CASE STUDY

  – 7 page  (not including cover page, citations, and reference page) 

– APA FORMAT

– NO PLAGIARISM 

– NEED PLAGIARISM REPORT

– INTEXT CITATION 

– QUESTION IS ATTACHED and SOURCE  VIDEO LINK TOO (TWO DIFFERENT FILES) 

After watching the video on Steelcase and completing your reading assignments, research the company Steelcase and in a single Word document, APA format, minimum 7 full pages, complete the following tasks:

1. Create a hypothetical demand forecast base on your research

2. Construct a process improvement model that optimizes operational yield at Steelcase

3. Analyze Steelcase operational practices and state your opinion about their ethical culture

4. Summarize your conclusions and recommendations to their supply chain, logistical, and operational problems

Video –

End-To-End Supply Chain Strategies: A Parametric
Study of the Apparel Industry

Shardul S. Phadnis*
Malaysia Institute for Supply Chain Innovation, 2A, Persiaran Tebar Layar, Seksyen U8, Bukit Jelutong, Shah Alam, 40150, Selangor,

Malaysia, sphadnis@misi.edu.my

Charles H. Fine
MIT Sloan School of Management, 77 Massachusetts Ave, E62-466, Cambridge, Massachusetts 02139, USA
Asia School of Business, Sasana Kijang, 2 Jalan Dato Onn, 50480, Kuala Lumpur, Malaysia, charley@mit.edu

T
his study examines the tradeoffs in sourcing and sales strategies (i.e., upstream and downstream supply chain strate-
gies) by considering them as components of an integral end-to-end supply chain strategy. We evaluate four end-to-

end supply chain strategies under various scenarios using a newsvendor model, and compare the model’s predictions
against the prescriptions in Fisher’s (1997) framework, which recommends “cost-efficient” supply chains for “functional”
products and “responsive” supply chains for “innovative” products. We considered combinations of offshore vs. near-
shore sourcing, and online vs. brick-and-mortar retailing. This study’s key finding is that sourcing and sales strategies are
not completely modular: an integral end-to-end strategy may not decompose into an optimal sourcing strategy and a sep-
arately computed optimal sales strategy. Our analyses sharpen strategic supply chain thinking by identifying realistic con-
ditions in which an end-to-end strategy with cost-efficient components could outperform one with responsive components
for innovative products, or when one with responsive components could be more profitable than one with cost-efficient
components for functional products

.

Key words: supply chain strategy; sourcing strategy; distribution strategy
History: Received: June 2016; Accepted: July 2017 by Edward Anderson, after 2 revisions.

1. Introduction

The twin forces of globalization and e-commerce have
dramatically changed supply chains in the last two
decades. Globalization has provided firms a choice in
their sourcing and operations strategies to either pro-
duce close to their markets or seek lower costs from
distant sources. The growth of the Internet has created
powerful new sales channels, where firms can sell
goods directly to consumers online instead of through
brick-and-mortar stores. However, firms around the
world still pursue a range of sourcing and sales strate-
gies, so that one cannot (yet) assert the emergence of a
dominant design for the 21st century’s supply chains.
Nonetheless, firms in Western countries have out-

sourced a large share of production to lower-cost
countries in Asia. A McKinsey study (Manyika et al.
2014) showed that one-third of all goods produced in
2012 (36% of global GDP) crossed national borders.
The United States alone imported $2.3 trillion worth
of goods for local consumption. On the other hand,
some Western firms have started bringing some of
their offshored production back into the home coun-
try. In a Boston Consulting Group (2015) survey, 31%

of senior manufacturing executives from firms with at
least $1 billion in annual revenue reported that their
companies were most likely to add production capac-
ity in the United States in the next five years.
On the sales side, e-commerce has grown as an

alternative to brick-and-mortar stores. The $395 bil-
lion e-commerce sales in the United States constituted
8.1% of total retail sales in 2016. From 2015 to 2016,
the U.S. e-commerce sales grew by 15.1%, while retail
sales increased by 2.9% (U.S. Census Bureau 2017).
This trend is projected to continue, with e-commerce
expected to account for 11% of all U.S. retail sales by
2018 (Forrester Research 2014). Although growth in e-
commerce channel has been linked to the demise of
established brick-and-mortar stores such as Borders
Book Stores and Circuit City, this trend does not spell
absolute doom for the brick-and-mortar sales channel.
In fact, Amazon.com, a pioneer of online retailing,
opened its first physical bookstore in 2015, and
announced the eighth bookstore to open in New York
City in Spring 2017 (New York Times 2017).
The plurality of supply chain strategies can also be

seen within numerous industries. The global apparel
industry provides one example of this apparent state

2305

Vol. 26, No. 12, December 2017, pp. 2305–2322 DOI 10.1111/poms.12779
ISSN 1059-1478|EISSN 1937-5956|17|2612|2305 © 2017 Production and Operations Management Society

of experimentation in end-to-end supply chain strate-
gies. This industry is a posterchild of globalization. In
2016, apparel manufacturers had estimated revenue of
$657 billion, 70% of which was generated from
exported goods (IBISWorld 2016). International Appa-
rel Federation (2017), the leading federation of apparel
manufacturers, retailers, and related companies, boasts
a membership of 150,000 companies employing over
5 million people in nearly 40 countries. Despite its
size, no dominant supply chain design has yet
emerged for the apparel industry. Equally successful
apparel brands in the West have either outsourced the
production to low-cost contract manufacturers in Asia,
or tightly integrated with producers located close to
the market (e.g., Ghemawat and Nueno 2003, Pisano
and Adams 2009). Similarly, numerous clothing
brands successfully use brick-and-mortar stores as the
primary sales channel (e.g., Zara) while others sell
only online (e.g., Amazon). With $63.8 billion online
sales, apparel is the largest category of e-commerce
sales in the United States (eMarketer 2017). This plu-
rality of supply chain configurations observed in prac-
tice raises a question: under what circumstances is a
particular end-to-end supply chain configuration more
profitable than the others?
The supply chain literature prescribes ideal supply

chain strategies based on different product and firm
attributes. Fisher (1997) suggests choosing a supply
chain strategy according to the predictability of the
product’s demand: cost-efficient supply chains for
products with predictable demand (“functional”) and
responsive supply chains for those with unpre-
dictable demand (“innovative”). Analytical and
empirical examinations partially support Fisher’s
guidelines (Li and O’Brien 2001, Selldin and Olhager
2007). The general guidelines help derive specific rec-
ommendations for sourcing and sales strategies. An
offshore source has lower out-of-pocket costs, but
may suffer from higher forecast error and be less
responsive due to longer lead time than a nearshore
source (de Treville and Trigeorgis 2010, Wu and
Zhang 2014). On the sales side, the online channel has
lower costs than the brick-and-mortar channel as it
has fewer physical assets and centralizes inventory in
one location. However, this comes at the expense of a
lack of pre-purchase product experience for the con-
sumer and results in a higher proportion of returned
sales for the online channel (WSJ 2013). Pre-purchase
experience may trump lower cost of online channel
for novel products, but not for popular products
(Balakrishnan et al. 2014, Brynjolfsson et al. 2009).
Although the tradeoffs among different sourcing

and sales strategies have been studied in the opera-
tions and supply chain management literature, the
two strategies have not been studied together as com-
ponents of one integral supply chain strategy. This

lack of a systemic approach may be due to the associa-
tion of the two strategic decisions with different func-
tions in firms or academic disciplines, or due to the
academic preference for parsimonious models of
managerial decisions. Regardless, a joint exploration
of the sourcing and sales strategies is likely to provide
novel insights if the two possess a high degree of
interdependence (Larsen et al. 2013). The sourcing
and sales strategies may have some complementarity,
because the firms that choose holistic supply chain
strategies (e.g., Zara) seem to outperform their com-
petition (Ghemawat and Nueno 2003).
This study explores these complementarities by eval-

uating sourcing and sales strategies together, which we
call the firm’s end-to-end supply chain strategy. Our
model assumes that a firm chooses its end-to-end sup-
ply chain strategy, and makes two operational deci-
sions in each season: decide the quantity to procure
and allocate the procured quantity to store(s). The firm
sources either from an offshore or a nearshore supplier,
and sells through either an online store or a number of
brick-and-mortar stores. We compare the expected
profits of four end-to-end supply chain configurations
under various scenarios defined by eight factors: con-
tribution margin, cost advantage of offshore supplier,
forecast accuracy, change in forecast accuracy over
time, variation in market demand, number of brick-
and-mortar stores, product returns rate, and the online
returns penalty. We evaluate the strategies using a
numerical exercise and codify the findings in six obser-
vations. We present industry executives’ critique of the
observations, and conclude by discussing the implica-
tions of the study’s findings for theory and practice.

2. Literature Review

Fisher’s (1997) seminal paper argued that products
could be categorized as functional or innovative
based on the predictability of their demand and rec-
ommended that they be served by efficient or respon-
sive supply chains, respectively. The efficient supply
chains would seek to meet “demand efficiently at the
lowest possible cost” through means such as selecting
suppliers “primarily for cost and quality” (e.g., using
low-cost offshore suppliers), shortening “lead time as
long as it doesn’t increase cost,” minimizing “inven-
tory throughout the chain” (e.g., by centralizing
inventory storage as in the online channel), and so on
(ibid, p. 108). Conversely, responsive supply chains
would seek to “minimize stockouts, forced mark-
downs, and obsolete inventory” through means such
as selecting suppliers “for speed, flexibility, and qual-
ity” investing “aggressively in ways to reduce lead
time,” (e.g., by sourcing nearshore), deploying “sig-
nificant buffer stocks of parts or finished goods” (e.g.,
through brick-and-mortar stores), and so on.

