REIMAGINING FASHION RETAILING IN A POST-COVID-19 ENVIRONMENT
A Summer Report
By
Nishat Alam Choudhury
Submitted On
June 15, 2020
Approved by the Academic Advisor and Area Chair
1. Prof. PRS Sarma [Academic Advisor]
2. Prof. Gopal Kumar [Area Chair]
Indian Institute of Management Raipur
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Abstract
The novel coronavirus has changed the way fashion retail functions. Absorbing the implicated effects
of the changes require building resilience internally and meticulous planning. In post-pandemic
environment fashion retailers would be trading-off between the performance of the brands in the
stores, its budget limitations and maintenance of merchandising standards. The main objective of the
study is to identify the new set of constraints caused due to the nature of the virus, form different
scenarios and identify resilient options. The new set of constraints is developed by a lexicographic goal
programming model considering both offline stores and online format. A case has been designed for
a retailer who earns major revenue through offline stores and also has online presence. The options
lead us to identification of new dynamics of fashion retailers in post-pandemic environment. The
results of the analysis of the case indicate intuitive measures to build resilience like focussing on online
capabilities due to the impact of coronavirus may not be the best option. Further discussion includes
theoretical and practical implications of the study
Keywords
Supply Chain Disruption, COVID-19, Fashion Retail, Merchandise Planning
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1. Introduction
The novel coronavirus (COVID-19), has made specialists rethink their supply chains. Weaknesses of
philosophies like Lean and Just-in-time are now getting reflected wide open. What is more intriguing
is the nature of the virus. So far, it has taken its own route touching almost all the borders of the globe.
The virus is following a logistic curve having its own saturation point for a given population (Baveja et
al., 2020). During this journey, it is seen to cause economic, humanitarian, financial, and supply chain
(SC) crisis. Many industries are facing the risk of crumbling down, as companies are facing demand
and supply shortages. Consumer facing businesses like tourism, hospitality and retail are the major
victims of this crisis. Unemployment rate has shown significant incline. By the third week of March,
more than 3 million citizens of US filed for unemployment benefits (Business Insider, 2020)
Governments and policymakers are trying to adopt measures to flatten the growth curve of new cases
of coronavirus but many emerging markets are still seeing rapid rise in cases till May end (Craven et
al., 2020).
While some sectors like healthcare and food by virtue of its importance during the pandemic has
witnessed importance (Bain & Company, 2020) and are transforming during the pandemic, fashion
retailers are facing the curse of it. A Mckinsey report predicts a minimum of 27 percent decline in
growth of fashion industry in the year 2020, and also expect 56 percent of the global fashion
companies to go bankrupt in a year or two (Amed et al., 2020). The report draws attention to the
alarming transition the fashion retail industry is going to face. Macroeconomic changes will shift
consumer behaviour. Closure of borders for months to contain the virus has caused supply disruption
aggravating the issue. Cautious planning is required to build resilience to survive the period of decline
of demand. Within 5 months from the onset of the disease, apparel retailers are either cancelling
orders from suppliers or are defaulting payments (Cline, 2020).
Fashion retailers need to reimagine a future or a new beginning keeping in mind the nature of the
disruption caused by the novel coronavirus (COVID-19). It requires changes at different stages like
designing, sourcing, manufacturing, buying, planning, store operations, online operations, marketing
etc. all that make a fashion retailer. It is imperative that while planning short term survival strategy, a
long term strategy also needs to be drawn for retailers (Roggeveen & Sethuraman, 2020)
This study focuses specially on merchandise planning in store and online format considering the
implications caused due to COVID-19 pandemic. The study makes three contributions to the literature.
First, the acknowledgement of the SC disruption caused by pandemic as rare event and developing
model based on the implications where the future is still uncertain adds to the literature of SC
disruption. Second, developing a theory of constraints perspective and applying goal programming
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(GP) technique to capture those priorities has not been done so far, for such kind of disruption. Third,
the results of the simulation suggest that the trend of shifting online due to the impact of the
pandemic may not be effective in building resilience for. This provides a guide for practitioners and a
direction for research.
The rest of the paper is organised as follows. Section 2 provides a background literature of the major
topics and themes related to the intended study. Section 3 develops a model based on lexicographic
goal programming technique to optimize merchandising resources. Section 4 analyses the model using
a case to develop scenarios for building resilience. Section 5 provides discussion and Section 6 covers
theoretical and practical implications. Section 7 concludes with the limitations and identifies directions
of further scope of research.
2. Related literature
2.1. Supply chain disruption
There has been growing literature related to supply chain disruption in the last decade (Kleindorfer &
Saad, 2009; Baghalian et al., 2013; Tang et al., 2014; Kim et al., 2015; Vahid Nooraie & Parast, 2016;
Kamalahmadi & Parast, 2017), indicating the rise in importance of it. Hishamuddin et al. defines supply
chain disruption as an event that breaks the flow of materials in a supply chain resulting in undesirable
ceasing of flow of goods (2013). For common supply disruptions like factory breakdown, supplier
failure etc. historical data can help in quantifying risk. But for low-probability high impact disruptions
like political disruptions, SARS outbreak, Hurricane Katrina, or earthquakes historical data is limited
and so it is difficult to quantify (Simchi-Levi et al., 2014). Hence, categorization of supply chain
disruptions, is required to develop mitigation, recovery and resilience building models. Tomlin
categorized disruptions into three categories: a) financially avoidable, b) operationally avoidable c)
operational contingency. Waters (2007) categorized it as internal risks and external risks, on the basis
of how controllable they are. Based on disruption sources, SC disruption can be categorized into
operational disruptions, natural disruptions, political or terrorism related disruptions (Kleindorfer &
Saad, 2009). Rao and Goldsby (2009) categorized supply chain risks into framework, problem specific,
and decision making risks. Sodhi et al. (2012) divided supply chain disruption literatures into four
categories: a) risk identification b) risk assessment c) risk mitigation and d) responsiveness to risk
incidents. These are also the stages from beginning of disruption to restoring to normalcy.
A substantial amount of literature resides in studies related to the upstream of supply chain, i.e. where
the cause of disruption is supply uncertainty (Bakshi & Kleindorfer, 2009; Ellis et al., 2010; Hu et al.,
2013; Kleindorfer & Saad, 2009; Meena et al., 2011; Tang et al., 2014). Tomlin established that the
supply side tactics that are internal to the firm like sourcing, inventory, and contingent rerouting play
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an important role in determining disruption mitigation strategies. Other strategies include process
improvement (Y. Wang et al., 2010), incentivizing supplier for restoring capacity (Hu et al., 2013), and
multiple sourcing (Sawik, 2014, 2019).
