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Over the past decades, we have witnessed the rapid development of giant fashion brands in the retail market which inspires a lot of operational research (OR) studies in fashion retail supply chains (FRSCs). In fact, FRSCs are highly consumer-demand driven and face many operational challenges coming from high demand and supply side uncertainties. Realizing the significance of fashion retail supply chain management (FRSCM) and a lack of comprehensive review on the topic, we develop this paper which examines the operational models on FRSCM in the mainstream OR literature. We organize this review systematically with respect to the core functional areas of FRSCs, namely the manufacturer, retailer, consumer, and fashion retail supply chain system. In each functional area, insights regarding the related studies as well as the specific OR model features and assumptions are generated. Finally, we conclude the review by summarizing the major findings and proposing promising future research areas (from both OR modeling and practical perspectives).
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Fashion Retail Supply Chain Management: A Review of
Operational Models
Xin Wen
, Tsan-Ming Choi
, Sai-Ho Chung2
First version: 22 March, 2018.
Revised: 28 August 2018.
Accepted: 23 October, 2018.
To appear in International Journal of Production Economics.
We sincerely thank the editor and reviewers for their very helpful and constructive comments on this paper. We are indebted to Mr.
Allen Y. Chen, the former commodity director of Guangzhou Jinyu Garment Co. Ltd. for his helpful and insightful inputs regarding the
fashion industry.
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University.
Corresponding author: Tsan-Ming Choi, Professor of Fashion Business, Business Division, Institute of Textiles and Clothing, The
Hong Kong Polytechnic University, email:, phone: 852-27666450.
Fashion Retail Supply Chain Management: A Review of
Operational Models
Abstract: Over the past decades, we have witnessed the rapid development of giant fashion brands in
the retail market which inspires a lot of operational research (OR) studies in fashion retail supply
chains (FRSCs). In fact, FRSCs are highly consumer-demand driven and face many operational
challenges coming from high demand and supply side uncertainties. Realizing the significance of
fashion retail supply chain management (FRSCM) and a lack of comprehensive review on the topic,
we develop this paper which examines the operational models on FRSCM in the mainstream OR
literature. We organize this review systematically with respect to the core functional areas of FRSCs,
namely the manufacturer, retailer, consumer, and fashion retail supply chain system. In each functional
area, insights regarding the related studies as well as the specific OR model features and assumptions
are generated. Finally, we conclude the review by summarizing the major findings and proposing
promising future research areas (from both OR modeling and practical perspectives).
Keywords: Supply chain management; fashion industry; retail; operational models; review.
We sincerely thank the editor and reviewers for their very helpful and constructive comments on this paper.
1. Introduction
1.1 Background
With the growing power of giant retail groups, retail supply chain management, in which the retailer
assumes the leadership role of the whole channel, has become an important topic in modern supply
chain management in recent years (Agrawal and Smith, 2015). There is no doubt that quick and reliable
supplies, proper product quality, accurate transportation and inventory control, efficient and valuable
information flow, competitive pricing strategy, and high-accuracy demand forecasting are all critical
elements in supply chain management (Hanne and Dornberger, 2016). Retailing, as the ultimate
element of retail supply chain directly facing consumers, has special emphasis on product availability
and customer satisfaction. In fact, consumers only need a limited quantity of certain types among
tremendous product assortments offered by the retailer. Therefore, it is crucial but immensely
challenging for retailers to learn exactly what customers want, when and where demand occurs, and
provide the needed information (e.g., demand forecasting, real-time sales data, consumer return and
feedback, inventory status) to other upstream members so as to improve the supply chain performance
(Afshari and Benam, 2011).
In the fashion industry, fashion retailing has received growing attention with the rapid growth of
global fashion industry and brands (fashion products are like fashion apparel, footwear, accessories,
and fashion beauty). Statistics of Fashion United
show that the worldwide value of fashion apparel
market reached three trillion US dollars, about 2% of global Gross Domestic Product (GDP), totally
creating 115.6 million employment opportunities in the world by 2014 which had increased by 69%
since 1990. Global fashion retail brands in luxury fashion (e.g., Hermes, Louis Vuitton), functional
apparel (e.g., Adidas, Nike) and fast fashion (e.g., Zara, H&M) have emerged as the leading brands
and consistently being ranked as top 100 brands in the world, together with retail giants like Apple
Computers, Amazon, and Walmart. As a consequence, fashion retail supply chain management
(FRSCM) has become prominent. According to Choi (2014), fashion retail supply chain (FRSC) is a
retailer-led supply chain coupled with forward and backward flows of fashion products, information,
and funds, where the decisions are driven by consumer demand in the market. The four core members
of a FRSC are illustrated in Figure 1. In this paper, for the definition of FRSCM, we adopt the one
proposed by Choi (2014), arising from the definition of Supply Chain Management given by The
Council of Supply Chain Management Professionals: “FRSCM encompasses the planning and
management of all activities involved in sourcing and procurement, conversion, and all logistics
management activities in the FRSC. It includes coordination and collaboration with supply chain
partners. In essence, FRSCM integrates supply and demand management within and across the FRSC
with a goal of satisfying the customer requirements under the leadership of the retailer.”
Figure 1. Core members in a FRSC.
According to Şen (2008), the fashion industry is characterized by short life cycle, huge variety in
products (e.g., color, size, style), inevitable inherent uncertainties (e.g., demand, supply), lengthy and
rigid supply procedures, and high impulse buying behavior. As a result, FRSCM is highly complicated
and challenging. Although the significance of FRSCM has been well-realized, very few studies have
been conducted to comprehensively examine FRSCM from an academic perspective. To the best of
our knowledge, Şen (2008) conducts an overview about the advanced technologies and operations,
including apparel manufacturing, retail operations, and trends such as quick response and e-commerce,
in fashion supply chain management in the US until 2006. Choi (2014), in the introductory chapter of
the book, summarizes several important topics in FRSCM, including customer service management,
inventory models, channel coordination, efficient consumer response, new product selection, and
information system. Shen et al. (2016a) review the literature related to fashion inventory management.
However, none of them focuses on exploring the related operational models in detail.
1.2 Methodology
Motivated by the significance of FRSCM and a lack of comprehensive review on the topic, we have
developed this paper which reviews 144 papers published in the recent decade (2006 to 2017). The
review focus is on those analytical studies which apply operational research (OR) techniques in
tackling the research problems. The reviewed papers apply OR techniques ranging from game theory
to diverse mathematical programming methods such as dynamic programming, integer programming,
and stochastic programming.
FRSC: A retailer-led
fashion supply chain,
where the decisions
are demand-driven
Supply chain system
Regarding the searching and selection criteria for papers, similar to Wang et al. (2015), we
concentrate on the dominant operations research and management science (OR/MS/OM) journals,
including INFORMS journals (Management Science, Operations Research, Manufacturing and
Service Operations Management, Interfaces, Information Systems Research, Marketing Science,
Service Science, Transportation Science, Mathematics of Operations Research, and INFORMS Journal
of Computing), Production and Operations Management
, IEEE Transaction (various)
Transactions, European Journal of Operational Research, International Journal of Production
Economics, Decision Sciences, Transportation Research (various parts)
, Naval Research Logistics,
Omega, Annals of Operations Research, International Journal of Production Research, Journal of the
Operational Research Society, Computers & Operations Research, Decision Support Systems, OR
Letters, and SIAM Journal on Optimization, with the keywords of “fashion”, “supply chainand
“retail”. In total, we have found 1295 papers. To fit our “fashionscope well, we select three categories
of publications that employ operational models to investigate fashion retail supply chain management
problems: i) Those considering the fashion industry directly (like Cachon and Swinney (2011)); ii)
those considering products like fashion items (like O'Neil et al. (2016)); iii) those with features of the
problem or findings that are relevant or applicable to the fashion industry (like Cui et al. (2016) and
Lee and Park (2016)). Therefore, 138 papers are retained. Besides, based on our knowledge and advice
from reviewers, we intentionally include 5 papers (e.g., Chiu et al. (2011) and Xiao et al. (2010))
published in other reputational journals (i.e., Automatica, and Computers & Industrial Engineering)
and one paper of Zhao et al. (2018)
that are crucial for the development of the FRSCM literature to
make our findings comprehensive. Consequently, there are totally 144 papers being reviewed in this
work. Table 1 illustrates the statistics of the collected 144 papers in each journal. We can see that the
majority of selected papers are published in European Journal of Operational Research, International
Journal of Production Economics, and IEEE transactions, while no relevant publications are found in
SIAM Journal on Optimization, OR Letters, Transportation Science, Mathematics of Operations
Research, and INFORMS Journal of Computing.
Figure 2 summarizes the number of publications every two years and shows the trend in the growth
of research interests in FRSCM in recent years. Specifically, the research attention steadily increases
after the year of 2009. In 2016-2017
, 36 related papers were published, which is more than a double
Notice that Journal of Operations Management is excluded because it only publishes empirical research in the recent decade.
IEEE Transactions on Engineering Management; IEEE Transactions on Systems, Man, and Cybernetics: Systems; IEEE Transactions
on Systems, Man, and Cybernetics - Part A: Systems and Humans
Transportation Research Part B: Methodological; Transportation Research Part E: Logistics and Transportation Review
Due to its significance and high relevance to fashion retail supply chain management, we deliberately include Zhao et al. (2018)
according to the insightful suggestion of an anonymous reviewer.
Zhao et al. (2018) is included in 2016-2017.
compared to ten years ago. Therefore, it is confident that there will be more publications in the future
and this research area will keep attracting researchers.
Table 1. Statistics of the papers selected in each journal.
European Journal of Operational Research
IIE Transactions
International Journal of Production Economics
Annals of Operations Research
IEEE Transactions (various)
Manufacturing and Service Operations Management
Management Science
Naval Research Logistics
Service Science
Decision Sciences
Computers & Operations Research
Production and Operations Management
Information Systems Research
Transportation Research (various parts)
Journal of the Operational Research Society
International Journal of Production Research
Decision Support Systems
Marketing Science
Operations Research
Figure 2. Trend of publications during the last decade.
As a remark, in this paper, we vote for a comprehensive review for recently published papers in
the basket of dominant OR/MS/OM journals for a number of reasons: (i) The collected papers are of
a high quality, timely and relevant to FRSCM. (ii) The result from this searching can show some
statistics regarding the trend of publications, and the popularity of different outlets for FRSCM
research. (iii) The searching result also indicates the topics that have been widely explored as well as
those under-explored, which helps with our proposal on future research opportunities.
Considering that most selected studies investigate: i) Operational strategies of upstream players
(fashion manufacturer / supplier), ii) decision frameworks of downstream players (fashion retailer), iii)
issues of consumers, or iv) the collaboration and coordination between these agents, this review is
systematically classified with respect to the four core members of a FRSC: Manufacturer, retailer,
consumer, and supply chain system. This classification strategy is easy for readers to follow and
understand what each stakeholder concerns in a FRSC. Specifically, Section 2 summaries four topics
mostly considered by fashion manufacturers (equivalent to “supplierin this review), while six
frequently concerned problems are reviewed for fashion retailers in Section 3. Next, we renew the
knowledge about consumer behavior, consumer demand, and customer return in Section 4, in order to
generate some managerial insights from the aspect of fashion customers. Then, recent developments
regarding the supply chain system are examined in Section 5. Finally, we conclude this review by
summarizing the major findings and proposing future research opportunities. The numbers of the
selected papers investigating the respective topics for the four functional core members are provided
in Table 2. It is revealed that the studies focusing on fashion retailers (72 papers) and consumers (39
papers) are much more than those on fashion manufacturers (25 papers). Besides, great attention has
been paid to the fashion retail supply chain system (87 papers), especially with regard to the
mechanism of channel coordination (62 papers).
Table 2. Statistics of the papers selected investigating the diverse research topics*.
Functional member
Major topics
Functional member
Major topics
Manufacturer (25)
Retailer (72)
Supplier, market & retail
channel selections
Product design
Inventory management
Channel selection
Retail in-store operations
Pricing strategies
Consumer (39)
Selling strategies
Consumer demand
Product operations
Consumer return
Supply chain system
Fast fashion
Risk management
* Note that it is possible for one paper appearing in more than one topic due to the multiple aspects it primarily considers.
1.3 Contribution
This review contributes to the OR literature by examining the most recent research development with
FRSCM operational models and updating the most advanced knowledge in the domain. Our focus is
to examine the works investigating FRSCM decisions with operational models to generate managerial
insights. Insights on the areas (from both OR modeling and practical perspectives) in which more
research should be conducted are proposed. This review helps to improve the understanding towards
FRSCM for both researchers and practitioners from the four functional areas. By identifying the
prospects arising from current research, we hope it will attract more attention in both the industry and
academy, and inspire more innovative OR studies on this important research area.
2. Manufacturer
In a FRSC, fashion manufacturers produce and supply fashion products to other downstream members
which ultimately reach consumers via retailers. Traditionally, manufacturers have the flexibility to set
prices to influence the market demand and further affect the profitability of the whole supply chain.
With the limitation of production capacity and lead time, manufacturers decide due dates to either
accept or reject orders from downstream partners. It is hence crucial for fashion manufacturers to make
production schedules according to demand forecasting ahead of the selling season (Charnsirisakskul
et al., 2006). In this section, we review the recent literature discussing the important aspects of
manufacturers in a FRSC, as shown in Figure 3.
Figure 3. Important issues considered by fashion manufacturers.
2.1 Production
Production is an activity performed by manufacturers. However, it is also closely related to retailers
and market demand. Considering the highly volatile demand and long lead time, it is critical and
challenging to make efficient and effective production decisions, including the optimal quantity to
produce, time to produce and ship, and price to sell. A good production plan can improve the
profitability of both manufacturers and retailers, increase market share, raise consumer satisfaction,
and reduce inventory costs. Some research works on improving the production decisions in the fashion
industry are conducted in recent years. For example, Charnsirisakskul et al. (2006) investigate the
optimal decisions on production scheduling, pricing, lead time flexibility, and order rejection /
acceptance in an integrated model. By applying mixed integer programming and heuristic initialization
methodology, the experimental results demonstrate the significance of integrating production with
other decisions on improving profitability. Similar to Charnsirisakskul et al. (2006), Cao (2014)
explores a decision model integrating pricing and production strategies for a dual-channel chain. In
addition to manufacturing by themselves, many fashion companies outsource production to specialized
third-party suppliers to gain a higher level of competitiveness and flexibility. Some research focuses
on improving decision making in this area. For instance, Ni and Srinivasan (2015) propose a matching
Manufacturer: A FRSC
member who produces
fashion products and provides
for downstream members
Product design
Channel Selection
model to identify the optimal outsourcing manufacturer for fashion companies with the consideration
of manufacturer’s tenure and location, while Georgiadis and Rajaram (2013) investigate the selection
strategies from outsourcing suppliers for private label fashion products. Besides, Choi et al. (2013)
propose to assign a third-party company to collect returned fashion products for remanufacturing.
