose that the fixed ordering cost (transportation, order processing) is given by $100 per order. The inventory holding cost is estimated to be 20% of the product’s value (annually) and the product value is equal to the unit wholesale price $200. The daily demand is 10. By using the EOQ formula, the optimal ordering quantity is 2TD ∗ Q = aw 2 × 100 × 10 × 365 = = 135. 0.2 × 200 The corresponding ordering frequency is: awD ∗ N = 2T 0.2 × 200 × 10 × 365 = 27. = 2 × 100 3.3 THE NEWSVENDOR MODEL-BASED RETAIL INVENTORY PROBLEM

3.3.1 An illustrative fast fashion retailing example A fast fashion retailer orders a short-life fashion product from its supplier with a unit ordering cost c. The product is sold with a unit retail selling price of r during a single short selling season. The unsold product by the end of the season will be cleared with a big discount sale at a price v (as a new fashion product is coming and no space is available for the obsolete one). The product’s demand, y, is uncertain and follows a probability density function f (·) and a cumulative distribution function F(·). The inverse function of F(·) is represented by F −1 (·). Denote the order quantity for this fashion product by q. The fast fashion retailer needs to determine the optimal ordering quantity for this fashion product. To avoid trivial cases, we have: r > c > v.

Inventory models and coordination in fashion retail supply chains

37

3.3.2 Profit maximization model For the problem defined in Section 3.3.1, we can express the profit function as follows: π(q) = r min(y, q) − cq + v max(q − y, 0), where r min(y, q) is the revenue, cq is the product cost, and v max(q − y, 0) is the revenue generated by the clearance sale. Obviously, since the product demand over the selling season is a random variable, the profit function π(q) is also uncertain. To determine the optimal ordering quantity, we assume that the fashion retailer aims to maximize the expected profit. Thus, by taking expectation, the expected profit can be found in the following: E[π(q)] = rE[min(y, q)] − cq + vE[max(q − y, 0)]. Since min(y, q) = q − max(q − y, 0), we can rewrite E[π(q)] as follows: E[π(q)] = (r − c)q − (r − v)E[max(q − y, 0)]  q F(y)fy. = (r − c)q − (r − v) 0

Differentiating E[π(q)] once and twice with respect to q yields: dE[π(q)] = (r − c) − (r − v)F(q), dq d 2 E[π(q)] = −(r − v)f (q) < 0. dq2 Thus, E[π(q)] is a strictly concave function and the optimal ordering quantity q∗ can be solved as follows: dE[π(q)] = 0 ⇒ (r − c) − (r − v)F(q) = 0 dq

∗ −1 r − c ⇒ q =F . r−v Notice that the above optimal ordering quantity formula is the classical newsvendor r−c represents the resulting inventory optimal quantity (Choi 2012), and the ratio r−v service level when the fast fashion retailer orders at q∗ .

3.3.3 A numerical example Back to the illustrative example as described in Section 3.3.1, suppose that we have the following estimates of the parameters: The unit ordering cost c = 80. The unit retail selling price r = 220. The unit clearance sale price v = 60.

38

Fashion Retail Supply Chain Management

The product’s demand y follows a normal distribution with mean 100, and standard deviation of 30. The optimal order quantity for this fashion product can be found as follows:

220 − 80 r−c = 93.3%. = First, r−v 220 − 70 Second, since the demand follows the normal distribution, we have the following formula to find the inverse function of the cumulative distribution function: If F(·) is a normal distribution with mean µ and variance σ 2 , then F −1 (z) can be expressed as: µ + σ−1 (z), where −1 (·) is the inverse function of the standard normal cumulative distribution function. Thus, the optimal ordering quantity for this example is: 100 + 30−1 (0.933) = 145.0326. As a remark, −1 (·) can be computed conveniently from many application software programs. For example, in Microsoft Excel, it can be computed by the built-in function of “normsinv()’’.

3.3.4 Remarks First of all, in the model formulation presented in Section 3.3.2, when there is a stockout, the profit function already reflects the fact that a loss of revenue results. As a consequence, the stockout-related penalty has been inherently considered in the model formulation. Of course, from a fashion retail supply chain management perspective, some fashion retailers may wish to achieve an excellent inventory service level with a minimal chance of stockout. For those cases, the fashion retailer can include a kind of consumer loss of good wil penalty cost into the model. To be specific, this additional cost further penalizes stockout (in addition to the loss of revenue) because consumers will feel bad when there is stockout in a fashion retail store. Suppose that we represent this penalty cost by θ > 0, then the optimal ordering quantity becomes the following: q∗θ = F −1



r+θ−c . r+θ−v

Undoubtedly, a larger θ means a larger inventory service level. Secondly, it is interesting to note that the same optimal ordering quantity can be derived if the fashion retailer aims to minimize the sum of the expected inventory overstocking and under-stocking costs. For details, readers can refer to Nahmias (2004). Thirdly, in Section 3.2, the fashion retailer’s optimization objective is expected profit maximization which means that it is risk neutral. However, if the fashion retailer is risk averse, then it is a classical result that under the profit model as given in Section 3.3.2 (without θ), a risk averse fashion retailer will order a quantity lower than the q∗ . See Choi et al. (2008) for further discussion.

Inventory models and coordination in fashion retail supply chains

3.4

39

COORDINATION IN FASHION RETAIL SUPPLY CHAIN SYSTEMS

3.4.1 EOQ model based inventory planning In Section 3.2, we have discussed the use of the classic EOQ model for fashion retail inventory planning. Suppose that the supplier gets the product at a unit cost of m, where m < w. Thus, from the fashion retail supply chain system’s perspective, the optimal ordering quantity will not be the same as the fashion retailer’s because the product value is different. In fact, if the supply chain is fully vertically integrated, the optimal ordering quantity is given as follows (the subscript SC denotes “Supply Chain’’): Q∗SC

=



2TD . am

By direct comparison, it is easy to find that Lemma 4.1 holds. Lemma 3.1. If m < w, then Q∗SC > Q∗ . Lemma 3.1 indicates that in the decentralized fashion retail supply chain, the fashion retailer will determine the optimal ordering quantity which is not the best for the supply chain. This leads to supply chain inefficiency and the supply chain not being coordinated. As we discussed in Chapter 2, the root problem here is the double marginalization effect. In order to coordinate the supply chain in terms of this optimal ordering quantity decision, there are a number of probable methods. For instance, the supplier can wholesale the product at cost and then get a certain percentage share of the retailer’s revenue or profit (i.e., using a form of consignment (Wang et al. 2004; Sarker 2013; see Chapter 2 for more details)). Alternatively, the supplier may also offer a quantity discount contract which can potentially provide the needed incentive for the fashion retailer to order at the supply chain’s optimal quantity.

3.4.2 Newsvendor model-based inventory planning In Section 3.3, the newsvendor model formulation has been discussed for exploring the fast fashion retail inventory control problem. Suppose that we consider a retailer-led fast fashion supply chain in which the supplier is a manufacturer who will not start production until the fashion retailer advises the order quantity. In other words, it is a make-to-order (MTO) supply chain. In this case, suppose that the unit manufacturing cost of the fast fashion product is u. From the fashion retail supply chain’s perspective, the optimal product quantity is given in the following: q∗SC = F −1



r−u . r−v

Since under the traditional business practices, the unit product manufacturing cost is less than the unit wholesale price, i.e. u < c, we have Lemma 3.2.

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Fashion Retail Supply Chain Management

Lemma 3.2. If u < c, then q∗SC > q∗ .    r−c  and q∗SC = F −1 r−u . If u < c, then Proof of Lemma 3.2: Note that q∗ = F −1 r−v r−v     r−c   r−u  r−c r−u −1 < . Since F (·) is increasing in its argument, < ⇒ q∗ < q∗SC . r−v r−v r−v r−v (Q.E.D.) Similar to the case summarized in Lemma 3.1, we notice that the double marginalization effect is also present here and it creates the scenario in which the fashion retailer will order a quantity different from the best one for the fashion retail supply chain. In order to coordinate the supply chain by making q∗SC = q∗ , various measures can be taken (see Cachon 2003 for a comprehensive study). For example, we can use the markdown contract. To be specific, the fashion retailer can negotiate with the manufacturer to set up a markdown scheme (Whang 2009) in their business transaction in which the fashion retailer will receive from the manufacturer a monetary sponsor, called markdown money, for each unit of product leftover at the end of the selling season (see Tsay 2001; Choi 2013; Chow et al. 2013 for more details of the markdown contracts). We denote this unit markdown money as γ. In the presence of this markdown contract, the fashion retailer’s optimal ordering quantity becomes:

r−c q∗ (γ) = F −1 . r − (γ + v) Lemma 3.3 shows the optimal setting of the markdown money to achieve coordination in the fashion retail supply chain system. Lemma 3.3. The fashion retail supply chain can be coordinated by setting the markdown money γ=

(r − v)(c − u) . (r − u)

Proof of Lemma 3.3: Note that in the presence of the markdown contract with ∗ the markdown  money of γ, the fashion retailer’s optimal ordering quantity q (γ) = r−c . Interestingly, the optimal quantity for the fashion retail supply chain F −1 r−(γ+v)   . To coordinate the fashion retail supsystem remains unchanged, i.e., q∗SC = F −1 r−u r−v ply chain system means setting a value of γ to make q∗ (γ) = q∗SC . Thus, we have the following: q∗ (γ) = q∗SC



r−c −1 r − u −1 =F ⇔F r − (γ + v) r−v



r−c r−u ⇔ = r − (γ + v) r−v ⇔ (r − c)(r − v) = (r − u)(r − v − γ) (r − c)(r − v) ⇔ γ = (r − v) − (r − u) (r − v)(c − u) . (Q.E.D.) ⇔γ = (r − u)

Inventory models and coordination in fashion retail supply chains

41

From Lemma 3.3, we have a few important findings: 1

2

3

3.5

The markdown contract can coordinate the fashion retail supply chain by the adjustment of the markdown money and also the wholesale price. The condition for achieving coordination is summarized in Lemma 3.3. Obviously, there exist multiple pairs of markdown money and wholesale price which can achieve coordination. Thus, upon the negotiation between the fashion retailer and the manufacturer, the best pair of these two critical contract parameters will be determined which in turn decides the respective expected profit share in the fashion retail supply chain. The condition for achieving coordination by the markdown contract is independent of the retail demand distribution. It is hence relatively easy to determine the contract parameters even under information asymmetry in terms of retail demand (i.e., even when the manufacturer does not know the demand information of the retailer, the markdown contract can still be easily set to coordinate the supply chain). This is one nice feature of the markdown contract. In order to avoid having the arbitrage opportunity for the fashion retailer to take advantage of the markdown contract, the markdown money γ must be less than the difference between the wholesale price and the market clearance price, i.e. γ < c − v. If γ ≥ c − v, then q∗ (γ) → ∞ as the fashion retailer will certainly make money for each unit ordered under this situation (i.e. arbitrage).

SENSITIVITY ANALYSIS

In this section, we conduct a numerical sensitivity analysis to illustrate how significant it is to coordinate the respective fashion retail supply chain systems.

3.5.1 The EOQ-based model We employ the set of basic numerical values for the parameters as in Section 3.2.3. Figures 3.1 to 3.5 depict the sensitivity analysis results towards the optimal ordering quantity of the fashion retailer EOQR , the system optimal quantity (for the whole fashion retail supply chain system) EOQSC , and the cost improvement of the fashion retail supply chain system if coordination is achieved DTCSC = TCSC (EOQR ) − TCSC (EOQSC ) when the respective parameters change, where TCSC (EOQR ) denotes the total cost of the fashion retail supply chain with the optimal ordering quantity of the fashion retailer, and TCSC (EOQSC ) represents the total cost of the fashion retail supply chain with the system optimal quantity. Tables 3.1 to 3.5 (in Appendix) show the detailed numbers. Note that: 2TD ∗ EOQR = Q = , aw 2TD , EOQSC = Q∗SC = am TCSC (Q) = TD/Q + amQ/2.

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Fashion Retail Supply Chain Management

Figure 3.1 The effects brought about by changes in a.

Figure 3.2 The effects brought about by changes in T.

From Figures 3.1 to 3.5, we draw the following observations and findings: Coordinating the fashion retail supply chain system yields a higher cost saving when the unit product manufacturing cost m is smaller, the unit wholesale price w is larger, the annual demand D is larger, the per order fixed ordering cost T is larger, and the inventory holding cost parameter a is larger. In fact, T and D act as the magnifying factors because they relate to the size of the optimal ordering quantity. If they are larger, the respective EOQs are larger. Since the decentralized fashion retailer’s optimal ordering quantity and the centralized system’s optimal quantity are also directly proportional to the square root of the product of T and D, a larger T and D will lead to this observed effect. For the inventory holding cost parameter a, since it is in the denominator of the

Inventory models and coordination in fashion retail supply chains

43

Figure 3.3 The effects brought about by changes in D.

Figure 3.4 The effects brought about by changes in w.

EOQ formula, a smaller a will help to enhance inventory cost saving and coordination. Finally, since the system inefficiency for this case comes from the difference between w and m, when w is larger or m is smaller, the effect on cost saving brought about by coordination is more prominent (because the double marginalization effect is more significant which means the decentralized system is less efficient).

3.5.2 The newsvendor problem-based model Based on the set of numerical values of parameters in Section 3.3.3, similar to the EOQ based model’s case, we conduct a numerical sensitivity analysis towards the

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Fashion Retail Supply Chain Management

Figure 3.5 The effects brought about by changes in m.

Figure 3.6 The markdown money versus r.

coordination scheme. Here, we focus on the coordinating markdown money. To be specific, we explore how the coordinating markdown money varies when different parameters change. Figures 3.6 to 3.9 show the results (the exact numerical values are placed in Tables 3.6 to 3.9 in the Appendix). From Figures 3.6 to 3.9, we have the following findings: The markdown money which coordinates the supply chain is decreasing in the market clearance price v, the product manufacturing cost u, the retail selling price r, while increasing in the wholesale price c. The markdown money is especially sensitive to the manufacturing cost u and the wholesale price c. As we can see from Tables 3.6 and 3.7, the markdown money which coordinates the fashion retail supply chain varies from less than 3 to over 18 when u or c varies.

Inventory models and coordination in fashion retail supply chains

Figure 3.7 The markdown money versus v.

Figure 3.8 The markdown money versus u.

Figure 3.9 The markdown money versus c.

45

46

3.6

Fashion Retail Supply Chain Management

CONCLUSION

In this chapter, we have examined two imperative inventory models and the coordination challenges in fashion retail supply chain systems. We have specifically reviewed and investigated two fundamental inventory models, namely the EOQ model and the newsvendor problem. We have studied how these models can relate to fashion retailing inventory problems. For both models, we have shown the existence of the optimal ordering decisions. Illustrative numerical examples have also been presented. After that, we have extended the analysis to the fashion retail supply chain systems and analytically examined the schemes which can optimize the systems, i.e., achieve supply chain coordination. We have proven that owing to the double marginalization effect, the fashion retail supply chains under both the EOQ-based model and the newsvendor problem-based model are not optimal under a decentralized setting. In fact, the fashion retailer’s optimal ordering quantities will always be less than the supply chain system’s optimal ordering quantities. Then, we have discussed the supply chain coordination mechanisms. Sensitivity analysis has been conducted to show further insights regarding how different modeling parameters affect the significance of coordination for the EOQ model-based fashion retail supply chain, as well as the parameter setting in the markdown contracts. In particular, we have revealed that coordinating the EOQ model-based fashion retail supply chain system will yield a higher cost saving when the product manufacturing cost is smaller, the wholesale price is larger, the annual demand is larger, the per order fixed ordering cost is larger, and the inventory holding cost parameter is larger. For the markdown contract which coordinates the newsvendor problem-based fashion retail supply chain, we have found that the equilibrium markdown money is larger when the market clearance price is smaller, the product manufacturing cost is smaller, the retail selling price is smaller, or the wholesale price is larger. Moreover, the equilibrium markdown money is very sensitive to the manufacturing cost and the wholesale price. REFERENCES Cachon, G. (2003) Supply chain coordination with contracts. In: Graves, S., T. de Kok. (Eds). Handbooks in Operations Research and Management Science: Supply Chain Management, North Holland, 229–340. Choi, T.M. (ed.) (2014) Handbook of EOQ Inventory Problems: Stochastic and Deterministic Models and Applications, International Series in Operations Research & Management Science, Vol. 197, Springer. Choi, T.M. (ed.) (2012) Handbook of Newsvendor Problems: Models, Extensions and Applications, International Series in Operations Research & Management Science, Vol. 176, Springer. Choi, T.M. (2013) Multi-period risk minimization purchasing models for fashion products with interest rate, budget, and profit target considerations. Annals of Operations Research, 10.1007/s10479-013-1453-x, in press. Choi, T.M., Li, D. & Yan. H. (2008) Mean variance analysis of the newsvendor problems. IEEE Transactions on Systems, Man, and Cybernetics: Part A, 38(5), 1169–1180. Chow, P.S., Wang, Y., Choi, T.M. & Shen, B. (2013) An experimental study on the effects of minimum profit share on supply chains with markdown contract: Risk and profit analysis. Omega, in press.