Phadnis and Fine: End-To-End Supply Chain Strategies
2306 Production and Operations Management 26(12), pp. 2305–2322, © 2017 Production and Operations Management Society

Tests of Fisher’s propositions have produced mixed
results. A survey of 128 manufacturing companies,
using self-reported performance measures, partially
supported Fisher’s dichotomy of products and supply
chain strategies: it found that companies using respon-
sive supply chains to deliver innovative (functional)
products considered their cost performance superior to
(worse than) their competitors (Selldin and Olhager
2007). However, the study did not find support for
Fisher’s propositions related to cost efficient supply
chains. Li and O’Brien’s (2001) test of Fisher’s proposi-
tions using an optimization model suggests that the
ideal supply chain strategy for products with different
levels of demand uncertainty is influenced by condi-
tions not considered in Fisher’s model, such as uncer-
tainty of material demand, product profit margin, etc.
Calvo and Martinez-de-Albeniz (2016) show that for
innovative products, contrary to Fisher’s argument, sole
sourcing with up-front price commitment is preferable
to the more flexible option of dual sourcing because it
enforces stronger competition between the suppliers.
Broadly, at least three different streams can be iden-

tified in the large body of OM literature examining
tradeoffs in sourcing (upstream supply chain) deci-
sions. One stream studies supplier selection using mod-
els to explore tradeoffs between single and multiple
suppliers. A common reason for using multiple sup-
pliers is to mitigate the risk of supply disruption due
to unpredictable events such as accidents or natural
disasters (Treleven and Schweikhart 1988). Dual sour-
cing can be optimal when suppliers face positive
entry costs (Klotz and Chatterjee 1995). It can also
provide better service levels than sole sourcing,
except when order costs are high and lead time vari-
ability is low (Chiang and Benton 1994). On the other
hand, single sourcing with up-front price commit-
ment enforces stronger competition and is superior to
dual sourcing for procuring products with short life
cycles from suppliers choosing prices endogenously
(Calvo and Martinez-de-Albeniz 2016). For both sole
and dual sourcing, Li (2013) describes when it is opti-
mal to commit price ex ante and when it is better to
negotiate ex post.
A large body of literature studies the properties of

optimal procurement decision in presence of demand uncer-
tainty, using the newsvendor model. Several extensions
of the basic newsvendor model—such as, the case of
price-setting retailer, retailer promotions, retailer com-
petition, etc.—are studied in this stream (e.g., Bernstein
and Federgruen 2005, Dana and Spier 2001, Taylor
2002). Some recent works extend these models by
incorporating the effects of competition for either the
buyer or the supplier (Li 2013, Wu and Zhang 2014). A
related stream of literature explores mathematical
properties of optimal procurement quantities from
multiple suppliers (Zhang et al. 2012).

A third stream of research on sourcing strategies
examines the strategic reasons and implications of out-
sourcing for the buyer firm with a descriptive lens
(e.g., Larsen et al. 2013, Reitzig and Wagner 2010).
The works in this stream examine the implications of
outsourcing in terms of cost, quality, responsiveness,
R&D productivity, and so on. One finding from this
stream of particular interest to the present work is
that the benefits of offshoring, expected to result from
the lower cost of goods, may be overestimated in
practice due to the difficulty of understanding the
interdependence between the tasks offshored and
those retained in-house (Larsen et al. 2013).
A large body of OM research also examines sales

(downstream supply chain) strategies. One stream of
work studies interactions between two sales channels of one
firm – one on-line and one in-store. Lenient returns poli-
cies that encourage online purchases in the absence of
pre-purchase product experience, yield returns rates
that are higher for online channels (average 18–35%,
depending on product category) than the average retail
returns rate of 8.7% (Ofek et al. 2011). Generous returns
policies also can signal higher product quality and
encourage online purchase (Wood 2001). In multi-
channel settings, policies that allow customers to Buy a
product Online and Pick it up at the retailer’s brick-and-
mortar Store (BOPS) enable the retailer to reach new
customers (Gao and Su 2017). The BOPS strategy can
also increase the contribution of lowest selling products
to the total sales (Gallino et al. 2017).
Another stream of literature examines competition

between brick-and-mortar and online retailers. Balasubra-
manian (1998) shows that for products well-suited to
the online channel (i.e., reliable products with low
need for immediate gratification), an online retailer
can obtain higher returns by increasing market pres-
ence and engaging in greater competition with the
brick-and-mortar retailers; conversely, for products
poorly suited for the online channel, the online retail-
er should lower its market presence and allow compe-
tition among brick-and-mortar retailers. Balakrishnan
et al. (2014) show that the browse-and-switch behavior
—where some customers examine a product in a
brick-and-mortar store but buy it from an online retai-
ler—is feasible in equilibrium, and it reduces profits
for both retailers. Empirical evidence (Brynjolfsson
et al. 2009) shows that the competition between brick-
and-mortar and online channels is limited to popular
products, and an increase in the density of brick-and-
mortar stores reduces the demand for popular
products from the remote channels.
The third stream of literature examines the competi-

tive dynamics between a brick-and-mortar retailer and its
manufacturer that introduces its own online sales channel.
Under several scenarios, the manufacturer and its
brick-and-mortar retailer can both earn higher profits;

Phadnis and Fine: End-To-End Supply Chain Strategies
Production and Operations Management 26(12), pp. 2305–2322, © 2017 Production and Operations Management Society 2307

this results from a decrease in double-marginalization
as manufacturer drops the wholesale price to ensure
that the traditional retailer’s demand for its product is
not diminished (Arya et al. 2007). This can occur even
when sales in the manufacturer’s direct channel are
zero (Chiang et al. 2003). Any selfish cost-reducing
investments made by the manufacturer could also spill
over to reduce the wholesale price paid by the retailer
(Yoon 2016). However, manufacturer’s encroachment
in retail can lead to loss of profit for both parties, if the
retailer’s selling process is more efficient and the prior
probability of a large market is low (Li et al. 2014).
A common feature of the modeling in all three

streams of this literature is the use of consumer choice
models to derive demands at the competing channels
of one or multiple firms, endogenously. In contrast,
our study assumes that the firm faces an exogenous
stochastic demand. We make this choice deliberately.
Our model examines four end-to-end strategies of one
firm, with only one sales channel (either brick-and-
mortar or online). Thus, no inter-channel competitive
dynamics are present in our model. This simplifica-
tion allows us to draw insights despite the presence of
additional complexity introduced by the joint consid-
eration of the sourcing and sales strategies.
In summary, a review of the vast literature on the

sourcing and sales strategies shows that upstream
and downstream supply chain strategies have largely
been studied in isolation. The consideration of sales
strategy in the sourcing studies is generally limited to
the dimensions of price or demand; and the costs and
uncertainties associated with physical distribution in
the sales channel are not typically considered in the
sourcing models. Similarly, the studies of sales strate-
gies focus on describing optimal pricing and return
policies without incorporating costs and uncertainties
encountered in making the sourcing decisions.
Against this backdrop, the present study seeks to
examine the tradeoffs between different sourcing and
retail strategies by considering them jointly as the
firm’s integral end-to-end supply chain strategy (Fine
2000, Fisher 1997).

3. Model

This section presents the model of one firm’s end-to-
end supply chain strategy choices. The modeling
choices are justified with the descriptions of industry
practices, obtained through industry publications and
interviews with eight executives in six firms (see
Table A1 in Appendix S1 for descriptions of the exec-
utives interviewed). The firm may source from either
an offshore or a nearshore supplier. The key tradeoffs
between the two are cost efficiency and responsive-
ness. The offshore supplier offers lower total landed
cost, whereas the nearshore supplier allows ordering

closer to the selling season when the demand forecast
is likely to be more accurate. The firm may sell the
products through either brick-and-mortar stores or
via an online store. The brick-and-mortar channel
“provides quicker gratification and offers an opportu-
nity for physical inspection” (Balasubramanian 1998,
p. 183). Retail presence makes the firm more respon-
sive by deploying “significant buffer stocks of [. . .]
finished goods” in order “to reduce lead time” (Fisher
1997, p. 108) between a customer’s order and receipt
of goods. Conversely, the online channel is more cost-
efficient as it allows pooling of inventory in a few
locations, which provides cost savings under a wide
range of demand distributions (Berman et al. 2011).
To prevent the lack of tactile pre-purchase product
experience from deterring shopping, online retailers
offer lenient returns policies (Wood 2001) and experi-
ence a higher returns rate than their brick-and-mortar
competitors (Ofek et al. 2011).
Our model assumes that a firm chooses to source

from only one type of supplier (offshore or nearshore)
and sells through only one type of channel (online or
brick-and-mortar). We do not consider hybrid sour-
cing or sales strategies. This assumption allows us to
explore the intricate interdependence between sour-
cing and sales decisions to draw insights about the
end-to-end strategy, without making the model
unwieldy. Such simplifying assumptions are com-
monly made in analytical models (Wu and Zhang
2014). Furthermore, the sourcing and sales strategies
modeled in this manner do exist in the real world.
Sole-sourcing is often practiced to minimize the costs
associated with developing and maintaining multiple
sources, and is superior to dual sourcing when order
costs are high or lead time is less variable (Chiang
and Benton 1994). R1, the ex-managing director of
Aretha (pseudonym, a major global retailer with a few
thousand stores), noted that Aretha uses either an off-
shore or a nearshore source, but not both, for a given
product. He added, “Producing in proximity is not
same as producing offshore;” Aretha produces high-
fashion items nearshore and cost-sensitive items off-
shore. A recent study of chief procurement officers of
“leading apparel companies, responsible for a com-
bined €28 billion in purchasing volume” shows that
apparel companies are beginning to engage more dee-
ply with their suppliers to improve productivity and
safety (Berg and Hedrich 2014). As a result, they may
reduce the number of suppliers.
On the sales side, a McKinsey survey of 3000 con-

sumers’ purchases at 17 apparel retailers in the United
Kingdom found that only 7% of the purchases at a
given retailer were made using both offline and online
channels, prompting the authors to conclude that “to-
day’s world is still on/offline and not yet omni” (Berg
et al. 2015, p. 4). S1, Executive Vice President at GN