Not restricted to manufacturers, production decisions can be made by fashion retailers (Georgiadis
and Rajaram, 2013). Such cases are commonly seen in fashion retail brands with private labels, such
as Zara, H&M, and GAP, which benefits the companies by differentiating the products from their
competitors in the market. However, the private label production strategy defines the responsibility of
retailers to make decisions for the whole supply chain with a minimum cost. In the work of Georgiadis
and Rajaram (2013), the retailer-led decision framework is formulated as a mixed integer programming
problem and solved by Lagrangian relaxation coupled with heuristics. The authors emphasize the
significance to consider inventory management decisions for upstream activities like production and
distribution. Besides, the costs arising from production and inappropriate inventory are proven to
constitute the major part of total supply chain cost which should be considered carefully during the
planning horizon.
Remanufacturing occurs in a closed-loop supply chain where (returned or used) products flow
backward to the manufacturer / remanufacturer for reproduction, the cost of which is usually lower
than producing a new one with a similar quality level. The significance of remanufacturing has been
recognized (Choi et al., 2013). The analysis conducted by Choi et al. (2013) illustrates that the
efficiency of remanufacturing strategy is highly associated with the distance between the supply chain
agents with the market.
Contract production is essentially a strategy to achieve quick response and supply chain
coordination. In contract production, the fashion manufacturer produces products according to the
contract signed with the retailer. For example, on one hand, the manufacturer can produce a contracted
quantity of fashion products long before the season and provide the retailer with a low cost. On the
other hand, the retailer can place an order to the supplier after the market demand is revealed when the
season is approaching with a higher cost. An alternative is that the retailer first orders a certain number
of products from the manufacturer, and requests further replenishment after the demand is known with
a higher cost. Note that the work conducted by Cattani et al. (2008) considers a fashion supplier which
contracts with a retailer about production. The authors identify the conditions under which the three
contract production strategies proposed are efficient. Moreover, Martínez-de-Albéniz and Simchi-Levi
(2009) examine a situation where the retailer reserves some production capacity of the manufacturer
in the early stage and then places the final order after the consumer demand is known. However, owing
to the uncertainty in demand, the retailer faces the risk of having redundant reserved capacity and
inventory. Therefore, it is important for the retailer to contract with several different suppliers with
unit capacity reservation fee and distribution fare considerations. However, their analysis shows that
diverse suppliers tend to cluster together (that is, applying the same strategies in small groups) during
the competition in order to win the retailer. In addition, Xiao et al. (2010) study a perishable-product
supply chain where the manufacturer could subcontract some of the retailer’s order to a subcontractor
to better satisfy the retailer’s demand. The authors uncover that both the manufacturer and the
subcontractor would offer a lower wholesale price when the lead time increases.
Production capacity is the major restriction faced by the fashion manufacturer. However, most
existing literature on FRSCM assumes infinite capacity. Only a few papers consider the capacity
restrictions. For example, Huang and Su (2013) take the capacity constraint into account when
designing a closed-loop supply chain. Cattani et al. (2008) identify the requirements on capacity when
making the optimal contracted production decisions. Moreover, Wang et al. (2011a) make the optimal
capacity allocation and order acceptance integrated decisions to achieve profit maximization. Both
manufacturers and retailers suffer from the limitation of production capacity. From the perspective of
fashion manufacturers, limited capacity restricts the ability to accept orders, which further influences
the development of the company. For fashion retailers, the incapability of manufacturers to supply
products timely to capture the ever-changing market trend leads to a loss in market share. In order to
deal with these problems, some fashion retailers decide to place orders long before the selling season.
However, a high inventory holding cost is incurred and the flexibility of reacting to real market demand
is impaired. To deal with the capacity restrictions, Huang et al. (2014a) propose two strategies:
Capacity expansion and wholesale price rebate for the fashion supplier. In the first strategy, it is
straightforward to improve production capacity so as to better satisfy the retailer’s demand promptly.
In the second strategy, the manufacturer offers the retailer a discount wholesale price to encourage it
to make an early order. Their research suggests that the capacity expansion cost, manufacturer’s
inventory holding cost, and retailer’s inventory holding cost are three key factors affecting the choice
on the optimal strategy. Particularly, the first strategy is preferred if the suppliers original capacity is
low while the second is advantageous when the supplier’s original capacity is relatively high.
2.2 Product design
Product design is the engine to drive the success of fashion companies considering the fast-changing
fashion trend and turbulent market demand. To capture consumers, giant brands, such as Zara and
H&M, have adopted the enhanced design strategy to quickly produce the most fashionable items by
extensively investigating the latest customer preference and tastes (Cachon and Swinney, 2011). The
improved design in fashion items benefits the companies by dealing with strategic consumers through
reducing their willingness to wait for discount. Different from the enhanced design measure, Caro and
Gallien (2007) propose to design during the selling season. This innovative strategy offers the
opportunity to improve the design according to the real market information collected from retail stores.
Moreover, instead of designing by themselves, many fashion companies outsource the design job to
professional third-party firms to keep a high level of competitiveness. According to Shen et al. (2016b),
many fashion brands move from the traditional Original Equipement Manufacturing (OEM) strategy
to the Original Design Manufacturing (ODM) strategy in recent years, in order to enjoy the advanteges
of low costs and professional design skills of the outsourcing service providers. In the OEM, the
outsourcing supplier is only responsible for production according to the clientsrequirements, while
in the ODM, the third-party manufacturer is responsible for both product design and manufacturing.
For example, famous fashion brands, Brooks Brothers and JC Penney, outsource their product design
and manufacturing function to TAL group, a leading apparel manufacturer in the world (Appelbaum,
2011). Besides, a popular Hong Kong fashion supplier, Crystal Group, reports that they have
transformed from a traditional OEM supplier to an ODM/OEM integrated service provider to design
and produce the most fashionable garments. Additionally, JEEP, a US fashion brand, appoints a
Chinese manufacturer to design and produce for it (Shen et al., 2016b).
Design for the Environment (DfE) is a growing topic in the literature (Raz et al., 2013). Nowadays,
more and more companies are seeking to provide products with less resource consumption and
pollution emission (Schmidheiny, 1992). Focusing on functional and innovative products (like fashion
clothes), Raz et al. (2013) examine the relationship among the effort paid in DfE, variation in consumer
demand, and change in total profit and cost. Exploring a newsvendor problem, the authors obtain the
optimal green efforts to be put in product design and the optimal production quantity for the
manufacturer. Their results illustrate that although the green efforts can always improve the
environmental effect of individual product, the deviation in total environmental influence remains
uncertain due to the rise in production quantity. The authors then find out the conditions where the
growth in production could be balanced by the eco-design efforts and show that overproduction is
2.3 Channel selection
In a FRSC, fashion manufacturers can distribute products to the end consumers either through the
traditional retailer channel or online direct channel. Therefore, there are two main streams of
distribution channel for suppliers being studied in the literature: Retailer (offline) channel and dual-
channel (an integration of the retailer channel with an online direct channel). It should be noted that a
fashion retailer can also play as an online seller, which will be discussed in Section 3.1.
Retailer channel: Most of the existing research works consider the manufacturers distributing
fashion products through retailers at a wholesale price (Cattani et al., 2008; Chen and Chen, 2007;
Huang and Su, 2013; Hung et al., 2013; Shao et al., 2013). For example, Chen and Chen (2007)
consider a multi-echelon supply chain for short life cycle products (like fashion items), where the
retailer makes replenishment decisions according to the Economic Ordering Quantity (EOQ) model
and the behavior of the manufacturer follows a lot-for-lot discipline. A profit-sharing channel
coordination contract is proposed for Pareto improvement. Besides, Shao et al. (2013) consider a
manufacturer distributing its fashion products through several competing retailers, where the decisions
on inventory and retailing price for individual retailers are shown to be different from the global
optimal solutions.
Dual channel: It is commonly seen in the current fashion retail industry that manufacturers sell
fashion products directly to end customers through online stores, rather than depending solely on the
traditional retailer channel, owing to the immense development of internet technology and rapid
growth in the logistics industry. According to Chen et al. (2012), the online direct sales help achieve
cost reduction, sales increase, and market expansion. Taleizadeh et al. (2016) point out that the dual-
channel strategy can not only reduce costs, but also decrease time and energy consumption by selling
online. Besides, it is convenient for customers to make comparisons on prices among various websites
and select the most preferred one. However, the authors comment that online stores cannot replace the
traditional retail stores because there are many customers who like to see, touch, and evaluate the
products in person before making a purchase. As a result, an increasing number of giant brands (e.g.,
Nike) are adopting the dual-channel supply chain strategy. For example, Cao (2014) examines a dual
channel supply chain coordination problem by a revenue sharing scheme for fashion companies, with
the objective to maximize the profit of the whole supply chain. Then, the effect of demand disruptions
on profitability and coordination contrasts is considered, and the value of knowledge of disruption in
management is quantified. His results show that for both the normal and disrupted situations, price
adjustment and production modification are the optimal decisions for the dual-channel supply chain.
He concludes that if the information concerning disruption is known by the decision-makers, the entire
dual-channel supply chain will benefit from it. A related work is found in Shang and Yang (2015),
where a profit-sharing contract is developed to coordinate a dual-channel supply chain and the optimal
contract parameters are identified to achieve Pareto-improvement. More importantly, they prove that
the three-player FRSC coordination contract negotiation is totally different from the two-player FRSC.
2.4 Shipment
Fashion products flow in a FRSC, which highlights the importance of shipping decisions (Caro et al.,
2010). Decisions about product shipment is important to both the fashion manufacturers and retailers,
because they affect costs, flexibility, and inventory management. For example, to deal with the
problem of stock out, Chen et al. (2013) develop a fast shipment strategy between the fashion apparel
retailer and the supplier ensuring that once the inventory level of the retailer decreases below a
contracted level, the supplier would help expedite the shipment of replenishment without an extra cost.
Three categories of fast shipment schemes are proposed. In the first scheme, the retailer determines
the amount of initial shipment and the subsequent fast shipment with an upper quantity limit imposed
by the manufacturer. Next, in scheme 2, the initial shipment is infinite to cover the retailer’s demand,
but there is a limit on the amount in the stage of fast shipment. Differently, scheme 3 allows the supplier
to decide the quantity limit after the order from the retailer is observed. Their mathematical analysis
shows that the manufacturers always choose scheme 1 when compared with scheme 2. Besides, the
manufacturers and the retailers always have opposite preferences regarding scheme 2 and scheme 3.
Beside the fast shipment strategy, suppliers offer a multi-shipment contract to help retailers
optimize their inventory control policy and better satisfy the market demand, in which the retailer
determines the ordering quantity for each shipment (Chen et al., 2016). From the perspective of fashion
manufacturers, the multi-shipment contract enables small batch productions to ease the peak season
pressure. For the fashion retailers, they can enjoy a reduction in the inventory holding cost while
maintaining a high level of customer service. Furthermore, green shipment, as discussed in Konur and
Schaefer (2014), relates to the optimal transportation policies under the carbon emission scheme.
Konur and Schaefer (2014) point out that truck emission is the major part of transportation pollution
in the world, which should be carefully studied in order to improve the sustainability of supply chains.
It is proven that the cost of shipment is not the only reason for the optimal selection of transportation
strategies, because the emission of freight is also crucial for the choice. Besides, outsourcing logistics
is commonly seen in the fashion industry. However, there is little research studying the outsourcing
logistics decisions faced by the fashion industry. Huang and Su (2013) is the only related literature we
found. Huang and Su (2013) investigate the conditions to apply an outsoucing shipment strategy,
assuming that the shipment of returned products could be operated by either third-party logistics
companies such as UPS or FedEx or in-house transportation departments. Their results show that the
number of products is crucial for the selection of third-party logistics service provider.
In addition to shipping vertically in a FRSC, lateral transshipment is also considered in the
literature. For example, Özdemir et al. (2013) formulate a transshipment problem within the same
echelon as a network flow problem, in which the impact of supply capacity of the fashion manufacturer
on the FRSC performance is analyzed. Besides, Huang and Soš(2010) consider a transshipment
problem in which the unsold inventory of a fashion retailer is transported to other retailers so as to
satisfy the unsatisfied demand. However, the authors mention that an independent third-party
organization is necessary to monitor the inventory and sales status to ensure the efficiency of the
optimal transshipment strategy.
3. Retailer
This section discusses the issues related to fashion retailers. Figure 4 shows the brief definition of the
fashion retailer and the related topics to be reviewed.
Figure 4. Important topics for the fashion retailer.
3.1 Supplier, market & retail channel selections
In this part, we discuss various selection problems faced by a fashion retailer. Specifically, a retailer
needs to choose a reliable supplier, a market to enter, and a channel to sell its products.
Supplier selection: Fashion retailers are facing with the risks from supply side, including the
uncertainties in supplier responsibility, lead time, supply quality, and supply quantity. Consequently,
the selection of a qualified and reliable supplier remains a challenge for fashion retailers. To deal with
these risks, Choi (2013b) develops a two-stage supplier selection strategy in which the fashion retailer
can filter poor suppliers in the first stage and then choose the optimal one from the remained suppliers
in the second stage by stochastic dynamic programming. Besides, Martínez-de-Albéniz and Simchi-
Levi (2009) select from several suppliers with different capacity reservation costs and wholesale prices,
while Bandyopadhyay and Paul (2010) make decisions between two competitive suppliers who offer
unsold product return contracts to the fashion retailer. Georgiadis and Rajaram (2013) highlight the
importance to integrate the consideration of manufacturer selection into the decisions on production
scheduling, distribution strategy, and inventory management. This is because a manufacturer who is
Retailer: A FRSC member
who orders fashion products
from manufacturers and
to end consumers
Supplier, market & retail
channel selections
Retail in-store
optimal for any individual decision might not be optimal for the whole FRSC. Besides, Choi (2013a)
studies a dual sourcing problem where the local suppliers are proposed to replace the offshore
manufacturers, with the objective to improve the environmental sustainability of the FRSC under a
carbon emission taxation policy. His result shows that a high-quality carbon emission taxation policy
can not only encourage the retailer to select a local supplier, but also reduce the risk level faced by the
retailer. Other studies related to dual sourcing problem could be found in Huang et al. (2017a),
Oberlaender (2011), and Serel (2015).
Market selection: For fashion retailers, the decisions on market selection is critical for its survival
and profitability. For example, Huang et al. (2014b) propose a secondary market strategy to deal with
the problem of returned and leftover inventory. Consumers return fashion products in the primary
market. Then, retailers trade the returned and unsold inventory in an internal market to determine the
optimal quantity of products to be salvaged in the secondary market. Their discussions show that the
secondary market plays a critical role in the profitability of the fashion retailer. However, the authors
mention that this strategy might lead to serious conflict between the retailer and the supplier regarding
the optimal decisions on inventory level. Abdel-Aal et al. (2017) integrate the market selection problem
with the product selection problem for a fashion retailer who sells multiple products in several markets.
