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Reshoring Manufacturing: Supply Availability,Demand Updating, and Inventory Pooling
Li ChenSamuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, NY 14853
Bin HuKenan-Flagler Business School, University of North Carolina, Chapel Hill, NC 27599
Reshoring refers to the emerging industry movement of once-offshoring manufacturers moving their factories
back onshore. Existing literature emphasizes reshoring’s demand responsiveness due to market proximity.
We however note that limited onshore supply availability may force reshoring manufacturers to remain
dependent on offshore suppliers for component sourcing. As a result, the advantages due to improved market
proximity may be offset by the disadvantages due to lost supply proximity. By accounting for the supply
availability issue, we show that manufacturers’ preferences toward reshoring boil down to trade-offs between
operational flexibilities under offshoring and reshoring. We characterize cost and demand scenarios wherein
manufacturers prefer reshoring, and further identify operational strategies that can swing such preferences.
Key words : newsvendor with demand updating, commonality, risk pooling
History : file version August 16, 2015
1. Introduction
For nearly three decades, manufacturing offshoring has been a predominant industry trend, espe-
cially in the United States. The top driver of this trend has been the substantially lower labor
costs in emerging economies. However, the previous labor “arbitrage” gradually tapers off as wages
in developing economies such as China and India have increased by 10-20% annually for the past
decade, putting a spotlight on the drawbacks of offshoring, including shipping costs and lead-times,
lost manufacturing expertise, potential intellectual property leakage, increased disruption risks,
and political pressure (The Economist 2013). Accordingly, a growing number of US-based com-
panies started to consider bringing factories back to the US—dubbed reshoring—and some have
taken actions. In December 2013, Apple announced that they had started producing the Mac Pro
computers in a Texas plant as part of a US$100 million Made-in-the-USA push (Burrows 2013).
Google also assembled its Moto X smartphones in the US and heavily advertised this initiative
(King 2013). Nevertheless, the adoption of reshoring has been slower than many have hoped, gen-
erating much discussion (Schoenberger 2013). Divided views are abundant among practitioners on
whether reshoring is viable and scalable (Hertzman 2014, Wang 2014).
1
2
Most existing literature on this topic emphasizes onshore manufacturing’s cost disadvantage
compared with offshoring (Wu and Zhang 2014, Wang et al. 2014). However, practitioners’ discus-
sions about reshoring have not always been around costs. Chen et al. (2015) survey multi-national
companies operating in China about their recent supply chain re-structuring decisions and moti-
vations. The survey results indicate that manufacturing jobs are generally not coming back to the
US, and supply availability is among the top non-cost considerations. An article in The Economist
(2014) also quotes, “the biggest problem with reshoring is that the decline in manufacturing over
the decades means that the supply chain has all but disappeared.” In fact, an analysis of the
Google Moto X smartphone that we mentioned above revealed that nearly all of its parts came from
overseas (King 2013). On the other hand, Duhigg and Bradsher (2012) depict the convenience and
flexibility of the iPhone supply chain in Shenzhen, China: “You need a thousand rubber gaskets?
That’s the factory next door. You need a million screws? That factory is a block away. You need
that screw made a little bit different? It will take three hours.” These articles highlight the reality
faced by many offshoring manufacturers contemplating reshoring: as they extensively sourced from
local (offshore) suppliers, onshore supply bases have gradually withered. The limited onshore sup-
ply availability may force them to continue sourcing from offshore suppliers even if they reshore
manufacturing, until the reemergence of full-fledged onshore supply bases.
How does a manufacturer’s dependence on offshore suppliers impact its consideration of
reshoring? A widely-perceived advantage of reshoring is that it allows a manufacturer to be closer
to the market, potentially making it easier to adjust production in response to demand changes.
However, the dependence on offshore suppliers means that a reshoring manufacturer also moves
away from its suppliers, which may make it more challenging to procure components in response
to demand changes. This is not a straightforward trade-off, prompting several research questions.
Under what conditions do the disadvantages of losing supply proximity outweigh the advantages
of obtaining market proximity? What are the underlying operational drivers? And what strategies
may influence the trade-off in favor of or against reshoring?
To answer these questions, we consider the following model. An expected-profit-maximizing
manufacturer sources components from an offshore supplier and converts the components into
finished goods to meet random onshore demands. Production takes place in the manufacturer’s
(or its strategic partner’s) factory, which may be placed close to the supplier in the offshoring
mode (the as-is case), or close to the market in the reshoring mode (the to-be case). The supply
chain operates in two sequential stages. Under offshoring, the two stages are (offshore) production
followed by shipping (of finished goods), whereas under reshoring, the two stages are shipping (of
components) followed by (onshore) production. We assume all common costs under offshoring and
reshoring to be identical to isolate non-cost drivers.
3
To capture potential demand information updates during the production-shipping (or shipping-
production) process, we assume that the onshore demand may have two different types—high and
low, which is initially unknown to the manufacturer. At certain time before the selling season, the
manufacturer learns the demand type through a marketing event such as a trade show (Fisher
and Raman 1996). We assume that the demand update occurs before the second stage in both
production modes so that the manufacturer can always respond to it (otherwise the comparison
of the two modes would become trivial). As such, the manufacturer faces a newsvendor problem
with demand updating in either production mode.
One can see that the most crucial distinction between offshoring and reshoring in our model
is in the sequence of events. Under offshoring production takes place before shipping, whereas
under reshoring shipping takes place before production. The different sequences of events entail
different operational flexibilities in response to the demand update before the second decision
events. Upon receiving new demand information, an offshoring manufacturer may either adjust
the final inventory level upward by rushing a production order before shipping (without worry-
ing about component supply), or adjust the final inventory level downward by not shipping all
finished goods. By contrast, a reshoring manufacturer may either adjust the final inventory level
upward by expediting more components from offshore before production begins, or adjust the final
inventory level downward by not processing all shipped components into finished goods. While the
manufacturer has both upward and downward flexibilities to adjust the final inventory level in each
mode, the costs of these flexibilities differ in nature. Under offshoring, utilizing the upward flexi-
bility (rushing production) incurs rush production costs, whereas utilizing the downward flexibility
(discarding finished goods) is at the expense of component sourcing and regular production costs.
Under reshoring, utilizing the upward flexibility (expediting component shipping) incurs expedited
shipping costs, whereas utilizing the downward flexibility (discarding shipped components) is at
the expense of component sourcing and regular shipping costs. Therefore, despite the identical cost
structure in each production mode, the costs of flexibilities may differ.
Since in our model offshoring and reshoring mainly differ in their operational flexibilities, when
a production mode has both the cheaper upward and downward flexibilities, we find that it always
outperforms the other (see Table 1’s diagonal quadrants). On the other hand, the comparison when
one production mode has the cheaper upward flexibility while the other has the cheaper downward
flexibility is less straightforward. Our analysis shows that in this case, whether reshoring is preferred
to offshoring depends on the prior probability of a high demand, or the demand prospect. The
intuition is as follows. Suppose a product has a low (high) demand prospect. This means that the
manufacturer is likely to plan a low (high) initial production quantity, which calls for an upward
(downward) flexibility in case demand turns out to be high (low). Therefore, for this product, the
4
manufacturer prefers the production mode with the cheaper upward (downward) flexibility. The
specific preferences depend on the cost parameters (see Table 1’s off-diagonal quadrants). In short,
the demand prospect determines which type of flexibility is needed, and the needed flexibility
then determines whether reshoring is preferred to offshoring. These results reveal insights about
reshoring with limited onshore supply availability. First, reshoring is advantageous for products
that are expensive to make but cheap to ship (in terms of both regular and rushed/expedited
costs), and disadvantageous otherwise. Second, when the regular production and shipping cost
comparison differs from that between the rushed/expedited costs, the manufacturer’s preference
toward reshoring goes beyond cost parameter comparisons, and demand prospects come into play.
r < e r > e
m< s Offshoring
Reshoring for products withlow demand prospects
Offshoring for products withhigh demand prospects
m>s
Offshoring for products withlow demand prospects
Reshoring for products withhigh demand prospects
Reshoring
m= regular production cost s= regular shipping costr= rush production premium e= expedited shipping premium
Table 1 A manufacturer’s preferred production mode in different cost scenarios
We further investigate operational strategies that can swing manufacturers’ preferences for
reshoring. We find that, due to the different sequences of production and shipping under offshoring
and reshoring, when a manufacturer makes two products that share a common component, it
enjoys component-pooling benefits only under reshoring and not under offshoring, which makes
reshoring more attractive. On the other hand, when a manufacturer makes a product for two sep-
arate markets, it enjoys product-pooling benefits only under offshoring and not under reshoring,
which makes reshoring less attractive. The above two effects, when coexisting, can offset each other
to some extent. These results make a connection between the classic inventory pooling strategy
and the emerging reshoring movement.