Phadnis and Fine: End-To-End Supply Chain Strategies
2308 Production and Operations Management 26(12), pp. 2305–2322, © 2017 Production and Operations Management Society

Sourcing (pseudonym, sourcing agent used by several
brands, has a few thousand suppliers), informed that
most of her retail customers “don’t do anything
online” and that brands with multiple channels still
operate as brick-and-mortar retailers used to. R3,
senior manager of global supply chain strategy at Pan-
theon (pseudonym, a major global retailer with over a
thousand stores), noted that her company set up its
online channel as a separate legal entity and could not
share inventory with its brick-and-mortar channel; it
also had separate inventory planners for the two chan-
nels until their recent integration. M1, President &
CEO of Lamina (pseudonym, virtual manufacturer,
helps brands design products) and M2, Senior Vice
President at Lamina, noted in separate interviews that
sales of online retailers and online stores of traditional
brick-and-mortar retailers are still small, and some of
the brick-and-mortar retailers value their online chan-
nel more for its ability to predict sales at the stores than
for its revenue. R2, co-founder of Ote (pseudonym, ten-
year old online retailer), noted that her firm uses its
brick-and-mortar stores primarily as showrooms for
customers to try the products before making the
purchase online. On the other hand, Amazon.com—
predicted to be the largest U.S. apparel retailer by 2017—
sells clothing only online (Weinswig 2017). Thus, the
online and brick-and-mortar channels are still largely
independent with regards to apparel sale and inven-
tory positioning.

3.1. Model Description
Most apparel retailers still plan for seasons. M3, the
vice president of the supply chain at Serene (pseudo-
nym, global brand of intimate apparel, owns manufac-
turing) noted that even its functional products (e.g.,
undergarments) have two primary selling seasons in
the United States: back-to-school (July) and winter
holidays (December). On this basis and following the
tradition in the OM literature to build on the
newsvendor model, we consider a firm that operates
in an industry with distinct selling seasons with
known beginnings and ends. Before each season, the
firm makes a strategic decision about its end-to-end
supply chain regarding the source and the sales chan-
nel for each product. After choosing its end-to-end
strategy (time t0), the firm has one opportunity to
order the merchandise to meet the season’s demand:
it buys the merchandise either at time t1 (if it chooses
an offshore supplier) or at t2 (near-shore supplier).
The goods are received at the beginning of the selling
season and allocated to the firm’s N stores (t3; N = 1
for online). Finally, the demand is realized (t4); the
firm sells the product for price p. A portion q of the
sold products are returned for a full refund at the end
of the season, with different returns rates for the two
channels: qB for brick-and-mortar and qO for online.

At the end of the season, the firm realizes revenue.
Figure 1 shows the timeline. If the demand exceeds
the purchased quantity, the firm does not have
another opportunity to buy additional product and
any future demand in the season is not converted into
sales. Conversely, if the demand is lower than the
procured quantity, the firm has to sell the leftover
product for a salvage price s. Let, cx and cm be the total
landed costs for the offshore and nearshore suppliers,
respectively. We assume, s ≤ cx ≤ cm ≤ p.

3.1.1. Apparel Demand Forecasting. Respondent
M2 highlighted two types of apparel forecasting: style
forecasting and quantity forecasting. He informed that
Lamina uses a proprietary database and algorithms to
forecast styles about one year in advance. Lamina’s
customer then uses historical sales data to forecast
quantity for each item and order the product eight
months before the selling season. R1 informed that a
team of forecasters in Aretha’s headquarters makes
the initial forecast for the season’s demand for each
product and the forecasts undergo several changes
over time. S1 and R1 both noted that the initial quan-
tity forecasts were based on the historic store and
aggregate sales data as well as input about fashion
trends from store managers and fashion experts. S2, an
Executive Director (Fashion) at GN Sourcing, corrobo-
rated the retailers’ use of detailed store-level sales data
for developing demand forecasts. Retailers are gener-
ally unable to change the order placed with the suppli-
ers. S2 noted that once a retailer commits to an order
quantity, GN Sourcing will buy the raw material for its
manufacturers, get the garments made, ship the
ordered quantity, and expect to be paid for it. M2
noted that once its retail customers commit to the
order quantity, Lamina buys the necessary raw mate-
rial and expects the customer to pay for it.
This forecast and sourcing process can be modeled

as the additive martingale model of forecast evolu-
tion, or a-MMFE (Graves et al. 1986, Heath and Jack-
son 1994). The model assumes that the information
available to predict a variable grows over time, and
the change in its forecast at a given time is uncorre-
lated with the information available at the time of the
previous change in forecast. These assumptions are
valid for the apparel industry where forecasts of the
season’s demand are updated based on sales and

Firm chooses its end-
to-end strategy

Firm orders goods from
offshore supplier at cost

per unit, according
to forecast 1

Selling season
Price

≤ ≤ ≤

Firm receives and allocates goods to a
warehouse for online store ( = 1) or each
brick-and-mortar store ( ∈ {10,25})

A portion ( or ) of
sales returned by cust-
omers; salvaged for
0 ≤ ≤ ≤ 1

Firm orders goods from
nearshore supplier at cost

per unit, according to
forecast 2

OR

Figure 1 Timeline of Decisions and Events

Phadnis and Fine: End-To-End Supply Chain Strategies
Production and Operations Management 26(12), pp. 2305–2322, © 2017 Production and Operations Management Society 2309

fashion trends observed, and the forecasts are likely
to be conditional expectations of demand given the
information available when making the forecast. In
fact, “almost all of the existing forecasting techniques
are based on assumptions” of a-MMFE (Heath and
Jackson 1994, p. 22).
Let, Fi be the forecast of the season’s market

demand made at time ti. The initial forecast of the sea-
son’s demand, F0, is made based on historic sales and
input from fashion experts. Let, l be the expected
demand at t0, i.e., E F0½ � ¼ l: The forecast is revised
over time as new information becomes available. Let
di be the change in the forecast of the season’s demand
between times ti-1 and ti, such that Fi = Fi-1 + di, for
i = {1, 2, 3, 4}. The forecast updates, Fi, are random
variables before time ti with E di½ � ¼ 0 (i.e., unbiased
forecast) and VarðdiÞ ¼ Nr2i . Let, F4 = Y be the market
demand realized in the season. Assuming a stationary
demand process with E Y½ � ¼ l and Var Yð Þ ¼
r2Y ¼ Nr24, the error in the forecast of market demand
made at time t1 (i.e., time of confirming the order
quantity from the offshore supplier), is E1 = Y � F1.
Note, Y ¼ F4 ¼ F3 þ d4 ¼ . . . ¼ F1þðð d2Þ þ d3Þ þ d4.
Therefore, the forecast error at t1 is E1 = d2 + d3 + d4.
Further, E E1½ � ¼ E d2 þ d3 þ d4½ � ¼ 0 and Var E1ð Þ ¼
Var d2 þ d3 þ d4ð Þ ¼ N r22 þ r23 þ r24

� �
. Similarly, error

in the forecast of the season’s demand made at time t2
(i.e., when procuring from a nearshore supplier) has
average 0 and variance of N r23 þ r24

� �
.

3.1.2 Apparel Sales. At time t0, the firm chooses
whether to sell the product through N brick-and-mor-
tar stores or an online store (N = 1). For simplicity, we
assume that the retail price (p) and the salvage price (s)
are identical in the two channels. Since the purpose of
this model is to understand the tradeoffs among differ-
ent end-to-end supply chain strategies of one firm, we
take price and demand as given, instead of making
them endogenous to the model. Without loss of gener-
ality, we assume that the season’s demands faced by
the stores are identically and independently dis-
tributed. Therefore, the individual store demand, X,

has expectation E X½ �¼E Y½ �
N

¼ l
N
and variance r2X ¼

r2
Y

N
¼r24.

After defining its end-to-end in strategy, the firm
makes two operational decisions every season. First,
it decides the quantity of the product to procure from
the chosen supplier to satisfy the market demand for
the entire season. We assume the firm considers the
deterministic product returns rate and uses the
newsvendor formulation to determine the purchase
quantity. The optimal quantity to buy at time ti (at
unit cost ci) for the season is Qi ¼ l þ zðwÞ � rðiÞ;where
z(w) is the safety factor corresponding with the
newsvendor critical ratio w and r(i) is the standard
deviation of error in the forecast of Y made at time ti.

Let, LðqÞ be the expected lost sales when the demand
exceeds the quantity available for sale, q.

LEMMA 1. The maximum expected profit for a season
from selling goods procured at time ti, where Qi is the
quantity procured for the season for per unit cost ci, after
all potential returned products are received and salvaged
by the firm is given by the following expression.

E P½ � ¼ 1 � qð Þ p � sð Þ l � L Qið Þð Þ � ci � sð ÞQi: ð1Þ

See proof in Appendix S1. For a given end-to-end
strategy, the optimal quantity procured from the cho-
sen supplier is determined by the supplier’s total
landed cost, distribution of errors in the demand fore-
casted at the time of procurement, and the product
returns rate for the sales channel.