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Chopra, S. & P. Meindl. (2013) Supply Chain Management: Strategy, Planning and Operations. Pearson, 5th edition. Nahmias, S. (2004) Production and Operations Analysis, McGraw Hill, 5th Edition. Tsay, A.A. (2001) Managing retail channel overstock: Markdown money and return policy. Journal of Retailing, 77(4), 457–492. Sarker, B.R. (2013) Consignment stocking policy models for supply chain systems: A critical review and comparative perspectives. International Journal of Production Economics, http://dx.doi.org/10.1016/j.ijpe.2013.11.005. Shen, B., Choi, T.M., Wang, Y. & Lo. C.K.Y. (2013) The coordination of fashion supply chains with a risk averse supplier by the markdown money policy. IEEE Transactions on Systems, Man, and Cybernetics – Systems, 43(2), 266–276. Wang, Y., Jiang, L. & Shen, Z.J. (2004) Channel performance under consignment contract with revenue sharing. Management Science, 50(1), 34–47. Whang, S.J. (2009) Markdown competition. In: Retail Supply Chain Management, Agrawal and Smith (eds.), Springer, 1–15.

APPENDIX: TABLES OF THE NUMERICAL ANALYSIS Table 3.1 The effects brought by changes in a. a

EOQR

EOQSC

TC SC (EOQR )

TC SC (EOQSC )

DTC SC

0.1 0.125 0.15 0.175 0.2 0.225 0.25 0.275 0.3 0.325

191 171 156 144 135 127 121 115 110 106

270 242 221 204 191 180 171 163 156 150

2866 3204 3510 3791 4053 4299 4531 4752 4964 5166

2702 3021 3309 3574 3821 4053 4272 4481 4680 4871

164 183 201 217 232 246 259 272 284 295

Table 3.2 The effects brought by changes in T. T

EOQR

EOQSC

TC SC (EOQR )

TC SC (EOQSC )

DTC SC

80 85 90 95 100 105 110 115 120 125

121 125 128 132 135 138 142 145 148 151

171 176 181 186 191 196 200 205 209 214

3625 3736 3845 3950 4053 4153 4251 4346 4440 4531

3418 3523 3625 3724 3821 3915 4007 4098 4186 4272

207 214 220 226 232 238 243 249 254 259

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Fashion Retail Supply Chain Management

Table 3.3 The effects brought by changes in D. D

EOQR

EOQSC

TC SC (EOQR )

TC SC (EOQSC )

DTC SC

3150 3250 3350 3450 3550 3650 3750 3850 3950 4050

125 127 129 131 133 135 137 139 141 142

177 180 183 186 188 191 194 196 199 201

3765 3824 3883 3940 3997 4053 4108 4162 4216 4269

3550 3606 3661 3715 3768 3821 3873 3924 3975 4025

215 219 222 225 229 232 235 238 241 244

Table 3.4 The effects brought by changes in w. w

EOQR

EOQSC

TC SC (EOQR )

TC SC (EOQSC )

DTC SC

140 150 160 170 190 200 210 220 230 240

161 156 151 147 139 135 132 129 126 123

191 191 191 191 191 191 191 191 191 191

3875 3900 3927 3956 4019 4053 4087 4122 4157 4193

3821 3821 3821 3821 3821 3821 3821 3821 3821 3821

54 79 106 135 198 232 266 301 336 372

Table 3.5 The effects brought by changes in m. m

EOQR

EOQSC

TC SC (EOQR )

TC SC (EOQSC )

DTC SC

80 85 90 95 100 105 110 115 120 125

135 135 135 135 135 135 135 135 135 135

214 207 201 196 191 186 182 178 174 171

3784 3851 3919 3986 4054 4121 4189 4256 4324 4391

3418 3523 3625 3724 3821 3915 4007 4098 4186 4272

366 328 294 262 233 206 181 159 138 119

Table 3.6 The markdown money versus r. Retail selling price r

Markdown money

200 205 210 215 220 225 230 235 240 245

10.77 10.74 10.71 10.69 10.67 10.65 10.63 10.61 10.59 10.57

Inventory models and coordination in fashion retail supply chains Table 3.7 The markdown money versus v. Clearance sale price v

Markdown money

50 52 54 56 58 60 62 64 66 68

11.33 11.20 11.07 10.93 10.80 10.67 10.53 10.40 10.27 10.13

Table 3.8 The markdown money versus u. Product manufacturing cost u

Markdown money

62 64 66 68 70 72 74 76 78

18.23 16.41 14.55 12.63 10.67 8.65 6.58 4.44 2.25

Table 3.9 The markdown money versus c. Wholesale price c

Markdown money

72 74 76 78 80 82 84 86 88 90

2.13 4.27 6.40 8.53 10.67 12.80 14.93 17.07 19.20 21.33

49

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Chapter 4

Efficient consumer response in fashion retail supply chain systems

SUMMARY Efficient consumer response (ECR) is a well-established practice in fashion retail supply chains. It refers to a quick responsive and consumer-driven system. In this chapter, we explore the ECR program in fashion retail supply chain systems. First, we build a formal analytical inventory model with Bayesian information updating. Second, we conduct profit analysis for the fashion retailer with respect to the benefit of using ECR. After that, we examine the all-win situation in which we consider the consumer, the retailer, the manufacturer and the whole fashion retail supply chain system. Finally, we explore the supply chain coordination challenge in the presence of the ECR program. Both analytical and numerical analyses are conducted. We reveal that: (i) the ECR program is beneficial to the fashion retailer; (ii) the consumer welfare coefficient plays a critical role in determining whether the ECR program is harmful or beneficial to consumer welfare, and the manufacturer; (iii) if the consumer welfare coefficient is sufficiently small, the ECR program creates the all-win situation which means the fashion retailer, the manufacturer, the consumer and the whole fashion retail supply chain all benefit; (iv) the markdown contract can be set to achieve supply chain coordination. Keywords Efficient consumer response, quick response, consumer welfare.

4.1

INTRODUCTION

Consider a fashion retailer that mainly sells highly seasonal fashion apparel. If the lead time is long and owing to such constraints as minimum order quantity (Chow et al. 2012), it has to place one big order for the products to be sold in the upcoming selling season (Choi et al. 2004). At that time, the fashion retailer will make a prediction about the market needs (Choi 2007). In other words, the fashion retailer will have to conduct demand forecasting long before the season starts and the forecasting result will drive the inventory decision. Unfortunately, it is well-known that all demand forecasting must be wrong and the forecasting accuracy is usually a decreasing function of lead time, i.e., a longer lead time brings a lower forecast accuracy (Donohue 2000; Fisher et al. 1994). As a result, the fashion retailer as well as the whole fashion retail supply

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chain system will suffer because it is tremendously difficult to plan the inventory so that supply and demand can match accurately1 . Nowadays, many fashion retail supply chains are implementing an efficient consumer response (ECR) program2 . According to Kurt Salman Associates (1993), “. . . ECR is a responsive consumer-driven system in which distributors and suppliers work together as business allies to maximize consumer satisfaction and minimize cost . . .’’. Thus, under ECR, the inventory ordering lead time is shortened and the fashion retailer and the supplier have an agreement to work together (Hoffman and Mehra 2000; Kurnia and Johnson 2001; Fisher et al. 2001) so as to provide the needed quantity of the product to the market efficiently. In this chapter, we examine the benefit of ECR and how the consumer welfare coefficient affects the impacts brought about by ECR to the fashion retail supply chain system, all the associated members and consumers. In addition, we study the supply chain coordination challenge. The organization of this chapter is as follows. The analytical model is presented in Section 4.2. The profit functions, with the consumer welfare function, are derived and discussed in Section 4.3. The all-win coordination challenge is examined in Section 4.4. The supply chain coordination problem is studied in Section 4.5. Numerical sensitivity analysis is discussed in Section 4.6. We conclude this chapter in Section 4.7. 4.2

BASIC ANALYTICAL MODEL

We construct the analytical model in this section. First of all, we consider a single manufacturer single retailer fashion retail supply chain selling a short-life fashion product. The fashion retailer is the leader of the supply chain who decides the ordering quantity. The manufacturer, acting as the supplier, reacts and fulfills the order. We assume that the manufacturer always has enough capacity to produce the needed quantity. For the cost revenue parameters, we have the following: The manufacturer produces the product at a unit expense of m. The fashion retailer pays the manufacturer a unit wholesale price c. In the consumer market during the regular season, the unit retail selling price is r. The product is highly fashionable and is sold only during a short season. Thus, by the end of the season, the fashion retailer will conduct a clearance sale and we assume all product leftovers can be cleared at a market clearance price v. Before the selling season starts, the fashion retailer has to decide the optimal ordering quantity of this product. We now present the Bayesian information update-based demand model (Azoury and Miller 1984; Azoury 1985; Choi et al. 2003, 2004, 2006). Following Iyer and Bergen (1997) and Choi et al. (2003), we consider the scenario when the retailer can place a single order at either one of the two points. The first time point, denoted by Time 0, is farther away from the selling season. This time point represents the more 1

See Fisher and Raman (1996) for the accurate response program which is also related to the use of early sales information to enhance inventory planning. 2 Note that the ECR system is very similar to the quick response system in the context of supply chain operations management. For more readings on quick response, refer to Kim (2003), Choi et al. (2006). In this chapter, we use the term ECR instead of quick response because we want to emphasize the role played by consumers in this program. Nevertheless, we include a measure on consumer welfare in the analysis. For more details and history of ECR, refer to Svensson (2002).

Efficient consumer response in fashion retail supply chain systems 53

traditional ordering decision with a long lead time, i.e. without ECR. Expectedly, the demand forecast is poor at Time 0 which implies that there is a great demand uncertainty. The second time point, denoted by Time 1, is closer to the selling season. If the fashion retailer chooses to order at Time 1, it can observe some market signal (e.g., on the popularity of the related color) during the time interval between Time 0 and Time 1, and use it to improve its demand forecast. Thus, the ordering at Time 1 has a smaller demand variance and hence a higher demand forecast accuracy. We represent the predicted demand of the product at Time 0 by x0 . Following the basic normal conjugate pair demand uncertainty structure as in Iyer and Bergen (1997) and Choi and Chow (2008), we model x0 as a normally distributed random variable with mean θ and variance δ: x0 ∼ N(θ, δ), where θ is uncertain and for modeling tractability, we model it to follow a normal distribution with mean µ0 and variance d0 , θ ∼ N(µ0 , d0 ). Notice that in the distribution of x0 , δ captures the basic demand uncertainty associated with the product which cannot be eliminated. With the above model, we can show that the unconditional distribution of x0 follows a normal distribution with mean µ0 and variance (d0 + δ), x0 ∼ N(µ0 , d0 + δ). During the time interval between Time 0 and Time 1, the fashion retailer can make a market observation on a related fashion product and use it to update its own knowledge on x0 (we denote this observation by z0 ). As a consequence, the distribution of θ is updated as follows (Iyer and Bergen (1997), Choi et al. (2003), Chow et al. (2012)), θ ∼ N(µ1 , d1 ), where



d0 δ z0 + µ0 , d0 + δ d0 + δ

µ1 =



d1 =

δ d0 . d0 + δ

We represent the predicted demand of the product at Time 1 by x1 . The distribution for x1 can be found to be the following: x1 ∼ N(µ1 , d1 + δ), where µ1 is also a normally distributed random variable at Time 0 (and it is known after the information updating with the observation of z0 ): µ1 ∼ N(µ0 , d02 /(d0 + δ)).

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Note that the above Bayesian information updating model is well studied and employed in the literature (Iyer and Bergen (1997), Choi et al. (2003), and Choi and Chow (2008)). We also use it here because it is analytically tractable and carries good physical meanings which fit the ECR program well.

4.3

PROFIT ANALYSIS FOR THE FASHION RETAILER

For notational purposes, we have the following: φ(·) = the standard normal probability density function, (·) = the standard normal cumulative distribution function, −1 (·) = the inverse function of (·), ∞ (x) = x (y − x)φ(y)dy = the standard normal right linear loss function, q = a decision variable to represent ordering quantity, EPR,0 (q) = the fashion retailer’s expected profit when ordering takes place at Time 0, EPM,0∗ = the manufacturer’s expected profit when the fashion retailer orders its best quantity at Time 0, EPSC,0 (q) = the supply chain’s expected profit when ordering takes place at Time 0, CWR,0 (q) = the consumer welfare when the fashion retailer’s ordering takes place at Time 0, EPR,1 (q|µ1 ) = the fashion retailer’s expected profit when ordering takes place at Time 1, for a given µ1 , EPM,1∗ (µ1 ) = the manufacturer’s expected profit when the fashion retailer orders its best quantity at Time 1, for a given µ1 , EPSC,1 (q|µ1 ) = the supply chain’s expected profit when ordering takes place at Time 1, for a given µ1 , CWR,1 (q|µ1 ) = the consumer welfare when the fashion retailer’s ordering takes place at Time 1, for a given µ1 , q∗R,0 = the fashion retailer’s optimal ordering quantity at Time 0, q∗R,1 (µ1 ) = the fashion retailer’s optimal ordering quantity at Time 1, for a given µ1 . Following the derivations in Iyer and Bergen (1997) and Choi and Chow (2008), it is easy to find that    q − µ0 EPR,0 (q) = (r − v)µ0 − (c − v)q − (r − v) d0 + δ  , d0 + δ    q − µ1 EPR,1 (q) = (r − v)µ1 − (c − v)q − (r − v) d1 + δ  . d1 + δ

In our analysis for the ECR program, we consider the situation in which the fashion retailer is concerned about both its expected profit and consumer welfare. Here, since the retail selling price is fixed, consumer welfare is mainly reflected by the product availability. To be specific, we define consumer welfare as follows: CWi (q) = τq,

i = 0, 1,

Efficient consumer response in fashion retail supply chain systems 55

where, τ > 0 represents the unit consumer welfare with respect to the product quantity available to the market. With this simple consumer welfare function, we argue that the consumers will benefit if more quantity is available.3 Note that τ relates to the type of products, too. For example, for those products about which the consumers care more and feel worse when it comes to stockout, then τ is larger (and hence more product being available is better). Thus, at Time 0, the fashion retailer’s optimal ordering quantity can be found by solving the following optimization problem: q∗R,0 = arg{maxUR,0 (q) = EPR,0 (q) + CW0 (q)} q





q − µ0 = arg max(r − v)µ0 − (c − v)q − (r − v) d0 + δ  q d0 + δ 





+ τq .

= ((a(q)) − 1) da(q) . Taking the first and second order derivatives of Note that d(a(q)) dq dq UR,0 (q) with respect to q yields the following:     dUR,0 (q) q − µ0 −1 +τ = −(c − v) − (r − v)   dq d0 + δ   q − µ0 = (r − c + τ) − (r − v)  , d0 + δ    q − µ0 (r − v) d 2 UR,0 (q) φ  < 0. = − dq2 d0 + δ d0 + δ

Thus, UR,0 (q) is a strictly concave function and the optimal ordering quantity at Time 0 is given as follows:

dUR,0 (q) ∗ qR,0 = arg =0 dq q

 r+τ−c . = µ0 + d0 + δ−1 r−v    Substitute q∗R,0 = µ0 + d0 + δ−1 r+τ−c into UR,0 (q) yields the optimal benefit r−v function for the fashion retailer as follows:

 r+τ−c UR,0∗ = (r − v)µ0 − (c − v − τ)q∗R,0 − (r − v) d0 + δ −1 . r−v 3

Note that this proposed consumer welfare is a very simple one for the sake of model tractability. In reality, it may not be linear and it should be diminishing when q becomes bigger. Furthermore, one may also model (expected) consumer welfare as the product between the consumer welfare coefficient and the expected quantity of goods sold. The analysis for this alternative case is similar to the current one and interested readers can try it themselves.