Our findings confirm many practitioners’ intuition that offshoring manufacturers’ dependence on
local (offshore) suppliers leads to operational trade-offs regarding reshoring, and that even under
5
identical cost structures, reshoring does not always provide operational advantages. The product
and operational characteristics that we identify as favoring reshoring can help policy makers deter-
mine which industries to target for promoting reshoring. In the long run, our study suggests that
policy makers should focus on promoting and fostering onshore supply base developments, so as
to reduce reshoring manufacturers’ dependence on offshore suppliers.
The rest of this paper is organized as follows. A literature review is provided in §2. We model
and analyze offshoring and reshoring in §3, and compare them in §4. This lays the foundation for
the investigation of common-component designs and serving multiple markets in §5. Finally, we
conclude the paper in §6. All proofs are found in the Appendix.
2. Literature review
In this paper we study offshoring manufacturers’ considerations of reshoring. A related concept
to offshoring is outsourcing. Tsay (2014) provides a lucid delineation between offshoring and out-
sourcing. Here is an excerpt from p. 129 of his monograph: “The hazards of both offshoring and
outsourcing can be interpreted as losing proximity, i.e., the creation of distance. In the case of
outsourcing, the distance is organizational in nature. An intervening corporate boundary obstructs
visibility and communication and causes divergence of incentives... With offshoring, the distance is
geographic. This increases the difficulty of moving materials, funds, information, knowledge, and
workers.” Accordingly, studies of outsourcing focus on the impact of decentralized decision-making
and the need for coordination (see Elmaghraby 2000 and Cachon 2003 for comprehensive reviews
of this literature), whereas we study how the geographic distance between offshore suppliers and
onshore markets impact offshoring manufacturers’ preferences toward reshoring.
At the core of our models are operational flexibilities, namely the ability to adjust final inventory
levels after receiving updated demand information. Therefore, our work is related to the literature
on production management with demand updating. This literature can be loosely divided into two
main categories. The first category focuses on optimal strategies and their benefits. For example,
Fisher and Raman (1996) study how to dynamically allocate production capacity in response to
information updates in a Quick Response system. Iyer and Bergen (1997) study the benefits of
Quick Response in a manufacturer-retailer supply chain. Gurnani and Tang (1999) further consider
optimal ordering policies with additional cost uncertainties under a similar setting. The second
category revolves around trade-offs between costs and responsiveness. Donohue (2000) studies
efficient contract design with forecast updating between two production modes, one less costly
and the other with a shorter lead-time. Two particularly related papers are by Wang et al. (2014)
and Wu and Zhang (2014), who, in the context of offshoring, study the interplay between cost,
responsiveness, competition, and information. A common feature of the above papers is that they
6
all model two-tier supply chains, each consisting of a manufacturer and a market, and consider one
operational flexility, either investigating its benefits or trading it off against costs. By contrast, we
model a three-tier supply chain consisting of a supplier, a manufacturer, and a market, and consider
two operational flexibilities. In our model, trade-offs between these operational flexibilities can drive
manufacturers’ preferences toward reshoring without any direct cost advantages for offshoring.
Therefore, both the premise and the insights of our paper are different from the above papers.
In our model extensions, we study common-component designs as well as serving multiple mar-
kets in the context of reshoring. The former strategy resembles delayed product differentiation
(Lee and Tang 1997), whereas the latter is essentially demand pooling (Eppen 1979). While such
strategies are widely studied and well understood, we make a contribution by recognizing that
common-component designs generate risk-pooling benefits only for reshoring manufacturers, and
serving multiple markets generates risk-pooling benefits only for offshoring manufacturers, thus
connecting the classic inventory pooling strategy and the emerging topic of reshoring.
Our problem is also related to the newsvendor network design literature. Van Mieghem and Rudi
(2002) offer an excellent review of this literature; here we focus on the two most relevant papers
by Lu and Van Mieghem (2009) and Dong et al. (2010). Their basic setting can be described as
one wherein a firm sells a product in two separate markets, and needs to decide whether to build
a centralized production facility for both markets, or build a dedicated facility in each market. In
short, the decision is about where to place a factory between two separate markets. Our research
problem, on the other hand, can be described as about where to place a factory between an offshore
supplier and an onshore market. Clearly, these research problems are different. Also, the basic
trade-off of Lu and Van Mieghem (2009) and Dong et al. (2010) is that between risk-pooling
benefits and production and shipping costs. Our base model considers a manufacturer making one
product for one market, thus does not involve risk pooling. In our extensions, as discussed above,
we study multiple products and/or multiple markets, which offers insights that are related to, but
different from those shown by Lu and Van Mieghem (2009) and Dong et al. (2010).
Recently, several papers have studied Quick Response and postponement in competitive envi-
ronments; examples include Van Mieghem and Dada (1999), Anand and Girotra (2007), Goyal and
Netessine (2007), Caro and Martınez-de-Albeniz (2010), Wang et al. (2014), and Wu and Zhang
(2014). As a first attempt to study operational flexibilities intrinsic to offshoring and reshoring, we
restrict our attention to a monopolistic setting. The insights from our paper will serve as a stepping
stone to understanding this problem in more complex settings such as competitive environments.
3. Base model and analyses
Our goal is to compare a currently-offshoring manufacturer’s profits before and after reshoring in
otherwise identical settings. We assume that an expected-profit-maximizing manufacturer depends
7
on an offshore supplier for sourcing components at unit cost c. The manufacturer can make one unit
of finished good from one unit of the component to meet a random onshore demand at retail price p
in a short selling season. The offshore supplier has no capacity limit or supply lead-time. Production
takes place in the manufacturer’s (or its strategic partner’s) factory, which may be placed close
to the supplier (offshoring), or close to the market (reshoring). Regardless of the location, regular
production costs m per unit. An offshoring manufacturer needs to ship finished goods, and a
reshoring manufacturer needs to ship components, from offshore to onshore. Regardless of what is
being shipped, the regular shipping cost is s per unit. We assume away cost differences between
offshoring and reshoring in order to isolate non-cost drivers. (The impacts of unequal costs are
straightforward: if offshore production is cheaper, then reshoring is less attractive; and if shipping
components is cheaper than shipping finished goods, then reshoring is more attractive.)
We assume the demand D to have a normal distribution N(µ,σ), where σ is the publicly known
standard deviation, and the mean µ can be µH (High demand) with prior probability γ, or µL (Low
demand) with prior probability 1− γ. We assume that µH > µL� σ, so that the probability of
having a negative demand is negligible. At the beginning of the decision horizon, the manufacturer
knows γ but not the demand type (i.e., whether µ= µH or µL). A large (small) γ means that the
demand is more likely to be high (low), which we refer to as a high (low) demand prospect.
Both regular production and shipping can require significant lead-times. For example, ocean
freight between Asia and America takes up to a month (Arnold 2009); Foxconn had to start
mass-producing iPhone 6 two months before the selling season (Culpan and Burrows 2014). We
do not quantitatively model lead-times, but rather treat production and shipping as two discrete
stages. For an offshoring manufacturer, the manufacturing process consists of (offshore) production
followed by shipping (of finished goods), whereas for a reshoring manufacturer, the manufacturing
process consists of shipping (of components) followed by (onshore) production.
At certain time before the selling season, the manufacturer learns the demand type through a
marketing event such as a trade show (Fisher and Raman 1996). We assume that the demand
update occurs before the second stage of the manufacturing process regardless of offshoring or
reshoring, so that the manufacturer can always respond to the updated information (otherwise the
comparison between the two modes would become trivial). The specific sequences of events under
offshoring and reshoring are illustrated in Figure 1.
Under offshoring, the manufacturer begins regular production in the offshore factory without
knowing the demand type. The regular production, once scheduled, cannot be changed. Once the
demand type is revealed, the manufacturer can adjust the production quantity upward by rushing
an order at additional cost r (on top of the regular production cost m) per unit, without delaying
shipping. (Component supply is not a constraint because production takes place in the proximity
8
Regularproduction
Rush production/Hold back shipping
Finished goodsin stock
Demand update Demand realization
Finished goodsin shipping
Componentsourcing
Expedite shipping/Hold back production
Finished goodsin stock
Demand update Demand realization
Componentsin shipping
Offshoring:
Reshoring:
time
time
ComponentProduct
Productionin progress
Productionin progress
Figure 1 Sequences of events when offshoring and reshoring
of the supplier.) Alternatively, the manufacturer can adjust its final inventory level downward by
holding back shipping and discarding some finished goods. As the shipped goods arrive onshore,
the actual demand realizes, and the manufacturer satisfies the demand subject to available stock.
Under reshoring, the manufacturer orders components to be delivered to the onshore factory
using regular shipping without knowing the demand type. Once the demand type is revealed, the
manufacturer can adjust the component inventory level upward by obtaining components using
expedited shipping at additional cost e (on top of the regular shipping cost s) per unit, without
delaying production. Alternatively, the manufacturer can adjust the final inventory level downward
by holding back production and discarding some shipped components. As the production is finished,
the actual demand realizes, and the manufacturer satisfies the demand subject to available stock.