LEMMA 2. The newsvendor critical ratio (w) for
products procured for cost c, sold at price p, and salvaged
for s when a proportion q of the sold products are
returned by customers for full refund is given by

w ¼ 1
1�q

� �
p�c
p�s

� �
� q

1�q
� �

. An end-to-end supply chain

strategy is feasible only if
p�c
p�s � q.

See proof in Appendix S1. Let, k ¼ p�c
p�s. Therefore,

w ¼ k�q
1�q. Observe that for 0 < q < k < 1, w ¼

k�q
1�q \k.

Thus, the newsvendor critical ratio given a determinis-
tic product returns rate (w) is smaller than the stan-
dard newsvendor critical ratio (k). As w is influenced
by the product returns rate, sales channels with differ-
ent returns rates would have different critical ratios,
even when the products are procured, sold, and sal-
vaged for identical prices in the two channels. This is
one manner in which the sales and sourcing strategies
are related to each other. If it has a feasible end-to-end
strategy, the firm procures quantity Q* that maximizes
the expected profit. Let, Q� ¼ Q�xC if the firm chooses
an offshore supplier, and Q� ¼ Q�mC if it chooses a near-
shore supplier; the subscript C denotes the sales chan-
nel, whose returns rate affects the quantity procured.

LEMMA 3. The optimal quantity procured from the off-
shore and the nearshore suppliers for sale in channel C,
with newsvendor critical ratio wC, is:

Q�xC ¼ l þ z wCð Þ
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
N r22 þ r23 þ r24
� �q

; ð2Þ

Q�mC ¼ l þ z wCð Þ
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
N r23 þ r24
� �q

: ð3Þ

See proof in Appendix S1. The demand at each
store is assumed to be identically distributed and
independent of the demand at the other stores.

Phadnis and Fine: End-To-End Supply Chain Strategies
2310 Production and Operations Management 26(12), pp. 2305–2322, © 2017 Production and Operations Management Society

Therefore, it is optimal to allocate the procured goods
among the stores evenly. The quantity available for
sale at each store is Q�/N (For Online: N = 1).

3.2. End-to-end Supply Chain Strategies
The two sourcing options (offshore vs. nearshore,
indicated with subscripts x and m, respectively) and
the two sales channel options (online vs. brick-and-
mortar, indicated with subscripts O and B, respec-
tively) combine to form four end-to-end supply chain
strategies. Let ~p ¼ ðp � sÞ denote the effective price.
Let ~cv :¼ ðc3 � sÞ ~cx :¼ ðc2 � sÞ and denote the effec-
tive total landed costs of the nearshore and offshore
suppliers, respectively. Let, LC qð Þ indicate the
expected lost demand in sales channel C, given pro-
cured quantity q. Since the model assumes that all
unsold products can be salvaged, salvage price can be
omitted from the analysis without affecting the valid-
ity of its qualitative insights. Using equation (1), we
can now describe the expected profit for each end-to-
end strategy.

PROPOSITION 1. The expected profits of four end-to-end
supply chain strategies, denoted by E[Πsource,Channel], are
as follows.

E½Px;O�¼ð1�qOÞ~pl�ð1�qOÞ~pLOðQ�xOÞ�~cxQ�xO; ð4Þ

E½Pm;O� ¼ ð1�qOÞ~pl�ð1�qOÞ~pLOðQ�mOÞ�~cmQ�mO; ð5Þ
E½Px;B� ¼ ð1�qBÞ~pl�ð1�qBÞ~pLBðQ�xBÞ�~cxQ�xB; ð6Þ
E½Pm;B� ¼ ð1 � qBÞ~pl � ð1 � qBÞ~pLBðQ�mBÞ � ~cmQ�mB: ð7Þ

We emphasize two observations from this proposi-
tion. In the first observation, the expected profit of a
particular end-to-end supply chain strategy depends
on three factors: the expected revenue from products
sold and not returned, ð1 � qÞ~pl; the expected rev-
enue loss from having insufficient goods to meet the
demand, ð1 � qÞ~pLðQ�Þ; and the total cost of procur-
ing the products, ~cQ�. For given p and s, the first factor
is a function of the sales channel alone (i.e., propor-
tion of sales returned, qO and qB); the second factor is
a function of the sales channel (i.e., the loss function
Lð�Þ for the probability distribution of the demand at
each sales location and product returns rate qO or qB)
as well as the sourcing strategy (i.e., procured quanti-
ties, which are determined by the suppliers’ total
landed costs—cm and cx—and the increase in variance
of the forecast error at the time of offshore sourcing
compared to variance at the time of nearshore
sourcing; r22); and the third factor is a function of the
sourcing strategy (i.e., total landed costs and procured
quantity) and one attribute of the sales channel (i.e.,

product returns rate). The second and the third fac-
tors embody the interdependence between the sour-
cing and sales strategies. In the second observation, it
is difficult to state whether an increase in cost would
always increase or decrease the expected profit. An
increase in cost would decrease the quantity procured
and would increase the second factor, which, ceteris
paribus, would lower the expected profit. However, it
may also decrease the third factor, which, ceteris pari-
bus, would increase the expected profit. Therefore,
instead of deriving optimality conditions through
first- and second-order derivatives, we describe the
conditions in which one strategy dominates the
others.

3.3. Comparison of End-To-End Supply Chain
Strategies
Let (S, C) denote an end-to-end strategy, where S and
C refer to the chosen source and sales channel, respec-
tively. Let �SC :¼ ð1 � qCÞLCðQ�SCÞ=l denote the lost
sales (adjusted for product returns rate of the sales
channel) as a fraction of the average market demand,
and jSC :¼ ~cSQ�SC=~pl denote the effective total landed
cost as a fraction of expected revenue before product
returns. kSC represents the sales inefficiency of the end-
to-end strategy of converting market demand into
sales by making the product available for sale through
the stores. It is a function of the choice of source (i.e.,
quantity procured, which depends on the supplier’s
total landed cost; and variance in demand forecast at
the time of procurement) as well as the choice of sales
channel (i.e., product returns rate; lost sales, which
depend on the number of store locations over which
the procured quantity is distributed for sale and vari-
ability of market demand). Similarly, jSC represents
the sourcing inefficiency of the end-to-end strategy of
procuring the product cost efficiently to generate the
expected revenue. It also is a function of the choice of
source (i.e., total landed cost, quantity procured) and
the choice of sales channel (i.e., quantity procured, as
influenced by product returns rate). Smaller values of
kSC and jSC are preferable. Three corollaries of
Proposition 1 describe the conditions under which
one end-to-end strategy dominates another. Proofs of
all corollaries are presented in Appendix S1.

COROLLARY 1. When selling products online, the strat-
egy of offshore sourcing is superior to nearshore sourcing,
i.e., ðx; OÞ � m; Oð Þ, when jxO � jmO < kmO � kxO. Similarly, when selling products through brick-and-mor- tar stores, the strategy of offshore sourcing is superior to nearshore sourcing, i.e., ðx; BÞ � m; Bð Þ, when jxB � jmB < kmB � kxB.

Thus, between two end-to-end strategies that use
the same sales channel C, strategy (s1, C) is superior

Phadnis and Fine: End-To-End Supply Chain Strategies
Production and Operations Management 26(12), pp. 2305–2322, © 2017 Production and Operations Management Society 2311

to strategy (s2, C) if the excess sourcing inefficiency of
(s1, C) compared to (s2, C) is smaller than the excess
sales inefficiency of strategy (s2, C) compared to
(s1, C). Thus, the preference between two end-to-end
supply chain strategies that use the same sales chan-
nel is determined not only by the characteristics of the
two sources but also by the attributes of the sales
channel.

COROLLARY 2. When sourcing from an offshore sup-
plier, the strategy of online sales is superior to the
brick-and-mortar channel, i.e., ðx; OÞ � x; Bð Þ, when
jxO � jxB\ �xB � �xOð Þ � qO � qBð Þ. Similarly, when
sourcing from a nearshore supplier, the strategy of
online sales is superior to brick-and-mortar stores,
i.e., ðm; OÞ � m; Bð Þ, when jmO � jmB\ �mB � �mOð Þ�
qO � qBð Þ.

Thus, between two end-to-end strategies that use
the same source S, strategy (S, c1) is superior to
strategy (S, c2) if the excess sourcing inefficiency of
(S, c1) compared to (S, c2) is smaller than the excess
sales inefficiency of strategy (S, c2) compared to
(S, c1) minus the surplus product returns rate for
channel c1 compared to channel c2. Thus, the prefer-
ence between two end-to-end strategies that use the
same sourcing strategy but different sales strategies
is determined by characteristics of the two sales
channels as expected, but also by the attributes of
the sourcing strategy.

COROLLARY 3. The strategy of online sales of products
sourced offshore is superior to brick-and-mortar sales of pro-
ducts sourced nearshore, i.e., ðx; OÞ � m; Bð Þ, when
jxO � jmB\ �mB � �xOð Þ � qO � qBð Þ. Similarly, the
strategy of online sales of products sourced nearshore is
superior brick-and-mortar sales of products sourced offshore,
i.e., ðm; OÞ � x; Bð Þ, when jmO � jxB\ �xB � �mOð Þ�
qO � qBð Þ.

Thus, between two end-to-end strategies that use
different sourcing as well as sales strategies, strat-
egy (s1, c1) is superior to (s2, c2) if the excess sour-
cing inefficiency of (s1, c1) compared to (s2, c2) is
smaller than the excess sales inefficiency of strategy
(s2, c2) compared to (s1, c1) minus the surplus pro-
duct returns rate for channel c1 compared to chan-
nel c2. Thus, as expected, the preference between
two end-to-end supply chain strategies that have no
common sources or sales channels is determined by
the attributes of the source as well as the sales
channel.
The three corollaries can jointly specify the condi-

tion in which one end-to-end strategy dominates the
remaining three. We illustrate this by stating the con-
ditions under which strategy (x, O) is superior to the

remaining three. Similar conditions can be developed
for each end-to-end supply chain strategy.