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Define: s=



r+τ−c . r−v

We consider only the case when τ < c − v (or else consumer welfare is so significant that the fashion retailer will have to order an infinite amount of the product). We can simplify UR,0∗ in the following:     UR,0∗ = (r − v)µ0 − (c − v − τ)(µ0 + d0 + δ−1 (s)) − (r − v) d0 + δ −1 (s)  = (r − c)µ0 − (r − v) d0 + δφ(−1 (s)).

Similarly, at Time 1, we define UR,1 (q|µ1 ) = EPR,1 (q|µ1 ) + CW1 (q), and derive the optimal ordering quantity as follows: q∗R,1 (µ1 ) = arg{max UR,1 (q|µ1 )} q



    q − µ1 = arg max(r − v)µ1 − (c − v)q − (r − v) d1 + δ  + τq . q d1 + δ d2 U

(q|µ )

R,1 1 It is straightforward to find that < 0 and hence UR,1 (q|µ1 ) is a strictly dq2 concave function. As a result, the optimal ordering quantity at Time 1 can be found as follows:

dUR,1 (q|µ1 ) ∗ =0 qR,1 (µ1 ) = arg dq q

 r+τ−c = µ1 + d1 + δ−1 . r−v

Substitute q∗R,1 (µ1 ) into UR,1 (q|µ1 ) gives the optimal benefit function for the fashion retailer at Time 1: UR,1 (q∗R,1 |µ1 ) = (r − v)µ1 − (c − v − τ)q∗R,1 (µ1 )

 r+τ−c −(r − v) d1 + δ −1 r−v  = (r − c)µ1 − (r − v) d1 + δφ(−1 (s)).

Define:

BR,ECR = UR,1 (q∗R,1 |µ1 ) − UR,0∗ ,

qR,ECR = q∗R,1 (µ1 ) − q∗R,0 .

Observe that BR,ECR denotes the net benefit gained by the fashion retailer upon employing ECR, and qR,ECR represents the quantity difference. However, since µ1 is

Efficient consumer response in fashion retail supply chain systems 57

unknown (and is a random variable) at Time 0, BR,ECR and qR,ECR are also random variables at Time 0. As such, to give a deterministic measure, we take expectation with respect to µ1 and we have the following notation: EBR,ECR = E [BR,ECR ] µ1

 = (r − c)µ0 − (r − v) d1 + δφ(−1 (s)) − [(r − c)µ0  − (r − v) d0 + δφ(−1 (s))]   = (r − v)( d0 + δ − d1 + δ)φ(−1 (s)),

EQR,ECR = E [qR,ECR ] µ1



 r+τ−c −1 r + τ − c = µ0 + d1 + δ − µ0 + d0 + δ r−v r−v

  r+τ−c = −( d0 + δ − d1 + δ) −1 . r−v 

−1



Proposition 4.1 summarizes the key findings. Proposition 4.1. (a) The efficient consumer response program can always bring a positive net benefit to the fashion retailer, i.e. EBR,ECR > 0. (b) The efficient consumer response program (i) hurts consumer welfare when τ > c − 0.5v − 0.5r, (ii) benefits consumer welfare when τ < c − 0.5v − 0.5r. Proof of Proposition 4.1.   (a) This is a directobservation from EB = (r − v)( d + δ − d1 + δ)φ(−1 (s)). R,ECR 0  −1 Since (r − v) > 0, ( d0 + δ − d1 + δ) > 0, and φ( (s)) > 0, we have: EBR,ECR > 0.

(b) Note that τ(EQR,ECR ) is the change of consumer welfare with the  use of an efficient consumer response program. Further observe that r +r −τ −v c > 0.5 ⇔   c − 0.5v − 0.5r < τ and r +r −τ −v c < 0.5 ⇔ c − 0.5v − 0.5r > τ. Since −1 (s) > 0 if s > 0.5 and −1 (s) < 0 if s < 0.5, we have τ(EQR,ECR ) > 0 if and only if c − 0.5v − 0.5r > τ and τ(EQR,ECR ) < 0 if and only if c − 0.5v − 0.5r < τ. (Q.E.D.) Proposition 4.1a shows an interesting result that the fashion retailer always benefits with the use of the efficient consumer response program (which is consistent with the findings reported in the literature, e.g., Iyer and Bergen 1997). However, this does not necessarily benefit the consumers as the expected quantity of product available to the market may be smaller which directly leads to lowered consumer welfare as indicated by Proposition 4.1b(i). To be specific, if the consumer welfare coefficient is too big (i.e., c − 0.5v − 0.5r < τ), Proposition 4.1b(i) shows that the efficient consumer response program is harmful to consumer welfare. However, if the consumer welfare coefficient is sufficiently small (i.e., c − 0.5v − 0.5r > τ), Proposition 4.1b(ii) proves that the efficient consumer response program is beneficial to consumer welfare.

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Thus, value of the consumer welfare coefficient plays an important role in determining whether the efficient consumer response program is doing more harm than good or not for consumer welfare.

4.4 ALL-WIN SITUATION In Section 4.3, we have explored the value of the efficient consumer response program to the fashion retailer and to the consumer welfare. In this section, we examine its impact on the manufacturer’s expected profit and the fashion retail supply chain system. As a remark, we assume in this chapter that the fashion supply chain is under the leadership of the fashion retailer which decides the ordering quantity and the manufacturer reacts and produces the needed quantity accordingly. Suppose that we represent the unit manufacturing cost for the product by m. Thus, the expected profits of the manufacturer under the cases with and without the efficient consumer response program are given below: At Time 0 (without ECR):

 r+τ−c EPM,0∗ = (c − m)q∗R,0 = (c − m) µ0 + d0 + δ−1 . r−v At Time 1 (with ECR): EPM,1∗ (µ1 ) = (c −

m)q∗R,1 (µ1 ) = (c

 −1 r + τ − c . − m) µ1 + d1 + δ r−v

Define: BM,ECR = EPM,1∗ (µ1 ) − EPM,0∗ , and EBM,ECR = E [BM,ECR ] µ1



= −(c − m)( d0 + δ −



−1 r + τ − c , d1 + δ)  r−v

where EBM,ECR represents the expected benefit for the manufacturer of using the efficient consumer response program. Define: EBSC,ECR = EBR,ECR + EBM,ECR . Proposition 4.2 shows the impact on the manufacturer brought about by the implementation of the efficient consumer response program. Proposition 4.2. The efficient consumer response program (i) hurts the manufacturer’s profit if and only if c − 0.5v − 0.5r < τ, (ii) improves the manufacturer’s profit if and only if τ < c − 0.5v − 0.5r.

Efficient consumer response in fashion retail supply chain systems 59

Proof of Proposition 4.2.   Since (c − m) > 0 and ( d0 + δ − d1 + δ) > 0, we have:   (c − m)( d0 + δ − d1 + δ) > 0. Notice that −1 (s) > 0 if and only if s > 0.5 ⇔ c − 0.5v − 0.5r < τ, and −1 (s) < 0 if and only if s < 0.5 ⇔ c − 0.5v − 0.5r > τ. Thus, EBM,ECR > 0 if and only if c − 0.5v − 0.5r > τ, and EBM,ECR < 0 if and only if c − 0.5v − 0.5r < τ. (Q.E.D.) Similar to Proposition 4.1, Proposition 4.2 shows that whether the manufacturer benefits from the use of the efficient consumer response program depends on the size of the consumer welfare coefficient. To be specific, if the consumer welfare coefficient is too big (i.e., c − 0.5v − 0.5r < τ), Proposition 4.2(i) shows that the efficient consumer response program brings harm to the manufacturer. On the contrary, if the consumer welfare coefficient is small enough (i.e., c − 0.5v − 0.5r > τ), Proposition 4.2(ii) proves that the efficient consumer response program brings benefits to the manufacturer. Thus, the value of the consumer welfare coefficient is crucially important. From Proposition 4.1 and Proposition 4.2, we have Proposition 4.3. Proposition 4.3. The efficient consumer response program achieves the all-win situation in the fashion retail supply chain system, i.e. it benefits the fashion retailer, the manufacturer, the consumers, and the fashion retail supply chain system all together, if and only if τ < c − 0.5v − 0.5r. Proof of Proposition 4.3: Observe that from Proposition 4.1, the fashion retailer always benefits from the efficient consumer response program. From Proposition 4.2 and Proposition 4.1b(ii), we know that the manufacturer’s profit and the consumer welfare will be improved under the efficient consumer response program if and only if τ < c − 0.5v − 0.5r. Since the fashion retail supply chain includes the fashion retailer, the manufacturer and the consumers, τ < c − 0.5v − 0.5r is the necessary and sufficient condition to guarantee that all of them will be benefited by the use of the efficient consumer response program. (Q.E.D.) Proposition 4.3 indicates concisely that it is possible to achieve the all-win situation in the fashion retail supply chain if the consumer welfare coefficient is sufficiently small, i.e. τ < c − 0.5v − 0.5r. This again indicates the critical role of the consumer welfare coefficient.

4.5

SUPPLY CHAIN COORDINATION

In Sections 4.3 and 4.4, the fashion retail supply chain is a decentralized system. The optimal ordering quantities (at Time 0 and Time 1) for the fashion retailer are different from the supply chain system’s respective optimal quantities (at Time 0 and Time 1).

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Define: q∗SC,0 = the fashion retail supply chain system’s optimal ordering quantity4 at Time 0, q∗SC,1 (µ1 ) = the fashion retail supply chain system’s optimal ordering quantity at Time 1, for a given µ1 . It is straightforward to show that the best quantities for the supply chain system are the following:

 −1 r + τ − m = µ0 + d0 + δ , r−v

 ∗ −1 r + τ − m . qSC,1 (µ1 ) = µ1 + d1 + δ r−v q∗SC,0

In order to achieve coordination, similar to the discussion in Chapter 2, the fashion retailer and the manufacturer can negotiate and employ the consignment contract in which the manufacturer supplies the product at cost and shares the retail revenue (in the right proportion) with the fashion retailer. Alternatively, they can also impose the markdown contract in which the manufacturer will sponsor the fashion retailer for the end of season leftover markdown with a unit markdown money of λ (Shen et al. 2013). Proposition 4.4 shows how the markdown money can be set to achieve fashion retail supply chain coordination. Proposition 4.4. The fashion retail supply chain system (for both the cases with and without the efficient consumer response program) can be coordinated by a markdown v)(c − m) . contract with the parameter λ = (r − r+τ −m

Proof of Proposition 4.4: We present the proof for the case when the fashion retailer orders at Time 1 (i.e. with the efficient consumer response program), the case when the fashion retailer orders at Time 0 is similar and we skip it for the sake of brevity. In the presence of the markdown contract, the optimalstocking quantity for the fashion  +τ −c  retailer at Time 1 is given below: q∗R,1 (µ1 , λ) = µ1 + d1 + δ−1 rr − . The fashion  v−λ   ∗ retail supply chain system’s optimal quantity is qSC,1 (µ1 ) = µ1 + d1 + δ−1 r +r τ−−v m . To achieve supply chain coordination, we need to find a value of λ which makes q∗R,1 (µ1 , λ) equal to q∗SC,1 (µ1 ): q∗R,1 (µ1 , λ) = q∗SC,1 (µ1 ) ⇔ µ1 +

4



d1 + δ

−1



 r+τ−c −1 r + τ − m = µ1 + d1 + δ r−v−λ r−v

The fashion retail supply chain system includes the fashion retailer, the manufacturer, and also the consumer (and hence the optimization objective includes consumer welfare).

Efficient consumer response in fashion retail supply chain systems 61

⇔

r+τ−c r+τ−m = r−v−λ r−v

⇔ (r + τ − c)(r − v) = (r − v − λ)(r + τ − m) ⇔λ=

(r − v)(c − m) . (Q.E.D.) r+τ−m

Proposition 4.4 illustrates the use of the markdown contract to coordinate the fashion retail supply chain system. It is important to observe that when the consumer welfare coefficient increases, the markdown sponsor λ decreases. As a remark, there are other related studies which propose different measures to enhance the supply chain performance under a program similar to the efficient consumer response program. For instance, Iyer and Bergen (1997) propose service level commitment, volume commitment and wholesale pricing commitment schemes to achieve Pareto improvement in the supply chain under quick response. Eppen and Iyer (1997a) propose a fashion buying enhancement scheme with information updating. Eppen and Iyer (1997b) study the backup agreement in the fashion supply chain with information updating. Their proposed backup agreement is a kind of incentive alignment scheme which helps coordinate the supply chain.

4.6

NUMERICAL ANALYSIS

To better illustrate the theoretical results, we conduct a numerical analysis in this section. First, we set the parameters as follows: r = 100, c = 80, m = 30, v = 20, τ = 10, µ0 = 150, d0 = 2000, δ = 500. The results are depicted in Figure 4.1 to Figures 4.8. A summary of the trends is shown in Table 4.1 (all the detailed numerical values are included in the Appendix).

Figure 4.1 Impacts brought about by r.

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Fashion Retail Supply Chain Management

Figure 4.2 Impacts brought about by c.

Figure 4.3 Impacts brought about by m.

Figure 4.4 Impacts brought about by v.

Efficient consumer response in fashion retail supply chain systems 63

Figure 4.5 Impacts brought about by τ.

Figure 4.6 Impacts brought about by δ.

Figure 4.7 Impacts brought about by d0 .

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Figure 4.8 Impacts brought about by µ0 . Table 4.1 Sensitivity results on the benefits of efficient consumer response for the fashion retailer, the manufacturer, and the fashion retail supply chain system (↑ = increases; ↓ = decreases; − = no change). Parameters r↑ c↑ m↑ v↑ τ↑ δ↑ d0 ↑ µ0 ↑

EBR,ECR

EBM,ECR

EBSC,ECR

↑ ↓ – ↓ ↑ ↓ ↑ –

↓ ↑ ↓ ↓ ↓ ↓ ↑ –

↓ ↑ ↓ ↓ ↓ ↓ ↑ –

From Table 4.1, we can see that the efficient consumer response system is especially significant to the fashion retail supply chain system when the wholesale price c increases, the reducible demand uncertainty d0 increases, the retail selling price r decreases, the production cost m decreases, the market clearance price v decreases, the coefficient of consumer welfare τ decreases, and δ decreases. It is also interesting to note that the mean of demand at Time 0 µ0 (the prior mean) does not affect the significance of the benefits brought by the efficient consumer response system to the fashion retailer, the manufacturer, and the whole fashion retail supply chain system. 4.7

CONCLUSION

We have explored the impacts brought by the efficient consumer response system to a fashion retail supply chain. Following the literature, we have employed the Bayesian

Efficient consumer response in fashion retail supply chain systems 65

information updating model (under the normal conjugate pair scheme) and constructed a formal analytical model to conduct analysis. Different from the previous related studies, we include consumer welfare in the analysis. Similarly to other previous studies, we have found that the efficient consumer response program is beneficial to the fashion retailer. However, it is interesting to reveal that the consumer welfare coefficient plays a critical role in determining whether the efficient consumer response program is harmful or beneficial to consumer welfare and the manufacturer. To be specific, we have revealed that if the consumer welfare coefficient is sufficiently big, the efficient consumer response program brings harm to the manufacturer and also reduces consumer welfare. On the contrary, if the consumer welfare coefficient is sufficiently small, the efficient consumer response program brings benefits to the manufacturer and improves consumer welfare, which directly implies that an all-win situation can be created, i.e., the fashion retailer, the manufacturer, the consumer and the whole fashion retail supply chain will all be benefited by the efficient consumer response program. Furthermore, we have investigated the fashion retail supply chain coordination challenge and proven analytically that the markdown contract can be set to achieve coordination. Finally, we have looked into the impacts brought about by different model parameters via conducting numerical sensitivity analysis.