In summary, we employ a newsvendor model with demand updating, wherein the sequence
of events differs based on the production mode (production-shipping when offshoring, shipping-
production when reshoring). In each mode, the manufacturer has the flexibilities to adjust the final
inventory level upward or downward in response to a demand update through different means.
Newsvendor models with demand updating are generally difficult to analyze, and closed-form char-
acterizations are often elusive (Fisher and Raman 1996, Iyer and Bergen 1997). Nevertheless, we are
able to analyze two newsvendor models with demand updating, and provide a simple, closed-form
characterization of their comparison.
To proceed, we formulate and analyze the offshoring and reshoring models in the rest of this
section, before comparing the manufacturer’s profits in both modes in the next section. Throughout
the paper, we use superscript 0 to denote offshoring, and superscript 1 to denote reshoring.
9
3.1. Offshoring
There are two decisions for an offshoring manufacturer: the regular production quantity before
learning the demand type, and the final shipped quantity (which may be higher or lower than the
former) after learning the demand type. We use subscript m to denote relation to regular produc-
tion, and subscript s to denote relation to shipping. Accordingly, the offshoring manufacturer’s
regular production quantity is denoted by x0m, and the shipped quantity by x0
s. Recall that p, c,
m, s, and r denote the retail price, the component sourcing cost, the regular production cost, the
regular shipping cost, and the rush production premium, respectively. An offshoring manufacturer’s
objective is to maximize its expected profit by choosing an optimal regular production quantity:
Π0m
.= max
x0m≥0{−(c+m)x0
m + γΠ0s(µH , x
0m) + (1− γ)Π0
s(µL, x0m)}, (1)
where Π0s(µ,x
0m) is the optimal profit due to adjusting the final inventory level after the mean
demand µ (= µH or µL) is revealed, given the regular production quantity x0m:
Π0s(µ,x
0m)
.= max
x0s≥0{−(c+m+ r)(x0
s−x0m)+− sx0
s + pE[min{D,x0s}|µ]}. (2)
We define
z.= Φ−1
(p− c−m− s
p
)where Φ is the cumulative distribution function of a standard normal distribution (we use φ to
denote the corresponding probability density function), and z is the critical fractile for the newsven-
dor problem with regular production only. It follows that the optimal solution x0∗m to problem (1)
must satisfy µL +σz ≤ x0∗m ≤ µH +σz.
Now consider the manufacturer’s decision after learning the demand type. Suppose that the
manufacturer adjusts the final inventory level downward when demand is high, then it must also
adjust the final inventory level downward when demand is low. Such a strategy cannot be optimal
as one can improve it by simply reducing the initial production quantity. Hence, in the event that
the demand type is revealed to be high, the manufacturer would either adjust the final inventory
level upward or do nothing, and the resulting profit function is given by
Π0s(µH , x
0m)
.= max
x0s≥x0m
{−(c+m+ r)(x0
s−x0m)− sx0
s + pE[min{D,x0s}|µH ]
}. (3)
The above problem is a standard newsvendor problem. We define
z0u.= Φ−1
(p− c−m− s− r
p
)which is the critical fractile for the newsvendor problem using the upward flexibility (rush produc-
tion). The optimal shipped quantity to problem (3) is given by x0∗sH = max{x0
m, µH + σz0u}, where
subscript H denotes relation to the high demand.
10
By an analogous argument, we can show that the manufacturer would either adjust production
downward or do nothing in the event that the demand type is revealed to be low, and the resulting
profit function is given by
Π0s(µL, x
0m)
.= max
x0s≤x0m
{−sx0
s + pE[min{D,x0s}|µL]
}. (4)
We define
z0d.= Φ−1
(p− sp
)which is the critical fractile for the newsvendor problem using the downward flexibility (holding
back shipping finished goods). The optimal solution to (4) is given by x0∗sL = min{x0
m, µL + σz0d},
where subscript L denotes relation to the low demand. Clearly, z0u < z < z0d. We further define
∆.=µH −µL
σ
as a measure of the difference between the high and low demands relative to the demand uncertainty.
It is straightforward to verify that µH + σz0u ≤ µL + σz0d if and only if ∆ = (µH −µL)/σ ≤ z0d − z0u.
It is also useful to define the following two thresholds for γ:
γ0u(∆)
.=
Φ(z0u + ∆)−Φ(z)
Φ(z0u + ∆)−Φ(z0u), γ0
d(∆).=
Φ(z0d)−Φ(z)
Φ(z0d)−Φ(z0d −∆). (5)
The following lemma characterizes the properties of these two thresholds:
Lemma 1. The threshold γ0u(∆) is increasing in ∆, with γ0
u(∆) = 0 for ∆≤ z−z0u. The threshold
γ0d(∆) is decreasing in ∆, with γ0
d(∆) = 1 for ∆≤ z0d− z. Thresholds γ0u(∆) and γ0
d(∆) intersect at
(∆0, γ0), where ∆0 .= z0d − z0u and γ0 .= c+mc+m+r
. For ∆≤∆0, µH + σz0u ≤ x0∗m ≤ µL + σz0d if and only
if γ0u(∆)≤ γ ≤ γ0
d(∆).
Figure 2 illustrates Lemma 1 with parameters p= 20, c= 2, m= 4, s= 2, e= 5, r= 3 (the same
as for all later figures). We also mark the regions characterized in Proposition 1 below.
For ease of exposition, we define two more critical fractiles:
z0mu.= Φ−1
(γr+ (1− γ)(p− c−m− s)
(1− γ)p
), for γ ≤ γ0 =
c+m
c+m+ r,
where it can be shown that z0mu increases from z to z0d as γ increases from 0 to γ0; and
z0md.= Φ−1
(γ(p− s)− c−m
γp
), for γ ≥ γ0 =
c+m
c+m+ r,
where it can be shown that z0md decreases from z to z0u as γ decreases from 1 to γ0. The next
proposition characterizes an offshoring manufacturer’s optimal strategies and profits.
Proposition 1. The offshoring manufacturer’s optimal strategies and profits are as follows:
11
0
0.2
0.4
0.6
0.8
1
0 1 2 3 4
γ
Δ
𝑧"# − 𝑧
𝛾"#(Δ) III
Δ#, 𝛾#Ι
𝛾*#(Δ) II
𝑧 − 𝑧*#
Figure 2 An offshoring manufacturer’s optimal strategies
Case I: ∆≤∆0 and γ0u(∆)≤ γ ≤ γ0
d(∆). The optimal solution is x0∗m = x0∗
sH = x0∗sL = x, where x
uniquely solves γΦ(x−µHσ
)+ (1− γ)Φ
(x−µLσ
)= Φ(z). The optimal profit is
−(c+m+ s)x0∗m + pE[min{D,x0∗
m}].
Case II: ∆ ≤∆0 and γ < γ0u(∆), or ∆ > ∆0 and γ ≤ γ0. The optimal solution is x0∗
m = x0∗sL =
µL +σz0mu, x0∗sH = µH +σz0u. The optimal profit is
pσγΦ(z0u)∆− pσ[γφ(z0u) + (1− γ)φ(z0mu)] + pΦ(z)µL.
Case III: ∆≤∆0 and γ > γ0d(∆), or ∆>∆0 and γ > γ0. The optimal solution is x0∗
m = x0∗sH =
µH +σz0md, x0∗sL = µL +σz0d. The optimal profit is
pσγΦ(z0md)∆− pσ[γφ(z0md) + (1− γ)φ(z0d)] + pΦ(z)µL.
Proposition 1 characterizes an offshoring manufacturer’s optimal strategy in three cases, as
illustrated in Figure 2. In Case I, where the high and low demand types are sufficiently similar
and/or the prior probability of a high demand is relatively moderate, the values of the flexibilities
cannot justify their costs, thus neither flexibility is used following the demand update. When
the two mean demands are further apart, however, the manufacturer may utilize the upward or
downward flexibilities depending on the demand prospect. In Case II with a low demand prospect
(small prior probability of a high demand), the manufacturer plans the initial production quantity
anticipating a low demand, and resorts to the upward flexibility (rush production) in the unlikely
12
event that the demand turns out to be high. In Case III with a high demand prospect (large prior
probability of a high demand), the manufacturer plans the initial production quantity anticipating
a high demand, and resorts to the downward flexibility (holding back shipping finished goods) in
the unlikely event that the demand turns out to be low.