COROLLARY 4. The strategy of online sales of products
sourced offshore, (x, O), is the most profitable of the four
end-to-end strategies formed by combining the two source
options (offshore, nearshore) and the two sales options
(online, brick-and-mortar) when the following holds:

ðjxO þ �xOÞ\ min
ðjmO þ �mOÞ � ðqO � qOÞ;

ðjxB þ �xBÞ � ðqO � qBÞ;
ðjmB þ �mBÞ � ðqO � qBÞ

0
B@

1
CA

¼ min
ðjmO þ �mOÞ;

ðjxB þ �xBÞ � ðqO � qBÞ;
ðjmB þ �mBÞ � ðqO � qBÞ
0
B@

1
CA:

This shows that the dominance of a particular
end-to-end strategy over the other three is deter-
mined by the sourcing and sales inefficiencies, as
defined earlier, of the end-to-end strategies and the
difference in product returns rates of the correspond-
ing sales channels. The sourcing and sales efficien-
cies depend on the quantity procured, which is a
nonlinear function of total landed cost, returns rate,
and variance of error in the forecast of season’s
demand at the time of procurement. The sales effi-
ciency also depends on the lost sales, which is also a
nonlinear function of procured quantity and the vari-
ance in market demand. Due to these complex inter-
dependencies, the expected profit for a given supply
chain strategy is an intricate nonlinear function that
is difficult to analyze analytically. Therefore, we use
a numerical exercise to compare the four end-to-end
strategies in various scenarios to generate theoretical
insights.

3.4. Numerical Exercise
We compare the four end-to-end strategies by varying
eight attributes: type of product (functional vs. inno-
vative) as modeled by varying the coefficient of varia-
tion of forecast error at the time of offshore

procurement (CV2þ3 ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
N r22 þ r2

3

� �q

=l), proportion

of variance of forecast errors resolved between off-
shore and nearshore procurement opportunities

r2
2

r2
2
þr2

3

� �
, coefficient of variation of market demand

(CV4 ¼
ffiffiffiffiffiffiffiffiffi
Nr24

q
=l), contribution margin if product is

sourced nearshore
p�cm
p

� �
, proportion of brick-and-

mortar sales returned by customers (qB), returns

penalty for online channel
qO�qB
qB

� �
, number of brick-

and-mortar stores in the market (N), and offshore cost
advantage (modeled as cx

cm
Þ. Exhibits 1 and 2 present

Phadnis and Fine: End-To-End Supply Chain Strategies
2312 Production and Operations Management 26(12), pp. 2305–2322, © 2017 Production and Operations Management Society

the results of the numerical exercise for 32 cases.
These cases are combinations of two extreme values
of each of the first five attributes above, and are sum-
marized in Table 1. Two extreme values each of the

next two attributes —
qO�qB
qB

� �
and N—are the data

series in the charts showing results of the 32 cases.
The last attribute, cx/cm, is varied along the X axis in
each chart. The Y axis in each chart shows the

High Contribution Margin: ( − )/ = . Low Contribution Margin: ( − )/ = .

2
2 = 0.9( 2

2 + 3
2) 2

2 = 0.1( 2
2 + 3

2) 2
2 = 0.9( 2

2 + 3
2) 2
2 = 0.1( 2
2 + 3

2)

H
ig

h
M

ar
ke

t D
em

an
d

V
ar

ia
ti

on
:

=
.

=
.
=
.

L
ow

M
ar

ke
t D

em
an

d
V

ar
ia

ti
on

:
=

.
=
.
=
.

Note: represents the product returns rate for the brick-and-mortar channel (low=0.1, high=0.3).
B&M (N=10) Online (10% pen.) Online (30% pen.)B&M (N=25)Offshore +…

Nearshore +… B&M (N=10) B&M (N=25) Online (10% pen.) Online (30% pen.)

Exhibit 1 Expected Profits (in 000s) for Functional Products (CV2+3 = 0.1) [Color figure can be viewed at wileyonlinelibrary.com]

Phadnis and Fine: End-To-End Supply Chain Strategies
Production and Operations Management 26(12), pp. 2305–2322, © 2017 Production and Operations Management Society 2313

expected profit of the end-to-end strategies. The
numeric values used in the exercise for several attrib-
uted are based on the practices in the apparel indus-
try. However, the qualitative insights obtained from

the exercise are generalizable to other industries with
similar attributes as well.
IBISWorld (2014a,b) reports that clothing retailers

spend an average of 57.4% and 61.9% of the revenue

High Contribution Margin: ( − )/ = . Low Contribution Margin: ( − )/ = .
2
2 = 0.9( 2
2 + 3
2) 2
2 = 0.1( 2
2 + 3
2) 2
2 = 0.9( 2
2 + 3
2) 2
2 = 0.1( 2
2 + 3
2)
H
ig
h
M
ar
ke
t D
em
an
d
V
ar
ia
ti
on
:
=
.
=
.
=
.
L
ow
M
ar
ke
t D
em
an
d
V
ar
ia
ti
on
:
=
.
=
.
=
.
Note: represents the product returns rate for the brick-and-mortar channel (low=0.1, high=0.3).
B&M (N=10) Online (10% pen.) Online (30% pen.)B&M (N=25)Offshore +…
Nearshore +… B&M (N=10) B&M (N=25) Online (10% pen.) Online (30% pen.)

Exhibit 2 Expected Profits (in 000s) for Innovative Products (CV2+3 = 1) [Color figure can be viewed at wileyonlinelibrary.com]

Phadnis and Fine: End-To-End Supply Chain Strategies
2314 Production and Operations Management 26(12), pp. 2305–2322, © 2017 Production and Operations Management Society

for procuring men’s and women’s clothing items,
respectively. This amounts to about 40% contribution
margin. Fisher and Raman (2010) note that the typical
gross margins for retailers are between 30% and 50%.
In the numerical exercise, we use two values for con-
tribution margin, 40% and 40%, for the goods pro-
cured from a nearshore source (i.e., cm = 0.6p and
cm = 0.3p). We then consider offshore cost advantage
by evaluating the strategies for a range of offshore-
cost-to-nearshore-cost-ratios (cx/cm) between 0.2 and
1.2, in increments of 0.05. We could not find statistics
about forecast accuracy in apparel sourcing. There-
fore, we use the statistic “average margin of error in
the forecast at the time production is committed”
from Fisher (1997) instead. This statistic is equivalent
to the uncertainty resolved by demand signals d2 and
d3 in our model. We model functional products as hav-
ing CV2+3 = 0.1, and innovative products with
CV2+3 = 1, according to the statistics by Fisher (1997,
p. 107). We tested the model with CV2+3 = 0.4, the
lower end of the range of margin of error for innova-
tive products suggested by Fisher (1997); the results
lie between those for CV2+3 = 0.1 and CV2+3 = 1, and
do not provide additional insights. Therefore, the
results for CV2+3 = 0.4 are not included in the study.
We also could not find statistics related to the deterio-
ration of forecast accuracy over procurement lead
time. Therefore, we consider two cases of proportion
of uncertainty about season’s demand resolved
between offshore and nearshore sourcing opportuni-
ties, modeled by a ¼ r22=ðr22 þ r23Þ. In the first case,
most of this uncertainty is resolved before the near-
shore procurement (but after the offshore procure-
ment opportunity) by demand signal d2; we assume
a = 0.9. In the second case, most of the uncertainty
still remains unresolved at the time of nearshore

procurement; for this we assume a = 0.1. The values
of a, although somewhat arbitrary, are chosen to test
the performance of end-to-end supply chains strategy
between two fairly extreme cases. Greater the value of
a, the more advantageous it is to source from a near-
shore source.1

On the sales side, a study using six years of data at
a large national catalog retailer of apparel and acces-
sories found that, on average, 16% of products sold
were returned by the customers (Petersen and Kumar
2009). A similar returns rate was found for L.L. Bean
—a retailer known for one of the most generous pro-
duct return policies—where eight out of 48 million
items it shipped in 2006 were returned (Loudin 2007).
Other studies of product returns have reported
returns rates of five-to-nine percent for hard goods
and up to 35% for high-end apparel (Guide et al.
2006). We test the end-to-end strategies for the returns
rates of 10% and 30% at the brick-and-mortar stores
(qB), with an addition penalty of 10% or 30% for the
same product sold online (i.e., qO = 1.1qB or = 1.3qB).
Thus, in the model, the returns rates for the products
sold online vary between 11% and 39%. We also vary
the number of brick-and-mortar stores in a market
(N = 10 and 25, for the brick-and-mortar channel).
Finally, we consider two values of variation of market
demand (CV4 ¼ r4l ¼ 0:01 and 0.1). These values are
chosen so that the coefficient of variation of demand
at a single brick-and-mortar store (CVS) does not
exceed 0.5, which is considered to be a satisfactory
threshold to ensure that the assumption of normal
distribution of store-level demand is valid (Berman
et al. 2011). For these values, the highest value of