However, a crisis situation like pandemic disease caused by COVID-19 (Munster et al., 2020) requires
a different approach. In such a situation supply chain disruption is caused by both supply and demand
disruption. Furthermore, the disruption is prolonged and impacting multiple supply chains. What
concerns more is recovery and building resilience. Pettit et al. (2010) derived the definition of
resilience from engineering, which is the tendency to return to its original shape on removal of the
stress. Similarly, Supply Chain Resilience(SCR) can also be defined as the ability of the network to
return to its original form once the disruption is over. After reviewing 67 articles on SCR, Hohenstein
et al. (2015) defined it as the ability to be prepared for unexpected risk events, responding and
recovering to return to its original situation or move to a more desirable state. Pettit et al. (2019)
formed a framework of supply chain resilience which is built around the drivers of disruptions called
“vulnerabilities” and building capacity to overcome those vulnerabilities called “capabilities”. Before
the capabilities are built, a clear understanding of the vulnerabilities is required.
2.2. COVID-19, emergence and effects
In late December 2019, few cases of Pneumonia with unknown cause emerged in China. Most of them
were linked to Wuhan seafood market in the Hubei Province of China. On December 31, an
investigation carried out by Chinese Center for Disease Control and Prevention, reported a novel
coronavirus in the patient (Zhu et al., 2020). In January 7,2020, scientists had isolated Novel
coronavirus and after four days, first fatal case was reported (C. Wang et al., 2020). On January 13,
Thailand reported its first case of a Chinese patient who travelled from Wuhan (World Health
Organizaton, 2020b). In January 16, World Health Organization (WHO), reports another case in Japan
(World Health Organization, 2020b). On January 19, Republic of Korea reported its first case of a
resident of Wuhan (World Health Organization, 2020a), China and on January 20, United States
reported its first case of a patient who returned from Wuhan (Centers for Disease Control and
Prevention, 2020). By January 23, studies of families of patients and healthcare workers indicated the
person-to-person transmission of the novel coronavirus (Chan et al., 2020). Moving forward in the
timeline, as of May 21, 2020, WHO reported 4,893,186 confirmed cases and 323,256 deaths globally
(World Health Organizaton, 2020a).
The nature of the virus is contagious and is impacting any environment that requires social gathering.
To curb the spread of virus many countries closed its borders and most of them followed severe lock
down, quarantine and social distancing measures (BBC, 2020). This implies severe economic and
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business implications. Factories are shut down, supply chains halted and disrupted, businesses are
struggling with cash flows and unemployment rate breaking records (Hutt, 2020). U.S. April
manufacturing output and retail sales fell to lowest since 2009; Production and Manufacturing Index
(PMI) across the eurozone fell to 13.5 in April from 29.7 in March and PMI of China contracted to 49.4
in April from 50.1 in March (Bain & Company, 2020). Similarly, retail sales also plunged to severe low.
Among other segments, clothing and accessories sales fell 89% in April in U.S. (Retail Dive, 2020). The
disruption is severe and prolonged and so businesses need to anticipate demand and calibrate their
supply chain. This study deals with repercussions in the fashion retail industry due to COVID-19.
2.3. Fashion retail
Fernie writes that fashion is a cultural and environmental reflection of particular frame of time within
a particular boundary of geography (Azuma & Fernie, 2003). Climate is another determinant. There
are myriads of other factors like social, political, regional, national and international. However, the
changing nature of fashion may be attributed to these reasons, the evolution of fashion and its
adoption globally over the last few decades is heavily associated with fashion retailers. In the late 80s
and 90s the emergence of retail brands like United Colors of Benetton, Zara and Hennes Mauritz, made
fashion retail a global concept (Fernie & Grant, 2015). Fashion, that was earlier a privilege for the elites
is now accessible to mass, a concept that was pioneered by Zara by calling it “democratization of
fashion” (Tungate, 2008). The duration of fashion from designer ramp to store has reduced. This was
fast fashion. Fast fashion can be defined in the view of supply chain as a system of supply network
where, short production times and distribution lead times enable quick response to uncertain demand
and the product design is ‘highly fashionable’ (Cachon & Swinney, 2011). Then came the online
revolution, with a new format of website and mobile catalogue of clothes (Fernie & Grant, 2015). This
somewhat is transitioning into omni-channel system. Omni-channel is characterised by a single
logistics system for any customer interface with products (Hübner, Wollenburg, et al., 2016).
The concept of Fashion retailing and its supply chain is interesting due to the presence of two elements
i.e. seasonality and uncertainty. Fashion season is a period of time with the following stages:
introduction and adoption by fashion leaders, rise in acceptance, conformation by the mass and
decline (Bhardwaj & Fairhurst, 2010). For offline stores and online formats, it can be described as the
frequency with which entire collection of merchandise is changed. To maximize the profit obtained
during ‘rise in acceptance’-phase, the number of seasons have increased (Gordon, 2019). Interestingly,
with climate change rapidly affecting the globe, there is a possibility of multiple fashion seasons
shrinking to few (Klerk, 2020). Now, with the novel coronavirus changing lifestyle with an impact even
greater than climate change, the prevalence of multiple seasons is to be questioned. E-commerce
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though is one channel that is managing unexpected growth during COVID-19 (Retail Dive, 2020).
Fashion industry experts believe that once the pandemic gets over, a recovery period will follow
marked by low spending and severe contraction in demand (Amed et al., 2020). Fashion retailers has
to anticipate demand and synchronize operations to build resilience and survive the impact. Planning
their upcoming merchandises with all the ongoing changes is critical issue.
Fashion retail has a unique value chain. Their growth and continuity depend on margin obtained from
seasonal products or economies of scale. Post pandemic, both are to be tested. For retail stores the
products reach the end customer as a result of multiple processes in the back-end. The processes
include sourcing, production, forecasting, buying, planning and merchandising (Azuma & Fernie,
2003). The apparel products can be categorized into two categories: basic products and fashionable
products. Basic products are sold round the year with less deviation in terms of trend or seasonality.
Fashionable products are products sold in different fashion seasons. Fashionable products when sold
in full price earns higher margin than basic products. This study revolves around merchandising
planning decision of a brand of fashionable products.
2.4. Merchandise Planning
The success of a fashion season of an apparel retailer is often attributed to assortment planning and
merchandise planning. The former signifies the determination of the variety of product offerings in a
store, the latter is defined as the quantity, type, and depth of the merchandises to be displayed in the
store (Kunz & Rupe, 1999). A lot of insight on taking a decision on merchandising is based on historical
data. Rigorous mathematical modelling have also found their place in the development of
merchandise planning tools (Smith et al., 1998). Rajaram (2001), develops a non-linear integer
programming model using demand forecasts based on historical data with the objective of increasing
profits. Such models maybe a guide under normal circumstances, but it may not be helping during
crisis situations. Tsafarakis et al. (2016) adopted a differential evolution approach to determine
assortment planning in economic recession to facilitate decisions on optimal merchandise planning of
private labels. Hübner et al. (2016), considers a newsvendor problem to maximize profit taking
consideration of out-of-assortment and out-of-stock effects and develops an algorithm for stochastic
customer demands. Hübner (2017), identified the constraint of limited space and developed a decision
system for merchandise planning to maximize profit. With similar objective, scope and boundaries,
Flamand et al. (2018) develops a mixed integer programming model and embedded in an optimization
based heuristic.