REFERENCES Azoury, K.S. (1985) Bayes Solution to dynamic inventory models under unknown demand distribution. Management Science, 31, 1150–1160. Azoury, K.S. & Miller, B.L. (1984) A comparison of the optimal ordering levels of Bayesian and non-Bayesian inventory models. Management Science, 30, 993–1003. Choi, T.M. (2007) Pre-season stocking and pricing decisions for fashion retailers with multiple information updating, International Journal of Production Economics, 106, 146–170. Choi, T.M. & Chow, P.S. (2008) Mean variance analysis of the quick response program. International Journal of Production Economics, 114, 456–475. Choi, T.M., Li, D. & H. Yan, (2003) Optimal two-stage ordering policy with Bayesian information updating. Journal of the Operational Research Society, 54, 846–859. Choi, T.M., Li, D. & Yan, H. (2004) Optimal single ordering policy with multiple delivery modes and Bayesian information updates. Computers and Operations Research, 31, 1965–1984. Choi, T.M., Li, D. & Yan, H. (2006) Quick response policy with Bayesian information updates. European Journal of Operational Research, 170, 788–808. Chow, P.S., Choi, T.M. & Cheng, T.C.E. (2012) Impacts of minimum order quantity on a quick response supply chain. IEEE Transactions on Systems, Man, and Cybernetics – Part A, 42, 868–879. Donohue, K.L. (2000) Efficient supply contract for fashion goods with forecast updating and two production modes. Management Science, 46, 1397–1411. Eppen, G.D. & Iyer, A.V. (1997) Improved fashion buying with Bayesian updates. Operations Research, 45, 805–819. Eppen, G.D. & Iyer, A.V. (1997) Backup agreements in fashion buying – the value of upstream flexibility. Management Science, 43, 1469–1484. Fisher, M., Hammond, J.H., Obermeyer, W.R. & Raman, A. (1994) Making supply meet demand in an uncertain world. Harvard Business Review, May–June, 83–93. Fisher, M. & Raman, A. (1996) Reducing the cost of demand uncertainty through accurate response to early sales. Operations Research, 44, 87–99.

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Fisher, M., Rajaram, K. & Raman, A. (2001) Optimizing inventory replenishment of retail fashion products. Manufacturing and Service Operations Management, 3, 230–241. Hoffman, J.M. & Mehra, S. (2000) Efficient consumer response as a supply chain strategy for grocery businesses. International Journal of Service Industry Management, 11, 365–373. Iyer, A.V. & Bergen, M.E. (1997) Quick response in manufacturer-retailer channels. Management Science, 43, 559–570. Kim, H.S. (2003) A Bayesian analysis on the effect of multiple supply options in a quick response environment. Naval Research Logistics, 50, 1–16. Kurnia, S. & Johnson, R.B. (2001) Adoption of efficient consumer response: The issue of mutuality. Supply Chain Management: An International Journal, 6, 230–241. Kurt Salmon Associates (1993) Efficient Consumer Response: Enhancing Consumer Value in the Grocery Industry. Food Marketing Institute, Washington, DC. Shen, B., Choi, T.M., Wang, Y. & Lo, C.K.Y. (2013) The coordination of fashion supply chains with a risk averse supplier by the markdown money policy. IEEE Transactions on Systems, Man, and Cybernetics – Systems, 43(2), 266–276. Svensson, G. (2002) Efficient consumer response – its origin and evolution in the history of marketing. Management Decision, 40, 508–519.

APPENDIX: TABLES OF THE NUMERICAL ANALYSIS Table 4.2 Impacts brought about by r. r

EBR,ECR

EBM,ECR

EBSC,ECR

90 92 94 96 98 100 102 104 106 108 110

476 505 532 558 583 607 629 651 672 692 711

566 508 456 407 361 319 279 241 205 172 140

1042 1013 988 965 944 925 908 892 877 863 851

Table 4.3 Impacts brought about by c. c

EBR,ECR

EBM,ECR

EBSC,ECR

70 72 74 76 78 80 82 84 86 88 90

638 637 633 627 618 607 593 576 556 534 508

0 53 111 174 243 319 401 490 587 693 809

638 690 744 801 861 925 993 1066 1144 1227 1318

Efficient consumer response in fashion retail supply chain systems 67 Table 4.4 Impacts brought about by m. m

EBR,ECR

EBM,ECR

EBSC,ECR

25 26 27 28 29 30 31 32 33 34 35

607 607 607 607 607 607 607 607 607 607 607

351 344 338 331 325 319 312 306 300 293 287

957 951 944 938 932 925 919 912 906 900 893

Table 4.5 Impacts brought about by v. v

EBR,ECR

EBM,ECR

EBSC,ECR

17.5 18 18.5 19 19.5 20 20.5 21 21.5 22 22.5

619 617 614 612 609 607 604 601 599 596 593

349 343 337 331 325 319 312 306 300 293 287

968 960 951 943 934 925 916 907 898 889 880

Table 4.6 Impacts brought about by τ. τ

EBR,ECR

EBM,ECR

EBSC,ECR

7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5

589 593 596 600 603 607 610 613 615 618 620

402 385 369 352 335 319 302 286 270 253 237

991 978 965 952 938 925 912 898 885 871 858

68

Fashion Retail Supply Chain Management Table 4.7 Impacts brought about by δ. δ

EBR,ECR

EBM,ECR

EBSC,ECR

400 420 440 460 480 500 520 540 560 580 600

628 624 619 615 611 607 603 599 595 591 587

330 328 325 323 321 319 317 314 312 310 309

958 951 944 938 931 925 919 913 907 902 896

Table 4.8 Impacts brought about by d 0 . d0

EBR,ECR

EBM,ECR

EBSC,ECR

1000 1200 1400 1600 1800 2000 2200 2400 2600 2800 3000

265 341 412 480 545 607 666 723 779 832 884

139 179 216 252 286 319 350 380 409 437 465

404 520 629 732 831 925 1016 1103 1188 1270 1349

Table 4.9 Impacts brought about by µ0 . µ0

EBR,ECR

EBM,ECR

EBSC,ECR

125 130 135 140 145 150 155 160 165 170 175

607 607 607 607 607 607 607 607 607 607 607

319 319 319 319 319 319 319 319 319 319 319

925 925 925 925 925 925 925 925 925 925 925

Chapter 5

New product selection in fashion retail supply chains

SUMMARY In this chapter, we study the new product selection problem in two-echelon single manufacturer single retailer fashion retail supply chains. We consider the situation when the fashion retailer is planning to offer a new product from a set of available product options. Since demand relates to the future market situation, we assume that there exist two states of the world for each new product candidate: One denoting the case when the market demand is expectedly high and one representing the case when the market demand is expectedly low. The probabilities for the occurrence of high and low market demands can be estimated. The fashion retailer needs to decide which new product to launch with a consideration of its own expected profit. By examining this problem under two different scenarios, we analytically show that under both scenarios the fashion retailer should select the new product which has the highest expected mean demand. Interestingly, this specific optimal new product will also be the best one for the manufacturer and the whole fashion retail supply chain system. Numerical sensitivity analysis is presented and reveals that the whole fashion retail supply chain system will enjoy a higher expected profit if the retail selling price increases, the market clearance sale price increases, the wholesale price decreases, and the product’s manufacturing cost decreases. Keywords New product development, fashion product extension, market states.

5.1

INTRODUCTION

In fashion retailing, offering new products is a commonly seen strategy (Caniato et al. 2014). These new products can be newly designed products, or a totally new category of products1 . Observe that Bossini, a fashion retailer in Hong Kong, launches many trendy cartoon tees during the summer season or the winter season. For example, in the winter of 2012, Bossini launched the Angry Birds series of cartoon tees with 1

See Soldani et al. (2013) for a recent discussion of the new product development process in the fashion industry.

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great success. A natural question hence arises: Since there are so many cartoon characters available in the market, why did Bossini select Angry Birds but not the others? What is the critical factor to determine the optimal new product choice? Motivated by this observed industrial issue and the importance of new product inventory management (Billington et al. 1998; Caniato et al. 2014) in fashion retailing, we conduct an analytical study in this chapter. First of all, we call these products “new products’’ because they are new to the fashion retailer and hence there is insufficient information to give a very precise prediction of the demand (Thomassey 2014). In fact, Kahn (2002) finds that there is no particularly accurate method for retailers to conduct demand forecasting of new products. In the operations management literature with new product related inventory management, Wanke (2008) proposes the use of the uniform distribution to help build inventory models because of the lack of information for totally new products. Shen et al. (2013) examine how the company prices products during the introduction of new products. They consider the company is capacity constrained. They integrate pricing, inventory and capacity decisions together and solve the problem by an optimal control approach. They find that the price adjustment capability is much more important than other operational strategies such as holding more inventory. Furthermore, they find that simple policies can work very well. Most recently, Ke et al. (2013) study how inventory cost affects the timing of introducing new products under the line extension marketing strategy. In this chapter, similar to the works reviewed above, we study inventory management for new products. However, we take a totally different perspective and focal point. To be specific, we consider the two separate scenarios listed below. Scenario One refers to the case in which we assume that the new products’ demands follow two market states which depend on, e.g., the economic situation. Under the “high’’ market state (the economy or the stock market is good during the selling season), the demands will distribute according to the “high’’ demand distribution. Under the “low’’ market state (the economy or the stock market is bad during the selling season), the demands will distribute following the “low’’ demand distribution. Whether the high or low market state will appear is unknown to the fashion retailer at the time of deciding which product to select even though the fashion retailer does know the chance of the occurrence of each market state (e.g., from financial forecast). Moreover, when it is just a month or so before the selling season starts, the fashion retailer will know whether the market state is high or low and can make the final optimal ordering decision accordingly. Scenario Two represents the case when each new product is associated with a specific event which influences consumer utility and preference towards the product, hence affecting its demand. For example, in the Bossini case mentioned above, suppose that Bossini is weighing up a few cartoon characters to be used as its new products for the coming summer season. The options include Toy Story, Monster Factory, etc which are all associated with movies showing soon. At the time of making the choice of which cartoon character to be selected as the theme of the new product, Bossini does not know precisely whether the popularity of the movies (and hence the associated demand for each cartoon character) will be high or low, but it does have an estimate on the chance associated with the success of each movie. Interestingly, many of these movies will first be shown in the US, and then in Hong Kong a month or so later. Thus,

New product selection in fashion retail supply chains

71

if the movies receive a good response in the US, it indicates that the demand belongs to the high distribution. If the movies receive a bad response in the US, it indicates that the demand belongs to the low distribution. Thus, the fashion retailer can decide the final optimal ordering quantity for the selected fashion product at that time after learning about the related movie’s popularity in the US accordingly. In the following, we first present the basic model in Section 5.2. After that, we conduct analysis for each scenario respectively in Sections 5.3 and 5.4. We discuss the numerical sensitivity analysis and the respective findings in Section 5.5. We conclude with a discussion of future research in Section 5.6.

5.2

BASIC MODEL

We consider the situation when there are n new product candidates available for the fashion retailer to select. These n products all belong to the same product category (e.g., all are cartoon tees) but differ significantly and are mutually exclusive (e.g., each represents a distinct series of cartoon characters). Thus, they have very similar cost and revenue parameters. Following the newsvendor model (Choi et al. 2008; Choi 2012), each of these n new products is sold in the market at a unit retail selling price of r. The fashion retailer gets the product with a unit wholesale price w. The unit market clearance sale price for clearing and “salvaging’’ leftover by the end of the season is represented by v. The unit manufacturing cost of the product by the manufacturer is c. For each new product i, there are two possible demand distributions, which refer to the high and low distribution cases, respectively: Di,H ∼ N(µi,H , σ 2 ), Di,L ∼ N(µi,L , σ 2 ),

where N(·) is a normal distribution, µi,H and µi,L are the means with µi,H > µi,L , and the standard deviation is σ. Notice that we assume the demand variance to be the same because for new products, there is no way to estimate demand variation precisely. What the fashion retailer will do is strive hard to get the best estimate it can from experience. Moreover, this variance reflects the market uncertainty regarding the demand and arguably would be the same no matter whether the average demand is high or low. At the point in time when the fashion retailer needs to decide which new product to select, it is unknown whether the demand distribution for product i is Di,H or Di,L . The fashion retailer does have an estimate of the probability of occurrence of Di,H and Di,L . To be specific, the fashion retailer knows that Di,H will appear with a chance of ρi , and Di,L with a chance of 1 − ρi . The sequence of decisions is given as follows: The fashion retailer first knows the two market states and the chances of the occurrence of each demand state. At that time, the fashion retailer needs to decide which new product to select among the set of n new products. We call this Stage 1. After that, when time passes to a point in time closer to the selling season for the new product, the fashion retailer observes and learns about the realization of the market state, i.e., whether Di,H or Di,L is the real demand distribution. We call this Stage 2. At Stage 2, the fashion retailer confirms with the manufacturer the

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exact ordering quantity (thus, the efficient consumer response scheme, as we discussed in Chapter 4 is implemented in the supply chain). After receiving the confirmed order, the manufacturer reacts to produce and ship the finished product on time before the season starts. With the above model, we know that the fashion retailer’s optimal ordering quantities (which maximize the respective expected profits) for the two possible market states, qi,H ∗ and qi,L∗ , are given below (it follows the standard newsvendor problem as discussed in Chapter 3):   For the high demand case Di,H : qi,H ∗ = µi,H + σ−1 rr−−wv .   For the low demand case Di,L : qi,L∗ = µi,L + σ−1 rr−−wv .

Followingmost of the observed real world situations, we consider in this chapter the case when rr−−wv , which represents the inventory service level, is always larger than 0.5. Thus, we have the following:

r−w > 0. −1 r−v

With qi,H ∗ and qi,L∗ , we can derive the expected optimal profit function for the fashion retailer with respect to the launch of the new product i (see Chapter 4 for some related details) in the following: 

 −1 r − w ∗ EPi,R = ρi (r − w)µi,H − (r − v)σφ  r−v

  r−w + (1 − ρi ) (r − w)µi,L − (r − v)σφ −1 r−v

r−w . (5.1) = (r − w)(ρi µi,H + (1 − ρi )µi,L ) − (r − v)σφ(−1 r−v The corresponding expected profit for the manufacturer is: EPi,M∗ = (w − c)[ρi qi,H ∗ + (1 − ρi )qi,L∗ ]



r−w r−w = (w − c) ρi µi,H + σ−1 + (1 − ρi ) µi,L + σ−1 . r−v r−v

(5.2)

Define: EPi,SC∗ = EPi,R∗ + EPi,M∗ . Lemma 5.1 summarizes some structural properties of EPi,R∗ , EPi,M∗ and EPi,SC∗ . Lemma 5.1. EPi,R∗ , EPi,M∗ and EPi,SC∗ are all increasing functions of ρi , µi,H and µi,L . Lemma 5.1 shows that for any new product i, the respective optimal expected profits of the fashion retailer, the manufacturer and the fashion retail supply chain

New product selection in fashion retail supply chains

73

are all increasing with the chance of having a high market demand state ρi , as well as the mean of demand for product i under each probable market demand distribution. Both results are very intuitive. Lemma 5.2 below shows the impacts brought about by inherent demand volatility of the demand distribution under each market state. Lemma 5.2. When σ increases: (a) EPi,R∗ and EPi,SC∗ decrease, and (b) EPi,M∗ increases. Lemma 5.2 shows the impacts brought by the inherent demand volatility σ to the expected profits of the fashion retailer, the whole fashion retail supply chain system, and the manufacturer. To be specific, a larger inherent demand volatility is harmful to the fashion retailer because the fashion retailer has to order more inventory in order to match supply and demand with a goal of maximizing its expected profit. However, this higher inherent demand volatility is indeed good for the manufacturer because the fashion retailer will order more quantity. For the whole fashion retail supply chain, unfortunately, an increased inherent demand volatility is harmful. As a result, from the whole supply chain system perspective, reducing this inherent demand volatility is important and helpful. 5.3 ANALYSIS: SCENARIO ONE Under Scenario One, we consider the case that the new products’ demands follow two market states which depend on a common external factor such as the economic situation. Since the chance for the economic situation to be high or low is independent of the specific new product i, we set ρ1 = ρ2 = · · · = ρn = ρ. For notational purposes, we add the subscript (1) to represent Scenario One. Define: µ ¯ i,(1) = ρµi,H + (1 − ρ)µi,L , which represents the expected mean of demand at Stage 1 under Scenario One. Under this scenario, we can rewrite (5.1) and (5.2) as follows:

r−w EPi,R,(1)∗ = (r − w)(ρµi,H + (1 − ρ)µi,L ) − (r − v)σφ −1 r−v

r−w . (5.3) = (r − w)µ ¯ i,(1) − (r − v)σφ −1 r−v



−1 r − w −1 r − w EPi,M,(1)∗ = (w − c) ρ µi,H + σ + (1 − ρ) µi,L + σ r−v r−v

r − w = (w − c) µ ¯ i,(1) + σ−1 . (5.4) r−v Define: EPi,SC,(1)∗ = EPi,R,(1)∗ + EPi,M,(1)∗ . With (5.3), (5.4) and (5.5), we have Proposition 5.1.