CVS ¼ r4=
ffiffiffi
N

p
l=N ¼

ffiffiffiffi
N

p
r4
l

� �
in the numerical exercise is 0.5

when N = 25 and r4l ¼ 0:1. Assuming that the demand

Table 1 Summary of Cases

Functional products Innovative products

Case CV2+3
p�cm
p CV4 qB

r2
2
r2
2
þr2

3 Case CV2+3
p�cm
p CV4 qB

r2
2
r2
2
þr2
3

1 0.1 0.7 0.01 0.1 0.9 17 1 0.7 0.01 0.1 0.9
2 0.1 0.7 0.01 0.1 0.1 18 1 0.7 0.01 0.1 0.1
3 0.1 0.7 0.01 0.3 0.9 19 1 0.7 0.01 0.3 0.9
4 0.1 0.7 0.01 0.3 0.1 20 1 0.7 0.01 0.3 0.1
5 0.1 0.7 0.1 0.1 0.9 21 1 0.7 0.1 0.1 0.9
6 0.1 0.7 0.1 0.1 0.1 22 1 0.7 0.1 0.1 0.1
7 0.1 0.7 0.1 0.3 0.9 23 1 0.7 0.1 0.3 0.9
8 0.1 0.7 0.1 0.3 0.1 24 1 0.7 0.1 0.3 0.1
9 0.1 0.4 0.01 0.1 0.9 25 1 0.4 0.01 0.1 0.9
10 0.1 0.4 0.01 0.1 0.1 26 1 0.4 0.01 0.1 0.1
11 0.1 0.4 0.01 0.3 0.9 27 1 0.4 0.01 0.3 0.9
12 0.1 0.4 0.01 0.3 0.1 28 1 0.4 0.01 0.3 0.1
13 0.1 0.4 0.1 0.1 0.9 29 1 0.4 0.1 0.1 0.9
14 0.1 0.4 0.1 0.1 0.1 30 1 0.4 0.1 0.1 0.1
15 0.1 0.4 0.1 0.3 0.9 31 1 0.4 0.1 0.3 0.9
16 0.1 0.4 0.1 0.3 0.1 32 1 0.4 0.1 0.3 0.1

Phadnis and Fine: End-To-End Supply Chain Strategies
Production and Operations Management 26(12), pp. 2305–2322, © 2017 Production and Operations Management Society 2315

at individual stores is normally distributed, the high-
est likelihood of experiencing negative demand is
only 2.3%.

4. Results

The expected profits for the four end-to-end strate-
gies in a given scenario are calculated using equa-
tions (4)–(7). Exhibits 1 and 2 present the results
graphically. The chart for each case shows the
expected profits (Y-axis) for a range of offshore cost
discounts, calculated as the ratios of offshore-cost-
to-nearshore-cost, cx/cm (X-axis). They are calculated
by keeping cm constant and varying cx from 0.2cm to
1.2cm. Four sales strategies—namely, brick-and-mor-
tar channel with 10 and 25 stores each, and online
channel with 10% and 30% returns penalty (i.e.,
qO�qB
qB

¼ 0:1 and = 0.3) each—are plotted as four data
series in each chart. The expected profit of an end-
to-end strategy involving a nearshore source is pro-
jected along the X-axis with a dotted line to show
its relation to the expected profits for various off-
shore cost conditions. We compare results from
multiple cases to draw theoretical insights about the
attractiveness of different end-to-end strategies
under various scenarios. These insights are pre-
sented as a series of observations in the following
sections.

4.1. End-To-End Supply Chain Strategies for
Functional Products
We first evaluate the four end-to-end strategies for
functional products (Exhibit 1). For all 16 cases pre-
sented in Exhibit 1, the expected profit for a given
sales channel is always higher when the products
are procured from an offshore source with a lower
cost than the nearshore source. Thus, consistent
with Fisher’s (1997) argument, the optimal sourcing
strategy for functional products is to choose a low-
cost offshore supplier, for a given sales strategy.
Furthermore, when the product returns rate is low
(qB = 0.1) and the market demand has low variabil-
ity (CV4 = 0.01; cases 1, 2, 9, and 10), the expected
profits in the online and the brick-and-mortar sales
channels for a given sourcing option are similar.
These cases represent the products in familiar cate-
gories (per hypotheses 3 and 4 in Petersen and
Kumar (2009)). Thus, the best supply chain strategy
for delivering familiar functional products is to
source them from an offshore supplier; they could
be sold either online or through brick-and-mortar
stores with similar level of profitability. The advan-
tage of an offshore source over a nearshore source
increases if the contribution margin of the product
is low (as in cases 9, 10 vs. cases 1, 2). Also, as
expected, an increase in the variation of market

demand makes the online channel more profitable
compared to the brick-and-mortar channel (cases 5–8
vs. cases 1–4, and cases 13–16 vs. 9–12) because of
the inventory pooling benefit of the online channel.

OBSERVATION 1. For functional products, (a) offshore
source is the dominant sourcing choice for a given sales
channel, and (b) brick-and-mortar and online channels
have similar profitability, for a given source, if the
product returns rate is low and the market demand is
predictable (i.e., low CV4).

However, for functional products with market
demand of low variability, if the offshore cost dis-
count is small, an appropriate end-to-end strategy
involving the responsive nearshore sourcing can
dominate a strategy involving the low-cost offshore
sourcing. If the functional products have high returns
rate (qB = 0.3) (cases 3, 4, 11, 12)—which may be the
case for the products in new categories (Petersen and
Kumar 2009)—it could be more profitable to source
them nearshore and sell through brick-and-mortar
stores, instead of sourcing offshore for selling online.
This is especially the case if the online returns penalty
is high. This counterintuitive result is due to the effect
of product returns rate on newsvendor critical ratio,
as stated in Lemma 2. In our model, the returns rate
of the online channel is higher than that of the brick-
and-mortar channel by a certain proportion (10% and
30% higher, in the numerical cases). When the returns
rate for the brick-and-mortar channel is high
(qB = 0.3), the (multiplicative) online returns rate is
even higher (qO = 0.33 or 0.39). When the online chan-
nel is paired with an offshore source whose total
landed cost is only marginally lower than the cost of
the nearshore source, the drop in the critical ratio due
to higher returns rate of the online channel is not com-
pensated adequately by the lower cost of the offshore
source. For instance, for a product with nearshore

contribution margin
p�cm
p

� �
of 0.4, the critical ratio for

the strategy of brick-and-mortar selling (qB = 0.3) is
w ¼ 1

1�0:3
� �

0:4 � 0:3
1�0:3
� �

¼ 0:143. If that product was
procured from an offshore source with 10% price dis-

count (i.e.,
p�cx
p

¼ 0:36) and sold online with a returns
penalty of 10% (i.e., qO = 0.33), the critical ratio is
w ¼ 1

1�0:33
� �

0:36 � 0:33
1�0:33
� �

¼ 0:045. As a result, the firm
with an end-to-end strategy of ‘offshore sourcing and
online sales’ will procure fewer pieces of the apparel
item compared to the firm with an end-to-end strat-
egy of ‘nearshore sourcing and brick-and-mortar
sales.’ Since the variability of market demand is low,
the risk-pooling benefit of the online channel over the
brick-and-mortar channel is small. The net result of
these effects is that the strategy of ‘nearshore sourcing

Phadnis and Fine: End-To-End Supply Chain Strategies
2316 Production and Operations Management 26(12), pp. 2305–2322, © 2017 Production and Operations Management Society

with brick-and-mortar sales’ can outperform the strat-
egy of ‘offshore sourcing and online sales.’ Thus, an
end-to-end strategy involving a responsive source
(i.e., nearshore) and a responsive sales channel (i.e.,
brick-and-mortar) can dominate the one involving a
cost-efficient source and a cost-efficient sales channel,
for functional products.

OBSERVATION 2. For functional products with pre-
dictable market demand (i.e., low CV4) but high product
returns rates, the responsive end-to-end strategy of
nearshore sourcing and brick-and-mortar retail can
outperform the cost-efficient strategy of offshore sourcing
and online retail, if the offshore price discount is low (i.e.,
cx/cm close to 1) and the online returns penalty (qO/qB) is
high.

4.2. End-to-end Supply Chain Strategies for
Innovative Products
We next consider end-to-end strategies for innovative
products, which are likely to have high uncertainty in
the aggregate season’s demand (CV2+3 = 1; results in
Exhibit 2). If a large portion of uncertainty in the sea-
son’s demand gets resolved between the times for
making the purchase decisions for offshore (t2) and
nearshore sourcing (t3)—as represented in the odd-
numbered cases in Exhibit 2 —a responsive nearshore
source can be more profitable than an offshore source
for a given sales strategy despite high offshore cost
discount. This is consistent with Fisher’s (1997) rec-
ommendation. The advantage of nearshore sourcing
is enhanced if the product has high contribution mar-
gin, as is likely for innovative products. Compared to
their equivalent cases of functional products (i.e.,
odd-numbered cases in 1–16), their expected profit
also rises more slowly with the increase in margin
resulting from an increase in the offshore cost advan-
tage. The range of offshore cost advantage for which
the nearshore source is more profitable than the
lower-cost offshore source varies for different sales
strategies. For the products with low returns rates
and low variability in market demand (cases 17–18,
25–26)—which are typically the products in familiar
categories (Petersen and Kumar 2009)—the online
and brick-and-mortar sales channels have similar
profitability for a given sourcing option, as was the
case with their equivalent functional products (cases
1–2 and 9–10). Thus, the best end-to-end supply chain
strategy for the innovative functional products is to
source them from a nearshore supplier; either sales
channel can be used with similar profitability. If the
variability of market demand is low, the end-to-end
strategy with a nearshore source and brick-and-mor-
tar stores is superior despite fairly high offshore cost
advantages. This is the supply chain strategy

famously associated with the Zara model (Ghemawat
and Nueno 2003).

OBSERVATION 3. For innovative products, (a) nearshore
source is the dominant sourcing choice for a given sales
channel for a wide range of offshore cost discounts, (b)
brick-and-mortar and online channels have similar
profitability, for a given source, if the product returns
rates are low and the market demand is predictable, but
(c) the brick-and-mortar channel can be more profitable
than the online channel if the product returns rates are
high.