3.2. Reshoring
There are two decisions for a reshoring manufacturer: the component ordering quantity before
learning the demand type, and the regular production quantity (which may be higher or lower than
the former) after learning the demand type. We use subscript c to denote relation to component
ordering. Accordingly, the offshoring manufacturer’s component ordering quantity is denoted by x1c,
and the regular production quantity by x1m. A reshoring manufacturer’s objective is to maximize
its expected profit by choosing an optimal component ordering quantity:
Π1c
.= max
x1c≥0{−(c+ s)x1
c + γΠ1m(µH , x
1c) + (1− γ)Π1
m(µL, x1c)}, (6)
where Π1m(µ,x1
c) is the optimal profit due to adjusting the final inventory level after the mean
demand µ (= µH or µL) is revealed, given the component ordering quantity x1c:
Π1m(µ,x1
c).= max
x1m≥0{−(c+ s+ e)(x1
m−x1c)
+−mx1m + pE[min{D,x1
m}|µ]}. (7)
By comparing (1)-(2) and (6)-(7), one can see that the offshoring and reshoring models struc-
turally identical, only with m and s, r and e switched. The identical structure reflects that in both
modes, the manufacturer has both upward and downward flexibilities. The switched parameters,
on the other hand, reflect the different natures of the flexibilities. To increase the final inventory
level, an offshoring manufacturer incurs extra cost r per unit due to rush production, whereas a
reshoring manufacturer incurs extra cost e per unit due to expedited shipping. To decrease the final
inventory level, an offshoring manufacturer holds back shipping of some finished goods, discarding
the worth of c+m per unit, whereas a reshoring manufacturer holds back processing some shipped
components into finished goods, discarding the worth of c+s per unit. The identical structure and
the switched parameters mean that we can present a reshoring manufacturer’s optimal strategy
without repeating the analysis. Mirroring the offshoring expressions, we define
z1u.= Φ−1
(p− c−m− s− e
p
), z1d
.= Φ−1
(p−mp
),
γ1u(∆)
.=
Φ(z1u + ∆)−Φ(z)
Φ(z1u + ∆)−Φ(z1u), γ1
d(∆).=
Φ(z1d)−Φ(z)
Φ(z1d)−Φ(z1d −∆).
The following lemma mirrors Lemma 1 for the offshoring model:
13
Lemma 2. The threshold γ1u(∆) is increasing in ∆, with γ1
u(∆) = 0 for ∆≤ z−z1u. The threshold
γ1d(∆) is decreasing in ∆, with γ1
d(∆) = 1 for ∆≤ z1d− z. Thresholds γ1u(∆) and γ1
d(∆) intersect at
(∆1, γ1), where ∆1 .= z1d− z1u and γ1 .= c+sc+s+e
. For ∆≤∆1, µH +σz1u ≤ x1∗c ≤ µL +σz1d if and only if
γ1u(∆)≤ γ ≤ γ1
d(∆).
We define two more critical fractiles:
z1cu.= Φ−1
(γe+ (1− γ)(p− c−m− s)
(1− γ)p
), for γ ≤ γ1 =
c+ s
c+ s+ e,
where it can be shown that z1cu increases from z to z1d as γ increases from 0 to γ1; and
z1cd.= Φ−1
(γ(p−m)− c− s
γp
), for γ ≥ γ1 =
c+ s
c+ s+ e,
where it can be shown that z1cd decreases from z to z1u as γ decreases from 1 to γ1. The next
proposition characterizes a reshoring manufacturer’s optimal strategies and profits (illustrated in
Figure 3 with the same parameters as for Figure 2):
Proposition 2. The reshoring manufacturer’s optimal strategies and profits are as follows:
Case I: ∆≤∆1 and γ1u(∆)≤ γ ≤ γ1
d(∆). The optimal solution is x1∗c = x1∗
mH = x1∗mL = x, where x
uniquely solves γΦ(x−µHσ
)+ (1− γ)Φ
(x−µLσ
)= Φ(z). The optimal profit is
−(c+m+ s)x1∗c + pE[min{D,x1∗
c }].
Case II: ∆ ≤∆1 and γ < γ1u(∆), or ∆ > ∆1 and γ ≤ γ1. The optimal solution is x1∗
c = x1∗mL =
µL +σz1cu, x1∗mH = µH +σz1u. The optimal profit is
pσγΦ(z1u)∆− pσ[γφ(z1u) + (1− γ)φ(z1cu)] + pΦ(z)µL.
Case III: ∆≤∆1 and γ > γ1d(∆), or ∆>∆1 and γ > γ1. The optimal solution is x1∗
c = x1∗mH =
µH +σz1cd, x1∗mL = µL +σz1d. The optimal profit is
pσγΦ(z1cd)∆− pσ[γφ(z1cd) + (1− γ)φ(z1d)] + pΦ(z)µL.
4. Profit comparison of offshoring and reshoring
In the previous section, we solved the manufacturer’s problem under offshoring and reshoring.
Despite the complexity of the optimal strategies and profit expressions, in this section we provide
a full structural characterization of their profit comparison.
14
0
0.2
0.4
0.6
0.8
1
0 1 2 3 4
γ
Δ
𝑧"# − 𝑧
𝛾"#(Δ) III
Δ#, 𝛾#Ι
𝛾*#(Δ) II
𝑧 − 𝑧*#
Figure 3 A reshoring manufacturer’s optimal strategies
4.1. The dominance case
We first show a simple dominance result.
Proposition 3. When m<s and r < e, the manufacturer weakly prefers to remain offshoring.
When m>s and r > e, the manufacturer weakly prefers reshoring.
To understand this result, recall that the offshoring model (1)-(2) and the reshoring model (6)-(7)
are structurally identical, and the unit product cost without using any flexibility is always c+m+s.
The only differences are in the “costs of flexibilities”. As discussed in Section 3.2, under offshoring,
the upward flexibility costs r per unit due to rush production, and the downward flexibility costs
c+m per unit due to discarding a finished good; whereas under reshoring, the upward flexibility
costs e per unit due to expedited shipping, and the downward flexibility costs c+s per unit due to
discarding a shipped component. When m< s and r < e (m> s and r > e), offshoring (reshoring)
has both cheaper upward and downward flexibilities than reshoring (offshoring), thus is preferred.
Let us consider the cost parameters m, s, r, and e. Both m and r are related to production,
whereas both s and e are related to shipping. Therefore, Proposition 3 can be interpreted as that
reshoring is less attractive for those products that are cheap to make but expensive to ship (e.g.,
furniture), and more attractive for those products that are expensive to make but cheap to ship
(e.g., designer apparel). These insights (especially the former) may be counterintuitive. One may
expect that bulky goods are suitable for reshoring due to the eliminated shipping cost. This notion
however neglects the fact that many reshoring firms continue to depend on offshore suppliers for
sourcing, and therefore the finished good shipping costs are merely replaced by component/material
15
shipping costs. In fact, if shipping is a dominant cost component, then a firm should make the
shipping decision with more available information, which means offshoring (where shipping takes
place after production) is more favorable than reshoring.
4.2. The conditional case
In the previous subsection, Proposition 3 establishes the comparison between offshoring and
reshoring when m < s, r < e and m > s, r > e, leaving two more cases to be analyzed. In this
subsection, we mainly explore the case of m> s and r < e (the analyses and results for the case
of m < s and r > e mirror those for this case). Recall that the offshoring and reshoring models
each have three solution cases, separated by γid(∆), γiu(∆), and γi, i= 0,1. When m>s and r < e,
one can straightforwardly verify that γ0u(∆)> γ1
u(∆), γ0d(∆)> γ1
d(∆), and γ0 > γ1. These relation-
ships mean that the offshoring and reshoring solution cases are positioned in a particular manner.
As a result, we need to compare the offshoring and reshoring profits in six case combinations in
an overlay of Figures 2 and 3, then “stitch” the preferences together. Despite the complexity of
the analysis, the resulting preference regions are surprisingly clean, as characterized in the next
proposition and illustrated in Figure 4 (generated with the same parameters as for Figure 2).
Proposition 4. Suppose that m > s and r < e. Thresholds γ0u(∆) and γ1
d(∆) intersect at
(∆∗, γ∗), where ∆∗.= z1d−z0u and γ∗
.= c+s
c+s+r. The manufacturer’s preferences toward reshoring are:
1. ∆≤∆∗ and γ0u(∆)≤ γ ≤ γ1
d(∆): the manufacturer is indifferent toward reshoring.
2. ∆≤∆∗ and γ < γ0u(∆), or ∆>∆∗ and γ ≤ γ∗: the manufacturer prefers to remain offshoring.
3. ∆≤∆∗ and γ > γ1d(∆), or ∆>∆∗ and γ > γ∗: the manufacturer prefers reshoring.