They propose three market entry strategies: Full entry, flexible entry, and partial entry. In the first
strategy, all products are sold in the selected market with a single introduction cost per period, while
in the second strategy, the retailer has the flexibility to decide what and how many products to sell in
the market with an introduction cost for each product per period. Last for the third strategy, the retailer
needs to pay an initial cost in order to enter the market, then pay an introduction cost for each particular
product sold in that market. In the work of Abdel-Aal et al. (2017), the retail price, market entry cost,
and consumer demand are dependent on the market selected. The authors claim that the constraint on
service level and retail price affect the optimal decisions on market selection significantly. Gray market,
referring to the sales through unauthorized channels, has attracted researchersinterest in recent years.
For example, Zhang and Feng (2017) point out that the existence of gray market could impair the brand
owner’s profit. The authors propose a pricing strategy to ease the impact of gray market sales on the
authorized channel. Besides, Autrey et al. (2014) investigate whether the centralized or decentralized
decisions benefit a brand owner with the existence of gray market. They conclude that decentralization
is preferred if the competition against gray market is based on quantity. Besides, the research interests
in market segmentation are emerging in the literature. For instance, Raza et al. (2016) investigate the
impact of market segmentation led by the price differentiation among environmental-friendly products
and standard items on the FRSC decisions. They build an integrated revenue management model to
identify the optimal decisions on pricing, green efforts, and ordering quantity. Moreover, they suggest
that the market segmentation possibly leads to demand leakage.
Retail channel selection: In recent years, e-business has grown intensively all over the world,
especially in the fashion retail industry. For example, an AIP survey
illustrates that the target
products for 58% of the online purchasers are fashion items in China. With the development of online
business, both the traditional offline physical stores and online stores are available for fashion retailers.
Through online channel, customers can buy products via internet directly without walking into a store.
In the literature, several studies explore online-offline integrated FRSCM decisions. For instance, Chen
et al. (2011) consider two fashion retailers (retailer A and retailer B) with physical stores that can also
accept the online orders from an e-tailer. Retailer A is of higher priority for the e-tailer to select.
However, considering that a part of revenue is required to be shared with the e-tailer when accepting
the online orders, retailer A always satisfies its own in-store demands with priority. A related work can
be found in Chen et al. (2015b), where the fashion retailer operates two channels: An on-site physical
store and a long distance online store. Different from the physical store where the consumer demand
should be fulfilled immediately, the orders from the online store could be delayed for a period. Besides,
these two channels share an inventory pool. On the other hand, some studies focus on the online fashion
retailers. For example, Hua et al. (2016) investigate the optimal shipping strategy (whether to ship the
products to consumers for free) and product return charging scheme for an online fashion retailer. The
insights derived from their research explain why some online retailers provide free shipping and return
strategies in the real world. Furthermore, Shen et al. (2017) build an analytical model to explore the
impact of the variation in consumer demand on the performance of a luxury fashion retailer with the
consideration of social influence. More online fashion retailing examples are illustrated in Altug and
Aydinliyim (2016), Aydinliyim et al. (2017), and Xiao and Chen (2014).
3.2 Inventory management
Inventory management is critical for the success of a fashion retailer. A high-performance inventory
management system can improve the competitiveness and profitability of retailers in the highly
unpredictable and fast-changing fashion market, by providing superior customer service with
minimum inventory related costs. The inventory replenishment strategy decides the time to place an
order and the quantity to be ordered. Efficient utilization of the information collected from the market
is crucial to make high-quality inventory decisions. In the fashion industry, retailers place orders to the
suppliers for inventory replenishment either before or during the season (Şen and Zhang, 2009). Plenty
of studies relate to inventory management, like Archibald et al. (2007), Choi (2007), de Brito and van
der Laan (2009), McCardle et al. (2007), and Wu et al. (2015). For example, Choi (2007) concentrates
on the pre-season inventory problem for a fashion retailer, while Archibald et al. (2007) evaluate
several inventory management models for the survival of start-up companies.
RFID technology is an efficient inventory management and information collection tool, which is
of great value for the fashion retail industry (Wong et al., 2012). RFID technology is based on radio
frequency and the RFID tags attached to the inventory items, with which tracking, control, and
replenishment become easier. However, the RFID readings sometimes lead to inaccuracy because the
radio waves are absorbed, and the RFID tags are detuned, which is known as the “false-negative
impact that should be carefully considered in the RFID-based inventory management systems
(Metzger et al., 2013). Besides, Chan et al. (2012) and Chan et al. (2015) investigate whether the RFID
technology outperforms the traditional bar-coding approach based on the health care apparel industry.
To be specific, Chan et al. (2012) explore the conditions under which the RFID technology achieves
better performance than the bar-coding approach with the consideration of safety stock. Later, Chan et
al. (2015) introduce the RFID system into a quick response supply chain and analyze the value of the
information updated.
Inventory operations: Due to the highly volatile consumer demand and short selling season,
leftover inventory (both the returned and unsold items) is commonly seen in the fashion industry.
Frequent approaches to dealing with leftover inventory are: Sharing with other retailers (Huang and
Sošić, 2010), clearing at a discount price (Caro and Gallien, 2012; Huang et al., 2014b; Lee, 2007; Lee
and Rhee, 2007), and returning to the suppliers (Lee and Rhee, 2007; Li et al., 2014a; Yue and
Raghunathan, 2007). An innovative research topic arising with the development of online fashion
business is inventory disclosure (Aydinliyim et al., 2017). Online fashion retailers can show either the
exact number of inventory items or just “in stockto consumers, to imply the risk of stock out.
Aydinliyim et al. (2017) firstly analyze the impact of inventory disclosure strategy on the profitability
of an online fashion retailer. They obtain an inventory threshold, above which the retailer prefers to
hide the exact inventory availability while below which it is better to reveal the real inventory level.
Therefore, they conclude that it is not always the optimal decision to disclose the actual inventory.
Besides, fashion retailers should identify the optimal inventory allocation strategies when they have
multiple retail stores, in order to decide the optimal quantity of inventory items to be allocated to each
store. Chen et al. (2015a) show that the multi-store inventory allocation problem is NP-hard when the
decisions of markdown pricing strategy are integrated, even if the consumer demand is deterministic.
They develop a rolling horizon approach coupled with Lagrangian relaxation technology to solve the
complicated problem and demonstrate that the proposed solution algorithm outperforms the
benchmark approach. Another important issue regarding retail inventory is the low inventory
assortment effect which usually appears in the late selling season when the inventory level declines
and there is a lack of non-homogenous products in the stores (Khouja, 2016). In such a case, the fashion
consumers might not buy the products on the shelf that fail to satisfy their secondary expectations (like
color, condition, size). Motivated by the significance of the low inventory assortment effect on the
decisions of fashion retailers, Khouja (2016) investigates the optimal ordering quantity for a
newsvendor retailer with the consideration of the low inventory assortment effect. His results show
that the low inventory assortment effect decreases the optimal ordering quantity and this impact is
significantly affected by the standard deviation of consumer demand. Besides, a discount strategy
before the end of selling season is proposed to improve the retailer’s profitability in this research.
Moreover, Lee and Park (2016) examine the inventory decisions of two retailers who could implement
transshipment between them to achieve cooperation. However, the two considered retailers may inflate
their orders in order to compete for the supplier’s capacity. The Nash equilibrium solutions of Lee and
Park (2016) show that the carefully selected transshipment prices could mitigate the order inflation
3.3 Retail in-store operations
Retail capacity, retail assortment, shelf allocation, labor planning, and service quality management are
fashion retailersmajor considerations in terms of in-store operations.
Retail capacity: In addition to the production capacity of fashion manufacturers discussed in
section 2.1, the retail capacity is another constraint to be considered in FRSCM decisions. The capacity
of fashion retailers refers to the limitation of shelf space, budget, and long lead time. However, the
giant fashion retailer, Zara, regards the retail capacity constraint as a strategy to sell products at a
higher price (Liu and Van Ryzin, 2008). According to Liu and Van Ryzin (2008), Zara rationalizes its
retail capacity strategy to ease the impact of strategic consumers on sales by creating rationing
understock. They explain the trade-off between the lost and holding sales which is commonly observed
in the fashion industry. However, the authors point out that this capacity rationalization strategy is not
always advantageous especially when the consumers are risk neutral. Besides, it also fails even if the
consumers are risk averse, but the proportion of high-valuation consumers is low.
Retail assortment: Fashion retailing is featured with huge product variety. However, what a
consumer expects from a fashion retailer is just a limited quantity of certain types from the tremendous
product assortment. Therefore, the retail assortment strategy is crucial for the performance of a fashion
retailer. Maddah et al. (2014) investigate the optimal retail assortment decisions for the most popular
products in a fashion store while Vaagen et al. (2011) explore to improve the robustness of retail
assortment plans. Besides, considering the dynamic consumer demand of seasonal apparel products,
Caro and Gallien (2007) improve the company’s profit by dynamically optimizing the retail assortment
strategy according to the real demand during the season. Moreover, Li et al. (2015) integrate the
optimal retail assortment plan into the procurement strategy through a screening mechanism, while
Caro et al. (2014) seek for trade-offs among the preference weights, profit margin, and short life cycle
through a retail assortment packing strategy in the fashion industry.
Shelf allocation: Shelf allocation is critical for the retailers’ performance, especially for the
fashion retailers selling products with short shelf life. According to Hübner and Schaal (2017), shelf
allocation even influences consumer demand. Although most of the related literature considers
deterministic models, Hübner and Schaal (2017) apply stochastic demand into the shelf allocation
problem for fashion apparel goods in order to maximize the retailer’s profit. A new modelling approach
is proposed to obtain the optimal solutions within short running times. Besides, they consider the
impact of space elasticity on the optimal shelf allocation strategy. In a related study, Abbott and Palekar
(2008) explore space elasticity by considering a retail replenishment problem where the consumer
demand is a linear function of the shelf space assigned to the product.
Labor planning: As fashion retail sales largely depend on the performance of in-store first-line
sales staff, highly trained and skilled sales assistants significantly help enhance the consumers’
purchasing intention and vice versa. Besides, labor cost is one of the largest compositions of a fashion
retailer’s total operational costs (Chuang et al., 2016). Therefore, the research interests in the fashion
retail labor planning problem are emerging in recent years. For example, Chuang et al. (2016)
investigate the effect of sales assistant on the sales performance based on a US women fashion apparel
retailer, and construct a planning model to help the firm arrange labor source efficiently. They claim
that the consumers’ buying behavior is dependent on the ratio of the number of sales assistants to
consumer traffic. Their numerical study demonstrates the efficiency of the proposed heuristic based
solution algorithm. Moreover, Sainathuni et al. (2014) focus on the problem of staff congestion which
affects the labor productivity of a US fashion clothing supply chain. An efficient local search based
heuristic algorithm is proposed to ease the impact of manpower variation.
Service quality management: It is known that service operations management has become a
popular research topic in recent years (Wang et al., 2015). In the fashion retail industry, the service
quality of fashion boutiques is critical for the customer experiences which affects purchase intentions.
One could expect that the level of service offered by the fashion boutiques should be high and the gap
between consumer expectation with the actual perceived level of service should be small. Based on
this knowledge, Choi et al. (2017) examine the service quality management issues through a revised
retail quality scale model. The closed-form analytical results reveal that the equilibrium demands only
rely on the service gap enhancement efficiency and the profit margin. Besides, Choi et al. (2017) prove
that the demand sensitivity which is dependent on the relative problem-solving service gap is
especially crucial for the fashion retailers.
Table 3 and Table 4 in the appendix provide a structured review of the operational models in the
literature examined in this part.
3.4 Pricing strategies
Fashion retail pricing is a high-risk game involving the guesses of competitorsdecisions, consumer
valuation, and the consideration of retailer’s own operational cost. Nowadays, many pricing
optimization software based on the analysis of enormous historical data help fashion retailers make
pricing decisions more intelligently and dynamically, in order to deal with the fast-changing market
(Elmaghraby et al., 2008). Besides, Charnsirisakskul et al. (2006), Huang et al. (2014c), and Şen and
Zhang (2009) even utilize pricing strategies to control consumer demand. Large body of the literature
has investigated the fashion retail pricing strategies, like Jadidi et al. (2017), Li et al. (2012), Liu et al.
(2012), Shao et al. (2013), Webster and Weng (2008), Wu et al. (2012), and Xiao et al. (2015). For
instance, Jadidi et al. (2017) study the pricing problem of a fashionable-product retailer where the
supplier offers an all-unit quantity discount scheme. Their results demonstrate that the quantity
discount scheme would benefit the supplier and retailer by increasing the profit pool. Moreover, the
end consumers could also benefit from this policy.
A special pricing strategy, the preannounced markdown pricing strategy, is introduced by
Elmaghraby et al. (2008) to deal with strategic consumers. In this scheme, the fashion retailer first sets
a high price and preannounces to the consumers that there will be several markdown periods in the
future. This pricing strategy is essentially a game between the retailer and the various sets of customers
holding different product valuations. If only a few customers bid for the high price, then the purchasing
intention of the high-valuation consumers will increase owing to the concern of stock out considering
that more customers would buy the product at a low price. Oppositely, to hedge against demand
uncertainty, the postponed pricing strategy does not determine the prices of fashion products until the
real consumer demand is known (Chernonog and Kogan, 2014). Besides, Aloysius et al. (2013)
propose the sequential pricing strategy to deal with demand uncertainty, where the price of one product
is determined after the consumer preference for another product is observed through online shopping
cart. On the other hand, the responsive pricing strategy, as proposed by Tang and Yin (2007), is a
flexible pricing strategy to manage supply uncertainty with deterministic consumer demand, which
enables the fashion retailer to determine the prices after knowing the actual supply yield. Compared
with the traditional strategy where the ordering and pricing decisions are made before the real supply
is observed, the responsive pricing strategy shows higher profitability.
Instead of static pricing, an increasing number of fashion retailers are adopting the dynamic pricing
strategy to deal with the fast-changing market. Lin (2006) points out that demand forecasting is the
key for the performance of dynamic pricing strategy. Plenty of research has investigated the related
decisions. For instance, Lin (2006) modifies the retail prices dynamically according to the consumer
traffic prediction which is corrected by real-time sales data to maximize revenue. Liu and Zhang (2013)
consider a dynamic pricing problem where two competing fashion retailers and strategic consumers
exist, while Chen et al. (2017) incorporate a menu cost into the dynamic price setting framework. Other
literature considering dynamic pricing includes Akçay et al. (2013), Aloysius et al. (2013), Chen et al.
(2015b), Elmaghraby et al. (2008), Huang et al. (2014c), Liu and Van Ryzin (2008), Şen and Zhang
(2009), and Wu et al. (2015). Table 5 and Table 6 in the appendix give a structured overview of the
operational models in the literature introduced in this part.