Obviously, the nearshore source is more prof-
itable when a large portion of uncertainty in the
season’s aggregate demand gets resolved between
the times when procurement decisions for offshore
and nearshore sources are made. Comparison of
the odd-numbered cases in Exhibit 2 (in which 90%
of the variance in forecast error at the time of pro-
curement gets resolved between the timings of
offshore and nearshore procurement) with the
even-numbered cases to their right (in which only
10% of this variance is resolved) shows that near-
shore procurement is advantageous only if delay-
ing the purchase decision to buy from the
nearshore supplier, instead of the offshore supplier,
helps resolve most of the uncertainty associated
with the season’s demand.
However, an end-to-end strategy with nearshore

sourcing can get dominated by one with offshore
sourcing even when a majority of uncertainty at the
time of procurement gets resolved between the times
when procurement decisions for offshore and
nearshore sources are made. Comparison of the end-
to-end strategy of ‘nearshore sourcing and brick-and-
mortar selling (N = 25)’ with ‘offshore sourcing and
online sales’ between cases 17 and 21, and cases 19
and 23 shows that the former strategy gets dominated
by the latter as the variation of market demand
increases from CV4 = 0.01 (cases 17 and 19) to
CV4 = 0.1 (cases 21 and 23). This superiority of the
offshore-online supply chain strategy is valid even
when the offshore discount is fairly low (cx/cm close
to 1). Admittedly, the best performing end-to-end
strategy in case 21 still maintains the responsive near-
shore source (but with the cost-efficient online chan-
nel) if the offshore cost discount is moderate
(approximately, 0.7 ≤ cx/p ≤ 1). However, the impor-
tant point to note here is that the supposedly ideal
responsive strategy of ‘nearshore sourcing with brick-
and-mortar sales’ can now get dominated by an
entirely cost-efficient strategy of ‘offshore sourcing
with online sales,’ and the best performing strategy
also has one cost-efficient element (i.e., the online
channel).

Phadnis and Fine: End-To-End Supply Chain Strategies
Production and Operations Management 26(12), pp. 2305–2322, © 2017 Production and Operations Management Society 2317

The reason for this result is as follows. At low off-
shore cost discounts, the newsvendor critical ratio for
the offshore source is only moderately higher than
that for the nearshore source. As a result, the quantity
procured from an offshore source is only moderately
higher than that from a nearshore source. However,
this quantity is then placed at a single location (ware-
house for the online store) instead of being distributed
among multiple brick-and-mortar stores. The inven-
tory pooling benefit of the online channel exceeds the
benefit obtained from reducing uncertainty in the sea-
son’s demand through nearshore sourcing, when
variability of the market demand is high.

OBSERVATION 4. For innovative products, if the store-
level demand has high uncertainty (i.e., large CV4), (a) a
cost-efficient end-to-end strategy of offshore sourcing and
online retail can outperform the responsive end-to-end
strategy of nearshore sourcing and brick-and-mortar
retail, (b) nearshore sourcing and online retail can be the
optimal strategy with low offshore cost discount, and (c)
offshore sourcing and online retail can be the optimal
strategy if the offshore cost discount is moderate.

4.3. Effect of Contribution Margin
The above observations compare different end-to-end
strategies for functional and innovative products.
Next, we examine how the performance of end-to-
end strategies is affected by changes in the product’s
contribution margins. We begin with the analysis of
functional products. Comparison of cases 1–8 (high
margin) with the corresponding cases 9–16 (low mar-
gin) shows that contribution margin does not affect
the preference ordering of the four end-to-end strate-
gies. The only noticeable change is that the benefit of
offshore sourcing over nearshore sourcing rises more
rapidly with an increase in offshore cost discount
when the products have low contribution margin.

OBSERVATION 5. For functional products, a change in
the contribution margin does not alter the preference
ordering of end-to-end supply chain strategies.

For innovative products, the preference ordering
between end-to-end strategies does not change if the
store-level demand is fairly predictable (CV4 = 0.01),
as observed by comparing cases 17–20 with the
corresponding cases 25–28. However, when the store-
level demand has higher variability, a drop in the
contribution margin can make the end-to-end strategy
of ‘offshore sourcing and online sales’ more profitable
than the strategy of ‘offshore sourcing and brick-and-
mortar sales’ for moderate level of offshore cost
discounts, especially when online returns penalty is
small (as observed by comparing cases 21–22 with

cases 29–30). This change in preference ordering of
end-to-end strategies can be explained by the
newsvendor ratio. At low contribution margin, the
newsvendor critical ratio is small, and as a result, pro-
cured quantity is small compared to the cases with
high contribution margin. In presence of higher vari-
ability in the store-level demand, it is more beneficial
to pool the procured quantity for sale though one (or
fewer) locations, as achieved in online selling, instead
of in the brick-and-mortar channel.

OBSERVATION 6. For innovative products, a change in
the contribution margin has the following effect on the
preference ordering of end to end strategies: (a) if the
store-level demand is fairly predictable (i.e., small CV4), a
change in the contribution margin does not alter the
preference ordering of end-to-end supply chain strategies,
(b) if the store-level demand has high variability (i.e.,
large CV4), a decrease in the contribution margin can
make online selling more attractive than brick-and-mortar
selling, when paired with offshore sourcing with
moderate-to-high levels of discount.

4.4. Practitioners’ Critique of the Study’s
Observations
We sought post hoc critique of the study’s observations
from eight executives in the apparel industry. Below,
we present a gist of their responses to the observation.
A more detailed version of the critique is presented in
the paper’s Appendix S1. Five executives (R1, R3, S1,
M1 and M3) commenting on Observation 1 all con-
curred with part (a), and agreed with part (b) with the
qualification that other factors—such as channel fixed
costs, sales volumes, etc.—also influence the respec-
tive channel’s profitability. Our respondents did not
have a strong reaction to Observation 2: those com-
menting (R1, R3, S1, and S2) tended to agree with the
observation but did not provide any corroborative
argument. Five executives (R1, R2, R3, S2, and M2)
agreed with Observation 3 that retailers need to use
nearshore sources for fashion products. All executives
commenting on Observation 4 (M1, R1, R2, and R3)
mentioned that Zara’s knowledgeable store managers
and its creation of impulse-purchase environment
made store demand more predictable to enable Zara’s
‘nearshore-sourcing, brick-and-mortar sales’ strategy.
Two executives stressed that creating this experience
online was difficult (M1, R2). All executives com-
menting on Observations 5 and 6 (R1, S2, M3) agreed
with them. They also cautioned that the retailer needs
to consider whether it should sell through the brick-
and-mortar channel if it does not understand the store
demand (R1, S2). The executives also mentioned a
few factors not considered in the model as being rele-
vant, such as channel fixed costs, channel’s ability to

Phadnis and Fine: End-To-End Supply Chain Strategies
2318 Production and Operations Management 26(12), pp. 2305–2322, © 2017 Production and Operations Management Society

generate additional sales, sources of raw materials
used for making garments, and use of brick-and-mor-
tar outlets as showrooms for the retailer’s online store.
These factors could be considered in future models of
apparel supply chains.

5. Discussion

This study advances the supply chain strategy litera-
ture (Fine 2000, Fisher 1997) by exploring the trade-
offs between different sourcing and sales strategies of
one firm in an end-to-end model of its supply chain
strategy. We compare the model’s predictions against
Fisher’s (1997) benchmark recommendations of ideal
supply chain strategies for delivering a firm’s func-
tional and innovative products under various scenar-
ios. Our analysis supports the recommendations in
several cases, but also highlights the scenarios in
which the recommended strategies may get domi-
nated by those considered to be inferior. The benefit
of the integral end-to-end approach is seen in the
intriguing insights and the corresponding practical
implications yielded by the study. They may explain
the “significant gap between expected and achieved
performance” observed in real-world offshoring pro-
jects (Larsen et al. 2013, p. 534) and other strategic
supply chain decisions.
Observation 1 supports Fisher’s (1997) recommen-

dations by showing that a cost-efficient offshore
supplier is the dominant source for functional prod-
ucts. It also shows that brick-and-mortar and online
channels may be employed equally effectively, in
conjunction with offshore sourcing, if the market
demand is predictable and product returns rate is
low. Familiar functional products typically have low
returns rate and predictable market demand (Petersen
and Kumar 2009). Based on observation 1, the most
effective end-to-end supply chain for such products
would pair cost-efficient sourcing with either online
or brick-and-mortar channel. Observation 2 describes
the instances in which Fisher’s recommendations for
functional products may not hold. It suggests that
functional products with predictable market demand
but high returns rate—such as, the products that need
to be examined physically to determine their fit with
the customer need before making the purchase deci-
sion—could be served more profitably by a respon-
sive supply chain involving a nearshore source and
brick-and-mortar stores, instead of a cost-efficient
supply chain consisting an offshore source with low-
to-moderate discount and the online channel.
However, the superiority of brick-and-mortar channel
diminishes and online selling becomes more attrac-
tive, even in presence of high online returns penalty,
when the demand at individual stores becomes less
predictable. These results suggest that online clothing

and shoe retailers (like Zappos) could become more
profitable if the demand at physical stores becomes
more variable. In such a scenario, the firms using
brick-and-mortar stores will need to change their end-
to-end strategies to consolidate the inventory at fewer
physical locations (not necessarily at one warehouse,
as for the online channel).
Observation 3 supports Fisher’s recommendations