Proposition 4 characterizes the case where each mode has an advantaged flexibility. For example,
m>s means that reshoring has the cheaper downward flexibility, and r < e means that offshoring
has the cheaper upward flexibility. When the high and low demand types are sufficiently similar
and/or the prior probability of a high demand is relatively moderate, the manufacturer is indifferent
toward reshoring. Otherwise, the manufacturer prefers reshoring if and only if the demand prospect
is sufficiently high, namely the prior probability of a high demand γ is above a threshold γ∗. It
is interesting to note that besides cost parameters, a product’s demand characteristics may also
influence its manufacturer’s preference toward reshoring. Furthermore, not all cost parameters
directly affect the preference: the threshold γ∗ depends on c, s, and r, but not on m or e.
To understand these results, recall that in our model offshoring and reshoring mainly differ in
their operational flexibilities. In Case 1 of Proposition 4, it is never optimal for the manufacturer to
use any flexibility. As a result, the manufacturer is indifferent toward reshoring. In Case 2 with low
demand prospects (the demand type is more likely to be low), the manufacturer will only commit
to a limited initial production quantity, thus no downward flexibility will be needed. However, if
16
0
0.2
0.4
0.6
0.8
1
0 1 2 3 4
γ
Δ
𝛾"#(Δ) 3. Reshoring
1. Indifferent𝛾∗ = )*+
)*+*,
𝛾-.(Δ) 2. Offshoring
Figure 4 A manufacturer’s preferences (solid), relative to the offshoring and reshoring solution cases (dotted)
the demand type turns out to be high, the manufacturer may need to resort to upward flexibilities
(rush production when offshoring, expedited shipping when reshoring). Since rush production is
cheaper than expedited shipping (r < e), offshoring yields higher profits than reshoring. Case 3 is
similar: with high demand prospects, the manufacturer will make to a large initial component order,
and may resort to downward flexibilities if the demand type turns out to be low. Since discarding
finished goods is more expensive than discarding shipped components (c+m> c+ s), offshoring
yields lower profits than reshoring. In short, the manufacturer always prefers the production mode
with the cheaper needed flexibility. This explains why the threshold γ∗ separating Cases 2 and 3
only depends on the costs of the cheaper upward and downward flexibilities, r and c+ s. In fact,
the expression (c+ s)/(c+ s+ r) exactly captures the relative magnitudes of these costs.
For the case of m<s and r > e, the manufacturer’s preferences mirror those of Proposition 4:
Proposition 5. Suppose that m < s and r > e. Thresholds γ0d(∆) and γ1
u(∆) intersect at
(∆†, γ†), where ∆†.= z0d−z1u and γ†
.= c+m
c+m+e. The manufacturer’s preferences toward reshoring are:
1. ∆≤∆† and γ1u(∆)≤ γ ≤ γ0
d(∆): the manufacturer is indifferent toward reshoring.
2. ∆≤∆† and γ < γ1u(∆), or ∆>∆† and γ ≤ γ†: the manufacturer prefers reshoring.
3. ∆≤∆† and γ > γ0d(∆), or ∆>∆† and γ > γ†: the manufacturer prefers to remain offshoring.
Our analyses in this section reveal that when a manufacturer depends on offshore suppliers,
its preferences toward reshoring boil down to trade-offs between operational flexibilities. If both
upward and downward flexibilities are cheaper in one production mode than the other, then the
manufacturer prefers the mode with the cheaper flexibilities. On the other hand, if the upward
17
flexibility is cheaper in one mode and the downward flexibility is cheaper in the other, then the
demand prospect comes into play: when it is high, the manufacturer prefers the mode with the
cheaper downward flexibility, otherwise it prefers the one with the cheaper upward flexibility.
5. Operational strategies concerning reshoring
We have shown that limited onshore supply availability may hamper reshoring. In the long run,
onshore supply bases may gradually grow, relieving reshoring manufacturers’ dependence on off-
shore suppliers. However, this process is likely to be slow. In some industries, building and main-
taining production capability is extremely expensive: Taiwan Semiconductor Manufacturing Com-
pany (TSMC)’s annual capital expenditures regularly amount to US$10 billion (Patterson 2014).
In other industries, the onshore environments lack the required resources to sustain their oper-
ations: Foxconn’s facilities in Shenzhen, China alone hire half a million workers—more than the
population of Atlanta (Mack 2011). Additionally, onshore suppliers are unlikely to rapidly develop
before reshoring reach a critical mass, whereas large-scale reshoring is unlikely to take place before
onshore supply bases become full-fledged, creating a chicken-and-egg dilemma. Until this dilemma
is resolved, those manufacturers who depend on offshore suppliers will face operational trade-offs
between remaining offshore to be closer to their suppliers and reshoring to be closer to their mar-
kets. If we could identify an operational strategy which, despite a manufacturer’s dependence on
offshore suppliers, improves reshoring’s attractiveness, then it might accelerate reshoring and help
resolve this dilemma. We discuss one such strategy in the following subsection.
5.1. Common-component designs
We argue that a common-component design is an operational strategy that makes reshoring more
attractive. To elaborate this idea, we make the following modifications to the base model. Suppose
now the manufacturer makes two different products a and b for the onshore market, which require
separate manufacturing processes, but share a component sourced from an offshore supplier. For
simplicity, we assume that both products have the same cost, price, and demand parameters as in
the base model (i.e., symmetric products), but their demand types and realizations are independent.
Figure 5 illustrates the sequence of events with a common-component design.
This modification has different implications for the manufacturer under offshoring and reshoring.
Under offshoring, the two products are manufactured separately from the very beginning. As a
result, the optimal strategy is identical to that of the base model under offshoring, and the profit
equals twice of Π0m determined by (1)-(2). Therefore, the common-component design brings no
benefit under offshoring. When reshoring, however, the manufacturer initially sources components
18
Regularproduction
Finished goodsin stock
Demand update Demand realization
Finished goodsin shipping
Componentsourcing
Finished goodsin stock
Demand update Demand realization
Componentsin shipping
Offshoring:
Reshoring:
time
time
Product a Component for products a and bProduct b
Rush production/Hold back shipping
Expedite shipping/Hold back production
Productionin progress
Productionin progress
Figure 5 Sequence of events with a common-component design
for both products, and allocates the component inventory between them only after learning each
product’s demand type. The reshoring manufacturer’s problem formulation is
Π1c
.= max
x1c≥0{−(c+ s)x1
c + γ2Π1m(µH , µH , x
1c) + 2γ(1− γ)Π1
m(µH , µL, x1c) + (1− γ)2Π1
m(µL, µL, x1c)},
(8)
Π1m(µa, µb, x
1c).= max
x1a+x1b=x1c
{Π1(µa, x1a) + Π1(µb, x
1b)}, (9)
Π1(µi, x1i ).= max
y1i≥0{−(c+ s+ e)(y1i − x1
i )+−my1i + pE[min{D, y1i }|µi]}, i= a, b, (10)
where subscripts a and b represent the two products. By comparing (8)-(10) to (6)-(7), one can see
that (8) and (10) respectively resemble (6) and (7), whereas the additional equation (9) reflects
a reshoring manufacturer’s additional flexibility of allocating the components between the two
products after learning their demand types, which yields component-pooling benefits. Since a
common-component design generates component-pooling benefits only under reshoring, it improves
reshoring’s attractiveness. This result is formally stated in the following proposition.
Proposition 6. Suppose that the manufacturer prefers or is indifferent toward reshoring in the
base model. Then, when the manufacturer makes two products that share a common component, it
strictly prefers reshoring.
We illustrate Proposition 6 in Figure 6 with a numerical example evaluated on a 20× 20 grid
(with the same parameters as for Figure 2). One can clearly see that, compared with the base
model’s preferences (dotted), when the manufacturer makes two products that share a common
component, the new reshoring region contains the original reshoring and indifferent regions.
19
0
0.2
0.4
0.6
0.8
1
0 1 2 3 4
γ
Δ
Reshoring
Offshoring
Figure 6 A manufacturer’s preferences with a common-component design (solid), compared with the base
model (dotted)
The essence of the common-component design strategy is component inventory pooling. While
inventory pooling is widely studied and well understood, we make a contribution by recognizing that
a common-component design generates risk-pooling benefits only under reshoring, and proposing
it as a strategy to improve reshoring’s attractiveness. This discovery connects the classic inventory
pooling strategy and the emerging topic of reshoring.
5.2. Serving multiple markets
In the previous subsection we identified a common-component design as an operational strategy to
improve reshoring’s attractiveness. Its essence is component inventory pooling. Another common
pooling form is product inventory pooling, which can be achieved with serving multiple markets.
In this subsection, we show that this strategy actually reduces reshoring’s attractiveness.
We make the following modifications to the base model. Suppose now the manufacturer makes
the same product for two separate onshore markets A and B, which are distant from each other and
require separate shipping (e.g., the US and Europe). For simplicity, we assume that both markets
have the same cost, price, and demand parameters as in the base model (i.e., symmetric markets),
but their demand types and realizations are independent. Figure 7 illustrates the sequence of events
with multiple markets. When offshoring, the manufacturer makes the product in its offshore factory
before sending two shipments to both markets. When reshoring, we assume that the manufacturer
has one factory near each market, and ships sourced components to each factory to be processed
into finished goods for their respective markets. (Clearly, two onshore factories require substantially
20
more capital investments than a single offshore factory; however, we will follow the convention of
ignoring capital investments, and focus on the operational aspects of the two production modes.