3.5 Selling strategies
Bundling selling, advance selling, probabilistic selling, and open-box selling are commonly used
selling strategies in the fashion industry. Firstly, the bundling selling strategy is to bundle several
products and sell them together, which could be found in many industries such as fashion apparel, food
industry, entertainment retailing, electronics, and cosmetics. The reasons for bundling selling include
logistics cost reduction, market share and profit improvement, and packaging issues. McCardle et al.
(2007) investigate the impact of bundling sales on the performance of a fashion retailer. They identify
the optimal decisions on ordering quantity and bundling price to optimize total profit. Finally, they
suggest that the profitability of the bundling selling strategy is determined by the demands for
individual products, the relationships among these demands, and the bundling cost. Secondly, the
advance selling strategy is studied by Li et al. (2014b), encouraging customers to pre-buy the fashion
products before the start of season. However, the retailers may face a high level of product returns due
to the uncertain consumer valuation. Therefore, Li et al. (2014b) point out that the fashion retailers
should consider the price and refund policy carefully to maintain profitability. Next, the probabilistic
selling strategy, as mentioned in Xiao and Chen (2014), is to provide an online consumer with another
product which is convenient for the fashion retailer to supply, rather than the original selected one (e.g.,
different color), with a discount price. In fact, the probabilistic selling strategy targets the consumer
group who is sensitive to price. Besides, the fashion retailer benefits from inventory pooling through
this strategy. However, if the future demand is predicted to be high while the inventory is limited, the
retailer may face a loss of revenue because many products are sold with a discount. Lastly, the open-
box selling strategy, emerging with consumer returns, is introduced by Akçay et al. (2013), in which
the returned fashion products are resold to consumers at a discount price with the boxes being opened.
Not limited to salvaging the returned inventory, the open-box selling strategy helps capture late
demand and further avoid lost sales.
3.6 Product operations
Fashion product operations include product variety, product substitution, and new product introduction.
Product variety: As discussed, fashion retailing is challenged by huge variety of products with
unique demands. Therefore, it is crucial for fashion retailers to determine the level of product variety
in retail stores. It is obvious that a high level of product variety contributes to high consumer
satisfaction, consumer demand stimulation, and brand image development. Besides, product variety
and portfolio alleviate the risk of demand uncertainties of individual products (Vaagen and Wallace,
2008). Furthermore, Choi (2016b) comments that a contract on product variety could coordinate a
quick response FRSC. However, a high product variety level does not necessarily lead to high profit
margin due to the increase in the costs of design, manufacturing, logistics, and inventory management.
For example, Huang and Su (2013) claim that enormous product variety affects both directions of a
reverse supply chain. Besides, Xiao et al. (2015) build two models with different channel leadership
to study the decisions of pricing and product variety in the fashion apparel industry. They reveal that
it is preferred to offer a higher level of product variety than just satisfying the market under a
centralized retailer-led supply chain, while for a decentralized scenario, it is wise to keep a level exactly
satisfying the market. Furthermore, in the other model with the supplier being the leader, the optimal
level of product variety increases due to economies of scope.
Product substitution: When the needed fashion product is out of stock, consumers usually turn to
a similar product for substitution (e.g., similar color, style). According to Vaagen et al. (2011), like
product variety, product substitution is beneficial for fashion retailers to ease the impact of demand
uncertainty. Several studies explore the impact of product substitution on FRSCs. For instance,
Maddah et al. (2014) investigate the optimal retail assortment, inventory management, and pricing
strategies when the product line contains substitutable fashion merchandise, while Stavrulaki (2011)
considers the inventory management problem of two fashion products that can substitute each other,
and suggests that under some conditions, product substitution helps increase total profit significantly.
New product introduction: For a fashion retailer, it is important to keep consumers interested in
the products on the shelf. Launching new products frequently is an efficient approach to enhancing
retailer’s presence, maintaining existing customers, attracting new ones, and improving market share.
Besides, initial sales, as pointed out by Gallien et al. (2015), play a pivotal role in the overall
performance for the whole selling season, because the stock out in the initial stage brings huge negative
impact on the later market. On the other hand, excessive initial inventory leads to a high inventory
holding cost and a waste of leftover inventory. Consequently, the decisions on new product
introduction are significant for the success of FRSCs. Gallien et al. (2015) develop a new data-driven
system for the fast fashion retailer Zara to improve the decision quality on initial shipment. They build
a heuristic based algorithm to obtain acceptable solutions with slight computational efforts. Their
experimental results show that the system helps Zara achieve 2% growth in seasonal sales and 4%
decline in leftover inventory. Additionally, Chiu et al. (2015b) determine the optimal launch time for
fashion products with unknown demand, by formulating the decision framework as a stock loan
problem that is commonly seen in the financial area. Besides, in the work of Caro et al. (2014), the
optimal new product introduction strategies are studied with the retail assortment problem.
4. Consumer
As discussed, FRSC decisions are driven by consumer demand with diverse uncertainties. Therefore,
studying consumer behavior, improving demand forecasting, and dealing with consumer returns are
significant to the success of a FRSC. This section presents the publications in terms of fashion
consumers from three aspects: Consumer behavior, consumer demand, and consumer return, as
depicted in Figure 5.
Figure 5. Important topics related to fashion consumers.
4.1 Consumer behavior
Consumer behavior concerns about the experience, knowledge, intellectuality, and psychological
characteristics of the customers. Consumers decide what to purchase, the quantity to purchase, and
when / where to purchase. Owing to the importance of consumer behavior on the profitability of a
FRSC, much research has been conducted to improve the efficiency of FRSC decisions by considering
consumer behavior.
One research stream investigates the impact of consumer behavior on FRSC decisions (Altug and
Aydinliyim, 2016; Chua et al., 2017; Taleizadeh et al., 2016). For instance, Chua et al. (2017) study
how customer behavior affects the optimal discount and inventory replenishment decisions of a retailer
who sells perishable products like fast fashion items. Besides, Taleizadeh et al. (2016) consider the
influence of customer preference on a fashion manufacturer’s selection between a direct online channel
Consumer: The end of a
FRSC, making decisions to
buy or return fashion
and a traditional retailer channel. They find that it is preferred for the supplier to select the online
channel if consumers enjoy purchasing online rather than offline. Besides, the online channel
contributes to lowering the product prices so as to enhance the consumer welfare. However, the
supplier needs to invest in marketing and advertising itself through the online channel. On the other
hand, if consumers prefer shopping in a physical retail store, the traditional retailer channel is hence
selected. The manufacturer is responsible for marketing and advertising if the retailer is unwilling to
make investments. Undoubtedly, the retail prices increase, and the consumer welfare is impaired
through this channel.
Another research stream believes that the decisions made by fashion retailers influence the
behavior of consumers (Aydinliyim et al., 2017; Chen and Chen, 2016; Xu et al., 2015; Yan and Cao,
2017; Yoo et al., 2015). For example, online fashion retailers can enhance the buying intention of
consumers by strategically disclosing the inventory level, signaling the risk of stock out by
intentionally hiding or showing the exact inventory (Aydinliyim et al., 2017). Besides, Xu et al. (2015)
impose deadlines for returning fashion products to influence the customer return behavior. They
suggest that customers decide whether to return or keep the product based on their valuation which is
affected by the return deadline regulated by the retailers. Longer return deadline helps improve the
consumer valuation for the product. Moreover, they suggest that the return policy should consider
product life cycle and historical return rate. Based on these ideas, Xu et al. (2015) identify the optimal
decisions on consumer return deadline, refund amount, pricing, and inventory level for a fashion
retailer. Besides, Yan and Cao (2017) study the impact of payment method, ordering quantity, and
assortment strategy on consumer behavior regarding returning products for a B2C US fashion retailer
while Chen and Chen (2016) and Yoo et al. (2015) show that the product return policy influences
consumersbuying behavior.
In the third research stream, the consumers who learn from experience, predict future discount,
and adjust their purchasing behavior are called strategic consumers. When consumers become strategic,
the fashion retailer’s profit is impaired and the efficiency of a FRSC is hurt (Li and Yu, 2017). Wu et
al. (2015) point out that strategic consumers will deliberately postpone the purchase with the
expectation for lower prices according to the historical pricing data while facing the risk of stock out.
They call the estimated lower price expected by strategic consumers as the reference price. To relieve
the impact of strategic consumers, Liu and Van Ryzin (2008) propose that the fashion retailers can
intentionally cause understock to motivate consumers to buy at a higher price. Besides, Altug and
Aydinliyim (2016) study how to utilize a product return policy to deal with strategic purchase
postponement of online customers. They suggest that high-price sales increase with a lenient return
policy because it is not too expensive for consumers to regret. Similar work is found in Elmaghraby et
al. (2008), where a markdown policy is applied to affect strategic consumers. Additionally, Cachon
and Swinney (2011) and Swinney (2011) believe that the efficiency of quick response strategies could
be impaired by strategic consumers. Table 7 and Table 8 in the appendix summarize the operational
models in the literature introduced in this part.
4.2 Consumer demand
In the fashion industry, consumer demand is highly turbulent and unpredictable. Demand uncertainty
is the major challenge considered by both researchers and practitioners. Stock-out caused by poor
demand forecasting leads to a consumer loss and huge damage to brand image. On the other hand, the
unnecessary inventory holding cost incurred by over-stock should be avoided. However, the fast-
changing fashion trend, short selling season, and unpredictable consumer preference make the demand
highly challenging to forecast. Besides, Sun and Debo (2014) insist that it is difficult for supply chain
members to keep long-term partnership under turbulent environment. Therefore, much research has
been conducted to improve the quality of demand forecasting (Lin, 2006; Swinney, 2011).
Fashion companies always conduct demand forecasting long before the selling season due to the
concerns of production, capacity, transportation, cost, or contract, which leads to inaccuracy in the
predicted demand. An efficient information updating mechanism contributes to improving the quality
of demand forecasting by updating the latest market information that is close to the coming season.
Besides, early sales data at the beginning of the season can be applied to update later demand
information to ease the impact of demand uncertainty (Şen and Zhang, 2009). Bayesian information
updating approach is widely used in the fashion industry. For example, Agrawal and Smith (2013)
propose a Bayesian model to update demand forecasting for a FRSC with multiple retail stores, and
further determine the optimal ordering and inventory allocation strategies. Choi et al. (2006) utilize
the Bayesian approach to update fashion productsdemand distribution using the sales data near the
beginning of the season. Two updating models, one with unknown mean and variance, and another
with unknown mean and pre-known variance, are compared. Their results show that the fashion retailer
always benefits from the quick response policy under the second model. Other applications of the
Bayesian information updating approach are observed in Berk et al. (2007), Caro and Gallien (2007),
Chan et al. (2015), Choi (2007), Huang et al. (2017b), and Şen and Zhang (2009). Furthermore, O'Neil
et al. (2016) utilize machine learning to predict the unknown demand without knowing the type of
distribution, variance, and mean. Their proposed method only requires information regarding the upper
and lower bounds on demand.
Demand nowcasting: With the development of online business and social media, the big data
technology has seen great potential in improving fashion demand forecasting. Big data moves the
traditional historical data-based demand forecasting to the advantageous big data-based demand
nowcasting. An analytical example of the application is reported in Choi (2016a), where the fashion
retailer adjusts its attitude towards future demand through analyzing the enormous consumer
comments collected from various social media platforms. The author finds that the penetration of social
media enables fashion brands to better identify consumer needs, complaints, and expectations, and
further reduce the bullwhip effect that widely exists in a FRSC due to information distortion.
4.3 Consumer return
Due to the uncertain product valuation, fashion companies always face the challenge of consumer
returns. Guide et al. (2006) report an over 100 billion US dollars of returned products (within ninety
days for any reason) every year in the US. However, only a small part of value of the returned products
are extracted by suppliers due to the long supply chains. As discussed in Taleizadeh et al. (2016), the
suppliers selling directly to customers through the online channel facilitate high utility of the returned
fashion products for reproduction, so as to improve environmental sustainability, while the traditional
retail store channel decreases the rate of reproduction unless the manufacturer contracts with the
retailer. Moreover, Reimann (2016) proposes to utilize the refurbished returned products collected in
the early sales stage, to fulfill the later demand.
Motivated by the significance of consumer returns on the performance of a FRSC, many
researchers pay attention to the decisions regarding consumer return policies, such as Chen and Bell
(2011), Choi (2013c), Huang et al. (2014b), Li et al. (2014b), Yan and Cao (2017), and Yoo et al.
(2015). For example, Chen and Bell (2013) examine two forms of product returns and their influence
on the profits for both retailers and suppliers. Their results show that the retailers always prefer a
manufacturer-led supply chain when the return rate is high. Finally, they demonstrate that the supply
chain profit would be greatly impaired if the customer return is ignored. Moreover, Akçay et al. (2013)
study a fashion retailer who applies a partial return policy and a full return policy. In the first policy,
only a part of value of the returned product is refunded to the customer, while the second policy
provides an 100% money-back guarantee. Then, the returned products are either salvaged or resold at
a discount price. An operational model is constructed to decide the optimal ordering quantity, new
product price, refund money, and discount price to resell the returned items. In a related work, Su
(2009) makes comparisons between these two return policies. He suggests that the full return policy is
not helpful to improve the FRSC performance while the optimal refund value should be lower than the
original selling price. This idea is supported by Xu et al. (2015) which insists that the optimal refund
money is equal to the salvage value of the returned product. However, a different finding is reported
in Hsiao and Chen (2014), suggesting that the full return policy is more profitable when the high-
valuation customers experience a low hassle cost. Besides, Heydari et al. (2017) comment that a
money-back guarantee FRSC could be coordinated with a dual-buyback contract. Additionally, as the
returned fashion products flow from customers to suppliers, there has been increasing attention paid
to the reverse fashion supply chain or closed-loop fashion supply chain issues (e.g., de Brito and van
der Laan, 2009; Guide et al., 2006; Huang and Su, 2013; Karakayali et al., 2013; Reimann, 2016).
5. Supply chain system
This section concludes the recent studies from the view of supply chain system in terms of channel
coordination, fast fashion, information management, and risk management (see Figure 6).
Figure 6. Important topics in the supply chain system.
5.1 Channel coordination
With individual different objectives of members in a FRSC as well as the presence of double
marginalization effect, the entire supply chain is usually unable to achieve optimality automatically.
Both in the literature and practice, contracts are widely used to provide incentives for the FRSC
members to achieve channel coordination. According to Cachon (2003), the supply chain is
coordinated when the actions taken by the players facilitate a Nash equilibrium. Besides, a supply
chain achieves Pareto optimality if at least one member is strictly better off, and no one is worse off
after the contract is applied (or realizes the win-win situation where all members are strictly better off).