However, the pandemic crisis caused by COVID-19 is different and is characterised by supply chain
disruption which is different from other disruptions. It is characterized by three components i.e.
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prolonged disruption existence, simultaneous disruption propagation and simultaneous disruption in
supply and demand (Ivanov, 2020). So, institutions like apparel retail, where crowd gathers,
propagation of the disease multiplies. It is also difficult to anticipate retail environment when business
reopens. The guidelines put forward by WHO to prevent human to human transmission, recurrence
of the disease and low transmission, that are related to retail environment are as follows (WHO, 2020):
“Increase physical distancing in crowded public spaces (e.g. public transportation, supermarkets,
markets, universities and schools, places of worship, mass gatherings such as sporting events, etc.)”
“Make sure your workplaces are clean and hygienic”
“Promote regular and thorough hand-washing by employees, contractors, and customers”
“Ensure that face masks or paper tissues are available at your workplaces, for those who
develop a runny nose or cough at work, along with closed bins for hygienically disposing
of them”
“Advise employees and contractors to consult national travel advice before going on business trips”
Based on the above guidelines, it is intuitive that when retailers open businesses they have to reassure
customers of their safety. Another consequence of such precautionary measures in retail is rapid rise
in online sales, which is safer. This necessitates fashion retailers to change their structure (Adhi &
Davis, 2020). However, online sales may not be a substitute for offline stores, but a shift of consumer
preference from pre-pandemic situation would be evident in the post-pandemic era (Gonzalo et al.,
2020). As reported by Mckinsey & Company, retailers are changing their priorities, by recalibrating
merchandise assortment planning, inventory replenishment system, and e-commerce business
(Aryapadi et al., 2020). In the post-pandemic environment retailers should still be cautious and
maintenance of social distancing norms would affect the fashion product display (Wu et al., 2013).
This implies brands may have to display lesser merchandise on the stores with distanced shelves.
Keeping this minimum standards would also be necessary to maintain the optimal density (Kunz &
Rupe, 1999).
It is clearly evident that priorities would be changing for retailers and profit maximization would not
be the only objective. The priorities chosen by a retailer would build or break its resilience during the
recovery period. This study aims to capture those priorities and develops a model based on GP
technique that shall help retailers to take decisions to build resilience when the dust settles.
GP was developed as an extension to linear programming (Lee, 1972) and has been later revised and
used in various contexts (Blake & Carter, 2002; Fine et al., 2005; Gökçen & Aǧpak, 2006; Kwak et al.,
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2005; Lee & Clayton, 1972; Z. J. Wang & Li, 2015). A review on Goal programming by Colapinto et al.
(2017), reflects how enormous literature used GP to solve different planning problems. Brauer &
Naadimuthu (1992), developed a GP model for aggregate inventory and distribution planning. Reyes
& Frazier (2007), developed a model for grocery shelf-space allocation. GP allows to develop multiple
objectives by minimizing their deviations and incorporate conflicting priorities and thereby optimize
the results. This technique fits the objective of the paper i.e. to analyse the merchandise planning
situation for fashion retailer in a post-pandemic environment.
3. Model Development
3.1. Brick-and-Mortar stores (Offline)
Most of the retail stores work with more than one product brand under the umbrella of their retail
brand. These product brands generally receive a fixed amount of shelf space. For basic products the
amount of shelf space does not vary. But fashionable products require dynamic planning as they drive
the top-line. A high performing store is always equipped with better products as it drives the growth
of the company. The mapping of shelf space of a product brand in respective stores is decided by its
performance, which is determined by multiple parameters. The total sale of the store is one
parameter. But not all product performs equally at any store. So, the product brands’ performance at
the store provides another dimension to judge the brand’s performance. However, another missing
dimension is the location of store. A high-street store or a store inside a mall generally tend to sale
more fashionable products than basic products. The contribution of current fashionable products is a
proxy for the effect of the location of store. Therefore, finally three of the defining factors chosen to
determine the performance of a brand in store of a retailer are a) Sales of the store, denoted by 𝑠𝑖𝑥 b)
Brand’s contribution, 𝑏𝑖𝑥 and c) Sales of Fashionable products, 𝑓𝑖𝑥 . Furthermore, these three criteria
have different importance, which is dependent on management decisions. The model adds weight to
the three criteria calculated using Analytic Hierarchy Process (AHP):
a) Sales of the store: weightage 𝑤1𝑥
b) Brand’s contribution: weightage 𝑤2𝑥
c) Sales of Fashionable products: weightage 𝑤3𝑥
the performance of the stores is denoted by 𝑝𝑖𝑥 .
𝑝𝑖𝑥 = 𝑤1𝑥 𝑠𝑖𝑥 + 𝑤2𝑥 𝑏𝑖𝑥 + 𝑤3𝑥 𝑓𝑖𝑥
(3.1)
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The stores have a fixed total capacity of items which include all brands. Each brand has a minimum
capacity of shelf space which they have to fulfil. This is because for a brand to exist, a standard number
of items is necessary to be displayed. Similarly, the brands high performing brands do not get more
space than required. For a multi-brand retailer, a balance of number of brands and number of products
in the brand is required. So, an upper limit to the capacity of each product brands is also present.
Let there be 10 stores available for a brand for displaying its products for the fashion season. These
10 stores must have capacities 𝑐𝑖𝑥 . Let the rents of each shelf space per item be 𝑟𝑖 . In practice, frontfacing and rear-sided shelfs have different rents. For simplification, it is assumed that the 𝑟𝑖 is the
average of rents of front facing and rear-sided shelves. Let B1 be the budget of the company for
merchandising the products of the brand on the stores. The decision variables are the number of items
on display and is denoted by 𝑥𝑖 . To illustrate, a shirt of size small and large are two separate items.
Since the objective is to generate better performance from the stores the problem can be formulated
as linear programming problem as shown below.
Maximize
𝑥
Z1 = ∑10
𝑖=1 𝑝𝑖 𝑥𝑖
Subject to
𝑥
∑10
𝑖=1 𝑐𝑖 𝑥𝑖 ≤ B1
𝑥𝑖 ≥ 𝑙𝑖𝑥 , 𝑥𝑖 ≤ 𝑢𝑖𝑥
for all 𝑖 = 1,2,3…10
(3.2)
where, 𝑙𝑖𝑥 is the minimum capacity of items and 𝑢𝑖𝑥 is the maximum capacity of items.