Considering capital investments will put reshoring at a disadvantage.)
Regularproduction
Finished goodsin stock
Demand update Demand realization
Finished goodsin shipping
Componentsourcing
Finished goodsin stock
Demand update Demand realization
Componentsin shipping
Offshoring:
Reshoring:
time
time
Component/product for market AProduct for markets A and B
Component/product for market B
Rush production/Hold back shipping
Expedite shipping/Hold back production
Productionin progress
Productionin progress
Figure 7 Sequence of events with multiple markets
This modification has different implications for the manufacturer under offshoring and reshoring.
Under reshoring, the components for the two markets are sourced and shipped separately to their
respective onshore factories from the very beginning. As a result, the optimal strategy is identical
to that of the base model under reshoring, and the profit equals twice of Π1c determined by (6)-(7).
Therefore, serving multiple markets brings no benefit under reshoring. When offshoring, however,
the manufacturer initially makes products for both markets, and allocates the product inventory
between them only after learning each market’s demand type. The offshoring manufacturer’s prob-
lem formulation is
Π0m
.= max
x0m≥0{−(c+m)x0
m + γ2Π0s(µH , µH , x
0m) + 2γ(1− γ)Π0
s(µH , µL, x0m) + (1− γ)2Π0
s(µL, µL, x0m)},
(11)
Π0s(µA, µB, x
0m)
.= max
x0A+x0
B=x0m
{Π0(µA, x0A) + Π0(µB, x
0B)}, (12)
Π0(µi, x0i ).= max
y0i≥0{−(c+m+ r)(y0i − x0
i )+− sy0i + pE[min{D, y0i }|µi]}, i=A,B (13)
where subscripts A and B represent the two markets. By comparing (11)-(13) to (1)-(2), one can
see that (11) and (13) respectively resemble (1) and (2), whereas the additional equation (12)
reflects an offshoring manufacturer’s additional flexibility of allocating the products between the
21
two markets after learning their demand types, which yields product-pooling benefits. Since serving
multiple markets generates product-pooling benefits only under offshoring, it reduces reshoring’s
attractiveness. This result is formally stated in the following proposition.
Proposition 7. Suppose that the manufacturer prefers to remain offshoring or is indifferent
toward reshoring in the base model. Then, when the manufacturer sells its product in two markets,
it strictly prefers to remain offshoring.
We illustrate Proposition 7 in Figure 8 with a numerical example evaluated on a 20× 20 grid
(with the same parameters as for Figure 2). One can clearly see that, compared with the base
model’s preferences (dotted), when the manufacturer sells its product in two markets, the new
reshoring region is contained in the original reshoring region.
0
0.2
0.4
0.6
0.8
1
0 1 2 3 4
γ
Δ
Reshoring
Offshoring
Figure 8 A manufacturer’s preferences with multiple markets (solid), compared with in the base model (dotted)
5.3. Common-component designs and serving multiple markets
In the previous two subsections, we studied the impacts of common-component designs and serving
multiple markets on a manufacturers’ preferences toward reshoring, which go in opposite directions.
It is thus natural to ask whether these impacts would offset each other in a model with both a
common-component design and multiple markets.
To answer this question, we make the following modifications to the base model. Suppose now
the manufacturer makes two products a and b for each of two markets A and B. The two products
share a common component but require separate production processes, and the two markets are
22
distant from each other and require separate shipping. For simplicity, we assume that both products
in both markets have the same cost, price, and demand parameters as in the base model (i.e.,
symmetric products and markets), but their demand types and realizations are independent.
Regularproduction
Finished goodsin stock
Demand update Demand realization
Finished goodsin shipping
Componentsourcing
Finished goodsin stock
Demand update Demand realization
Componentsin shipping
Offshoring:
Reshoring:
time
time
Component/products for market AProducts a, b for markets A and B
Component/products for market B
Rush production/Hold back shipping
Expedite shipping/Hold back production
Productionin progress
Productionin progress
Figure 9 Sequence of events with a common-component design and multiple markets
Figure 9 illustrates the sequence of events with a common-component design and multiple mar-
kets. When offshoring, the manufacturer makes two separate products in its offshore factory before
sending two shipments (each containing both products) to the two markets. When reshoring, the
manufacturer sends out two separate shipments of sourced components to the two onshore factories,
which then make both products for their respective markets.
This modification has different implications for the manufacturer under offshoring and reshoring.
Under offshoring, the two products are made separately from the very beginning, but the manu-
facturer can allocate the product inventory between the two markets after learning each market’s
demand types. As a result, the offshoring profit equals twice of that with multiple markets, Π0m,
determined by (11)-(13). When reshoring, the components for the two markets are sourced and
shipped separately to their respective onshore factories from the very beginning, but each onshore
factory can allocate the component inventory between the two products after learning each prod-
uct’s demand type. As a result, the reshoring profit equals twice of that with a common-component
design, Π1c, determined by (8)-(10). Note that (8)-(10) and (11)-(13) are structurally identical, only
with m and s, r and e switched—similar to (1)-(2) and (6)-(7). This allows us to partially recover
Propositions 3-5 for the case with a common-component design and multiple markets.
23
Proposition 8. Suppose that the manufacturer makes two products that share a common com-
ponent for two markets.
1. When m<s and r < e, the manufacturer prefers to remain offshoring. When m>s and r > e,
the manufacturer prefers reshoring.
2. For any γ, the manufacturer is indifferent toward reshoring when ∆ is sufficiently small.
In Figure 10 we present a numerical example evaluated on a 20×20 grid (with the same param-
eters as for Figure 2). One can see that the manufacturer’s preferences with a common-component
design and multiple markets are structurally similar to those of the base model (dotted). This
confirms our intuition that component-pooling benefits due to a common-component design and
product-pooling benefits due to serving multiple markets can offset each other to some extent.
0
0.2
0.4
0.6
0.8
1
0 1 2 3 4
γ
Δ
Reshoring
Indifferent
Offshoring
Figure 10 A manufacturer’s preferences with a common-component design and multiple markets (solid),
compared with the base model (dotted)
6. Concluding remarks
In this paper, we introduce a framework for understanding currently-offshoring manufacturers’
preferences toward reshoring, accounting for their dependence on offshore suppliers due to lim-
ited onshore supply availability. For a manufacturer that depends on offshore suppliers, reshoring
reduces its distance to the market at the expense of increasing its distance to the suppliers. The
market and supply proximities under reshoring and offshoring respectively enable different oper-
ational flexibilities for the manufacturer to adjust the final inventory level upward or downward
when new demand information becomes available. We show that the manufacturer’s preferences
24
toward reshoring boil down to trade-offs between such flexibilities. When a production mode (off-
shoring or reshoring) has both the cheaper upward and downward flexibilities, the manufacturer
prefers this mode to the other. If the upward flexibility is cheaper in one mode and the downward
flexibility is cheaper in the other, then the demand prospect comes into play: when it is high, the
manufacturer prefers the mode with the cheaper downward flexibility, otherwise it prefers the one
with the cheaper upward flexibility.
The above findings confirm many practitioners’ intuition that offshoring manufacturers’ depen-
dence on local (offshore) suppliers leads to operational trade-offs regarding reshoring, and that even
under identical cost structures, reshoring does not always provide operational advantages. Our anal-
yses can help identify product and operational characteristics that may or may not favor reshoring
given limited onshore supply availability. For example, designer apparels, which are expensive to
make but relatively cheap to ship (in terms of both regular and rushed/expedited costs), may be
suitable for reshoring, whereas furniture, which is bulky and relatively expensive to ship, may not
be suitable for reshoring (the diagonal quadrants in Table 1). Another example is electronics. They
are typically more expensive to make than to ship by sea, but expedited shipping can cost much
more than rush production (The World Bank 2011 estimates air freight to cost 12 to 16 times as
much as ocean freight). For such a product, reshoring is more suitable if the manufacturer has high
confidence that the product will be a hit (the lower-left quadrant in Table 1). These results may
inform policy makers in determining which industries to target for promoting reshoring.
We then investigate operational strategies that may swing manufacturers’ preferences for
reshoring. We show that implementing a common-component design generates component-pooling
benefits only under reshoring, hence improves reshoring’s attractiveness. On the other hand, serv-
ing multiple markets generates product-pooling benefits only under offshoring, hence reduces
reshoring’s attractiveness. These two effects, when coexisting, can offset each other to some extent.