For instance, in the problem of Yang et al. (2011), the retailer could firstly place an order to a long-
lead-time supplier. After the demand information is updated through market observation, the retailer
could cancel part of the order placed to the long-lead-time supplier and place a new order to a short-
lead-time supplier. In this case, the long-lead-time supplier may suffer a loss from the order cancelation,
despite that the retailer benefits from demand updating. Therefore, Yang et al. (2011) propose a return
policy contract by charging an order-cancelation penalty to the retailer to coordinate the supply chain,
which benefits the supplier. They show that it is easier to achieve coordination when the future market
Supply chain system: The
collaboration and
coordination among
diverse members in a FRSC
Fast fashion
demand is observed to be sufficiently large. For the comprehensive review on supply chain
coordination, we refer to Arshinder et al. (2011) and Cachon (2003). Table 9 in the appendix lists the
10 coordination contracts most frequently applied in the selected literature and the corresponding
example references. Table 10 (appendix) summarizes the major assumptions and extensions of the
operational models in the example literature. Particularly, the buyback contract and wholesale pricing
contract are the two most popular coordination strategies. Other coordination contracts are like
quantity compensation contract (Lee et al., 2013), two-part price contract (Yan and Cao, 2017),
advance-purchase contract (Deng and Yano, 2016), inventory subsidizing contract (Chen et al., 2016),
supply contract (Wang et al., 2012), gain / loss sharing contract (Wang and Webster, 2007), linear
margin contract (Xiao et al., 2015), cost sharing contract (Wang et al., 2011b), and supply option
contract (Zhao et al., 2018). For example, Wang et al. (2011b) build optimal cooperative advertising
policies and a cost-sharing contract to solve a cooperative advertising problem between a supplier with
competing retailers. Besides, Zhao et al. (2018) propose a novel supply option contract to coordinate
a two-echelon apparel supply chain by developing a two-stage model where a stochastic spot market
exists. The authors evaluate the expected benefits gained per unit of the option under different market
situations. Zhao et al. (2018) greatly contribute to the literature through the analytical characterization
of the option contract and deriving original managerial insights for the application of the supply option
5.2 Fast fashion
Considering the long lead time and volatile consumer demand of the fashion industry, the quick
response (QR) strategy is developed to reduce lead time and improve demand forecasting. QR helps
the FRSC react to the change in market rapidly, which contributes to the development of fast fashion.
According to Mehrjoo and Pasek (2016), fast fashion is to provide customers with the most fashionable
products within the shortest time, which requires flexible and responsive FRSC structures, even if high
costs are resulted. Giant fast fashion brands, such as H&M and Zara, have shortened the lead time
from several months to only a few weeks. The postponement product differentiation strategy,
postponing the final product design (e.g., color) near the season when the market information collected
is more accurate, is applied to reduce lead time (Choi, 2016c; Zhang et al., 2013). However, even
though the retailers benefit from the fast fashion strategy through more precise demand information
and a lower inventory level, the manufacturers usually suffer from the reduction in ordering quantity
and the high investment cost in constructing a flexible supply chain. In order to achieve fast fashion
supply chain coordination, strategic contracts must be developed. Such contracts could be inventory
service level commitment (Chan et al., 2015; Choi and Chow, 2008), minimum ordering quantity
commitment (Chow et al., 2012), price commitment (Choi and Chow, 2008; Chow et al., 2012),
minimum quantity with price commitment (Chan et al., 2015), buyback contract (Choi and Chow,
2008), or surplus-sharing and tariff contract (Choi, 2016a). Many works investigate the fast fashion
strategies from different perspectives, like Cachon and Swinney (2011) (enhanced design and strategic
consumers), Li et al. (2014a) (the impact of product return strategy), Choi and Chow (2008) (mean-
variance analysis), and Mehrjoo and Pasek (2016) (risk management). For a detailed review about fast
fashion, the readers are referred to Caro and Martínez-de-Albéniz (2015).
5.3 Information management
A high-quality information management system is of great significance to the success of a FRSC. For
a fashion company in the current highly competitive market, information refers to not only the
historical data, but also the real-time operations data (e.g., inventory level, customer traffic, product
return, and consumer feedback), future trend, and knowledge of competitors. The importance of
efficiently managing information flows within the fashion companies and across the FRSC are
emphasized in Anand and Goyal (2009). It is crucial for a fashion firm to manage what it knows, what
other FRSC members know, and what the competitors know. Moreover, Anand and Goyal (2009)
highlight the significance of balancing information flows with material flows in a FRSC on
maximizing total profit. Many studies explore the impact of information management on the
performance of FRSCs. For instance, de Brito and van der Laan (2009) demonstrate that the unreliable
and inaccurate historical product return information imposes great negative impacts on the decisions
of fashion retailers and suppliers. Besides, information updating plays an important role in improving
demand forecasting (as discussed in section 4.2).
Information asymmetry refers to a situation where the members in a FRSC possess their own
private information which is not observable by others. Fashion manufacturers generally have inferior
information regarding consumers than fashion retailers. Information asymmetry affects the supply
chain coordination strategies significantly. For example, Li et al. (2014a) and Yue and Raghunathan
(2007) study whether a return contract could coordinate a FRSC with information asymmetry, while
Chiu et al. (2016) investigate the influence of information asymmetry on the FRSC coordination
decisions under different channel leadership. Moreover, Burnetas et al. (2007) design a quantity
discount contract for a fashion manufacturer with worse knowledge of consumer demand, in order to
affect the retailer’s inventory decisions.
Information sharing helps improve the quality of FRSC decisions. The research interests in
information sharing are emerging in recent years. For instance, Liu and Özer (2010) highlight the
importance of demand information sharing on improving supply chain performance, and propose a
buyback contract to achieve information sharing between the supplier and the retailer. Besides, Chen
and Bell (2013) show that the consumer return information sharing helps avoid the revenue loss caused
by information distortion. Moreover, Chen (2011) shows that with a buyback contract, the fashion
retailer prefers to share the consumer return information if the supplier overestimates the information.
A similar study is found in Yan and Cao (2017), where a two-part pricing contract is proposed to
motivate the fashion apparel retailer to share the consumer return information with its supplier.
Moreover, the information regarding inventory status and consumer demand is shared in a FRSC,
which is demonstrated to positively influence the supply chain coordination strategies and improve
total profit (Huang et al., 2017b). Instead of passively receiving the information shared by retailers,
fashion suppliers could also share the information they collect from the market to affect the behavior
of retailers (Guo and Iyer, 2010; He et al., 2008; Liu and Özer, 2010). In addition to the information
sharing along a FRSC, the sharing within the same echelon is studied. For example, Chen et al. (2011)
investigate the optimal online order acceptance decisions for two fashion retailers who share inventory
information with each other.
5.4 Risk management
A FRSC encounters inherent diverse uncertainties, which leads to challenges and risk. On the
consumer side, apart from demand, uncertainties in consumer valuation (Akçay et al., 2013; Chen and
Chen, 2016; Li et al., 2014b; Swinney, 2011; Xu et al., 2015), comsumer preference (Aloysius et al.,
2013; Xiao et al., 2015), consumer population (Swinney, 2011), consumer reservation price (Huang et
al., 2014c; Lin, 2006), consumer return (de Brito and van der Laan, 2009; Liu et al., 2012), and
consumer arrival (Lin, 2006; Xiao et al., 2016) are frequently studied in the literature. Besides, a FRSC
also faces various uncertainties arising from the supply side (e.g., supply yield, supplier responsibility,
and lead time). For example, Tang and Yin (2007) deal with the uncertain supply yield using a
responsive pricing strategy for a fashion retailer. Facing the supply quality uncertainty, Lee et al. (2013)
show that the traditional buyback and revenue sharing contract fail to coordinate the FRSC. Instead,
they propose a new coordination scheme, named the quality-compensation contract, to realize FRSC
coordination. Besides, to deal with the supplier responsibility risk, Chen and Lee (2016) propose three
common approaches: Supplier certification, contingency payment, and process audit. Furthermore, the
supply lead time uncertainty is studied in Wu et al. (2012). Other uncertainties in a FRSC are like
market condition switch time (Chiu et al., 2015b) and market size (Li and Yu, 2017; Wu et al., 2015).
It is critical for all the members in a FRSC to consider the various types of uncertainties and risks
carefully, so as to improve the efficiency of decision making. Therefore, the research interests in
FRSCM risk management have grown rapidly in recent years. For example, based on the fashion
industry, Chow et al. (2015) study risk management with the consideration of minimum profit share.
Their results imply that when the ratio of minimum profit share grows, the risk level of the
manufacturer declines while that of the retailer increases. Besides, Zhao et al. (2014) investigate the
impact of consumer demand uncertainty on the applicability of a buyback contract in the fashion
industry. They conclude that it is crucial to consider the demand uncertainty risk when constructing
contracts. Furthermore, mean-variance analysis, a risk analysis tool widely used in the financial
industry, has seen great potential for the application in FRSCM (e.g., Chiu et al., 2011; Chiu et al.,
2015a; Choi, 2016c; Choi and Chow, 2008; Choi et al., 2008; Cui et al., 2016; Li et al., 2014a; Shen
et al., 2013; Wei and Choi, 2010). For instance, Cui et al. (2016) investigate the optimal store brand
introduction strategies using the mean-variance formulation for a risk-averse retailer, and prove that
the substitution factor and the risk deducted surplus of the store brand product are crucial for the
decision making. Besides, based on the classic Markowitz portfolio theory in finance, Chiu et al. (2011)
utilize the mean-variance approach to analyze the effect of the target sales rebate contracts on supply
chain coordination when the retailer is risk-averse. We refer to Chiu and Choi (2016) as a
comprehensive review of the application of mean-variance analysis in FRSCM. Besides, Mehrjoo and
Pasek (2016) utilize the Conditional Value at Risk (CVaR) measure to assess FRSC risks.
Several risk management strategies are found in the recent literature. Firstly, the risk sharing
strategy is widely applied both in the industry and academia. For example, the supply and inventory
risks arising from consumer demand uncertainty are proposed to be shared among several FRSCs when
the vacant reserved lead-time capacity is regarded as options on futures or futures for trading (Hung
et al., 2013). Besides, Chen et al. (2016) develop a risk sharing based contract and an inventory
subsidizing strategy to achieve FRSC coordination. Moreover, the return policy between fashion
suppliers with retailers is to share the risk of leftover inventory (Archibald et al., 2007; Lee and Rhee,
2007). Next, the mentioned postponement strategies, like the postponed pricing strategy in Section 3.4
and the postponement product differentiation strategy in Section 5.2, substantially help minimize
FRSC risks by postponing decision making nearer to the season. Similarly, mass customization, a
strategy to postpone final product design and assembly just before passing the products to consumers,
is actually a risk pooling strategy which greatly eases the impact of demand uncertainty by satisfying
individual consumer’s specific preferences with a lower inventory level (Liu et al., 2012). We refer
readers to Fogliatto et al. (2012) for a thorough review of mass customization. Besides, the information
updating strategy to improve the accuracy of demand forecasting is a method to reduce the risk from
demand side (as discussed in Section 4.2).
6. Conclusion and Future Research Agenda
The decisions in FRSCM are highly retailer-led and consumer demand driven. Owing to the diverse
inherent uncertainties from both the demand and supply sides, FRSCM has become an important and
challenging aspect in the domain of operations research and management science. A high level of
research interests in FRSCM is confirmed by the trend of publications in the recent decade. However,
there is a lack of comprehensive review of analytical modeling studies in FRSCM in the existing
literature. Motivated by the significance to update the most recent knowledge in this research area, we
review 144 papers selected from the mainstream OR/MS/OM journals published in the recent decade
(2016-2017). It should be noted that the main contribution of this review is to investigate those
analytical studies which utilize OR techniques and models to explore FRSCM problems.
Considering that most FRSCM studies investigate problems arising from the different functional
areas of a FRSC, we systematically structure this review according to the four core members, namely
the manufacturer, retailer, consumer, and supply chain system. In each section, the related studies and
research development are examined. In particular, fashion manufacturers are concerned with the
problems of production, product design, channel selection (retailer channel or dual channel), and
shipment most. Besides, we highlight that there are several critical issues involved in the consideration
of fashion retailers: Retailer selection (supplier, market, and retail channel), inventory management,
retailer in-store operations (retail capacity, retail assortment, shelf allocation, and labor planning),
pricing strategies, selling strategies, and product operations (product variety, product substitution, and
new product introduction). Then, regarding fashion customers, we identify that consumer behavior,
consumer demand, and consumer return concern the FRSCM decision makers most. Finally, the supply
chain system is investigated from the perspectives of crucial areas such as channel coordination, fast
fashion, information management, and risk management. As a concluding remark, we have the
following findings from the reviews:
1. Most popular problem setting: Regarding problem setting, the newsvendor model is employed
much frequently in the selected FRSCM literature (76 times in total). In fact, the newsvendor
problem refers to a situation where the decision maker should decide the optimal inventory level
of a product with uncertain demand that will be obsolete at the end of a short selling season, in
order to achieve profit maximization (Petruzzi and Dada, 1999). Therefore, it is extensively
applied in OR modeling research in FRSCM where the products are featured with short lifecycles
and highly uncertain demands.
2. Multi-echelon nature: Most of the FRSCM decisions are multi-member involved in order to
yield the supply chain system-wide optimal decisions. For example, fashion manufacturers make
production schedules according to the orders from retailers, while the ordering strategies of
retailers are based on consumer demand. On the other hand, the inventory decisions of fashion
retailers could be influenced by manufacturers through FRSC coordination contracts, while
retailers could affect consumer demand through strategic pricing, selling, and return policies.
3. Downstream oriented: The review results clearly show that there is more research concentrating
on downstream FRSC members (retailers and customers) than upstream agents (manufacturers).
This finding is as expected as the definition of FRSCM which implies that the focus of FRSCM
study is on the retailer and customer sides. However, operations of fashion manufacturers are still
worthy of investigation because they relate to downstream members closely and significantly
affect the performance of the whole FRSC. For example, as the start of a FRSC, fashion
manufacturers should work smoothly with retailers to provide sufficient and accurate inventory
to avoid stock out or overstock, so as to better satisfy the market. Besides, designing products that
capture the latest fashion trend and transporting the products to the right place at the right time
are profit engine of a FRSC.
4. Uncertainties: Diverse sources of uncertainties are the major challenges faced by the FRSCM
decision makers. As shown in the operational model summary tables, demand uncertainty is the
primary risk source considered by researchers, which is consistent with the nature of a FRSC.
Therefore, risk management has become a central part of FRSCM studies. Widely used risk
management strategies include updating demand forecasting close to the season, postponing the
pricing, product differentiation, and product design decisions, and sharing risks with other
stakeholders. All of these mechanisms are reactive actions. However, more studies are needed to
investigate the effect of proactive strategies like stimulating demand, setting or influencing
fashion trend.
5. Analysis approach: The summarized characteristics of the operational models from Table 3 to
Table 10 show that most works employ the “close-formanalysis approach, while a few studies
conduct the computational-based analysis. For the close form analysis, game theory is widely
applied. In the computational-based analysis, (mixed) integer programming, stochastic
programming, Lagrangian relaxation, and heuristics are frequently used. Additionally, more
research considers stochastic demand instead of deterministic, which is consistent with the
essence of the fashion industry. Besides, most studies pursue exact solutions rather than
approximate outputs. It is also interesting to notice that the majority of models seek for profit
(revenue) maximization rather than cost minimization.