(5.5)

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Proposition 5.1. Under Scenario One: The new product i with the largest µ ¯ i,(1) leads to the largest EPi,R,(1)∗ , EPi,M,(1)∗ and EPi,SC,(1)∗ for all i = 1, 2, . . ., n. ¯ i,(1) − Proof of Proposition  5.1: From (5.4), we observe that EP  i,R,(1)  ∗ = (r  − w)µ  (r − v)σφ −1 rr−−wv . Since, (r − w) is positive and (r − v)σφ −1 rr−−wv is the same for all new products under consideration, a larger µ ¯ i,(1) implies  a larger EPi,R,(1)∗ . From (5.4), we notice that EPi,M,(1)∗ = (w − c) µ ¯ i,(1) + σ−1 rr−−wv . Thus, with the similar argument for the case of EPi,R,(1)∗ , the new product i with the highest expected mean demand µ ¯ i,(1) also gives the largest EPi,M,(1)∗ . Since the supply chain system’s expected profit EPi,SC,(1)∗ is equal to EPi,R,(1)∗ + EPi,M,(1)∗ , the new product i with the largest µ ¯ i,(1) yields the largest EPi,SC,(1)∗ . (Q.E.D.) Proposition 5.1 shows a very neat analytical result for the optimal new product selection problem under Scenario One. To be specific, the new product which has the highest expected mean of demand is the optimal choice. Interestingly, this optimal choice is not only the best for the fashion retailer, but actually also the best for the fashion retail supply chain system and the manufacturer. This is a very appealing result in which all supply chain members will favour the same decision, and the supply chain system is automatically “coordinated’’ in terms of this new product selection decision.

5.4 ANALYSIS: SCENARIO TWO Under Scenario Two, we consider the situation when each product has the same high and low demand distribution parameters. However, the chance of occurrence of each demand distribution (i.e. ρi ) is different among the n new product candidates. We use the subscript (2) to represent the mathematical notations under Scenario Two. To be specific, we have the following: For the high demand case: D1,H = D2,H = · · · = Dn,H = DH,(2) , where µ1,H = µ1,H = · · · = µn,H = µH . For the low demand case: D1,L = D2,L = · · · = Dn,L = DL,(2) , where µ2,L = µ2,L = · · · = µn,L = µL . Define: µ ¯ i,(2) = ρi µH + (1 − ρi )µL , which represents the expected mean of demand at Stage 1 under Scenario Two. Under this scenario, we can rewrite (5.1) and (5.2) in the following way: EPi,R,(2)∗

−1 r − w = (r − w)(ρi µH + (1 − ρi )µL ) − (r − v)σφ  r−v

r − w = (r − w)µ ¯ i,(2) − (r − v)σφ −1 . r−v

(5.6)

New product selection in fashion retail supply chains

75

Similar to Scenario One, the corresponding expected profit for the manufacturer is:

EP

i,M,(2)∗



−1 r − w −1 r − w + (1 − ρi ) µL + σ = (w − c) ρi µH + σ r−v r−v

r−w = (w − c) µ ¯ i,(2) + σ−1 . (5.7) r−v

Define: EPi,SC,(2)∗ = EPi,R,(2)∗ + EPi,M,(2)∗ .

(5.8)

With the proof similar to Proposition 5.1, we can derive a similar proposition under Scenario Two. Proposition 5.2. Under Scenario Two: (a) The new product i with the largest µ ¯ i,(2) leads to the largest EPi,R,(2)∗ , EPi,M,(2)∗ and EPi,SC,(2)∗ for all i = 1, 2, . . ., n. (b) The new product i with the largest ρi leads to the largest EPi,R,(2)∗ , EPi,M,(2)∗ and EPi,SC,(2)∗ for all i = 1, 2, . . ., n. Proof of Proposition 5.2: (a) Similar to the proof of Proposition 5.1. (b) By direct observation of the problem nature, we find that: If ρi is the largest among all ρi ∀i = 1, 2, . . ., n, then µ ¯ i,(2) will also be the largest among all µ ¯ i,(2) . This, together with Proposition 5.2a, proves Proposition 5.2b. (Q.E.D.) Even though Scenario Two is different from Scenario One, Proposition 5.2a indicates that a very similar conclusion can be drawn in which the new product which has the highest expected mean of demand is the optimal choice under Scenario Two. Furthermore, as with the findings under Scenario One, this optimal choice under Scenario Two is optimal for the fashion retailer, the manufacturer and the whole fashion retail supply chain system. Most interestingly, under Scenario Two, essentially, we only need to check the chance of having the high market demand distribution ρi . The product i with the largest ρi will be the optimal choice. We summarize the findings of Proposition 5.1 and Proposition 5.2a in Theorem 5.1. Theorem 5.1. Under both Scenarios One and Two: (a) The fashion retailer should select the new product i which has the highest expected mean demand (at Stage 1), for all i = 1, 2, . . ., n. (b) This optimal new product i will also be the best one for the manufacturer and the whole fashion retail supply chain system. Note that although the finding in Theorem 5.1a is intuitive, we should not take for granted that the proposed rule based on the expected mean demand must work for the new product selection problem. In fact, if the variances of the demand distributions are not the same, and µi,H , µi,L and ρi do not follow Scenarios One and Two, the findings in Theorem 5.1a may not hold.

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5.5

Fashion Retail Supply Chain Management

NUMERICAL ANALYSIS

5.5.1 Scenario one To start the numerical analysis, we have the following basic parameters: r = 100, w = 50, c = 30, v = 15, σ = 20, ρ = 0.6. The values of µi,H and µi,L are shown in Table 5.1. It is straightforward to find µ ¯ i,(1) for the products and they are shown in Table 5.2. From Table 5.2, since product 3 has the largest expected mean demand µ ¯ i,(1) , it is the optimal choice for the fashion retailer to develop as the new product for the upcoming selling season. Next, we examine how the expected profits (associated with product 3) of the fashion retailer, the manufacturer, and the fashion retail supply chain vary when each of the various important model parameters varies. Figures 5.1 to Figure 5.5 show the results. Table 5.1 Values of µi,H and µi,L under Scenario One. Product

µi,H µi,L

1

2

3

100 60

90 70

110 50

1

2

3

84

82

86

Table 5.2 Values of µ ¯ i,(1) . Product

µ ¯ i,(1)

Figure 5.1 Values of EPi,R,(1)∗ , EPi,M,(1)∗ , EPi,SC,(1)∗ versus σ under Scenario One.

New product selection in fashion retail supply chains

77

Figure 5.2 Values of EPi,R,(1)∗ , EPi,M,(1)∗ , EPi,SC,(1)∗ versus r under Scenario One.

Figure 5.3 Values of EPi,R,(1)∗ , EPi,M,(1)∗ , EPi,SC,(1)∗ versus w under Scenario One.

From Figures 5.1 to Figure 5.5, we can observe some clear patterns for the changes. We show these trends in Table 5.3. From Table 5.5 (in Appendix) and Table 5.3, we can see that Lemma 5.2 holds in which EPi,M,(1)∗ is increasing in σ whereas EPi,R,(1)∗ , and EPi,SC,(1)∗ are decreasing in it (note that the inventory service level associated with the ordering decision is larger than 0.5). Then from Table 5.3, it is interesting to note that the whole fashion retail supply chain system will be benefited if (i) the retail selling price r increases, (ii) the market clearance sale price v increases, (iii) the wholesale price w decreases, and (iv) the product’s manufacturing cost c decreases. Furthermore, we can see that except for

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Figure 5.4 Values of EPi,R,(1)∗ , EPi,M,(1)∗ , EPi,SC,(1)∗ versus c under Scenario One.

Figure 5.5 Values of EPi,R,(1)∗ , EPi,M,(1)∗ , EPi,SC,(1)∗ versus v under Scenario One. Table 5.3 Sensitivity results (note that for all cases, the inventory service level > 0.5) (↑ = increases; ↓ = decreases; − = no change). σ↑ r↑ w↑ c↑ v↑

EPi,R,(1)∗

EPi,M,(1)∗

EPi,SC,(1)∗

↓ ↑ ↓ – ↑

↑ ↑ ↑ ↓ ↑

↓ ↑ ↓ ↓ ↑

New product selection in fashion retail supply chains

79

Table 5.4 Values of ρi . Product

ρi

4

5

6

0.5

0.6

0.7

the impact brought about by the product’s manufacturing cost, for all the other parameters under study (and shown in Table 5.3), the fashion retailer and the fashion retail supply chain system will be benefited or hurt by the change of the other parameters in the same manner (e.g., increasing the retail selling price benefits the fashion retail supply chain system and the fashion retailer together). However, it is not necessary the case for the manufacturer and the fashion retail supply chain. For instance, an increase of the wholesale price w benefits the manufacturer but hurts the fashion retail supply chain.

5.5.2 Scenario two Similar to Scenario One, we have the following basic set of parameters for Scenario Two: r = 100, w = 50, c = 30, v = 15, σ = 20, µH = 100, and µL = 70. The values of ρi are shown in Table 5.6 (in Appendix). From Table 5.9, according to Proposition 5.2b, product 6 has the largest ρi and hence it is the optimal choice for the fashion retailer to develop as the new product. For the sensitivity analysis, since the results show the same trends as the ones reported under Scenario One, we do not repeat them here. 5.6

CONCLUSION

In this chapter, we have explored the new product selection problem in a two-echelon single manufacturer single retailer fashion retail supply chain system under two different scenarios. We have considered the situation in which the fashion retailer plans to offer a new product from a set of given options. We have argued that since the market demand relates to the future market situation, the fashion retailer will not have sufficient information regarding it at the time of selecting the new product. As a consequence, we have assumed that there exist two distinct states of the world for each new product candidate: One denoting the case when the market demand is expectedly high and one representing the case when the market demand is expectedly low. The probabilities for the occurrence of high and low market demands can be estimated by the fashion retailer. With this piece of information, the fashion retailer needs to decide which new product, among the given options, to launch with a goal of optimizing its own expected profit. We have investigated this problem under two different scenarios. In Scenario One, the new products’ demands follow two market states which depend on a common external factor (such as the financial stock market). Thus, the chance for the financial stock market to be high or low is independent of the specific new product candidate and hence we assume the same probability of occurrence of high or low market

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demand distribution for all new product candidates even though their specific demand distributions have differences in terms of the parameters. In Scenario Two, we consider the situation when each product has exactly the same high and low demand distribution parameters. However, the chance of occurrence of each demand distribution is different for each individual new product candidate. We have analytically proven that the fashion retailer can identify the optimal new product to launch by finding the one with the highest expected mean demand. In addition, under Scenario Two, it suffices to identify the optimal new product choice by simply checking the probabilities of having high demand distribution (ρi ) for the new products, in which the new product with the highest ρi will be the optimal choice. Interestingly, we have revealed that the optimal new product as selected by the fashion retailer will also be the best for the manufacturer and the whole fashion retail supply chain system.Thus, the fashion retail supply chain system is coordinated with respect to the decision on optimal new product selection. Numerical sensitivity analysis has been conducted. We have found that the whole fashion retail supply chain system will be benefited if the retail selling price increases, the market clearance sale price increases, the wholesale price decreases, and the product’s manufacturing cost decreases. As an on-going research, since the new product selection problem involves risk, incorporating risk analysis into the optimization model is an important topic and deserves deeper exploration (Choi 2013). Another future research direction is to extend the model to a more general setting, e.g., with more than two market states and a higher degree of heterogeneity of demand distributions and products. Furthermore, since for many new products the respective demand estimations are hard to make precisely, the consideration of fuzzy demand (Bellman and Zadeh 1970; Xu and Zhai 2008; Ryu and Yucesan 2010) and interval only demand (Lin and Ng 2011) can be an interesting area for further studies.

REFERENCES Bellman, R.E. & Zadeh, L.A. (1970) Decision-making in a fuzzy environment. Management Science, 17, B141–B164. Billington, C., Lee, H.L. & Tang, C.S. (1998) Successful strategies for product rollovers. Sloan Management Review, Spring April 15. Caniato, F., Caridi, M., Moretto, A.Sianesi, A. & Spina, G. (2014) Integrating international fashion retail in to new product development. International Journal of Production Economics, 147, 294–306. Choi, T.M. (ed.)(2012) Handbook of Newsvendor Problems: Models, Extensions and Applications, International Series in Operations Research & Management Science, Vol. 176, Springer. Choi, T.M. (2013) New fashion product selection under a mean-variance framework. Working paper, The Hong Kong Polytechnic University. Choi, T.M., Li, D. & Yan, H. (2008) Mean-variance analysis for the newsvendor problem. IEEE Transactions on Systems, Man, and Cybernetics – Part A, 38, 1169–1180. Kahn, K.B. (2002) An exploratory investigation of new product forecasting practices. Journal of Product Innovation Management, 19, 133–143. Ke, T.T., Shen, Z.J.M. & Li. S. (2013) How inventory cost influences introduction timing of product line extensions. Production and Operations Management, 22(5), 1214–1231.

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Lin, J. & Ng, T.S. (2011) Robust multi-market newsvendor models with interval demand data. European Journal of Operational Research, 212, 361–373. Ryu, K. & Yucesan, E. (2010) A fuzzy newsvendor approach to supply chain coordination. European Journal of Operational Research, 200, 421–438. Shen, W., Duenyas, I. & Kapuscinski, R. (2013) Optimal pricing, production, and inventory for new product diffusionunder supply constraints. Manufacturing and Service Operations Management, dx.doi.org/10.1287/msom.2013.0447. Soldani, E., Rossi, M., Bandinelli, R. & Terzi, S. (2013) New product development process in fashion industry:Empirical investigation within Italian companies.In Product lifecycle management for Society – IFIP Advances in Information and Communication Technology, (Bernard, Rivest, & Duuta (eds.)) Vol. 409, Springer, pp. 481–490. Thomassey, S. (2014) Sales forecasting in apparel and fashion industry: A review. In: Intelligent Fashion Forecasting Systems: Models and Applications (Choi ed.), Springer, pp. 9–27. Wanke, P.F. (2008) The uniform distribution as a first practical approach to new product inventory management. International Journal of Production Economics, 114, 811–819. Xu, R. & Zhai, X. (2008) Optimal models for single-period supply chain problems with fuzzy demand. Information Sciences, 178, 3374–3381.