This implies that manufacturers can potentially utilize common-component designs to support
their reshoring initiatives before onshore supply bases become fully-fledged. In fact, this insight is
not limited to one firm; if multiple reshoring firms share a component, they can potentially source
the component via a third-party purchasing agent and share the inventory, which generates similar
component-pooling benefits and improves reshoring’s attractiveness for all firms. In general, lim-
ited onshore supply availability may hamper reshoring, which suggests that in the long run, policy
makers should focus on fostering onshore supply base developments in order to promote reshoring.
Our discrete-period model with a single information update implicitly assumes equal information
quality under offshoring and reshoring. If information is instead continuously updated, then the
information quality for decision-making under offshoring and reshoring may differ based on the
relative lengths of the production and shipping lead-times. In practice, shipping and production
25
can both take significant amounts of time: ocean freight between Asia and America takes up to a
month (Arnold 2009); Foxconn had to start mass-producing iPhone 6 two months before the selling
season (Culpan and Burrows 2014). Therefore, each of production and shipping may take longer
than the other. Recall that production takes place before shipping under offshoring, and shipping
takes place before production under reshoring. If shipping takes longer (shorter) than production,
then reshoring allows more (less) accurate information before the production (shipping) decision
than offshoring, which improves (reduces) reshoring’s attractiveness. Aside from this additional
effect, such a model will likely generate the same insights as ours.
In our model, we study a manufacturer’s problem of placing a factory either offshore or onshore.
Therefore, we consider offshoring and onshoring as exclusive production modes. One can also imag-
ine a hybrid mode wherein the manufacturer owns two factories—one offshore and one onshore—
between which it can freely allocate production. The hybrid mode obviously requires substantially
more capital investments than the exclusive modes. We follow the convention of ignoring capital
investments in the following discussion (considering capital investments will put the hybrid mode
at a disadvantage). In the hybrid mode, the manufacturer always has access to the cheaper upward
and downward flexibilities. For example, when m>s and r < e, the manufacturer can use onshore
factory for its downward flexibility (discarding shipped components) and the offshore factory for
its upward flexibility (rush production), thus the hybrid mode performs better than both exclusive
modes in general. However, as we have shown, the manufacturer will need at most one flexibility
for any specific product, thus can choose the exclusive mode that offers the cheaper needed flex-
ibility, which would match the performance of the hybrid mode. Therefore, although the hybrid
mode can always achieve the optimal performance for any products, it performs no better than
the manufacturer’s preferred exclusive mode for a specific product.
Our work presents a first step to understanding the impact of limited onshore supply avail-
ability on reshoring. To isolate the pure operational trade-offs regarding reshoring, we consider a
monopolistic, centralized decision-making setting, and obtain insights into first-best profit com-
parisons between offshoring and reshoring. The insights from studying this setting will serve as a
stepping stone for understanding the problem in more complex settings, such as one with compe-
tition, asymmetric information, and/or decentralized decision-making (outsourcing), all of which
are interesting extensions that merit future research investigation.
Appendix
Offshoring analysis (proof of Lemma 1)
By (5), it is straightforward to verify that γ0u(∆) is increasing in ∆, γ0
d(∆) is decreasing in ∆, γ0u = 0
for ∆≤ z−z0u, and γ0d = 1 for ∆≤ z0d−z. Therefore, γ0
u(∆) and γ0d(∆) intersect at most once. Since
when ∆0 = z0d − z0u, γ0u(∆0) = γ0
d(∆0) = γ0 = c+m
c+m+r, (∆0, γ0) characterizes the intersection.
26
Recall that ∆ ≤ ∆0 = z0d − z0u implies µH + σz0u ≤ µL + σz0d. From the analysis preceding the
lemma, when x0m ∈ [µH + σz0u, µL + σz0d], the manufacturer would do nothing after learning the
demand type. The problem thus becomes
maxx0m≥0
{−(c+m+ s)x0
m + pE[min{D,x0m}]}.
It is straightforward to show that the optimal solution solves the FOC:
γΦ((x−µH)/σ) + (1− γ)Φ((x−µL)/σ) = Φ(z).
Note that the left-hand-side of the FOC is increasing in x and decreasing in γ. Therefore, x ≥
µH +σz0u if and only if γ ≤ γ′, where γ′ is determined by
γ′Φ((µH +σz0u−µH)/σ) + (1− γ′)Φ((µH +σz0u−µL)/σ) = Φ(z)⇒ γ′ =Φ(z0u + ∆)−Φ(z)
Φ(z0u + ∆)−Φ(z0u)
which is the same as the threshold γ0u(∆) defined in (5).
Symmetrically, x≤ µL +σz0d if and only if γ ≤ γ′′, where γ′′ is determined by
γ′′Φ((µL +σz0d −µH)/σ) + (1− γ′′)Φ((µL +σz0d −µL)/σ) = Φ(z)⇒ γ′′ =Φ(z0d)−Φ(z)
Φ(z0d)−Φ(z0d −∆)
which is the same as the threshold γ0d(∆) defined in (5). Therefore, we conclude that µH + σz0u ≤
x0∗m ≤ µL +σz0d if and only if γ0
u(∆)≤ γ ≤ γ0d(∆). �
Offshoring profit (proof of Proposition 1)
We first introduce a relation that follows straightforward integration by parts. Recall that Φ and φ
denote the standard normal cumulative distribution and probability density functions, respectively.
Suppose ξ follows a normal distribution with mean µ and standard deviation σ. Then∫ x
−∞
ξ√2πσ
e− (ξ−µ)2
2σ2 dξ = µΦ
(x−µσ
)−σφ
(x−µσ
). (14)
Case I: ∆≤∆0 and γ0u ≤ γ ≤ γ0
d . Due to Lemma 1, we know that the manufacturer would do
nothing after learning the demand type. Therefore, the optimal solution is x0∗m = x0∗
sH = x0∗sL = x,
where x uniquely solves γΦ((x − µH)/σ) + (1 − γ)Φ((x − µL)/σ) = Φ(z). The optimal profit is
−(c+m+ s)x0∗m + pE[min{D,x0∗
m}].
Case II: ∆≤∆0 and γ < γ0u, or ∆>∆0 and γ ≤ γ0. First consider the subcase of ∆≤∆0 and
γ < γ0u. Due to Lemma 1, we know that x0∗
m < µH + σz0u ≤ µL + σz0d, hence it is optimal for the
manufacturer to adjust the final inventory level upward to µH +σz0u if the demand type turns out
to be high and do nothing if it turns out to be low. The problem becomes the following:
Π0m
.= max
x0m≥0{−(c+m)x0
m + γΠ0s(µH , x
0m) + (1− γ)Π0
s(µL, x0m)}, (15)
27
where Π0s(µH , x
0m) =−(c+m+ r)(x0∗
sH − x0m)− sx0∗
sH + pE[min{D,x0∗sH}|µH ] with x0∗
sH = µH + σz0u,
Π0s(µL, x
0m) =−sx0
m+pE[min{D,x0m}|µL]. It is straightforward to show that the optimal x0∗
m solves
the following FOC:
Φ((x0m−µL)/σ)) =
γr+ (1− γ)(p− c−m− s)(1− γ)p
.
Therefore, x0∗m = µL +σz0mu. Plugging x0∗
m into Π0m and utilizing (14), we obtain the optimal profit
Π0m = pσγΦ(z0u)∆− pσ[γφ(z0u) + (1− γ)φ(z0mu)] + pΦ(z)µL. (16)
Now consider the subcase of ∆>∆0 and γ ≤ γ0. Recall that ∆>∆0 implies µL+σz0d <µH +σz0u,
hence if x0m ≤ µL +σz0d, then the manufacturer would adjust the final inventory level upward if the
demand type turns out to be high and do nothing if it turns out to be low. The problem becomes
the same as (15), and it follows that x0∗m = µL + σz0mu. It is straightforward to verify that z0mu
increases from z to z0d as γ increases from 0 to γ0 = c+mc+m+r
. Therefore, when ∆>∆0 and γ ≤ γ0,
the optimal solution is x0∗m = µL +σz0mu and the optimal profit is given by (16).
Case III: ∆≤∆0 and γ > γ0d , or ∆>∆0 and γ > γ0. Following an argument symmetric to Case
II, we can show that in this case, it is optimal for the manufacturer to adjust the final inventory
level downward to µL +σz0d if the demand type turns out to be low and do nothing if it turns out
to be high. The problem becomes the following:
Π0m
.= max
x0m≥0{−(c+m)x0
m + γΠ0s(µH , x
0m) + (1− γ)Π0
s(µL, x0m)},
where Π0s(µH , x
0m) =−sx0∗
m + pE[min{D,x0∗m}|µH ]], and Π0
s(µL, x0m) =−sx0∗
sL + pE[min{D,x0∗sL}|µL]
with x0∗sL = µL +σz0d. Similar to Case II, the optimal x0∗
m solves the following FOC:
Φ((x0m−µH)/σ)) =
γ(p− s)− c−mγp
.