3.2. Online format
Customers buying from online portals are served from the regional warehouses or distribution
centres. So, the product brands find shelve spaces for their items in the warehouses. This means the
warehouses have different performances analogous to the stores. The sale of the warehouse is
representative of the sales of the region, denoted by 𝑠𝑗𝑦 . The contribution of the brand in the
warehouse, 𝑏𝑗𝑦 and sale of fashionable products, 𝑓𝑗𝑦 are other parameters to decide shelf space for
online format. Each of the parameters have different importance for online format which is dependent
on management decisions. The weights are calculated by AHP.
a) Sales of the warehouse: weightage 𝑤1𝑦
b) Product Brand’s contribution: weightage 𝑤2𝑦
c) Sales of Fashionable products: weightage 𝑤3𝑦
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the performance of the warehouses is denoted by 𝑝𝑗𝑦 .
𝑦
𝑦
𝑦
𝑦
𝑦
𝑦
𝑦
𝑝𝑗 = 𝑤1 𝑠𝑗 + 𝑤2 𝑏𝑗 + 𝑤3 𝑓𝑗
(3.3)
Since the warehouses serve a region, any brand would want a minimum standard of items always
displayed on the online portal. However, contrary to stores warehouses do not follow strict
standardization of shelves for brands. So, keeping a minimum inventory items of a brand are
achievable but a limit on the maximum items to be kept would restrict operational flow in warehouse.
Therefore, for simplicity of the model, an upper limit of items of a brand in a warehouse is relaxed.
Let the total online market be served by 5 warehouses demarcating their specific regions. Their
capacities are denoted by 𝑐𝑗𝑦 . Let the cost of carrying inventory per items be 𝑘𝑗 and B2 be the budget
of inventory carrying cost. The decision variables are the number of items of the brand to be kept at
each of the warehouses, denoted by 𝑦𝑗 .
Similar to offline stores the online performance can be formulated as linear programming problem as
given below.
Maximize
Z2 = ∑5𝑗=1 𝑝𝑗𝑦 𝑦𝑗
Subject to
∑5𝑗=1 𝑐𝑗𝑦 𝑦𝑗 ≤ B2
𝑦
𝑦
𝑦𝑗 ≥ 𝑙𝑗 , 𝑦𝑗 ≤ 𝑢𝑗
for all 𝑗 = 1,2,3,4,5
𝑦
(3.4)
𝑦
where, 𝑙𝑖 is the minimum capacity of items and 𝑢𝑖 is the maximum capacity of items.
Together, this forms a multi-objective deterministic problem, written as follows:
Maximize
𝑥
Z1 = ∑10
𝑖=1 𝑝𝑖 𝑥𝑖
Maximize
Z2 = ∑5𝑗=1 𝑝𝑗𝑦 𝑦𝑗
Subject to
𝑥
∑10
𝑖=1 𝑐𝑖 𝑥𝑖 ≤ B1
∑5𝑗=1 𝑐𝑗𝑦 𝑦𝑗 ≤ B2
𝑥𝑖 ≥ 𝑙𝑖𝑥 , 𝑥𝑖 ≤ 𝑢𝑖𝑥
for all 𝑖 = 1,2,3…10
𝑦𝑗 ≥ 𝑙𝑗𝑦 , 𝑦𝑗 ≤ 𝑢𝑗𝑦
for all 𝑗 = 1,2,3,4,5
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(3.5)
To adopt the changes caused in the priorities of the objective due to measures taken for containing
pandemic spread, a goal programming model based on lexicographic priorities has been designed to
solve the problem.
3.3. Goal programming formulation
After introducing deviational variables to the problem, the goal programming formulation is as
follows:
𝑥
∑10
𝑖=1 𝑐𝑖 𝑥𝑖 + η3 – ρ3 = B1
∑5𝑗=1 𝑐𝑗𝑦 𝑦𝑗 + η4 – ρ4 = B2
(3.6)
where η3 and η4 are the negative deviational variables and ρ3 and ρ4 are the positive deviational
variables for the two goals.
The goal programming formulations for the lower limit and upper limit of fulfilling capacity limitations
of offline retail stores are as follows:
𝑥𝑖 + 𝜂𝑙𝑖𝑥 - 𝜌𝑙𝑖𝑥 = 𝑙𝑖𝑥 ;
𝑥
𝑥
𝑥𝑖 + 𝜂𝑢𝑖
- 𝜌𝑢𝑖
= 𝑢𝑖𝑥 ;
for all 𝑖 = 1,2,3…10
(3.7)
𝑥
𝑥
where 𝜂𝑙𝑖𝑥 and 𝜂𝑢𝑖
are the negative deviational variables and 𝜌𝑙𝑖𝑥 and 𝜌𝑢𝑖
are the positive deviational
variables for the lower limits and upper limits of the fulfilling capacities at stores.
Similarly, the goal programming formulations for the lower limit capacity of warehouses for online
format are as follows:
𝑦
𝑦
𝑦𝑗 + 𝜂𝑙𝑗
- 𝜌𝑙𝑗
= 𝑙𝑗𝑦 ;
for all 𝑗 = 1,2,3,4,5
(3.8)
Both retail stores and online format, run on periodic targets and are constantly evaluated. These
targets serve as one of their major objectives while planning operations. Let, A1 be the aspirational
performance objective for retail stores as a whole, A2 be the aspirational performance objective for
online format. To maximize the performance objective, a chance constrained goal programming
problem is designed, such that the aspirational objective is achieved with a desirable probability, as
described below. Bhattacharya (2009) used a similar chance constrained objective for advertising
planning problem.
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𝑥
Probability(∑10
𝑖=1 𝑝𝑖 𝑥𝑖 ≥ 𝐴1 ) ≥ α;
𝑦
Probability(∑5𝑗=1 𝑝𝑗 𝑦𝑗 ≥ 𝐴2 ) ≥ β;
where A1 and A2 are estimated performance level.
∑10 𝑝𝑥 𝑥 −𝐸(𝐴1 )
P( 𝑖=1 𝑖 𝑖
√𝑉𝑎𝑟(𝐴1)
𝐴 − 𝐸(𝐴1)
or, P( 1
√𝑉𝑎𝑟(𝐴1 )
where the term
≥
√𝑉𝑎𝑟(𝐴1 )
√𝑉𝑎𝑟(𝐴1 )
)≥α
𝑥
∑10
𝑖=1 𝑝𝑖 𝑥𝑖 −𝐸(𝐴1 )
≤
𝐴1− 𝐸(𝐴1 )
𝐴1− 𝐸(𝐴1 )
√𝑉𝑎𝑟(𝐴1)
)≥α
represents a normal variate with mean of zero and a variance of one. Let v
be the value at which ƒ(v) = α,
∑10 𝑝𝑥 𝑥 −𝐸(𝐴1)
ƒ( 𝑖=1 𝑖 𝑖
√𝑉𝑎𝑟(𝐴1 )
or,
) ≥ ƒ(v)
𝑥
∑10
𝑖=1 𝑝𝑖 𝑥𝑖 −𝐸(𝐴1 )
√𝑉𝑎𝑟(𝐴1 )
≥v
𝑥
or, ∑10
𝑖=1 𝑝𝑖 𝑥𝑖 − 𝐸 (𝐴1 ) − v√𝑉𝑎𝑟(𝐴1 ) ≥ 0
Similarly,
∑5𝑗=1 𝑝𝑗𝑦 𝑦𝑗 − 𝐸(𝐴2 ) − w√𝑉𝑎𝑟(𝐴2 ) ≥ 0
Adding deviational variable to formulate into a goal equation, the performance objectives are as
follows:
𝑥
∑10
𝑖=1 𝑝𝑖 𝑥𝑖 + η1 – ρ1 = 𝐸 (𝐴1 ) + w√𝑉𝑎𝑟(𝐴1 ) ;
(3.9)
∑5𝑗=1 𝑝𝑗𝑦 𝑦𝑗 + η2 – ρ2 = 𝐸(𝐴2 ) + w√𝑉𝑎𝑟(𝐴2 ) ;
(3.10)
For the discussed problem the following priority structure has been developed.