6. Future model extensions: By analyzing the review results of the analytical models in four
research topics, namely retail in-store operations, pricing strategies, consumer behavior, and
channel coordination (Table 3-10), we have identified several future probable extensions of the
analytical modeling FRSCM research according to the number of selected papers applying the
model features. For example, regarding the model objectives, nearly all the selected papers are
dedicated to maximizing profits/revenues, instead of minimizing costs. Besides, except retail in-
store operations, all the other three research topics utilize the close-form analysis approach much
more frequently than the computational-based analysis approach. Therefore, we summarize the
potential future model extensions in Table 11. Specifically, more computational-based analysis,
real case research, and multi-product studies are needed for pricing strategies, consumer behavior,
and channel coordination. In terms of real data and cost minimization studies, there is a large open
space left for future research in all the four areas. For consumer behavior and channel coordination
related research, most existing works examine the single-period problems; therefore, more
attention should be paid on the multi-period cases. Finally, implementation of the OR models and
techniques is critically important, and future research should pay attention to it.
Table 11. Future exploration areas in the analytical OR models.
Retail in-store
based analysis
Real case
Real data
Minimize cost
Lastly, as we can see from the findings derived from our literature review, despite that plenty of
research studies have already been devoted to FRSCM, there still exist some under-explored topics
which call for future research. We propose some most important ones as follows.
a) Outsourcing logistics: With the advantages of third-party firms professional knowledge and
skills, outsourcing (e.g., product design, manufacturing, logistics) is commonly seen in the fashion
industry. According to the review results, product design and manufacturing decisions in
outsourcing have been widely investigated. However, the issues on outsourcing logistics are
underexplored, e.g., transportation scheduling in FRSCs, as well as the role of third party logistics
service providers in the “buy online pick up in storeretail operations (e.g., in companies like
Uniqlo). Therefore, more attention should be paid to build practical models and efficient
algorithms to generate better outsourcing logistics solutions.
b) Cross selling/up selling: According to Wong et al. (2012), the cross selling strategy helps improve
profit on the existing consumers by selling them additional items that are associated with the
product they originally purchase, while the up selling strategy is to persuade consumers to buy an
upgraded version of the original intended product, which is usually achieved by a sales assistant.
These selling strategies are beneficial for fashion retailers and commonly seen in practice.
However, there is no analytical literature considering fashion products cross selling or up selling.
Future studies can develop applicable models and efficient solution methods to generate valuable
insights regarding cross/up selling strategy, which will benefit the fashion retailers a lot.
c) Horizontal collaboration: Collaboration within the same FRSC echelon is commonly seen in the
fashion industry. For example, the fashion retailers under the same brand umbrella usually share
consumer information and conduct promotions together. Besides, different fashion retailers may
work with the department store to have an accumulated point system during some promotion
periods. Moreover, small fashion manufacturers usually collaborate to take up big orders from
giant retailers. However, the horizontal collaboration in FRSCs is underexplored. Consequently,
future research can fill this gap by studying the decision frameworks and identifying the optimal
strategies for the horizontally collaborative FRSC members.
d) Social and environmental responsibility: Lee and Tang (2017) propose that researchers are
going beyond the traditional supply chain management research areas such as production, quality,
inventory, and scheduling. Social and environmental responsibility of the companies that operate
in developing economies has attracted increasing research interests. Our review shows that green
product design and green shipment have been integrated into FRSCM decisions. However, this
area is still underexplored and deserves more research. Issues such as social and economic well-
being, non-profit activities, and environmental sustainability of FRSCs should be investigated.
More analytical and operational models and solution methodologies with high efficiency should
be developed to improve the firmssocial and environmental responsibility.
e) Proactive risk management: Due to the diverse inherent uncertainties of the fashion industry,
risk management is crucial for the success of FRSCs. As discussed, most of the existing risk
management strategies applied in the literature are reactive mechanisms, which leaves open space
for researchers to investigate more efficient strategies, develop novel models, and construct high-
performance solution approaches from the perspective of proactive risk management.
To summarize, this paper examines the most recent literature that applies OR techniques and
models to improve FRSCM decisions and updates the most advanced knowledge for the four FRSC
core members. Future research opportunities are proposed. We hope this paper will lay the foundation
for the topic and help inspire more future research to address many challenging issues in FRSCM.
The authors are grateful to the editors and anonymous reviewers for their constructive comments and
suggestions for the development of this manuscript. Besides, this research was partially supported by
General Research Fund by Research Grants Council (Hong Kong) with the project number PolyU
152294/16E, and The Hong Kong Polytechnic University (under account code RUE7).
Abbott, H., & Palekar, U. S. (2008). Retail replenishment models with display-space elastic demand. European
Journal of Operational Research, 186(2), 586-607.
Abdel-Aal, M. A., Syed, M. N., & Selim, S. Z. (2017). Multi-product selective newsvendor problem with service
level constraints and market selection flexibility. International Journal of Production Research, 55(1),
Afshari, H., & Benam, F. H. (2011). Retail logistics. In Logistics Operations and Management: Concepts and
Models (R. Farahani, S. Rezapour, & K. Laleh (Eds.), 267-289): Elsevier.
Agrawal, N., & Smith, S. A. (2013). Optimal inventory management for a retail chain with diverse store
demands. European Journal of Operational Research, 225(3), 393-403.
Agrawal, N., & Smith, S. A. (2015). Retail Supply Chain Management: Quantitative Models and Empirical
Studies (Vol. 223): Springer.
Akçay, Y., Boyacı, T., & Zhang, D. (2013). Selling with money-back guarantees: The impact on prices,
quantities, and retail profitability. Production and Operations Management, 22(4), 777-791.
Aloysius, J., Deck, C., & Farmer, A. (2013). Sequential pricing of multiple products: Leveraging revealed
preferences of retail customers online and with auto-id technologies. Information Systems Research,
24(2), 372-393.
Altug, M. S., & Aydinliyim, T. (2016). Counteracting strategic purchase deferrals: The impact of online retailers
return policy decisions. Manufacturing & Service Operations Management, 18(3), 376-392.
Anand, K. S., & Goyal, M. (2009). Strategic information management under leakage in a supply chain.
Management Science, 55(3), 438-452.
Appelbaum, R. (2011). Transnational contractors in East Asia. In The Market Makers: How Retailers Are
Reshaping the Global Economy (G. Hamilton, B. Senauer, & B. Petrovic (Eds.), 255-270). New York:
Oxford University Press.
Archibald, T. W., Thomas, L. C., & Possani, E. (2007). Keep or return? Managing ordering and return policies
in start-up companies. European Journal of Operational Research, 179(1), 97-113.
Arshinder, K., Kanda, A., & Deshmukh, S. (2011). A review on supply chain coordination: Coordination
mechanisms, managing uncertainty and research directions. In Supply Chain Coordination Under
Uncertainty (T.-M. Choi & T. C. E. Cheng (Eds.), 39-82): Springer.
Autrey, R. L., Bova, F., & Soberman, D. A. (2014). Organizational structure and gray markets. Marketing
Science, 33(6), 849-870.
Aydinliyim, T., Pangburn, M. S., & Rabinovich, E. (2017). Inventory disclosure in online retailing. European
Journal of Operational Research, 261(1), 195-204.
Bandyopadhyay, S., & Paul, A. A. (2010). Equilibrium returns policies in the presence of supplier competition.
Marketing Science, 29(5), 846-857.
Berk, E., Gürler, Ü., & Levine, R. A. (2007). Bayesian demand updating in the lost sales newsvendor problem:
A two-moment approximation. European Journal of Operational Research, 182(1), 256-281.
Burnetas, A., Gilbert, S. M., & Smith, C. E. (2007). Quantity discounts in single-period supply contracts with
asymmetric demand information. IIE Transactions, 39(5), 465-479.
Cachon, G. P. (2003). Supply chain coordination with contracts. In Handbooks in Operations Research and
Management Science (A. G. de Kok & S. C. Graves (Eds.), Volume 11, 227-339): Elsevier.
Cachon, G. P., & Swinney, R. (2011). The value of fast fashion: Quick response, enhanced design, and strategic
consumer behavior. Management Science, 57(4), 778-795.
Cao, E. (2014). Coordination of dual-channel supply chains under demand disruptions management decisions.
International Journal of Production Research, 52(23), 7114-7131.
Caro, F., & Gallien, J. (2007). Dynamic assortment with demand learning for seasonal consumer goods.
Management Science, 53(2), 276-292.
Caro, F., & Gallien, J. (2012). Clearance pricing optimization for a fast-fashion retailer. Operations Research,
60(6), 1404-1422.
Caro, F., Gallien, J., Díaz, M., García, J., Corredoira, J. M., Montes, M., Ramos, J. A., & Correa, J. (2010). Zara
uses operations research to reengineer its global distribution process. Interfaces, 40(1), 71-84.
Caro, F., & Martínez-de-Albéniz, V. (2015). Fast fashion: Business model overview and research opportunities.
In Retail Supply Chain Management (N. Agrawal & S. A. Smith (Eds.), 237-264): Springer.
Caro, F., Martínez-de-Albéniz, V., & Rusmevichientong, P. (2014). The assortment packing problem:
Multiperiod assortment planning for short-lived products. Management Science, 60(11), 2701-2721.
Cattani, K. D., Dahan, E., & Schmidt, G. M. (2008). Tailored capacity: Speculative and reactive fabrication of
fashion goods. International Journal of Production Economics, 114(2), 416-430.
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(1), 803-811.
Chan, H.-L., Choi, T.-M., Hui, C.-L., & Ng, S.-F. (2015). Quick response healthcare apparel supply chains:
Value of RFID and coordination. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(6),
Charnsirisakskul, K., Griffin, P. M., & Keskinocak, P. (2006). Pricing and scheduling decisions with leadtime
flexibility. European Journal of Operational Research, 171(1), 153-169.
Chen, H. W., Gupta, D., & Gurnani, H. (2013). Fast-ship commitment contracts in retail supply chains. IIE
Transactions, 45(8), 811-825.
Chen, J. (2011). The impact of sharing customer returns information in a supply chain with and without a
buyback policy. European Journal of Operational Research, 213(3), 478-488.
Chen, J., & Bell, P. C. (2011). Coordinating a decentralized supply chain with customer returns and price-
dependent stochastic demand using a buyback policy. European Journal of Operational Research,
212(2), 293-300.
Chen, J., & Bell, P. C. (2013). The impact of customer returns on supply chain decisions under various channel
interactions. Annals of Operations Research, 206(1), 59-74.
Chen, J., & Chen, B. (2016). Competing with customer returns policies. International Journal of Production
Research, 54(7), 2093-2107.
Chen, J., Chen, Y., Parlar, M., & Xiao, Y. (2011). Optimal inventory and admission policies for drop-shipping
retailers serving in-store and online customers. IIE Transactions, 43(5), 332-347.
Chen, J., Dong, M., Rong, Y., & Yang, L. (2017). Dynamic pricing for deteriorating products with menu cost.
Omega, in press.
Chen, J., Zhang, H., & Sun, Y. (2012). Implementing coordination contracts in a manufacturer Stackelberg dual-
channel supply chain. Omega, 40(5), 571-583.
Chen, J. M., & Chen, T.-H. (2007). The profit-maximization model for a multi-item distribution channel.
Transportation Research Part E: Logistics and Transportation Review, 43(4), 338-354.
Chen, L., & Lee, H. L. (2016). Sourcing under supplier responsibility risk: The effects of certification, audit,
and contingency payment. Management Science, in press.
Chen, M., Chen, Z.-L., Pundoor, G., Acharya, S., & Yi, J. (2015a). Markdown optimization at multiple stores.
IIE Transactions, 47(1), 84-108.
Chen, S., Lee, H., & Moinzadeh, K. (2016). Supply chain coordination with multiple shipments: The optimal
inventory subsidizing contracts. Operations Research, 64(6), 1320-1337.
Chen, W., Fleischhacker, A. J., & Katehakis, M. N. (2015b). Dynamic pricing in a dual-market environment.
Naval Research Logistics, 62(7), 531-549.
Chernonog, T., & Kogan, K. (2014). The effect of risk aversion on a supply chain with postponed pricing.
Journal of the Operational Research Society, 65(9), 1396-1411.
Chiu, C.-H., & Choi, T.-M. (2016). Supply chain risk analysis with mean-variance models: A technical review.
Annals of Operations Research, 240(2), 489-507.
Chiu, C.-H., Choi, T.-M., Hao, G., & Li, X. (2015a). Innovative menu of contracts for coordinating a supply
chain with multiple mean-variance retailers. European Journal of Operational Research, 246(3), 815-
Chiu, C.-H., Choi, T.-M., Li, X., & Yiu, C. K.-F. (2016). Coordinating supply chains with a general price-
dependent demand function: Impacts of channel leadership and information asymmetry. IEEE
Transactions on Engineering Management, 63(4), 390-403.
Chiu, C.-H., Choi, T.-M., & Li, X. (2011). Supply chain coordination with risk sensitive retailer under target
sales rebate. Automatica, 47(8), 1617-1625.
Chiu, C.-H., Hou, S.-H., Li, X., & Liu, W. (2015b). Real options approach for fashionable and perishable
products using stock loan with regime switching. Annals of Operations Research, in press.
Choi, T.-M. (2007). Pre-season stocking and pricing decisions for fashion retailers with multiple information
updating. International Journal of Production Economics, 106(1), 146-170.
Choi, T.-M. (2013a). Local sourcing and fashion quick response system: The impacts of carbon footprint tax.
Transportation Research Part E: Logistics and Transportation Review, 55, 43-54.
Choi, T.-M. (2013b). Optimal apparel supplier selection with forecast updates under carbon emission taxation
scheme. Computers & Operations Research, 40(11), 2646-2655.
Choi, T.-M. (2013c). Optimal return service charging policy for a fashion mass customization program. Service
Science, 5(1), 56-68.
Choi, T.-M. (2014). Fashion Retail Supply Chain Management: A Systems Optimization Approach. Netherlands:
CRC Press.
Choi, T.-M. (2016a). Incorporating social media observations and bounded rationality into fashion quick
response supply chains in the big data era. Transportation Research Part E: Logistics and
Transportation Review, in press.
Choi, T.-M. (2016b). Inventory service target in quick response fashion retail supply chains. Service Science,
8(4), 406-419.
Choi, T.-M. (2016c). Multi-period risk minimization purchasing models for fashion products with interest rate,
budget, and profit target considerations. Annals of Operations Research, 237(1-2), 77-98.
Choi, T.-M., & Chow, P.-S. (2008). Mean-variance analysis of quick response program. International Journal
of Production Economics, 114(2), 456-475.
Choi, T.-M., Chow, P.-S., Shen, B., & Wan, M.-L. (2017). Service analysis of fashion boutique operations: An
empirical and analytical study. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(11),
Choi, T.-M., Li, D., & Yan, H. (2006). Quick response policy with Bayesian information updates. European
Journal of Operational Research, 170(3), 788-808.