APPENDIX: TABLES OF THE NUMERICAL ANALYSIS Table 5.5 Values of EPi,R,(1)∗ , EPi,M,(1)∗ , EPi,SC,(1)∗ versus σ under Scenario One. σ

EPi,R,(1)∗

EPi,M,(1)∗

EPi,SC,(1)∗

10 12 14 16 18 20 22 24 26 28 30

3969 3903 3837 3771 3705 3639 3572 3506 3440 3374 3308

1765 1774 1782 1791 1800 1809 1818 1827 1836 1845 1854

5734 5677 5619 5562 5505 5448 5391 5333 5276 5219 5162

Table 5.6 Values of EPi,R,(1)∗ , EPi,M,(1)∗ , EPi,SC,(1)∗ versus r under Scenario One. r

EPi,R,(1)∗

EPi,M,(1)∗

EPi,SC,(1)∗

90 95 100 105 110 115 120 125 130 135 140

2844 3240 3639 4040 4444 4849 5257 5665 6075 6486 6899

1753 1783 1809 1833 1854 1874 1892 1909 1925 1939 1953

4597 5023 5448 5873 6298 6723 7149 7574 8000 8426 8852

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Fashion Retail Supply Chain Management Table 5.7 Values of EPi,R,(1)∗ , EPi,M,(1)∗ , EPi,SC,(1)∗ versus w under Scenario One. w

EPi,R,(1)∗

EPi,M,(1)∗

EPi,SC,(1)∗

40 42 44 46 48 50 52 54 56 58 60

4574 4382 4192 4005 3821 3639 3459 3282 3107 2934 2764

968 1146 1319 1487 1650 1809 1964 2114 2259 2400 2536

5543 5528 5511 5492 5471 5448 5423 5395 5366 5334 5300

Table 5.8 Values of EPi,R,(1)∗ , EPi,M,(1)∗ , EPi,SC,(1)∗ versus c under Scenario One. c

EPi,R,(1)∗

EPi,M,(1)∗

EPi,SC,(1)∗

20 22 24 26 28 30 32 34 36 38 40

3639 3639 3639 3639 3639 3639 3639 3639 3639 3639 3639

2714 2533 2352 2171 1990 1809 1628 1447 1266 1086 905

6352 6171 5991 5810 5629 5448 5267 5086 4905 4724 4543

Table 5.9 Values of EPi,R,(1)∗ , EPi,M,(1)∗ , EPi,SC,(1)∗ versus v under Scenario One. v

EPi,R,(1)∗

EPi,M,(1)∗

EPi,SC,(1)∗

10 11 12 13 14 15 16 17 18 19 20

3589 3599 3608 3618 3628 3639 3649 3660 3671 3682 3693

1776 1782 1789 1795 1802 1809 1816 1824 1831 1839 1847

5365 5381 5397 5414 5430 5448 5466 5484 5502 5521 5541

Chapter 6

Mean-risk analysis for fashion retail supply chain information systems projects

SUMMARY In this chapter, we examine the use of the classic mean-risk models to evaluate the information systems projects for fashion retail supply chain management. To be specific, in light of different sources of uncertainty in the market (e.g., interest rate and consumer demand), the benefits and even costs associated with the implementation and operation of fashion retail supply chain management information systems are highly uncertain. As such, when the fashion retail companies evaluate these information systems projects, they have to consider both the anticipated benefits and risk. We first present two related models for conducting benefit and risk analysis, namely the classical mean-variance model, and the revised mean-semi-variance model. After that, we discuss the safety-first objective model and illustrate its relationship with the meanvariance model. Numerical examples are included to illustrate the applicability and decision making process based on the proposed models. Furthermore, the information systems portfolio related to fashion retail supply chain management is discussed. Keywords Information systems management, project evaluation, benefit and risk analysis, mean-risk analysis, safety-first objective.

6.1

INTRODUCTION

In modern fashion retail supply chain management (FRSCM), computerized information systems play a crucial role. For example, in order to implement a vendor-managed inventory scheme, JC Penney (the retailer) and TAL (the manufacturer) are both equipped with the EDI platform for information sharing. In addition, TAL has its own enterprise resource planning (ERP) system (Hui et al. 2010) to facilitate the respective operations. In Asia, Levi’s implemented a state-of-the-art information system to facilitate its operations in China – Hong Kong (Choi et al. 2013b). In fact, it is commonly believed that the presence of a versatile computerized information system for supply chain management can help reduce lead time, reduce manual errors, improve response time and better utilize information (Yang et al. 2011), which

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ultimately would reduce costs in a lot of areas (e.g., inventory, transportation and communications costs). However, information systems projects are notorious for having over-budgeting and over-run problems. In particular, larger scale information systems such as supply chain management information systems and ERP systems (Bertolini et al. 2004) are known to have a high failure rate and many companies even cannot complete their planned projects because of a shortage of financial resources (Laudon and Laudon 2012). It is especially true in fashion retailing because most fashion companies are relatively small in scale and information systems are big investments (Choi et al. 2013b). To them, information systems projects are highly risky1 . Undoubtedly, a careful planning and project evaluation scheme is essential to help ensure the success of fashion retail supply chain management information systems. In this chapter, we examine the use of the classic mean-risk models, together with the popularly known safety-first objective method, to evaluate the information systems projects for fashion retail supply chain management. Illustrative numerical examples are included to demonstrate how the tools can be applied. The rest of this chapter is organized as follows. Section 6.2 presents the problem formulation. Section 6.3 discusses the mean-variance analysis. Section 6.4 shows the mean-semi-variance analysis. Section 6.5 studies the safety-first objective and highlights its relationship with the mean-variance analysis. Section 6.6 is devoted to the application of the information systems portfolio for prioritizing fashion retail supply chain management information systems projects. Section 6.7 concludes this chapter.

6.2

PROBLEM FORMULATION

Consider the case whereby a fashion retailer plans to start a FRSCM information systems project which will help to enhance its supply chain management operations. In the market, there are n software vendors which can provide the needed FRSCM information systems. These projects are all mutually exclusive. It is assumed that all these n FRSCM information systems software vendors offer the FRSCM information systems projects with the same duration of m years. Each software vendor prepares a spreadsheet showing the future cash flow projection associated with its proposed (i) FRSCM information systems project (yak = the net cash flow of FRSCM information systems project i, the probable net cash flow outcome a, in year k). However, since the future is uncertain, the fashion retailer further estimates the chances associated with different possible scenarios of the cash flows (i.e., outcomes of the net cash flows) associated with each project in the future. We represent the chance by pij which denotes the probability of having outcome j with the FRSCM information systems project provided by software vendor i. To be specific, the fashion retailer can set up Table 6.1 for a FRSCM information systems project as proposed by the software vendor i. For each specific outcome j, under the FRSCM information systems project i, the net cash flows over the m years horizon can be combined and represented by the

1

Supply chain management-related information systems projects are not only risky to smaller scale companies. Even industrial giants like Nike also suffered with the implementation of its demand planning systems years ago.

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85

Table 6.1 The problem specification. Chance

Net cash flow of each year

pi1

Year 1: y11 (i) Year 2: y12 … (i) Year m: y1m

pi2

Year 1: y21 (i) Year 2: y22 … (i) Year m: y2m …

… pin

(i)

(i)

(i)

Year 1: yn1 (i) Year 2: yn2 … (i) Year m: ynm

Table 6.2 The revised problem specification with NPV. Chance

Net present value (NPV) of the net cash flows

pi1 pi2 … pin

xi1 xi2 … xin

net present value (NPV) xij with respect to the market interest rate estimated by the respective case, denoted by Iij . Table 6.2 shows the result. Now, for each FRSCM information systems project i, we have prepared the revised table which appears as Table 6.2. Since there is uncertainty associated with the net present value outcomes and because, in general, some outcomes are negative, the FRSCM information systems projects under study are risky. We hence need an analytical framework to help evaluate the risk and the benefit of these information systems projects.

6.3

MEAN-VARIANCE ANALYSIS

6.3.1 Model details Following the classic Markowitz mean-variance framework (Markowitz 1959), for each risky information systems project under investigation, we calculate two statistics,

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Table 6.3 The mean-variance analysis.

Expected payoff Variance of payoff

Project 1

Project 2

m 

m 

j=1 m  j=1

p1j x1j p1j (x1j − µ1 )2

j=1 m  j=1

p2j x2j p2j (x2j − µ2 )2

… … …

Project n m 

j=1 m  j=1

pnj xnj pnj (xnj − µn )2

namely the expected payoff2 (i.e., the “mean’’) and the variance of payoff (i.e., the “variance’’). Here, under the mean-variance framework, the expected payoff represents the benefit associated with the project whereas the variance of payoff denotes the level of risk. In the problem formulation described in Section 6.2, for each FRSCM information systems project i, the expected payoff and the variance of payoff are given as follows: m  The expected payoff µi = pij xij . j=1 m 

The variance of payoff σi =

j=1

pij (xij − µi )2 .

With all the expected payoffs and the variances of payoff calculated, we can construct Table 6.3. From Table 6.3, any projects with a lower expected payoff and a higher variance of payoff than the other(s) will be called inferior and are being dominated. As a rule, these projects will be eliminated. The remaining projects are called non-inferior and they are the “mean-variance efficient’’ projects. The fashion retailer should select the optimal one among the set of mean-variance efficient projects with respect to its own preference towards the expected payoff and the variance of payoff. Note that the above analysis is well explored in engineering economics. There are also various other scenarios and extensions. See Park and Sharp-Bette (1990) for some more discussions.

6.3.2 Example 1 Suppose that a fashion retailer is given four mutually exclusive FRSCM information systems with the details as shown in Table 6.4 below. Suppose that the interest rate is fixed at 5% for all cash flows and years. We can find the net present value for each outcome. For example, for FRSCM Information Systems Project 1, the first outcome’s net present value is equal to: −10 + 2

5.5 5.5 5.5 5.5 + + + = 9.50. 1 + 5% (1 + 5%)2 (1 + 5%)3 (1 + 5%)4

In this chapter, the expected payoff represents the expected NPV of the cash flows; the variance of payoff means the variance of the NPV of the cash flows. Note that the payoff here can be negative or positive.

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87

Table 6.4 Example 1: The problem specification (The cash flows are measured in millions of dollars).

Chance 0.7

0.3

FRSCM Information Systems Project 1

FRSCM Information Systems Project 2

FRSCM Information Systems Project 3

FRSCM Information Systems Project 4

Year 0: −10 Year 1: 5.5 Year 2: 5.5 Year 3: 5.5 Year 4: 5.5 Year 0: −10 Year 1: −4 Year 2: −4 Year 3: 4 Year 4: 4

Year 0: −10 Year 1: 5 Year 2: 5 Year 3: 5 Year 4: 5 Year 0: −10 Year 1: −9 Year 2: 6 Year 3: 6 Year 4: 6

Year 0: −10 Year 1: 3 Year 2: 3 Year 3: 3 Year 4: 3 Year 0: −10 Year 1: −9 Year 2: 7 Year 3: 7 Year 4: 7

Year 0: −10 Year 1: 5.5 Year 2: 4 Year 3: 5.5 Year 4: 4 Year 0: −10 Year 1: −9 Year 2: 5.5 Year 3: 6 Year 4: 5.5

Table 6.5 Example 1: The net present value of the outcomes (The cash flows are measured in millions of dollars).

Chance 0.7 0.3

NPV of FRSCM Information Systems Project 1

NPV of FRSCM Information Systems Project 2

NPV of FRSCM Information Systems Project 3

NPV of FRSCM Information Systems Project 4

9.50 −10.69

7.73 −3.01

0.64 −0.42

6.91 −3.87

Table 6.6 Example 1: The expected payoff and variance of payoff. FRSCM Information Systems Project 1 Expected payoff (in millions of dollars) Variance of payoff (in millions of dollars squared)

FRSCM Information Systems Project 2

FRSCM Information Systems Project 3

FRSCM Information Systems Project 4

3.44

4.51

0.32

3.67

85.64

24.22

0.23

24.42

Similarly, we can run this equation for all the others, resulting in Table 6.5. With Table 6.5, we can calculate the expected payoff and the variance of payoff of each FRSCM information systems project and establish Table 6.6. From Table 6.6, we derive the following analysis: 1

Comparing FRSCM Information Systems Projects 1, 2, and 4, we can see that FRSCM Information Systems Projects 1 and 4 are inferior to and dominated by FRSCM Information Systems Project 2 because in terms of the expected payoff,

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2

3

Fashion Retail Supply Chain Management

theirs are smaller; in terms of variance of payoff, which represents risk under the mean-variance model, theirs are bigger. FRSCM Information Systems Projects 2 and 3 are the mean-variance efficient projects as they are non-inferior. To be specific, even though FRSCM Information Systems Project 2 has a much larger expected payoff, the corresponding variance of payoff is also larger. Thus, the fashion retailer should choose between FRSCM Information Systems Projects 2 and 3. Suppose that the fashion retailer has an expected payoff target in terms of net present value of 2 million dollars. Then, since between FRSCM Information Systems Projects 2 and 3, only FRSCM Information Systems Project 2 satisfies this constraint, the fashion retailer’s optimal choice is FRSCM Information Systems Project 2.

6.3.3 Example 2 Now, we consider another situation in which a fashion retailer is given two mutually exclusive FRSCM information systems projects with the details as shown in Table 6.7 below. Similarly to Example 1, suppose that the interest rate is 5%. We can find the net present value for each of the four outcomes under each project and develop Table 6.8. Following Table 6.8, we proceed to calculate the expected payoff and the variance of payoff of each FRSCM information systems project and arrive at Table 6.9.

Table 6.7 Example 2: The problem specification (The cash flows are measured in millions of dollars). Chance

FRSCM Information Systems Project A

FRSCM Information Systems Project B

0.3

Year 0: −10 Year 1: 7 Year 2: 7 Year 3: 7 Year 4: 7 Year 0: −10 Year 1: 6 Year 2: 6 Year 3: 6 Year 4: 6 Year 0: −10 Year 1: 5 Year 2: 5 Year 3: 5 Year 4: 5 Year 0: −10 Year 1: 4 Year 2: 4 Year 3: 4 Year 4: 4

Year 0: −10 Year 1: 4 Year 2: 4 Year 3: 4 Year 4: 4 Year 0: −10 Year 1: 4 Year 2: 4 Year 3: 3 Year 4: 4 Year 0: −10 Year 1: 4 Year 2: 4 Year 3: 3 Year 4: 4 Year 0: −10 Year 1: −4 Year 2: 4 Year 3: 3 Year 4: 4

0.3

0.2

0.2

Mean-risk analysis for fashion retail supply chain information systems projects

89

From Table 6.9, following the mean-variance decision making framework, FRSCM Information Systems Projects A and B are non-inferior. However, is this really true? Suppose that any negative outcome is judged as unfavourable and any positive outcome is favourable. Then from Tables 6.7 and 6.8, it is obvious that in both the individual cash flows and the net present value perspectives, FRSCM Information Systems Project A should dominate FRSCM Information Systems Project B in all cases! However, by the mean-variance analysis, the recommendation is different. So, is our intuition wrong or does the mean-variance framework have bugs? In fact, our intuition is correct: FRSCM Information Systems Project A should really dominate FRSCM Information Systems Project B. The problem comes from a needed condition for the mean-variance model to work properly: The cash flows must be symmetrical in terms of the outcome distribution. To be specific, the variance of payoff is a measure of the dispersion of the outcomes from the mean (which is the expected payoff). However, it is known that variation of payoff leads to risk only if (some of) the payoff outcomes are unfavourable and uncertain. The variance of payoff, by definition, does not differentiate between favourable and unfavourable outcomes. Thus, unless the distribution of the outcomes (i.e. “payoffs’’) follows a symmetrical distribution, the variance of payoff suffers an inherent theoretical limitation that it captures both upside variation of the favourable outcomes and the downside variation of the unfavourable outcomes. As such, the use of variance of payoff as the risk measure is far from perfect. In particular, in Example 2, FRSCM Information Systems Project A does have a big variation of payoffs but all these payoffs are classified as favourable outcomes. Thus, the variation of “good outcomes’’ should not be interpreted as risk. To overcome this limitation with respect to the use of the variance of payoff as a measure for risk, we have the mean-semi-variance approach which will be discussed next.

Table 6.8 Example 2: The net present value of the outcomes (The cash flows are measured in millions of dollars). Chance

NPV of FRSCM Information Systems Project A

NPV of FRSCM Information Systems Project B

0.3 0.3 0.2 0.2

14.82 11.28 7.73 4.18

4.18 3.32 3.32 −4.30

Table 6.9 Example 2: The expected payoff and variance of payoff.

Expected payoff (in millions of dollars) Variance of payoff (in millions of dollars squared)

FRSCM Information Systems Project A

FRSCM Information Systems Project B

10.21 15.21

2.06 10.23

90

6.4

Fashion Retail Supply Chain Management

MEAN-SEMI-VARIANCE APPROACH

As we explored in Section 6.3, the “variance measure’’ has a serious limitation. As shown by Example 2, the mean-variance framework will give a misleading recommendation to the fashion retailer. A quick solution to this problem is to revise the mean-variance model by substituting the variance by a downside risk measure called the semi-variance. The semi-variance of payoff for FRSCM Information Systems Project i is defined as follows: svi =



j∈DS,i (b)

pij (xij − b)2 ,

where b is the threshold below which the outcomes are termed as unfavourable, DS,i (b) is the set of outcomes xij below b, and the subscript DS represents downside. Obviously, from its definition, we can clearly see that svi measures only the variation of the downside payoff (i.e. cash flow outcome) below the threshold b. It is hence a more precise measure for risk. If we employ the mean-semi-variance approach for studying Example 2, the means of the two projects remain the same as calculated in Table 6.9. For the semi-variance, we set b = 0, and have the following: For FRSCM Information Systems Project A: svA = 0. For FRSCM Information Systems Project B: svB = 0.2 × (−4.30 − 0)2 = 3.70. We summarize the figures in Table 6.10. From Table 6.10, it is obvious that FRSCM Information Systems Project A dominates FRSCM Information Systems Project B because the expected payoff is much higher and the semi-variance of payoff (which measures risk) is much lower. As a remark, FRSCM Information Systems Project A is actually a risk-free project as its semi-variance of payoff is zero. Note that both the mean-variance framework and the mean-semi-variance framework are closely related to Markowitz’s (1959) Nobel prize winning mean variance portfolio management theory in finance. We usually call the mean-variance or meansemi-variance based models the Markowitz mean-risk models, which have already been widely applied to different domains, e.g., in inventory management in a supply chain system (see Choi and Chiu 2012a, 2012b; Choi 2013; Li et al. 2013).