Priority 1: The minimum capacity standards at both offline stores and warehouses for online format
should be fulfilled
Priority 2: The budget condition of offline stores should be met.
Priority 3: The budget condition of online stores should be met.
Priority 4: The aspirational performance level objective of online format should be met.
Priority 5: The aspirational performance level objective of offline stores should be met.
13
Priority 6: The maximum capacity standards at offline stores should not exceed.
Scenario development
The priorities are numbered to serve an identity to the priority and is not to be considered as an order.
The order of priorities is defined by the scenarios developed subsequently.
Scenario 1:
The retailer wants to survive by maintaining the minimum standards of visibility of its products. It also
realizes the constraint of shrinkage of budget and hence then prioritizes budget of both offline stores
and online format. The rising trend of online purchases needs to be leveraged, hence Priority 4 comes
next. Performance level of offline stores can be considered next, followed by Priority 6.
The goal programming formulation is as follows:
Minimize
𝑦
𝑥
5
10
𝑥
P1{∑10
𝑖=1 𝜂𝑙𝑖 + ∑𝑗=1 𝜂𝑙𝑗 }, P2 {𝜌3 }, P3 {𝜌4 }, P4 {𝜂2 }, P5 {𝜂1 }, P6 {∑𝑖=1 𝜌𝑙𝑖 }
(P1 indicates first priority, P2 second and so on)
Such that
𝑥
∑10
𝑖=1 𝑝𝑖 𝑥𝑖 + η1 – ρ1 = 𝐸 (𝐴1 ) + w√𝑉𝑎𝑟(𝐴1 ) ;
∑5𝑗=1 𝑝𝑗𝑦 𝑦𝑗 + η2 – ρ2 = 𝐸(𝐴2 ) + w√𝑉𝑎𝑟(𝐴2 ) ;
and
𝑥𝑖 + 𝜂𝑙𝑖𝑥 - 𝜌𝑙𝑖𝑥 = 𝑙𝑖𝑥 ;
𝑥
𝑥
𝑥𝑖 + 𝜂𝑢𝑖
- 𝜌𝑢𝑖
= 𝑢𝑖𝑥 ;
for all 𝑖 = 1,2,3…10
and
𝑦
𝑦
𝑦𝑗 + 𝜂𝑙𝑗
- 𝜌𝑙𝑗
= 𝑙𝑗𝑦 ;
for all 𝑗 = 1,2,3,4,5
(3.11)
Scenario 2:
Retailer is highly concerned over budget. It maintains store budget and online budget as top priority.
It focusses on maintaining minimum standard capacity at stores followed by its performance. Online
performance and maximum capacity are the last priorities. So, the goal formulation is as follows:
𝑦
Minimize
𝑥
5
10
𝑥
P1{𝜌3 }, P2{𝜌4 }, P3 {∑10
𝑖=1 𝜂𝑙𝑖 + ∑𝑗=1 𝜂𝑙𝑗 }, P4 {𝜂1 }, P5 {𝜂2 }, P6 {∑𝑖=1 𝜌𝑙𝑖 }
such that
Constraints of (3.11)
Scenario 3:
14
Retailer is concerned about achieving aspirational performance objectives of both. Its brick-andmortar presence being stronger it prioritizes it over online format. Maintaining minimum standard
capacity comes close next, followed by budget objectives of store and online format. Finally, maximum
capacity objective at stores is to be met. The goal formulation is as follows:
Minimize
𝑦
𝑥
5
10
𝑥
P1{𝜂1 }, P2 {𝜂2 }, P3 {∑10
𝑖=1 𝜂𝑙𝑖 + ∑𝑗=1 𝜂𝑙𝑗 }, P4 {𝜌4 }, P5 {𝜌3 }, P6 {∑𝑖=1 𝜌𝑙𝑖 }
such that
Constraints of (3.11)
Scenario 4:
Retailer is highly concerned over budget and tries to maintain minimum standard capacity at stores.
It then prioritizes the performance of online format knowing there is a surge in demand. Finally, it
covers performance objective of offline stores and maximum capacity limits. The goal formulation is
as follows:
Minimize
𝑦
𝑥
5
10
𝑥
P1{𝜌3 }, P2{𝜌4 }, P3 {∑10
𝑖=1 𝜂𝑙𝑖 + ∑𝑗=1 𝜂𝑙𝑗 }, P4 {𝜂2 }, P5 {𝜂1 }, P6 {∑𝑖=1 𝜌𝑙𝑖 }
such that
Constraints of (3.11)
Scenario 5:
Retailer is getting a rise in demand online and wants to leverage the opportunity. However, keeping
minimum standard capacity at stores remains a second level priority. It takes budget objectives of
store and online format as third level and fourth level priorities. It is not very keen on achieving
performance targets in stores and hence it is a fifth level priority. Finally, it aims to meet maximum
capacity objective at stores. The goal formulation is as follows:
Minimize
𝑦
𝑥
5
10
𝑥
P1{𝜂2 }, P2{∑10
𝑖=1 𝜂𝑙𝑖 + ∑𝑗=1 𝜂𝑙𝑗 }, P3{𝜌3 }, P4{𝜌4 }, P5 {𝜂1 }, P6{∑𝑖=1 𝜌𝑙𝑖 }
such that
Constraints of (3.11)
Scenario 6:
Retailer has invested in its offline presence in the past and wants the stores to bail out by achieving
performance aspirations. It also aims to fulfil minimum standard of capacity. It then targets budget
objectives of both stores and online formats. Online format performance remains as the second last
priority while satisfying maximum capacity limits is the last priority. The goal formulation is as follows:
Minimize
𝑦
𝑥
5
10
𝑥
P1{𝜂1 }, P2{∑10
𝑖=1 𝜂𝑙𝑖 + ∑𝑗=1 𝜂𝑙𝑗 }, P3{𝜌4 }, P4{𝜌3 }, P5 {𝜂2 }, P6{∑𝑖=1 𝜌𝑙𝑖 }
such that
Constraints of (3.11)
15
4. Case Problem
An apparel retailer has its presence in both store and online format. It is facing the aftermath of
pandemic caused by novel coronavirus (COVID-19). The following are the descriptions of offline stores
of the retailer:
Table 1. Merchandise capacities of Stores
Store 1
Store 2 Store 3 Store 4 Store 5 Store 6 Store 7
Store 8 Store 9
Store 10
10000
3000
3640
8000
6000
8800
7200
3200
4800
11200
Table 2. Performance measures of Stores
Store 1
Total Sale
1950
Contribution(Sale)
109.20
of brand in the
Stor
Stor
Stor
Stor
Stor
Stor
Stor
Stor
Stor
e2
e3
e4
e5
e6
e7
e8
e9
e 10
400
950
110
100
750
150
800
200
150
0
0
0
0
0
16.8
77.0
33.5
86.6
40.8
103.