Choi, T.-M., Li, D., Yan, H., & Chiu, C.-H. (2008). Channel coordination in supply chains with agents having
mean-variance objectives. Omega, 36(4), 565-576.
Choi, T.-M., Li, Y., & Xu, L. (2013). Channel leadership, performance and coordination in closed loop supply
chains. International Journal of Production Economics, 146(1), 371-380.
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: Systems and Humans,
42(4), 868-879.
Chow, P.-S., Wang, Y., Choi, T.-M., & Shen, B. (2015). An experimental study on the effects of minimum profit
share on supply chains with markdown contract: Risk and profit analysis. Omega, 57, 85-97.
Chua, G. A., Mokhlesi, R., & Sainathan, A. (2017). Optimal discounting and replenishment policies for
perishable products. International Journal of Production Economics, 186, 8-20.
Chuang, H. H. C., Oliva, R., & Perdikaki, O. (2016). Traffic-based labor planning in retail stores. Production
and Operations Management, 25(1), 96-113.
Cui, Q., Chiu, C.-H., Dai, X., & Li, Z. (2016). Store brand introduction in a two-echelon logistics system with
a risk-averse retailer. Transportation Research Part E: Logistics and Transportation Review, 90, 69-89.
de Brito, M. P., & van der Laan, E. A. (2009). Inventory control with product returns: The impact of imperfect
information. European Journal of Operational Research, 194(1), 85-101.
Deng, S., & Yano, C. A. (2016). Designing supply contracts considering profit targets and risk. Production and
Operations Management, 25(7), 1292-1307.
Elmaghraby, W., Gülcü, A., & Keskinocak, P. (2008). Designing optimal preannounced markdowns in the
presence of rational customers with multiunit demands. Manufacturing & Service Operations
Management, 10(1), 126-148.
Fogliatto, F. S., Da Silveira, G. J., & Borenstein, D. (2012). The mass customization decade: An updated review
of the literature. International Journal of Production Economics, 138(1), 14-25.
Gallien, J., Mersereau, A. J., Garro, A., Mora, A. D., & Vidal, M. N. (2015). Initial shipment decisions for new
products at Zara. Operations Research, 63(2), 269-286.
Georgiadis, G., & Rajaram, K. (2013). The retail planning problem under demand uncertainty. Production and
Operations Management, 22(5), 1200-1213.
Guide Jr, V. D. R., Souza, G. C., Van Wassenhove, L. N., & Blackburn, J. D. (2006). Time value of commercial
product returns. Management Science, 52(8), 1200-1214.
Guo, L., & Iyer, G. (2010). Information acquisition and sharing in a vertical relationship. Marketing Science,
29(3), 483-506.
Hanne, T., & Dornberger, R. (2016). Computational Intelligence in Logistics and Supply Chain Management.
Switzerland: Springer.
He, C., Marklund, J., & Vossen, T. (2008). Research note—Vertical information sharing in a volatile market.
Marketing Science, 27(3), 513-530.
Heydari, J., Choi, T.-M., & Radkhah, S. (2017). Pareto improving supply chain coordination under a money-
back guarantee service program. Service Science, 9(2), 91-105.
Hsiao, L., & Chen, Y. J. (2014). Return policy: Hassle-free or your money-back guarantee? Naval Research
Logistics, 61(5), 403-417.
Hua, Z., Hou, H., & Bian, Y. (2016). Optimal shipping strategy and return service charge under no-reason return
policy in online retailing. IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press.
Huang, H., Li, Z., & Xu, H. (2017a). Wholesale price auctions for dual sourcing under supply risk. Decision
Sciences, in press.
Huang, K. L., Kuo, C.-W., & Lu, M.-L. (2014a). Wholesale price rebate vs. capacity expansion: The optimal
strategy for seasonal products in a supply chain. European Journal of Operational Research, 234(1),
Huang, S. M., & Su, J. C. (2013). Impact of product proliferation on the reverse supply chain. Omega, 41(3),
Huang, X., Gu, J.-W., Ching, W.-K., & Siu, T.-K. (2014b). Impact of secondary market on consumer return
policies and supply chain coordination. Omega, 45, 57-70.
Huang, X., & Sošić, G. (2010). Repeated newsvendor game with transshipments under dual allocations.
European Journal of Operational Research, 204(2), 274-284.
Huang, Y.-S., Ho, C.-H., & Fang, C.-C. (2017b). Information sharing in the supply chains of products with
seasonal demand. IEEE Transactions on Engineering Management, 64(1), 57-69.
Huang, Y.-S., Hsu, C.-S., & Ho, J.-W. (2014c). Dynamic pricing for fashion goods with partial backlogging.
International Journal of Production Research, 52(14), 4299-4314.
Hübner, A., & Schaal, K. (2017). A shelf-space optimization model when demand is stochastic and space-elastic.
Omega, 68, 139-154.
Hung, Y.-H., Li, L. Y. O., & Cheng, T. C. E. (2013). Transfer of newsvendor inventory and supply risks to sub-
industry and the public by financial instruments. International Journal of Production Economics,
143(2), 567-573.
Jadidi, O., Jaber, M. Y., & Zolfaghari, S. (2017). Joint pricing and inventory problem with price dependent
stochastic demand and price discounts. Computers & Industrial Engineering, 114, 45-53.
Karakayali, I., Akçalı, E., Çetinkaya, S., & Üster, H. (2013). Capacitated replenishment and disposal planning
for multiple products with resalable returns. Annals of Operations Research, 203(1), 325-350.
Khouja, M. (2016). Late season low inventory assortment effects in the newsvendor problem. Omega, 63, 123-
Konur, D., & Schaefer, B. (2014). Integrated inventory control and transportation decisions under carbon
emissions regulations: LTL vs. TL carriers. Transportation Research Part E: Logistics and
Transportation Review, 68, 14-38.
Lee, C. H. (2007). Coordination on stocking and progressive pricing policies for a supply chain. International
Journal of Production Economics, 106(1), 307-319.
Lee, C., & Park, K. S. (2016). Inventory and transshipment decisions in the rationing game under capacity
uncertainty. Omega, 65, 82-97.
Lee, C. H., & Rhee, B.-D. (2007). Channel coordination using product returns for a supply chain with stochastic
salvage capacity. European Journal of Operational Research, 177(1), 214-238.
Lee, C. H., Rhee, B.-D., & Cheng, T. C. E. (2013). Quality uncertainty and quality-compensation contract for
supply chain coordination. European Journal of Operational Research, 228(3), 582-591.
Lee, H. L., & Tang, C. S. (2017). Socially and environmentally responsible value chain innovations: New
operations management research opportunities. Management Science, in press.
Li, J., Choi, T.-M., & Cheng, T. C. E. (2014a). Mean variance analysis of fast fashion supply chains with returns
policy. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(4), 422-434.
Li, T., & Yu, M. (2017). Coordinating a supply chain when facing strategic consumers. Decision Sciences, 48(2),
Li, Y., Wei, C., & Cai, X. (2012). Optimal pricing and order policies with B2B product returns for fashion
products. International Journal of Production Economics, 135(2), 637-646.
Li, Y., Xu, L., Choi, T.-M., & Govindan, K. (2014b). Optimal advance-selling strategy for fashionable products
with opportunistic consumers returns. IEEE Transactions on Systems, Man, and Cybernetics: Systems,
44(7), 938-952.
Li, Z., Shao, L., & Sun, D. (2015). Designing multi-attribute procurement mechanisms for assortment planning.
Decision Sciences, 46(3), 597-622.
Lin, K. Y. (2006). Dynamic pricing with real-time demand learning. European Journal of Operational Research,
174(1), 522-538.
Liu, H., & Özer, Ö. (2010). Channel incentives in sharing new product demand information and robust contracts.
European Journal of Operational Research, 207(3), 1341-1349.
Liu, N., Choi, T.-M., Yuen, C.-W. M., & Ng, F. (2012). Optimal pricing, modularity, and return policy under
mass customization. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and
Humans, 42(3), 604-614.
Liu, Q., & Van Ryzin, G. J. (2008). Strategic capacity rationing to induce early purchases. Management Science,
54(6), 1115-1131.
Liu, Q., & Zhang, D. (2013). Dynamic pricing competition with strategic customers under vertical product
differentiation. Management Science, 59(1), 84-101.
Maddah, B., Bish, E. K., & Tarhini, H. (2014). Newsvendor pricing and assortment under Poisson
decomposition. IIE Transactions, 46(6), 567-584.
Martínez-de-Albéniz, V., & Simchi-Levi, D. (2009). Competition in the supply option market. Operations
Research, 57(5), 1082-1097.
McCardle, K. F., Rajaram, K., & Tang, C. S. (2007). Bundling retail products: Models and analysis. European
Journal of Operational Research, 177(2), 1197-1217.
Mehrjoo, M., & Pasek, Z. J. (2016). Risk assessment for the supply chain of fast fashion apparel industry: A
system dynamics framework. International Journal of Production Research, 54(1), 28-48.
Metzger, C., Thiesse, F., Gershwin, S., & Fleisch, E. (2013). The impact of false-negative reads on the
performance of RFID-based shelf inventory control policies. Computers & Operations Research, 40(7),
Ni, J., & Srinivasan, K. (2015). Matching in the sourcing market: A structural analysis of the upstream channel.
Marketing Science, 34(5), 722-738.
O'Neil, S., Zhao, X., Sun, D., & Wei, J. C. (2016). Newsvendor problems with demand shocks and unknown
demand distributions. Decision Sciences, 47(1), 125-156.
Oberlaender, M. (2011). Dual sourcing of a newsvendor with exponential utility of profit. International Journal
of Production Economics, 133(1), 370-376.
Özdemir, D., Yücesan, E., & Herer, Y. T. (2013). Multi-location transshipment problem with capacitated
production. European Journal of Operational Research, 226(3), 425-435.
Petruzzi, N. C., & Dada, M. (1999). Pricing and the newsvendor problem: A review with extensions. Operations
Research, 47(2), 183-194.
Raz, G., Druehl, C. T., & Blass, V. (2013). Design for the environment: Life-cycle approach using a newsvendor
model. Production and Operations Management, 22(4), 940-957.
Raza, S. A., Rathinam, S., Turiac, M., & Kerbache, L. (2016). An integrated revenue management framework
for a firm's greening, pricing and inventory decisions. International Journal of Production Economics,
in press.
Reimann, M. (2016). Accurate response with refurbished consumer returns. Decision Sciences, 47(1), 31-59.
Sainathuni, B., Parikh, P. J., Zhang, X., & Kong, N. (2014). The warehouse-inventory-transportation problem
for supply chains. European Journal of Operational Research, 237(2), 690-700.
Schmidheiny, S. (1992). Changing Course: A Global Business Perspective on Development and the
Environment (Vol. 1): MIT Press.
Şen, A. (2008). The US fashion industry: A supply chain review. International Journal of Production Economics,
114(2), 571-593.
Şen, A., & Zhang, A. X. (2009). Style goods pricing with demand learning. European Journal of Operational
Research, 196(3), 1058-1075.
Serel, D. A. (2015). Production and pricing policies in dual sourcing supply chains. Transportation Research
Part E: Logistics and Transportation Review, 76, 1-12.
Shang, W., & Yang, L. (2015). Contract negotiation and risk preferences in dual-channel supply chain
coordination. International Journal of Production Research, 53(16), 4837-4856.
Shao, J., Krishnan, H., & Thomas McCormick, S. (2013). Distributing a product line in a decentralized supply
chain. Production and Operations Management, 22(1), 151-163.
Shen, B., Chan, H.-L., Chow, P.-S., & Thoney-Barletta, K. A. (2016a). Inventory management research in the
fashion industry. International Journal of Inventory Research, 3(4), 297-317.
Shen, B., Choi, T.-M., Wang, Y., & Lo, C. K. (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, 43(2), 266-276.
Shen, B., Li, Q., Dong, C., & Quan, V. (2016b). Design outsourcing in the fashion supply chain: OEM versus
ODM. Journal of the Operational Research Society, 67(2), 259-268.
Shen, B., Qian, R., & Choi, T.-M. (2017). Selling luxury fashion online with social influences considerations:
Demand changes and supply chain coordination. International Journal of Production Economics, 185,
Stavrulaki, E. (2011). Inventory decisions for substitutable products with stock-dependent demand.
International Journal of Production Economics, 129(1), 65-78.
Su, X. (2009). Consumer returns policies and supply chain performance. Manufacturing & Service Operations
Management, 11(4), 595-612.
Sun, J., & Debo, L. (2014). Sustaining long-term supply chain partnerships using price-only contracts. European
Journal of Operational Research, 233(3), 557-565.
Swinney, R. (2011). Selling to strategic consumers when product value is uncertain: The value of matching
supply and demand. Management Science, 57(10), 1737-1751.
Taleizadeh, A. A., Sane-Zerang, E., & Choi, T.-M. (2016). The effect of marketing effort on dual-channel closed-
loop supply chain systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press.
Tang, C. S., & Yin, R. (2007). Responsive pricing under supply uncertainty. European Journal of Operational
Research, 182(1), 239-255.
Vaagen, H., & Wallace, S. W. (2008). Product variety arising from hedging in the fashion supply chains.
International Journal of Production Economics, 114(2), 431-455.
Vaagen, H., Wallace, S. W., & Kaut, M. (2011). Modelling consumer-directed substitution. International
Journal of Production Economics, 134(2), 388-397.
Wang, C. X., & Webster, S. (2007). Channel coordination for a supply chain with a risk-neutral manufacturer
and a loss-averse retailer. Decision Sciences, 38(3), 361-389.
Wang, Q., Chu, B., Wang, J., & Kumakiri, Y. (2012). Risk analysis of supply contract with call options for
buyers. International Journal of Production Economics, 139(1), 97-105.
Wang, Q., Gong, X., Deng, H., & Leong, G. K. (2011a). The use of switching point and protection levels to
improve revenue performance in order-driven production systems. Decision Sciences, 42(2), 495-509.
Wang, Y., Wallace, S. W., Shen, B., & Choi, T.-M. (2015). Service supply chain management: A review of
operational models. European Journal of Operational Research, 247, 685-698.
Wang, S.-D., Zhou, Y.-W., Min, J., & Zhong, Y.-G. (2011b). Coordination of cooperative advertising models in
a one-manufacturer two-retailer supply chain system. Computers & Industrial Engineering, 61(4),
Webster, S., & Weng, Z. K. (2008). Ordering and pricing policies in a manufacturing and distribution supply
chain for fashion products. International Journal of Production Economics, 114(2), 476-486.
Wei, Y., & Choi, T.-M. (2010). Mean-variance analysis of supply chains under wholesale pricing and profit
sharing schemes. European Journal of Operational Research, 204(2), 255-262.