Table 6.10 Example 2: The expected payoff and semi-variance of payoff.

Expected payoff (in millions of dollars) Semi-variance of payoff (in millions of dollars squared)

FRSCM Information Systems Project A

FRSCM Information Systems Project B

10.21 0

2.06 3.70

Mean-risk analysis for fashion retail supply chain information systems projects

6.5

91

SAFETY-FIRST OBJECTIVE

Another popular measure in evaluating FRSCM information systems projects is the safety-first objective approach (Roy 1952; Choi et al. 2011; Choi et al. 2013a). Under the safety-first objective approach, the optimization objective is to minimize the probability of suffering a loss, e.g., if any outcome less than β is unfavourable, the optimization problem can be stated as follows, min P(NPVi ≤ β), i

where NPVi is the outcome of the FRSCM information systems project i. For example, suppose that β = 0, in Example 2, we see the results in Table 6.11. Obviously, the optimal project under the safety-first objective is FRSCM Information Systems Project A. As a remark, the safety-first objective is a probability measure which relates to the mean-variance model. Following Roy (1952) and Choi et al. (2011), we know that by the Bienayme-Tchebycheff inequality, the following holds, P(|NPVi − E[NPVi ]| ≥ E[NPVi ] − β) ≤

V[NPVi ] , (E[NPVi ] − β)2

(6.1)

where E[·] is the expectation operator, V[·] is the variance operator, P(·) means probability. Assuming that NPVi distributes following a symmetrical distribution like the normal distribution or an almost symmetrical distribution, (6.1) can be approximated as follows, P(E[NPVi ] − NPVi ≥ E[NPVi ] − β) ≤ ⇔ P(NPVi ≤ β) ≤

V[NPVi ] . (E[NPVi ] − β)2

V[NPVi ] . (E[NPVi ] − β)2

(6.2)

(6.3)

Define: S(NPVi ) =

V[NPVi ] . (E[NPVi ] − β)2

(6.4)

From (6.3) and (6.4), we can see that S(NPVi ) is the upper bound of P(NPVi ≤ β), which is the probability measure employed in the safety first objective. Thus, for the Table 6.11 The numerical example 2: The expected payoff and semi-variance of payoff.

P(NPV i ≤ 0)

FRSCM Information Systems Project A

FRSCM Information Systems Project B

0

0.2

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Table 6.12 An example of the information system portfolio related to FRSCM. High risk

Low risk

High expected benefit

Larger scale ERP system for FRSCM

Low expected benefit

Replacing the existing merchandising system with a similar one developed by another software company without any real additional improvement in terms of functionality and performance

Barcoding-based information system with enhanced scanning operations (e.g., automatic scanning) Software upgrade

optimal information systems project selection problem, when one employs the safetyfirst objective method, one can actually approximate the computation by employing the mean-variance approach following (6.3) in which a minimization of S(NPVi ) means the minimization of the upper bound of P(NPVi ≤ β). This is an important result because it relates the important mean-variance analysis with the safety-first objective analysis. Fashion retailers can simply compute the expected payoff and the variance of payoff following the mean-variance approach and then employ them for both mean-variance analysis and safety-first objective analysis (with the computation of S(NPVi ) for each project i).

6.6

INFORMATION SYSTEM PORTFOLIO

Based on the mean-risk mindset, we can classify the FRSCM-related information systems projects into different types by a simple information system portfolio in which we consider the level of risk and the expected benefit. Table 6.12 shows a simple information system portfolio with examples in FRSCM-related information systems projects3 . Table 6.12 provides the information system portfolio for the fashion retailer to decide which one to go for first. As expected, the larger scale systems projects, such as implementing the ERP systems, involve a level of high risk while they can lead to high potential benefits. Thus, this kind of larger information systems projects require careful planning and budgeting before the fashion retailer decides to proceed. For the low risk low expected benefit information systems projects, they usually refer to the standard upgrading of the existing application software or machine replacement. They are safe options but the expected benefit is marginal. Among all four types of classified information systems projects, the most interesting one is the low risk high expected benefit project because the fashion retailer should put priority there. As shown in Table 6.12, an example is the barcoding-based information system with enhanced

3

See Laudon and Laudon (2006), p. 513, for the general concept of system portfolio.

Mean-risk analysis for fashion retail supply chain information systems projects

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operations. Undoubtedly, the barcoding scheme is critically important in fashion retail supply chain management. It helps reduce manual errors and provides a needed tool for digitizing the inventory record, the transportation profile, etc. If there is an opportunity to further improve it or if the company switches from the “no barcoding scheme’’ to the “automatic scanning with barcoding scheme’’, the potential benefit is immense. Since the barcoding technology is well-established and mature, the level of risk is low. Finally, some information systems projects involve a change which might not bring any real benefits. Usually, it happens when some senior managers or directors of the company are personally in favour of some information systems which they would like everybody to follow (or it enhances their own work because of their prior experience of using that kind of information system). However, there is always a switching cost and learning cost for people to get used to a new information system. This kind of change is hence unwise as it brings a low benefit (to just a few individuals) but a high level of risk (a lot of problems).

6.7

CONCLUSION

Information systems are important for modern fashion retail supply chain management. However, versatile information systems are expensive and the larger scale information systems implementation projects for supporting fashion retail supply chain management are usually risky. In this chapter, we have discussed the use of the classic mean-risk models to evaluate the information systems projects for fashion retail supply chain management. To be specific, with respect to different sources of uncertainty in the market, the benefits and operational costs associated with the fashion retail supply chain management information systems are highly volatile. As a consequence, when the fashion retailers evaluate the business values of these fashion retail supply chain management information systems-related projects, they have to take into consideration both expected benefit and risk. In this chapter, we have explored two related models for conducting benefit and risk analysis, namely the classic mean-variance model, and the revised mean-semivariance model. Both of them originated from Markowitz mean-variance portfolio theory. We have shown the analytical details as well as illustrated the applicability of the models via numerical examples. We have also discussed the safety-first objective approach and showed its analytical relationship with the mean-variance model. Furthermore, we have presented how the information systems portfolio can be used to help the fashion retailers decide the priority of information systems projects.

REFERENCES Bertolini, M., Bevilacqua, M., Bottani, E. & Rizzi, A. (2004) Requirements of an ERP enterprise modeler for optimally managing the fashion industry supply chain. The Journal of Enterprise Information Management, 17(3), 180–190. Choi, T.M. (2013) Multi-period risk minimization purchasing models for fashion products with interest rate, budget, and profit target considerations. Annals of Operations Research, Doi: 10.1007/s10479-013-1453-x, in press.

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Choi, T.M. & Chiu, C.H. (2012a) Mean-downside-risk and mean-variance newsvendor models: Implications for sustainable fashion retailers. International Journal of Production Economics, 135, 552–560. Choi, T.M. & Chiu, C.H. (2012b) Risk analysis in atochastic supply chains. Vol. 178, Springer’s International Series in Operations Research/Management Science. Choi, T.M., Chiu, C.H. & To, K.M.C (2011) A fast fashion safety first inventory model. Textile Research Journal, 81, 819–826. Choi, T.M., Chow, P.S., Kwok, B., Liu, S.C. & Shen, B. (2013a) Service quality of online shopping platforms: A case based empirical and analytical study. Mathematical Problems in Engineering, Doi: 10.1155/2013/128678, Article ID 128678. Choi, T.M., Chow, P.S. & Liu, S.C. (2013b) Implementation of fashion ERP systems in China: Case study of a fashion brand, review and future challenges. International Journal of Production Economics, 146, 70–81. Hui, P.C.L., Tse, K., Choi, T.M. & Liu, N. (2010) Enterprise resource planning systems for the textiles and clothing industry, In: Cheng, T.C.E. & Choi, T.M. (eds), Innovative Quick Response Programs in Logistics and Supply Chain Management, Springer. Laudon, K.C. & Laudon, J.P. (2006) Management Information Systems: Managing the Digital Firms, 9th Ed, Prentice-Hall. Laudon, K.C. & Laudon, J.P. (2012) Management Information Systems, 12th Ed, Prentice-Hall. Li, J., Choi, T.M. & Cheng, T.C.E (2013) Mean-variance analysis of two-echelon fast fashion supply chains with returns contract. IEEE Transactions on Systems, Man and Cybernetics – Systems, Doi: 10.1109/TSMC.2013.2264934, published online. Markowitz, H.M. (1959) Portfolio Selection: Efficient Diversification of Investment. John Wiley & Sons, New York. Park, C.S. & Sharp-Bette, C.P. (1990) Advanced Engineering Economics. John Wiley & Sons, Canada. Roy, A.D. (1952) Safety first and the holding of assets. Econometrica, 20(3), 431–449. Yang, D., Choi, T.M., Xiao, T. & Cheng, T.C.E. (2011) Coordinating a two-supplier and one-retailer supply chain with forecast updating. Automatica, 47, 1317–1329.

Chapter 7

Fashion retail supply chain management – Concluding remarks

SUMMARY Fashion retail supply chain management is a critically important topic. In this concluding chapter, we first present a concise summary of the core managerial insights and implications generated by the analyses discussed in the book. After that, we propose some probable extensions of the models. In particular, extending the efficient consumer response systems to the fast fashion systems will be an important and promising area for future study. Finally, we discuss a few areas that have not been covered in the book thus far, which include category captainship, risk management, technology-operations interfaces, sustainability, and mass customization, for future research on fashion retail supply chain management. Keywords Managerial insights, future research, fashion retail supply chain management, customer service, inventory management, efficient consumer response, new product development, mean-risk analysis. In this book, we have explored several timely topics related to fashion retail supply chain management via an analytical optimization approach. In this concluding chapter, we summarize the core managerial insights and explore future research opportunities in the following.

7.1

MANAGERIAL INSIGHTS

In this section, we concisely present some core managerial insights. 1

Customer service management (Thirumalai and Sinha 2005) is crucial in fashion retail supply chains. Our analysis has shown that under the pure wholesale pricing contract in a decentralized fashion retail supply chain, the optimal retail customer service level is lower than the optimal customer service level for the fashion retail supply chain system. As a consequence, the decentralized fashion retail supply chain with the pure wholesale pricing contract is not most efficient. A “wholesale pricing and revenue sharing scheme’’-based consignment contract can be employed

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2

3

4

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to coordinate the fashion retail supply chain system and achieve the all-win situation in which the upstream supplier, the downstream fashion retailer and the consumers will all benefit. Owing to the double marginalization effect, the fashion retail supply chains under both the EOQ-based model and the newsvendor problem-based model will not be coordinated by themselves under a decentralized setting. To coordinate, incentive alignment contracts such as markdown, consignment, etc can be used. The efficient consumer response program is beneficial to the fashion retailer. However, whether it is beneficial or harmful to the manufacturer and the consumers depends on the consumer welfare coefficient. If the consumer welfare coefficient is sufficiently small, the efficient consumer response program creates an all-win situation which means the fashion retailer, the manufacturer, the consumers and the whole fashion retail supply chain all benefit; otherwise, the efficient consumer response program hurts the manufacturer and the consumers. Finally, a simple markdown contract can be set to coordinate the fashion retail supply chain with the efficient consumer response program. New product development is a commonly seen practice in fashion retailing (Caniato et al. 2014). For the new product selection problem, our analysis shows that the fashion retailer should select among the new product candidates the one which has the highest expected mean demand. Interestingly, this specific optimal new product is not only the optimal choice for the fashion retailer, but also the best one for the manufacturer and the whole fashion retail supply chain system. Moreover, for the new product offering, the whole fashion retail supply chain system will enjoy a higher expected profit if the retail selling price is higher, the market clearance sale price is higher, the wholesale price is lower, or the product’s manufacturing cost is lower. For the evaluation of fashion retail supply chain management (FRSCM) information systems projects, the mean-variance approach provides a convenient and systematic framework to conduct the benefit and risk analysis. Under the conditions that the mean-variance approach does not work1 , the mean-semivariance approach can be a good alternative. In addition, the safety-first objective approach is also useful. It is interesting to note that the safety-first objective model and the mean-variance model are closely related. The fashion retailer can compute the “mean’’ and “variance’’ of the involved cash flows and employ both the (approximated) safety-first objective approach and the mean-variance approach.

From the insights discussed above as well as other findings from this book, Table 7.1 below summarizes several concise issues and the respective managerial implications under each topic.

1

The mean-variance approach does not work well if the cash flows or payoffs are not distributed following a symmetric distribution which implies that the “variance’’ is not a good measure for risk. In particular, its performance will be poor if there are too many upside variations and too few downside variations under the asymmetric cash flows’ distribution scenario.

Fashion retail supply chain management – Concluding remarks 97 Table 7.1 The managerial implications derived from the insights. Topics

Issues

Managerial Implications

Customer service management

Performance measures

Quantitative performance measures are critical for customer service control and improvement

The RSQS model

This conceptual model is useful for examining fashion retail customer services

Coordination challenge

Consignment contract can achieve all win coordination

Deming’s Quality Management Framework

Deming’s fourteen points provide important guidelines for customer service improvement

EOQ and news vendor models

They are fundamental and help to build analytical models for fashion retail supply chain management analysis

Coordination issue

Decentralized supply chains based on these two models are not optimal by themselves under a simple pure wholesale pricing contract. Incentive alignment schemes are needed to coordinate the fashion retail supply chains

Benefits

The efficient consumer response program is always beneficial to the fashion retailer but not necessarily for the manufacturer and the consumers. Thus, the manufacturer and the consumers may have reasons to feel bad about it

All-win situation

The efficient consumer response program creates the all-win situation in which the fashion retailer, the manufacturer, the consumers and the whole fashion retail supply chain are all benefited only if the consumer welfare coefficient is sufficiently small. In other words, we cannot take it for granted that the all-win situation will always occur

Coordination

Coordination under the efficient consumer response program is similar to the case with the traditional inventory models:A simple markdown contract is capable of achieving the coordination

Stochastic nature of the problem

New product development is a stochastic problem which involves more than a commonly assumed simple demand distribution. Moreover, it can be a two-stage (in terms of time point) model

Simple decision rule

In the settings we explored for the new product selection problem, the fashion retailer should select among the new product candidates the one which has the highest expected mean demand. This is a simple and intuitive decision rule

Coordination

The optimal choice of new product is optimal for the fashion retailer, the manufacturer and the whole supply chain system. Thus, the fashion retail supply chain system is automatically coordinated with respect to this optimal decision

Inventory models and coordination

Efficient consumer response

New product development

(Continued)

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Table 7.1 Continued. Topics

Issues

Managerial Implications

Mean-risk analysis of information systems projects

Mean-variance model

The mean-variance model provides a good tool for conducting mean-risk analysis of information systems projects for fashion retail supply chain management. In addition, by calculating the “mean’’ and the “variance’’, we can also use them to apply the safety-first objective method

Mean-semi-variance model

In those cases where the involved cash flows follow distributions which are too asymmetric, the mean-variance approach will not work well and hence the mean-semi-variance approach can help

Information systems portfolio

It is an “easy-to-use’’ classification scheme for prioritizing the projects according to the level of risk and the expected benefit. This tool helps senior managers and directors to visualize the project options available and is hence a very practical tool

7.2

FUTURE RESEARCH DIRECTIONS

Note that from the findings above, we can reveal some areas for further explorations. For example: 1

2

More general fashion retail supply chain systems: The models explored in the book are relatively simple. This is deemed appropriate because it helps with the derivations of analytical closed form results and simple models are easier to understand for book readers. However, from a research perspective, exploring more complex and general fashion retail supply chains is a natural extension. For example, we can explore the longer (N-echelon) supply chain systems (van der Rhee et al. 2010). We may investigate the multiple period models. We may also consider the competition effect in the fashion retail supply chain system. As a remark, as a general rule, more complex supply chains require more sophisticated supply contracts to coordinate. Thus, hybrid supply chain incentive contracts design (Chiu et al. 2011) will be an important future research area. Fast fashion systems: In this book, the efficient consumer response (ECR) program has been explored. The fast fashion system (Choi 2014), which is a more advanced system than ECR, is both an important industrial trend (Bhardwaj and Fairhurst 2010) and a good research topic for supply chain analysis. Thus, by incorporating the model features such as enhanced design (Cachon and Swinney 2011), minimum order quantity (Chow et al. 2012), branding elements (Choi et al. 2010), and logistics requirements (Cagliano et al. 2011) into the ECR model, we can comprehensively explore the fast fashion strategy and reveal how it affects the fashion retail supply chain’s operations and strategies.