for innovative products by highlighting the superior-
ity of responsive nearshore sourcing over the cost-
efficient offshore sourcing. The obvious caveat for the
nearshore source to be more effective is that signifi-
cant amount of uncertainty about the aggregate
market demand experienced at the time of procuring
goods from the offshore source needs to be resolved
by the time of ordering goods from the nearshore
source. This justifies the practice of sourcing products
with high demand uncertainty, such as fashion items
and new products, close to the market. Observation 3
also shows that the brick-and-mortar channel is supe-
rior to the online channel when the product returns
rates are high. This economic advantage of ‘nearshore
source with brick-and-mortar sales channel’ for fash-
ion products justifies the Zara model. However, the
superiority of this end-to-end model requires that the
uncertainty in the store-level demand is low. The cele-
brated Zara model minimizes uncertainty about the
store-level demand by actively involving its store
managers in design and ordering of the merchandise.
This is such an important aspect of Zara’s strategy
that its CEO called availability of store managers who
understand the store demand “the single most impor-
tant constraint on the rate of store additions” (Ghe-
mawat and Nueno 2003, p. 14). In the case of high
uncertainty in the store-level demand, as noted in
Observation 4, we see a complete reversal of the pref-
erence for end-to-end strategies: the cost-effective
end-to-end supply chain consisting of ‘offshore sour-
cing and online selling’ can become more profitable
than the responsive supply chain involving ‘near-
shore sourcing and brick-and-mortar selling.’ This is
particularly true when the total landed cost of the
offshore supplier is close to that of the nearshore
supplier! Two aspects of this reversal highlight the
need for taking an end-to-end perspective. First, the
reversal occurs when offshore cost is high and closer
to that of the nearshore source, which is counter-intui-
tive. The reason for this effect is that the quantity pro-
cured from the offshore source is not much higher
than that procured from the nearshore supplier, but is
now centralized in one location for online sales,
where the benefits of inventory pooling is high due to
the high uncertainty of store-level demand. Second,
the reversal shows how change in one attribute of the
business environment—namely, the predictability of
market demand in this case—can cause a eulogized

Phadnis and Fine: End-To-End Supply Chain Strategies
Production and Operations Management 26(12), pp. 2305–2322, © 2017 Production and Operations Management Society 2319

and emulated business model to get dethroned by its
polar opposite. If the increase in mobile e-commerce
results into an increased uncertainty of the store-level
demand, the revered Zara business model could get
deposed by the one using the end-to-end strategy of
‘offshore sourcing and online sales.’
Observations 5 and 6 relate to the robustness of

end-to-end strategies to changes in contribution mar-
gin. For functional products, it is always preferable to
source from a cost-efficient offshore supplier, and sell
via brick-and-mortar stores (online) if the store-level
demand is (not) predictable (Observation 5). For inno-
vative products, the end-to-end strategy is robust to
changes in contribution margin if the store-level
demand is fairly predictable; however, if the store-
level demand is unpredictable, a decrease in contribu-
tion margin can rapidly make online selling more
attractive than the brick-and-mortar channel (Obser-
vation 6). This suggests that firms using the Zara
model of brick-and-mortar selling need to ensure that
their products enjoy a high margin to ensure the
viability of the responsive nearshore source.

5.1. Generalization to Other Industries
Some of this study’s conclusions may differ in other
industries if one of the underlying premises, peculiar
to the apparel industry, does not hold. In 2015, 54.6%
of the $340 billion e-commerce revenue in the U.S.
was generated by four product categories (eMarketer
2017): apparel & accessories, computers & computer
electronics, auto & parts, and books/music/video.
Apparel products differ from those in the other three
industries in that buyers can judge the fit of apparel
items completely only after their physical examina-
tion. As a result, apparel products may experience
much higher returns rate for online sales than the
brick-and-mortar sales. Observation 2 states that func-
tional products with predictable market demand but
high returns rates could be sold more profitably using
a responsive ‘nearshore sourcing and brick-and-mor-
tar selling’ than a cost-efficient offshore-online strat-
egy, if the online returns penalty is high. This
contradiction of Fisher’s (1997) prescription of the
ideal supply chain strategy may not be observed if a
product’s fit to a buyer’s needs can be assessed with-
out experiencing the product physically, such as
based on technical specifications and online reviews
(e.g., electronics, auto parts, or books).
The contradiction of Fisher’s (1997) recommenda-

tion for innovative products (Observation 4) is possible
only when the store-level demand is difficult to pre-
dict. Apparel retailers, like Zara, use vast data of store
sales and store managers’ knowledge of the local
trends to estimate the store-level demand more accu-
rately for allocating inventory to the brick-and-mortar
stores (Ghemawat and Nueno 2003). However, the

industries that experience highly unpredictable store-
level demands for the innovative products (e.g., new
movies at the movie halls, new trade books in book
stores, etc.) could be served more profitably by the
online channel than the brick-and-mortar stores.

5.2. Limitations and Future Research
Our analysis focused on the tradeoffs among four
‘pure’ end-to-end strategies. In reality, different end-
to-end strategies may be ideal for a firm’s different
functional and innovative products based on their
returns rates, predictability of market demand, off-
shore cost discounts, and so on. Whether a firm
adopts the strategy best suited for each product
depends on the tradeoff between the marginal benefit
obtained by choosing that strategy and the fixed costs
associated with the implementation of that strategy.
The model presented in this work does not consider
these financial tradeoffs involving the fixed costs.
A second limitation of this work is the assumption

that the firm uses the newsvendor model to determine
its purchase quantity for each selling season.
Empirical research shows that managers making pro-
curement decisions in newsvendor settings exhibit
pull-to-center bias, i.e., the quantity they order is
between the average demand and the newsvendor
optimum (Schweitzer and Cachon 2000). Thus, the
quantity procured in all four end-to-end configura-
tions would be lower than the standard newsvendor
optimal when the contribution margin is high (cases
1–8, 17–24), and higher when the margin is low (cases
9–16, 25–32). The newsvendor critical ratio with a
deterministic product returns rate, as used in this
study, is lower than the standard newsvendor critical
ratio (lemma 2). Therefore, in presence of the pull-to-
center bias, the procured quantity will be closer to
(farther from) optimal for high-margin (low-margin)
products. However, in absence of field data of how
decision makers consider product returns rate when
deciding procurement quantities, this remains specula-
tive and provides an opportunity for future research.
Another limitation of the newsvendor assumption is

that some items may be replenished using a multi-
period periodic-review inventory policy, in which the
leftover inventory is not salvaged but available for sale
in the following period. Such a policy can be modeled
as newsvendor, with the cost of carrying inventory to
the next period as the cost of excess inventory. To
model replenishment using a multiperiod inventory
policy, additional parameters, such as inventory carry-
ing charge, will need to be considered.
A limitation related to the model of the sales chan-

nel is the assumption that the retailer sells through
either brick-and-mortar stores or an online store, but
not both. Many current industry practices justify this
assumption (Berg et al. 2015). However, the model in

Phadnis and Fine: End-To-End Supply Chain Strategies
2320 Production and Operations Management 26(12), pp. 2305–2322, © 2017 Production and Operations Management Society

this study can be extended to examine end-to-end
strategies with omnichannel retailing. At least two
forms of omnichannel retail should be considered:
showrooming, in which shoppers examine products at
a brick-and-mortar store and complete the purchase
online (Balakrishnan et al. 2014) and webrooming or
buy-online-pick-at-store, in which shoppers read pro-
duct reviews and check store availability online, and
complete the purchase at a brick-and-mortar store
(Gao and Su 2017). To compare end-to-end strategies
with brick-and-mortar, online, and omnichannel
sales, a demand model to specify product demand
from different types of consumers—such as, those
who shop exclusively in either brick-and-mortar or
online channels, as well as the omnichannel shoppers
—may need to be used. Additionally, product returns
rates for omnichannel shopping may be assumed to
be between the returns rates for the brick-and-mortar
and online channels.
The model presented in this study could be

extended to include a profit multiple for the brick-and-
mortar channel to reflect that customers may make
additional purchases after visiting a store (Gao and Su
2017). Future modeling works could also evaluate
mixed strategies, in which a firm uses more than one
type of source or sales channel (Balakrishnan et al.
2014, Klotz and Chatterjee 1995). The end-to-end
model for one firm developed here could be extended
to study competition between sales channels (Balasub-
ramanian 1998) or the choice of sourcing strategy
under competition (Wu and Zhang 2014). Future
empirical studies could test the observations in this
study. Even though the model was evaluated for the
parametric values observed in the apparel industry,
the observations from the numerical analysis are gen-
eral and could be tested in other industry contexts.
Case studies of end-to-end supply chain strategies of
different firms in the same industry could be used to
validate or challenge the observations.
In summary, this study illustrates the interdepen-

dence of two important decisions in apparel supply
chains: sourcing and sales strategies. This interdepen-
dence is demonstrated by revealing the conditions in
which the ideal sourcing and sales strategies for func-
tional and innovative products (Fisher 1997) can be
outperformed by those considered inferior, when
used as the components of an integral end-to-end
strategy. Such interdependencies could explain the
unexpected results, such as the hidden costs of out-
sourcing resulting from an increase in the firm’s ‘con-
figuration complexity’ (Larsen et al. 2013).

Acknowledgments

We are grateful to Fred Abernathy, Inga-Lena Darkow,
David Gonsalvez, Steve Graves, John Gray, Nitin Joglekar,

Sang Jo Kim, Leonard Lane, Angel Poyato, and the seminar
participants at the Asia School of Business, Industry Studies
Conference (2015), and Academy of Management Annual
Meeting (2015) for many helpful comments and for connect-
ing us with industry practitioners who helpfully critiqued
the study’s propositions. We sincerely thank the editor, the
senior editor, and the anonymous reviewers for their
insightful suggestions.

Note

1We thank one of the anonymous referees for pointing this
out.

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