Wong, W. K., Leung, S. Y. S., Guo, Z. X., Zeng, X. H., & Mok, P. Y. (2012). Intelligent product cross-selling
system with radio frequency identification technology for retailing. International Journal of Production
Economics, 135(1), 308-319.
Wu, S., Liu, Q., & Zhang, R. Q. (2015). The reference effects on a retailer’s dynamic pricing and inventory
strategies with strategic consumers. Operations Research, 63(6), 1320-1335.
Wu, Z., Kazaz, B., Webster, S., & Yang, K. K. (2012). Ordering, pricing, and lead-time quotation under lead-
time and demand uncertainty. Production and Operations Management, 21(3), 576-589.
Xiao, T., Choi, T.-M., & Cheng, T. C. E. (2015). Optimal variety and pricing decisions of a supply chain with
economies of scope. IEEE Transactions on Engineering Management, 62(3), 411-420.
Xiao, T., Choi, T.-M., & Cheng, T. C. E. (2016). Delivery leadtime and channel structure decisions for make-
to-order duopoly under different game scenarios. Transportation Research Part E: Logistics and
Transportation Review, 87, 113-129.
Xiao, Y., & Chen, J. (2014). Evaluating the potential effects from probabilistic selling of similar products. Naval
Research Logistics, 61(8), 604-620.
Xiao, T., Jin, J., Chen, G., Shi, J., & Xie, M. (2010). Ordering, wholesale pricing and lead-time decisions in a
three-stage supply chain under demand uncertainty. Computers & Industrial Engineering, 59(4), 840-
Xu, L., Li, Y., Govindan, K., & Xu, X. (2015). Consumer returns policies with endogenous deadline and supply
chain coordination. European Journal of Operational Research, 242(1), 88-99.
Yan, R., & Cao, Z. (2017). Product returns, asymmetric information, and firm performance. International
Journal of Production Economics, 185, 211-222.
Yang, D., Choi, T.-M., Xiao, T., & Cheng, T. (2011). Coordinating a two-supplier and one-retailer supply chain
with forecast updating. Automatica, 47(7), 1317-1329.
Yoo, S. H., Kim, D., & Park, M.-S. (2015). Pricing and return policy under various supply contracts in a closed-
loop supply chain. International Journal of Production Research, 53(1), 106-126.
Yue, X., & Raghunathan, S. (2007). The impacts of the full returns policy on a supply chain with information
asymmetry. European Journal of Operational Research, 180(2), 630-647.
Zhang, J., Shou, B., & Chen, J. (2013). Postponed product differentiation with demand information update.
International Journal of Production Economics, 141(2), 529-540.
Zhang, Z., & Feng, J. (2017). Price of identical product with gray market sales: An analytical model and
empirical analysis. Information Systems Research, in press.
Zhao, Y., Choi, T.-M., Cheng, T. C. E., Sethi, S. P., & Wang, S. (2014). Buyback contracts with price-dependent
demands: Effects of demand uncertainty. European Journal of Operational Research, 239(3), 663-673.
Zhao, Y., Choi, T.-M., Cheng, T., & Wang, S. (2018). Supply option contracts with spot market and demand
information updating. European Journal of Operational Research, 266(3), 1062-1071.
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Companies deal with different customer groups, requirements differ among them, which makes it important to define the service level precisely and improve customer service through different supply chain strategies for each group. An alternative to deal with imprecision related to the segmentation processes suggested by either the Leagile or the Dynamic Alignment Schools is the application of fuzzy set theory. The objective of this work is to develop a quantitative model that uses the fuzzy set theory and, based on sales data, assess the company’s supply chain(s). The model's aim is to facilitate managers' decision-making processes to achieve the dynamic alignment. It was possible to identify the supply chains that serve the client groups evaluated, providing answers faster than the analysis proposed by the models found in the literature. The application in two real situations validated the model since the results obtained were consistent with the reality pointed out by the experts of the companies assessed. The model indicates possible actions for the realignment of the supply chain by their managers. Results obtained should improve practice, preparing managers to cope with the organizations` multiple supply chains. This study is the first one that aims to segment quantitatively supply chains on a company applying fuzzy set theory, providing a novel approach to align operations and supply chain strategy dynamically.
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One of the goals that a company sets is the customer's satisfaction, which is a priority item. This clearly shows that the concerns of companies are focused on the customer and how this later could be served quickly. This work addresses the problem of optimization of the location of the distribution centers of a final product to customers, in a given region. The problem addressed in this paper is the location of the position (emplacement) to build a new distribution center (warehouses), taking into account the constraint of the distance between a distribution center and the various points of sale, and the constraint of the cost of land to build a distribution center. The optimization method used for this problem is the artificial bee colony algorithm (ABC), which was initially applied to numerical function optimization problems, inspired from the behavioral model bees during foraging.
Sustainable operations management (SOM) is a well-established field, which is crucially important to fashion business nowadays. Traditional SOM studies employ methods such as theoretical modeling (e.g., mathematical analyses), quantitative empirical (e.g., surveys), and qualitative empirical (e.g., interviews, case studies). In recent years, to enhance research rigor, more and more SOM studies employ a multi-methodological approach to explore research problems related to the fashion industry. In this paper, we discuss applications of the multi-methodological approach for fashion SOM. We examine how the multi-methodological approach has been applied in the related literature. We propose new methods as well as new areas for future fashion SOM research in which the multi-methodological approach can be applied. Important insights are derived.
Fashion is a global, multi-trillion dollar industry devoted to producing and selling clothing, footwear, and accessories to individuals or groups of people. Its sheer numbers, together with social and environmental sustainability concerns, and the move towards digitalization of customer-centric operations, make the fashion business a prime target for Decision Support Systems (DSSs). On the other hand, decision support in fashion retail is particularly problematic and embraces all major supply chain domains. Decisions in an online fashion retail supply chain (FRSC) are highly dependent on time-varying customers' preferences and product availability, often leading to a combinatorial explosion. To address such a problem, DSSs could greatly benefit from high-quality information stored in customer models (CMs), constructed by using Artificial Intelligence techniques, allowing informed decisions on how to personalize (adapt) to match the customer's needs and preferences. Combinations of CMs with recommender systems (RSs) have been increasingly utilized in fashion e-commerce to provide personalized product recommendations. Nevertheless, works on enhancing CMs for e-commerce or other decision-making chain domains are scanty. This paper offers a systematic review of the literature on fashion CMs with applications to decision-making in FRSCs, mining topics for a research agenda. Research on the theme is relevant and urgent for the fashion business, which is still in its infancy. Work on the agenda topics could benefit distinct fashion stakeholders, not just customers, and produce well-grounded decision-making in varied FRSC contexts and dynamics.
In this paper, we investigate the optimal operational decisions and financing strategies of a supply chain with one financial-constrained supplier and one reputable retailer. In particular, two pre-shipment financing mechanisms, buyback support buyer-backed purchase order financing (BSBPOF) and buyback support advance payment discount (BSAPD), are chosen to ease the supplier's financial pressures. Both financing methods indicate that the supplier promises to buy back the unsold items after the sales season, but the difference is that the former means that the financial institution lends money to the supplier which the retailer needs to guarantee, while the latter means that the retailer pays in advance. Our research shows that: 1) the difference between the buyback price of the unsold product and its salvage value has an impact on the optimal operational decisions (such as the supplier's capacity level, the discount rate under BSAPD, and the financial institution's interest rate under BSBPOF); 2) when both financing instruments are available, if the buyback price is above a certain level and the difference between the buyback price and the salvage value is less than a certain range, the financing equilibrium is BSBPOF. Otherwise, BSAPD is preferred. Furthermore, the results provide managerial insights on how to address the operational and financing issues, and relevant conclusions are drawn by extending the model to consider stockout costs.
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The fashion industry has been continuously growing over the years, yet it is an industry that was greatly affected by the COVID-19 pandemic. The purpose of the study was to determine the factors affecting buying behavior of Filipinos towards clothing apparel during the COVID-19 pandemic. There were 457 respondents who voluntarily participated and answered an online questionnaire. Structural equation modeling (SEM) indicated that marketing mix was found to have significant effects on attitude, subjective norm, and perceived behavioral control, which subsequently led to purchase intention. In addition, the COVID-19 latent variable was found to have significant effects on self-perceived severity and self-efficacy, which subsequently led to attitude and purchase intention. Interestingly, marketing mix was found to have the highest effect on actual purchase, which indicated that innovation dynamics are the keys for the buying behavior. The SEM construct can be applied to determine the clothing apparel buying behaviors of consumers in other countries, particularly during the COVID-19 pandemic. Finally, advertisements, promotions, sales, and health safety should be considered as innovation dynamics during the COVID-19 pandemic.
Motivated by the industrial practices, this paper develops a two-stage model to explore the supply option contract in a two-echelon supply chain, taking into account a stochastic spot market and demand information updating of general correlation. We develop a concept called EUOS (expected unit opportunity saving) by which to examine the expected benefit received per unit of the option under different market scenarios. With the concept of EUOS, we develop intuitive analytical results to characterize the optimal strategies for portfolio procurement via the option contract and spot markets, and to design the appropriate option mechanism for supply chain coordination. Furthermore, we reveal that EUOS can be a viable alternative for real option pricing in the supply chain. Detailed comparisons between the mixed market scenario (in which the spot market and the option contract market co-exist) and the scenario with pure spot market or pure option contract market are made, and thereby we analytically examine the effects of supply competition between the spot market and the option contract market. Our paper contributes to the literature by developing intuitive characterizations for the option contract models and generating some new insights with respect to the use of supply option contracts in the supply chain in the presence of a stochastic spot market and demand information updating.
The market today is very dynamic and competitive. Many products, such as fashion apparel and technology and Christmas items, are short lived and become obsolete. Another example is grocery items. Some are either perishable or have expiry dates. They become less appealing to customers as they become less fresh with time. Many companies consider pricing and inventory decisions important to minimize losses and improve profitability due to either obsolescence or perishability. They also try to maximize truck capacity and reduce transport cost to reap additional savings. For this reason, suppliers often provide buyers with an all-unit quantity discounts. This study addresses these issues by examining a joint pricing and inventory decision problem with transport capacity and cost. It considers a single period model where a buyer purchases a single item from a dominant supplier offering all-unit quantity discounts. Product demand is stochastic and price-dependent. Two new mathematical models were developed. The results showed that discounts increase the pool of profits for the supplier and the buyer and provide a better utilization of truck capacity and lower transportation cost. The models also benefit the end customer by providing a product at a competitive price.
Wholesale price bids commonly occur in procurement auctions where dual sourcing is usually employed to mitigate supply risks. When at most two winners are required, unreliable wholesale price bidders face the unique trade‐off between unit margin and quantity allocation that depends on both winners' bids and the reliabilities. Motivated by above facts and considerations, we investigate several auction formats accommodating wholesale price bids for dual sourcing under newsvendor market, with unreliable suppliers possessing private costs. Generalized first‐price auction (GFA) is designed under known cost distribution, while generalized English auction (GEA) and optimal auction with learning (OAL) are examined under unknown distribution. Through a preannounced allocation rule to curb the bidding behavior, both GFA and OAL balance information rent and quantity allocation and generate various diversification degrees. Information asymmetry under GFA depresses the quantity proportion for high‐cost winner, because he charges higher unit information rent than low‐cost winner does. In contrast, two winners in GEA collude at the same price and thus split the order quantity evenly. The full diversification possibly enables GEA to generate higher service level than GFA under high supply risk, although the expected output quantity of GEA is always lower. Regarding the practical requirement for auction format choice, when the costs of implementation and distribution acquisition are not negligible, the previous analysis suggests that the favorable format is driven from GFA or OAL to GEA, as supply risk or the number of bidders increases, or as retail price exceeds a threshold. We also extend to multisourcing cases and examine the impacts of salvage and lost sale on auction format choice.
Money-back guarantee (MBG) is a well-known service offered by many retailers. Under the MBG policy, a purchased item by a consumer can be returned for a full refund. In this paper, we explore a supply chain (SC) system comprising of one retailer, which offers the money-back guarantee policy and faces a stochastic demand. With a given supply contract offered by the supplier, the retailer makes decisions on the order quantity and the market coverage of the money-back guarantee service. With reference to real-world practices, we examine three models, namely, (1) the traditional wholesale pricing model without the buyback contract, (2) the model with a buyback contract (between the supplier and the retailer) for unsold items, and (3) a dual-buyback (DB) contract model for both unsold and consumer returned items. We find that using the buyback contract alone for unsold items cannot achieve Pareto improving supply chain coordination, whereas the DB contract can. Numerical findings also indicate that the DB contract can lead to an expansion of the money-back guarantee service program to cover more customers. This paper’s findings support the implementation of the DB contract, which is commonly seen in the industry, for efficient supply chain management in the presence of the money-back guarantee service program.
The sale of genuinely branded products through unauthorized channels (also known as gray markets) is a growing problem for many firms that operate in separate markets. It is generally believed that the existence of such unauthorized sales will cannibalize the profits of brand owners. In this paper, we develop a pricing model for a firm that sells an identical product in two distinct markets but faces the threat of potential gray market sales. The firm chooses prices in each market. A consumer chooses whether to buy the product from one of the markets including a gray market. We derive the optimal prices in the two markets and examine their effects on consumer demand and the total profit. We show that the higher price in one market transfers part of its demand into the gray market, thus influencing the consumer demand in the low-priced market as well. Additionally, the price gap between the two separate markets positively influences gray market sales and, under certain conditions, can lead to an increase in firm profit. Using authorized sales data from a Fortune 100 company and a separate data set on online gray market sales, we find empirical evidence in support of our model results.
By examining the state of operations management (OM) research from 1980 to 2015 and by considering three new industry trends, we propose new OM research directions in socially and environmentally responsible value chains that fundamentally expand existing OM research in three dimensions: (a) contexts (emerging and developing economies); (b) objectives (economic, environmental, and social responsibility); and (c) stakeholders (producers, consumers, shareholders, for-profit/nonprofit/social enterprises, governments, and nongovernmental organizations). In this paper, we describe some examples of this new research direction that are intended to stimulate more exciting OM research, to contribute to the economic and social well-being of both developing and developed economies. This paper was accepted by Teck-Hua Ho, operations management.
Retail operation practice has empirically demonstrated that menu costs (i.e., the costs of adjusting a price) play a critical role in retailer pricing decisions. In this paper, we propose four dynamic pricing models for deteriorating products based on different pricing adjustment frequencies with and without menu costs. Under Poisson demand, we provide 1) the rank of these models in terms of expected profits and note that 2) the optimal prices of deteriorating products consistently decrease over time when menu costs are negligible. To investigate the impact of menu costs on dynamic pricing decisions, we conduct several numerical experiments based on different menu costs, decay rates, holding costs and initial inventories. The results demonstrate that 1) the one-time price adjustments typically employed in practice provide the most benefit from dynamic pricing when menu costs are moderate and that 2) it is typically preferable to adjust the price in the middle of the products shelf life rather than to make early or late adjustments. The same conclusions follow under compound Poisson demand and general customer utility functions.