72.4
120.
132.
0
5
5
0
8
05
0
00
00
252
304
528
590
390
855
496
130
660
store
Sale of fashionable 1073
products
of
the
0
store
Table 3. Rentals per capacity of Stores
Store 1
Store 2 Store 3 Store 4 Store 5 Store 6 Store 7
Store 8 Store 9
Store 10
6.5
3
2.5
6
4.5
300
300
3
4.5
2.5
2.5
3
Table 4. Brand capacities at stores
Minimum
300
300
300
300
number of items
of brand at store
16
300
300
300
300
Maximum
700
400
500
600
500
400
400
400
700
600
number of items
of brand at store
The calculation of weights for criteria of stores using AHP is as shown below in Table 5 and Table 6.
Table 5. Comparison matrix
Contribution(Sale) of
Sale of fashionable
brand in the store
products of the store
1
0.33
0.20
3
1
0.33
5
3.00
1
9
4.33
1.53
Rank
Sale
Sale
Contribution(Sale) of
brand in the store
Sale of fashionable
products of the store
Column total
Table 6. Calculation of weights
Contribution(Sal
Sale
e) of brand in the
store
Sale
Sale of
fashionable
products of the
Row Average
store
0.11
0.08
0.13
0.11
0.33
0.23
0.22
0.26
0.56
0.69
0.65
0.63
Contribution(Sale)
of brand in the
store
Sale of fashionable
products of the
store
Consistency test:
𝑤1𝑥 = 0.11, 𝑤2𝑥 = 0.26, 𝑤3𝑥 =0.63
17
1 0.33
Aw = [3
1
5
3
0.2 0.11
0.32
0.33] [0.26]= [0.79]
1
0.63
1.94
λmax = 0.32 + 0.79 + 1.94 = 3.05
Consistency Index of A =
3.05−3
3−1
= 0.026
Random Consistency Index of A = 0.58 (Obtained from Random Consistency Index table)
Consistency Ratio =
CI
RI
= 0.045
The consistency ratio is within acceptable limits (less than 0.1).
Similarly, the following are the descriptions of online format of the retailer (Table 7,8,9,10) :
Table 7. Merchandise capacities of warehouses
Warehouse 1
Warehouse 2
Warehouse 3
Warehouse 4
Warehouse 5
100000
30000
60000
88000
72000
Table 8. Performance measures of warehouses
Sale
Warehous
Warehous
Warehous
Warehous
Warehous
e1
e2
e3
e4
e5
806625
448125
478000
746875
507875
41945
19718
26290
44813
12697
451710
228544
286800
336094
294568
Contribution(Sale) of brand
in the region
Sale of fashionable products
in the region
Table 9. Cost of storage at warehouse per capacity
Warehouse 1
Cost per item
0.195
Warehouse 2 Warehouse 3
0.250
0.190
18
Warehouse 4 Warehouse 5
0.200
0.225
Table 10. Capacity limits of the brand
Minimum items to be kept of the brand
3000
900
1800
2640
2160
The calculation of weights for criteria of warehouses using analytic hierarchy process is as shown
below in Table 11 and Table 12.
Table 11. Comparison matrix
Contribution(Sale) of
Sale of fashionable
brand in the store
products of the store
1
3.00
0.33
0.33
1
0.14
3
7.00
1
4.33
11
1.47
Rank
Sale
Sale
Contribution(Sale) of
brand in the store
Sale of fashionable
products of the store
Column total
Table 12. Calculation of weights
Contribution(Sal
Sale
e) of brand in the
store
Sale
Sale of
fashionable
products of the
Row Average
store
0.23
0.27
0.22
0.24
0.08
0.09
0.10
0.09
0.69
0.64
0.68
0.67
Contribution(Sal
e) of brand in the
store
Sale of
fashionable
products of the
store
𝑦
𝑦
𝑦
𝑤1 = 0.24, 𝑤2 = 0.09, 𝑤3 =0.67
The consistency ratio calculated is -0.0020, which is within acceptable limits (less than 0.1).
19
Let the annual budget for rentals for stores and warehouses be 15000 and 4000 respectively.
The performance of the stores and warehouses are obtained as follows:
Table 13. Performance measure of stores
Store
Store
Store
Store
Store
Store
Store
Store
Store
Store
1
2
3
4
5
6
7
8
9
10
99.43
69.91
73.22
60.28
73.91
117.6
162.4
118.9
1
9
5
95.80
92.85
Perfor
manc
e of
stores
Table 14. Performance measures of warehouses
Warehouse 1
Warehouse 2
Warehouse 3
Warehouse 4
Warehouse 5
4.12
7.40
4.16
4.13
3.56
Performance
of
warehouses
So, the linear programming formulation of the above retailer is obtained as:
Maximize
Z1 = 99.43𝑥1 + 69.91𝑥2 + 73.22𝑥3 + 60.28𝑥4 + 73.91𝑥5 + 117.61𝑥6 + 162.49𝑥7 +
118.95𝑥8 + 95.80𝑥9 + 92.85𝑥10
subject to
6.5𝑥1 + 3𝑥2 + 3𝑥3 + 4.5𝑥4 + 2.5𝑥5 + 2.5𝑥6 + 3𝑥7 + 2.5𝑥8 + 6𝑥9 + 4.5𝑥10 ≤ 15000;
𝑥1 ≥ 300, 𝑥1 ≤ 700;
𝑥2 ≥ 300, 𝑥2 ≤ 400;
𝑥3 ≥ 300, 𝑥3 ≤ 500;
𝑥4 ≥ 300, 𝑥4 ≤ 600;
𝑥5 ≥ 300, 𝑥5 ≤ 500;
𝑥6 ≥ 300, 𝑥6 ≤ 400;
𝑥7 ≥ 300, 𝑥7 ≤ 400;
𝑥8 ≥ 300, 𝑥8 ≤ 400;
20
𝑥9 ≥ 300, 𝑥9 ≤ 700;
𝑥10 ≥ 300, 𝑥10 ≤ 600;
Maximize
Z2 = 4.12𝑦1+ 7.40𝑦2 + 4.16𝑦3 + 4.13𝑦4 + 3.56𝑦5
subject to
0.195𝑦1+ 0.25𝑦2 + 0.19𝑦3 + 0.20𝑦4 + 0.225𝑦5
𝑦1≥ 3000;
𝑦2≥ 900;
𝑦3 ≥ 1800;
𝑦4 ≥ 2640;
𝑦5 ≥ 2160;
The goal programming formulation of the above multi-objective problem for the scenarios explained
above is designed as follows:
The aspirational performance level is obtained by solving equation 3.3 and 3.4 separately. For online
format the aspirational level is 99351 and for offline stores it is 394289.