Fashion retail supply chain management – Concluding remarks 99

3

Stochastic models for RSQS-based customer service management: In Chapter 2, we explore the customer service management in fashion retail supply chain systems under a deterministic setting. However, the fashion retail supply chain is in general a dynamic system in a market filled with uncertainties. Thus, future research can be conducted to explore the customer service management problems related to the RSQS model in a stochastic setting.

In addition to these topics, there are a few under-explored topics in the area of fashion retail supply chain management which are open for future research. Some of them are proposed below: 1

2

3

Category captainship: In retailing, a category captain refers to the brand which is a leader in the particular category and helps the retailer in planning visual merchandising and making many other operational decisions for the respective category. In fashion apparel, it is reported that VF Corp. acts as the category captain for many retailers in the product category of jeans (Kurtulus and Toktay 2009). The function of VF Corp. includes giving advice on inventory and assortment planning of the jeans category of its retailer partners. Note that there exists an inherent conflict of interest between the category captain and other suppliers, and even between the category captain and the retail partners. It is also a controversial issue whether the category captainship scheme is harmful or beneficial to the consumers and the competing non-captain suppliers. One argument centres on the probable “monopolization’’ of the category as a result of being dictated by the category captain which may lead to higher prices and fewer options (as the degree of competition drops). As a result, the involvement of category captainship is being challenged from the perspective of fair trade and anti-trust related issues (Desrochers et al. 2003). Undoubtedly, this issue is still relatively new in fashion retail supply chain management and has not been well explored in the literature. Thus, it provides a very promising area for future research studies. Risk management: Business operations in the fashion industry are facing all kinds of risks (Vaagen and Wallace 2008; Choi and Chiu 2012a). For instance, there is risk from demand, risk from supply, risk from exchange rate, risk from international trading barriers, risk from political stability, etc. Thus, in addition to our discussions in Chapter 6 on the mean-risk analysis of the fashion retail supply chain management information systems projects, proper risk management in many other operations in fashion retail supply chains is imperative. As a consequence, the topic of risk management opens rich research opportunities for both future empirical research and analytical studies (Markowitz 1959, Olson and Wu 2011, Choi and Chiu 2012b, Dekker et al. 2013, Shen et al. 2013, Yu and Goh 2014). Technology-operations interface: Nowadays, information technology, such as RFID (Szmerekovsky and Zhang 2008; Choi 2011; Chan et al. 2012), is playing a crucial role in fashion retail supply chains. The pros and cons these technological tools bring to the fashion retail supply chain systems are still not yet fully understood. The optimal levels of technological adoption by fashion retailers are still topics deserving further exploration. Moreover, from a managerial perspective, how the fashion retailer leads the deployment of information technology

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Table 7.2 Future research topics, issues and related references. Topics

Research Issues

Related References

More general fashion retail supply chain systems

Studying more complex fashion retail supply chain systems and supply contracts will yield more insights, but the analysis will expectedly be technically more challenging

van der Rhee et al. (2010), Chiu et al. (2011), He and Zhou (2012), Schmitt and Singh (2012), Choi et al. (2013), Li et al. (2013), Pan and Ngai (2013)

Fast fashion systems

Modelling fast fashion systems with all kinds of features helps advance our knowledge regarding this timely industrial practice

Bhardwaj and Fairhurst (2010), Cachon and Swinney (2011), Cagliano et al. (2011), Li et al. (2013); Choi (2014)

Stochastic models for RSQS-based customer service systems

Impacts brought about by customer service strategies are in general stochastic. Building and investigating stochastic RSQSbased fashion retail customer service models would generate many new insights

Swaminathan and Srinivasan (1999), Xiao et al. (2012)

Category captainship

Analytically reveal the benefits and drawbacks of the category captainship

Desrochers et al. (2003), Bandyopadhyay et al. (2009), Kurtulus and Toktay (2009)

Risk management

Both strategic and operational risk management schemes for fashion retail supply chain systems

Martinez-de-Albeniz and Simchi-Levi (2006),Wei and Choi (2010),Wu and Olson (2010), Choi and Chiu (2012b), Olson and Wu (2011), Dekker et al. (2013), Shen et al. (2013), Yu and Goh (2014)

Technology-operations interfaces

Optimal level of technology adoption and retailer-led technology deployment scheme in the fashion retail supply chain systems

Thirumalai and Sinha (2005), Szmerekovsky and Zhang (2008), Chan et al. (2012), Fan et al. (2014)

Sustainability issues

Fashion retail supply chain system’s sustainability

Choi and Chiu (2012a),Tang and Zhou (2012), Brandenburg et al. (2014)

Mass customization

Identify the critical success factors for fashion mass customization

Labarthe et al. (2007),Yao and Liu (2009), Liu et al. (2012), Choi (2013)

4

in the fashion retail supply chain is an important area which requires further investigation. Sustainability issues: Nowadays, sustainability is an incredibly important and timely issue (Choi and Chiu 2012a). For fashion retail supply chain management, how to better balance and achieve the optimal supply chains with the consideration of environmental sustainability, economics sustainability, and consumer

Fashion retail supply chain management – Concluding remarks 101

5

welfare is crucial (Tang and Zhou 2012). The effect of different channel leadership is substantial (Choi et al. 2013). The rules imposed by the governments also influence the sustainability issues considerably. Thus, the fashion retail supply chain systems with the consideration of sustainability issues are another promising area for deeper exploration. Mass customization: In fashion retailing, mass customization is a popular measure (Liu et al. 2012; Choi 2013). However, it is interesting to note that both successes and failures are observed. For example, over the past few years, many big fashion retailers stopped their mass customization programs (at least temporarily). However, a lot of fashion retails are starting or expanding mass customization programs (e.g., Nike’s NIKEiD program has expanded to Hong Kong recently (Yeung and Choi 2011), and Adidas, LV, Polo Ralph Lauren, Burberry, Brooks Brothers, etc are all offering mass customization services). Thus, the operational issues in the fashion retail supply chain systems probably play a determining role in the success of the mass customization program. This calls for new research to reveal the truth and the respective managerial insights.

Table 7.2 summarizes the above-proposed future research topics, research issues, and some related references.

REFERENCES Agrawal, N. & Smith, S.A. (2009) Retail supply chain management: Quantitative models and empirical studies. International Series in Operations Research & Management Science, Vol. 122, Springer. Bandyopadhyay, B., Rominger, A. & Basaviah, S. (2009) Developing a framework to improve retail category management through category captain arrangements. Journal of Retailing and Consumer Services, 16(4), 315–319. Bhardwaj, V. & Fairhurst, A. (2010) Fast fashion: Response to changes in the fashion industry. The International Review of Retail, Distribution and Consumer Research, 20, 165–173. Brandenburg, M., Govindan, K., Sarkis, J. & Seuring, S. (2014) Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233(2), 299–312. Cachon, G.P. & Swinney, R. (2011) The value of fast fashion: Quick response, enhanced design, and strategic consumer behavior. Management Science, 57, 778–795. Cagliano, A.C., DeMarco, A. & Rafele, C. (2011) Using system dynamics in warehouse management: A fast fashion case study. Journal of Manufacturing Technology Management, 22, 171–188. Caniato, F., Caridi, M., Moretto, A., Sianesi, A. & Spina, G. (2014) Integrating international fashion retail into new product development. International Journal of Production Economics, 147, 294–306. Chan, H.L., Choi, T.M. & Hui, C.L. (2012) RFID versus bar-coding systems: Transactions errors in health care apparel inventory control. Decision Support Systems, 54, 803–811. Chiu, C.H., Choi, T.M. & Tang, C.S. (2011) Price, rebate, and returns supply contracts for coordinating supply chains with price dependent demand. Production and Operations Management, 20, 81–91. Choi, T.M. (2013) Optimal return service charging policy for a fashion mass customization program. Service Science, 5, 56–68.

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Choi, T.M. (2011) Coordination and risk analysis in VMI supply chain with RFID technology. IEEE Transactions on Industrial Informatics, 7, 497–504. Choi, T.M. (ed.). Fast Fashion Systems: Theories and Applications. CRC Press, 2014. Choi, T.M. & Chiu, C.H. (2012a) Mean-downside-risk and mean-variance newsvendor models: Implications for sustainable fashion retailers. International Journal of Production Economics, 135, 552–560. Choi, T.M., Chiu, C.H. (2012b) Risk Analysis in Stochastic Supply Chains. Vol. 178, Springer’s International Series in Operations Research/Management Science. Choi, T.M., Li, Y. & Xu, L. (2013) Channel leadership, performance and coordination in closed loop supply chains. International Journal of Production Economics, 146, 371–380. Choi, T.M., Liu, N., Liu, S.C., Mak, J. & To, Y.T. (2010) Fast fashion brand extensions: Consumer behaviours and preferences. Journal of Brand Management, 17, 472–487. Choi, T.M. & Sethi, S. (2010) Innovative quick response programmes: A review. International Journal of Production Economics, 127, 1–12. Chow, P.S., Choi, T.M. & Cheng, T.C.E. (2012) Impacts of minimum order quantity on a quick response supply chain. IEEE Transactions on Systems, Man, and Cybernetics – Part A, 42, 868–879. Dekker, H.C., Sakaguchi, J. & Kawai, T. (2013) Beyond the contract: Managing risk in supply chain relations. Management Accounting Research, 24(2), 122–139. Desrochers, D.M., Gundlach, G.T. & Foer, A.A. (2003) Analysis of antitrust challenges to category captain arrangements. Journal of Public Policy and Marketing, 22, 201–215. Fan, T.J., Chang, X.Y., Gu, C.H., Yi, J.J. & Deng, S. (2014) Benefits of RFID technology for reducing inventory shrinkage. International Journal of Production Economics, 147(C), 659–665. He, Y. & Zhao, X. (2012) Coordination in multi-echelon supply chain under supply and demand uncertainty. International Journal of Production Economics, 139(1), 106–115. Kurtulus, M. & Toktay, L.B. (2009) Category captainship practices in the retail industry. In Agrawal and Smith (eds.). Retail Supply Chain Management: Quantitative Models and Empirical Studies, Springer, 79–98. Labarthe, O., Espinasse, B., Ferrarini, A. & Montreuil, B. (2007) Toward a methodological framework for agent-based modelling and simulation of supply chains in a mass customization context. Simulation Modelling Practice and Theory, 15(2), 113–136. Li, J., Choi, T.M. & Cheng, T.C.E. (2013) Mean-variance analysis of two-echelon fast fashion supply chains with returns contract. IEEE Transactions on Systems, Man and Cybernetics – Systems, Doi: 10.1109/TSMC.2013.2264934, published online. Liu, N., Choi, T.M., Yuen, M. & Ng, F. (2012) Optimal pricing, modularity and return policy under mass customization. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 42, 604–614. Markowitz, H.M. (1959) Portfolio Selection: Efficient Diversification of Investment, John Wiley & Sons, New York. Martinez-de-Albeniz, V. & Simchi-Levi, D. (2006) Mean-variance trade-offs in supply contracts. Naval Research Logistics, 53(7), 603–616. Olson, D.L. & Wu, D.D. (2011) Risk management models for supply chain: A scenario analysis of outsourcing to China. Supply Chain Management: An International Journal 16(6): 401–408. Pal, B., Sana, S.S. & Chaudhuri, K. (2012) A multi-echelon supply chain model for reworkable items in multiple-markets with supply disruption. Economic Modelling, 29(5), 1891–1898. Pan, F. & Nagi, R. (2013) Multi-echelon supply chain network design in agile manufacturing. Omega, 41(6), 969–983.

Fashion retail supply chain management – Concluding remarks 103 Schmitt, A.J. & Singh, M. (2012) A quantitative analysis of disruption risk in a multi-echelon supply chain. International Journal of Production Economics, 139(1), 22–32. Shen, B., Choi, T.M., Wang, Y. & Lo, C.K.Y. (2013) The coordination of fashion supply chains with a risk averse supplier under the markdown money policy. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 42(3): 266–276. Swaminathan, J.M. & Srinivasan, R. (1999) Managing individual customer service constraints under stochastic demand. Operations Research Letters, 24(3), 115–125. Szmerekovsky, J.G. & Zhang, J. (2008) Coordination and adoption of item-level RFID with vendor managed inventory. International Journal of Production Economics, 114, 388–398. Tang, C.S. & Zhou, S. (2012) Research advances in environmentally and socially sustainable operations. European Journal of Operational Research, 223(3), 585–594. Thirumalai, S. & Sinha, K.K. (2005) Customer satisfaction with order fulfillment in retail supply chains: Implications of product type in electronic B2C transactions. Journal of Operations Management, 23, 291–303. Vaagen, H. & Wallace, S.W. (2008) Product variety arising from hedging in the fashion supply chains. International Journal of Production Economics, 114, 431–455. van der Rhee, B., van der Veen, J.A.A., Venugopal, V. & Nalla, V.R. (2010) A new revenue sharing mechanism for coordinating multi-echelon supply chains. Operations Research Letters, 38(4), 296–301. Wei, W. & Choi, T.M. (2010) Mean-variance analysis of supply chains under wholesale pricing and profit sharing scheme, European Journal of Operational Research, 204(2), 255–262. Wu, D. & Olson, D.L. (2010) Enterprise risk management: Coping with model risk in a large bank. Journal of the Operational Research Society, 61(2), 179–190. Xiao, T., Choi, T.M., Yang, D. & Cheng, T.C.E. (2012) Service commitment strategy and pricing decisions in retail supply chains with risk-averse players. Service Science, 4(3), 236–252. Yao, J. & Liu, L. (2009) Optimization analysis of supply chain scheduling in mass customization. International Journal of Production Economics, 117(1), 197–211. Yeung, H.T. & Choi, T.M. (2011) Mass customization in the Hong Kong apparel industry. Production Planning and Control, 22, 298–307. Yu, M.C. & Goh, M. (2014) A multi-objective approach to supply chain visibility and risk. European Journal of Operational Research, 233(1), 125–130.

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COMMUNICATIONS IN CYBERNETICS, SYSTEMS SCIENCE AND ENGINEERING

7

7

COMMUNICATIONS IN CYBERNETICS, SYSTEMS SCIENCE AND ENGINEERING

Fashion Retail Supply Chain Management: A Systems Optimization Approach is a comprehensive reference source that provides the state-of-the-art findings on many important emerging research issues related to retail supply chain management and opimizaion problems. The book takes an explicit systems approach, and discusses retailled fashion supply chain coordinaion mechanisms and consumer market informaiondriven fashion retail supply chain models, as well as suggesing future research avenues. This volume will be of interest not only to those involved in the fashion industry, but also to academics and praciioners in the wider fields of business, manufacturing engineering, systems engineering and supply chain management. ABOUT THE BOOK SERIES Communicaions in Cyberneics, Systems Science and Engineering (CCSSE) is a crossdisciplinary book series devoted to theoreical and applied research contribuions, that cater to a rapidly growing worldwide interest in a cyberneic and systemic methodology with an ever-increasing capacity to deal with new challenges in a way that tradiional science cannot. The series aims to become a comprehensive reference work on and guide to developments within the field and strategies required for beter implementaion of advances, with a view to environmental protecion and sustainable social and economic development. The CCSSE series targets all working in theoreical and applied fields of cyberneics, systems science and engineering, e.g. academics, researchers and consultants, computer and informaion scienists, development and systems engineers, mathemaicians, management cyberneicists and systemists, medical scienists, and intelligent and manufacturing engineers in industry, as well as leading decision- and policy-makers. SERIES EDITOR: JEFFREY ‘YI-LIN’ FORREST

Tsan-Ming Choi

A Systems Opimizaion Approach

ISSN: 2164-9693

an informa business