For scenario 1:
Minimize
𝑦
𝑥
5
10
𝑥
P1{∑10
𝑖=1 𝜂𝑙𝑖 + ∑𝑗=1 𝜂𝑙𝑗 }, P2 {𝜌3 }, P3 {𝜌4 }, P4 {𝜂2 }, P5 {𝜂1 }, P6 {∑𝑖=1 𝜌𝑙𝑖 }
such that
99.4305*𝑥1 + 69.9067*𝑥2 + 73.2197*𝑥3 + 60.275*𝑥4 + 73.908*𝑥5 + 117.6093*𝑥6 +
162.4937*𝑥7 + 118.945*𝑥8 + 95.8035*𝑥9 + 92.85*𝑥10 + η1 – ρ1 = 394289.59 +
1.96*1000;
[Assumption: standard deviation=1000]
4.1202*𝑦1 + 7.3955*𝑦2 + 4.1586*𝑦3 + 4.129*𝑦4 + 3.5621*𝑦5 + η2 – ρ2 = 99351.048 +
1.96*100;
and
[Assumption: standard deviation=100]
𝑥𝑖 + 𝜂𝑙𝑖𝑥 - 𝜌𝑙𝑖𝑥 = 𝑙𝑖𝑥 ;
𝑥
𝑥
𝑥𝑖 + 𝜂𝑢𝑖
- 𝜌𝑢𝑖
= 𝑢𝑖𝑥 ;
for all 𝑖 = 1,2,3…10
Values of 𝑙𝑖𝑥 and 𝑢𝑖𝑥 are presented in Table 4.
and
𝑦
𝑦
𝑦
𝑦𝑗 + 𝜂𝑙𝑗 - 𝜌𝑙𝑗 = 𝑙𝑗 ;
for all 𝑗 = 1,2,3,4,5
21
Values of 𝑙𝑖𝑦 are presented in Table 10.
Optimal values for scenario 1 obtained is shown in Table 15 and Table 16:
Table 15. Optimal values of stores
𝒙𝟏
𝒙𝟐
𝒙𝟑
𝒙𝟒
𝒙𝟓
𝒙𝟔
𝒙𝟕
𝒙𝟖
𝒙𝟗
𝒙𝟏𝟎
300
300
300
300
300
300
300
300
300
300
Table 16. Optimal values of warehouses
𝒚𝟏
𝒚𝟐
𝒚𝟑
𝒚𝟒
𝒚𝟓
3000
8236
1800
2640
2160
The solution obtained for scenario 1:
P1 = 0, P2 = 0, P3 = 0, and P4 = 196
The algorithm for the scenarios are developed using Python software and Table 17 shows the results
of all scenarios:
Table 17. Results of all scenarios
Performa
Scen
ario
nce
obtained
(Offline,
Online)
1
2
289332.5
7, 45097
289332.5
7, 45097
Overall
from
𝒙𝟏 , 𝒙𝟐 , 𝒙𝟑 , 𝒙𝟒 , 𝒙𝟓 , 𝒙𝟔 , 𝒙𝟕 , 𝒙𝟖 , 𝒙𝟗 , 𝒙𝟏𝟎 ;
aspirati
𝒚𝟏 , 𝒚𝟐 , 𝒚𝟑 , 𝒚𝟒 , 𝒚𝟓
8,
99550.3
ase
Priority
Cost
t
level
300,300,300,300,300,300,300,300,
-32.25%
300,300;
3000,8236,1800,2640,2160
304,400,500,300,500,400,400,400,
-32.25%
304, 600;
3000,900,1800,2640,2160
700,400,500,600,500,400,400,400,
19.37%
from
budge
onal
489684.7
3
Decre
increase
700,600;
3000,8262,1801,2640,2160
22
P1=0,P2=0,P3
154
=0,P4=196
00
P2=0,P3=0,P1
171
=0,P5=1960
60
P5=0,P4=0,P1
253
=0,P3=6.69
06
19%
10%
-33%
4
289332.5
7, 45097
300,300,300,300,300,300,300,300,
-32.25%
3000,8236,1800,2640,2160
289332.5
5
7,
304,400,500,300,500,400,400,400,
-21.22%
99550.3
6
489684.7
8, 45097
300,300;
304,600;
3000,8262,1801,2640,2160
300,400,500,301,500,400,400,400,
8.33%
328,600;
3000,900,1800,2640,2160
P2=0,P3=0,P1
154
=0,P4=196
00
P4=0,P1=0,P2
154
=0,P3=6.69
06
P5=0,P1=0,P3
172
=0,P2=122
88
19%
19%
9%
5. Discussions
The impacts of globalisation and a connected supply chain has multiplied the risk factor of supply
chain. The supply chain disruption brought by COVID-19, demands careful planning and develop
operational capabilities at the retailers’ end in order to build resilience and recover the crisis. As
discussed in Section 2, retailers are bearing the brunt of both supply disruption and demand
disruption. Fashion retailers, therefore need reimagination to thrive through a stunted economy and
carefully optimize resources at hand. The discussed model, intends to add three factors: merchandise
capacity, budget and performance to redevelop the planning process to survive through the crisis. The
case is a generalisation of major apparel retailers across the globe, whose major revenue source is
offline stores than online format. So, merchandise planning of stores for the forthcoming seasons
remain a pivotal activity.
Apparel retail stores can be categorized based on the density of merchandises it displays, and the
fashion-quotient of the merchandises. The density of merchandise is the number of products a retailer
displays in its area of shelf-space (Kunz & Rupe, 1999; Wu et al., 2013). So, a retailer like Peacock in
the United Kingdom should be considered as high-density retailer, while Armani as low-density
retailer. Fashion however is not easy to define, as Joanne Entwistle relates with constant change and
newness (Briggs, 2001). In view of supply chain, fashion-quotient for an apparel retailer can be defined
as the amount of merchandises having demand of getting sold at full price (Fernie & Grant, 2015). So,
Zara can be considered a high-fashion retailer, while Express may be considered as low-fashion
retailer. To illustrate the above description of fashion and density trade-off the below matrix is
developed on a scale of 0 to 1 for both the dimensions.
23
Figure 1. Illustration of categories of retailers based on fashion-quotient and merchandise density
In a post-pandemic environment, the retailers need to modify their density to accommodate close
contact preventive measures. With reference to Figure 1, there could be a likely shift from High density
(Green and Yellow) to Low density (Blue and Red) category. Similarly, a post-pandemic environment
would likely face demand being disrupted as policies like work from home, restricted social gathering,
prevention of festivities etc. being promoted implies lack of demand of high-fashion apparels and
accessories. On the other side, supply from foreign countries will need longer time to recover,
meaning availability of global high-fashion would take time. This means High-fashion (Blue and Green)
retailers would tend to move into the category of Low fashion (Red and Yellow) category. To make
way for these changes, merchandise planners have to fit the priorities similarly.
If the pandemic has helped any sector then online retail business would be one of them (Amed et al.,
2020). However, it is difficult for the online retail business to completely replace offline stores, due to
their different nature. This however indicates potential for retailers to survive the crisis period by
giving importance to their strengths and weaknesses. To illustrate, a retailer with strong brick-andmortar presence and weak online presence may relax its targets of brick-and-mortar stores and
develop online capabilities, to leverage the surge in online demand. Another possibility of retailers
with strong online presence could be to innovate and develop new offline and omni-channel
24
capabilities and strongly recover the crisis. Figure 2 illustrates a matrix of firms categorized on a scale
of 0 to 1 depending on their online and offline presence.
Figure 2. Illustration of retailers categorized into offline and online strength
For merchandise planners this would mean prioritizing online and offline budgets to develop
capabilities.
In the case analysis of the model, a key assumption is that the retailer earns major chunk of its revenue
from offline store and a lesser amount of revenue comes from online format. The assumption is in
congruence with the current retail market structure (O’Connell, 2020). The 6 priorities in the model
above are different from each other yet they can be grouped into 3 categories: capacity, budget and
performance. P1 and P6 are capacity constraints; P2 and P3 are budget constraints; and P4 and P5 are
performance constraints. P1 and P6 are conflicting priorities. The importance of P6 constraint is
severely reduced because of the impact of supply disruption caused by pandemic, while importance
of P1 has increased tremendously. P2 conflicts with P3, as P5 conflicts with P4 only when the retail
format i.e. online or offline, becomes a key decision to make. P2 and P3 together conflicts with P4 and
P5 when the key decision lies in a trade-off between performance objective and budget constraints.
Insight 1:
25
As discussed, in a post-pandemic environment retailer based on high density merchandise would shift
to low density merchandise. If the minimum capacity standards become the top priority, performance
of the product would drop significantly compared to the aspirational objective (Scenario 1). This would
imply drop in revenue. To overcome the decline in revenue, they may have to cut operational cost, or
increase their fashion-quotient to garner better margin. If they intend to increase the fashion-quotient
a shift from yellow quadrant to blue quadrant in Figure 1 would be evident.
Insight 2:
For retailers with highly constrained budget and are willing to maintain minimum standard as the
second priority after budget, should focus on online over offline (Scenario 2 vs Scenario 4) as this
would bring savings in the budget. This may help survive the crisis.
Insight 3:
Retailers (Scenario 5) who pursue online performance over offline stores while meeting minimum
capacity standards would end up saving from the budget but decline in overall performance so much
that it may exacerbate the loss of cash-flow. However, retailers prioritizing offline stores performance
(Scenario 6) would save less from the budget but perform slightly better than aspirational objective.
This would help them with cash-flow and survive the crisis. This is in contradiction to the obvious view
that capturing rise in online demand would lead to growth.
6. Theoretical and Practical Implications
The pandemic caused by COVID-19 is a new phenomenon in the literature of supply chain disruption.
The initial reproduction number of the novel coronavirus was between 2-2.5 (Callaway et al., 2020)
which has increased to 6 (World Economic Forum, 2020). This means an infected person can spread
the disease to 2 to 6 persons. As a result, touch, close contact, physical distancing and travel limitations
are becoming more prevalent. Supply chain as a network is difficult to operate under such limitations.
A March 2020 McKinsey report suggests, companies that inherited a Lean and Just-in-time philosophy
were hit the most (McKinsey, 2020). It also reports that fashion retailer has only 2 to 3 months of
inventory and many of them may lead to bankruptcy. Merchandise planning which broadly means
putting the right merchandise at the right place at the right time (Bruce & Daly, 2006; Rajaram, 2001;
Tsafarakis et al., 2016), is now a daunting task. The right merchandise means revaluating demand
during the crisis. New season merchandise already planned and are in the pipeline will need to be
calibrated, meaning ‘right merchandise’ is a big challenge. Brick-and-mortar stores may not see a
complete recovery of footfall in the pandemic and post-pandemic environment. So online formats or
newer innovative formats could be the ‘right place’. Prolonged supply disruption means fulfilling the
26
‘right time’ criteria would also be another gigantic task. Realizing the nature of retail merchandising,
this study borrows the principles of theory of constraints originally developed by Eli Goldratt which
explains that removing a bottleneck improves performance (Gupta & Boyd, 2008; Rahman, 1998). In
this study, the constraints are identified and are prioritised based on the merchandising strategy the
retailers intend to adopt to recover through the crisis. The idea of exploiting and elevating the
constraints are captured under the scenarios. The conflicting priorities would help identify the
bottleneck resources and non-bottleneck resources.
Some of the practical implications are as follows. A supply-based disruption in a fashion retail chain
indicate merchandises for upcoming fashion seasons are unlikely to reach on time. This means that
the normal merchandise assortment decided during the preparation of fashion season has to be
tweaked, while maintaining the minimum of the merchandising standard of the retailer. This
constraint is captured in Priority 1. The demand disruption caused by the pandemic is likely to stay for
a long time before it recovers to pre-pandemic level. The spread of the disease has caused factories
and workplaces to work at lesser capacities (PWC united states, 2020). As a result, performance of
fashion retailers is very likely to decline. This implies reduction of sales targets and aspiration levels of
performance of both online and offline. This also indicates a slower rate of sales and lesser over filling
of merchandise capacities. The former implication is captured in Priority 4 and 5, while the later in
Priority 6. A post COVID-19 environment is likely to be hit by recession (Faulconbridge & Mackenzie,
2020) and lower cash flow for retailers. Budgets for merchandise planning would be serious
constraints for offline stores as well as online format. These constraints are added in Priority 2 and 3.
7. Limitations and future scope
The developed model assumes a fashion retailer who has both online and offline presence. Although
the situation is a representative of the scenario around the globe, there are retailers who operate
either online or offline. Such retailers definitely do not fit the models. The model draws the constraints
from performance, capacity and budget. Other constraints that may be prevalent in a post pandemic
situation would be unavailability of workforce, inventory flow, logistics’ constraints etc. are not
considered. Such constraints can be studied in the future.
A possible upgrade of the model could be using dynamic programming to anticipate how the stages
could be developed when the desired state is known.
27
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