Therefore, the optimal solution is x0∗m = x0∗
sH = µH + σz0md, x0∗sL = µL + σz0d. Plugging x0∗
m into Π0m
and utilizing (14), we obtain the optimal profit
Π0m = pσγΦ(z0md)∆− pσ[γφ(z0md) + (1− γ)φ(z0d)] + pΦ(z)µL.
�
Reshoring analysis (proof of Lemma 2)
The proof mirrors that of Lemma 1, only with m and s, r and e switched, thus is omitted. �
Reshoring analysis (proof of Proposition 2)
The proof mirrors that of Proposition 1, only with m and s, r and e switched, thus is omitted. �
28
Profit comparison: the dominance case (proof of Proposition 3)
From Propositions 1 and 2, we know that after learning the demand type, there are three possible
decision cases for the manufacturer: I) do nothing regardless of the demand type; II) adjust the
final inventory level upward if the demand type turns out to be high and do nothing if it turns out
to be low; and III) adjust the final inventory level downward if the demand type turns out to be
low and do nothing if it turns out to be high. Suppose that m<s and r < e.
In reshoring’s Case I, since the manufacturer does not use any flexibility, the final inventory costs
c+m+ s per unit. An offshoring manufacturer can achieve the same cost by making the same
initial quantity and not using any flexibility. Therefore, offshoring can do no worse than reshoring.
In reshoring’s Case II, when the manufacturer adjusts the final inventory level upward, the initial
production costs c+m+ s per unit and the additional production costs c+m+ s+ e per unit. In
comparison, it would cost an offshoring manufacturer c+m+ s per unit for the initial production
and c+m+ s+ r per unit for the additional production. Since r < e, offshoring incurs lower costs
than reshoring for the same decision quantities, thus generating higher profits.
In reshoring’s Case III, when the manufacturer adjusts the final inventory level downward, the
shipped inventory costs c+m+ s per unit and the discarded inventory costs c+ s per unit. In
comparison, it would cost an offshoring manufacturer c+m+ s per unit for the shipped inventory
and c+m per unit for the discarded inventory. Since m < s, offshoring incurs lower costs than
reshoring for the same decision quantities, thus generating higher profits. Therefore, we conclude
that in general the manufacturer prefers to remain offshoring when m<s and r < e.
The proof for the case of m>s and r > e is similar and omitted. �
Profit comparison: the conditional case (proof of Proposition 4)
Recall that m>s and r < e. Due to Lemmas 1 and 2, we know that γ0u(∆) is increasing in ∆ and
γ1d(∆) is decreasing in ∆. It is easy to verify that γ0
u(∆) and γ1d(∆) intersect at (∆∗, γ∗), where
∆∗ = z1d − z0u and γ∗ = c+sc+s+r
. It is also easy to verify that γ0u(∆) > γ1
u(∆), γ0d(∆) > γ1
d(∆), ∆∗ <
min{∆0,∆1}, and γ1 <γ∗ <γ0, hence when ∆≤∆∗, µH +σz1u ≤ µH +σz0u ≤ µL +σz1d ≤ µL +σz0d.
We first consider Case 1: ∆≤∆∗ and γ0u(∆)≤ γ ≤ γ1
d(∆). Due to Lemmas 1 and 2, we know that
µH + σz0u ≤ x0∗m ≤ µL + σz1d and µH + σz0u ≤ x1∗
c ≤ µL + σz1d. In this case, the manufacturer would
do nothing after learning the demand type, and the offshoring and reshoring problems become
identical, hence x0∗m = x1∗
c and the manufacturer is indifferent toward reshoring.
We next consider Case 2: ∆ ≤ ∆∗ and γ < γ0u(∆), or ∆ > ∆∗ and γ ≤ γ∗. The first subcase
is ∆ ≤ ∆∗ and γ < γ0u(∆), which has two possible scenarios: 1) γ1
u(∆) < γ < γ0u(∆), and 2) γ ≤
γ1u(∆). In Scenario 1), due to Propositions 1 and 2, we know that if the demand type turns
out to be high, an offshoring manufacturer would adjust the final inventory level upward, but
29
a reshoring manufacturer would do nothing. Because an offshoring manufacturer always has the
option of doing nothing, offshoring must yield higher profits than reshoring. In Scenario 2), due to
Propositions 1 and 2, we know that if the demand type turns out to be high, both an offshoring
and a reshoring manufacturer would adjust the final inventory level upward, which incurs r per
unit under offshoring and e per unit under reshoring. Since r < e, offshoring yields higher profits
than reshoring. Therefore, the manufacturer prefers to remain offshoring in this subcase.
The second subcase is ∆>∆∗ and γ ≤ γ∗. Recall that γ1 < γ∗ < γ0. Due to Proposition 1, we
know that an offshoring manufacturer would adjust the final inventory level upward if the demand
type turns out to be high. On the other hand, due to Proposition 2, a reshoring manufacturer may
do nothing, adjust the final inventory level upward if the demand type turns out to be high, or
adjust the final inventory level downward if it turns out to be low. When a reshoring manufacturer
does nothing or adjusts the final inventory level upward if the demand type turns out to be high,
offshoring yields higher profits than reshoring following similar arguments as the first subcase.
It remains to compare offshoring and reshoring profits with ∆>∆∗ and max{γ1d(∆), γ1}<γ < γ∗,
when an offshoring manufacturer adjusts production upward if the demand type turns out to be
high, and a reshoring manufacturer adjusts the component inventory level downward if the demand
type turns out to be low. Due to Propositions 1 and 2, the optimal offshoring profit is given by
Π0m = pσγΦ(z0u)∆− pσ[γφ(z0u) + (1− γ)φ(z0mu)] + pΦ(z)µL
= (p− c−m− s− r)γσ∆− pσ[γφ(z0u) + (1− γ)φ(z0mu)] + pΦ(z)µL,
whereas the optimal reshoring profit is given by
Π1c = pσγΦ(z1cd)∆− pσ[γφ(z1cd) + (1− γ)φ(z1d)] + pΦ(z)µL
= [(p−m)γ− (c+ s)]σ∆− pσ[γφ(z1cd) + (1− γ)φ(z1d)] + pΦ(z)µL.
The difference between the two expressions is
Π0m−Π1
c = σ[(c+ s)− γ(c+ s+ r)]∆− pσ[γφ(z0u) + (1− γ)φ(z0mu)− γφ(z1cd)− (1− γ)φ(z1d)].
Note that for any γ′ ∈ (max{γ1d(∆), γ1}, γ∗) where γ∗ = (c+ s)/(c+ s+ r), the above expression is
strictly increasing in ∆. Define ∆ as the solution to γ1d(∆) = γ′. We know that at the point (∆, γ′),
the reshoring manufacturer would do nothing regardless of the revealed demand type. Therefore,
offshoring yields higher profits than reshoring. It then follows that offshoring yields strictly higher
profits than reshoring for all ∆> ∆ and γ′ ∈ (max{γ1d(∆), γ1}, γ∗). Combining the above cases, we
conclude that the manufacturer prefers to remain offshoring in Case 2.
This leaves Case 3: ∆≤∆∗ and γ > γ1d(∆), or ∆>∆∗ and γ > γ∗. The proof is similar to Case
2 and omitted. �
30
Profit comparison: the conditional case (proof of Proposition 5)
The proof mirrors that of Proposition 4 and is omitted. �
Common-component designs (proof of Proposition 6)
It is straightforward to see that under reshoring, if the manufacturer sets x1a = x1
b = x1c/2 instead
of allocating the component inventory optimally as in (9), then Π1c = 2Π1
c. Also recall that the
offshoring profit with a common-component design is 2Π0m. Therefore, under this allocation the
manufacturer’s preference toward reshoring would be identical to that in the base model. However,
such an allocation is strictly suboptimal, because in the event that one demand type is high and
the other is low, this allocation does not satisfy the well-known equal-fractile optimality condition.
Thus we know under the optimal allocation, the manufacturer will strictly prefer reshoring where
it prefers or is indifferent toward reshoring in the base model. �
Serving multiple markets (proof of Proposition 7)
The proof mirrors that of Proposition 6 and is omitted. �
Common-component designs and serving multiple markets (proof of Proposition 8)
For Part 1, note that (8)-(10) and (11)-(13) are structurally identical, only with m and s, r and e
switched, hence the conclusion follows from the same argument as for Proposition 3.
For Part 2, consider (8)-(10). First, it is straightforward to see that the optimal allocation of
the component inventory, x1c, after the two products’ mean demands µa and µb are revealed, is
x1∗a = (x1
c+µa−µb)/2 and x1∗b = (x1
c+µb−µa)/2. (This allocation satisfies x1∗a −µa = x1∗
b −µb, which
ensures equal fractiles for the two products.) Also note that when ∆→ 0, the high and low demand
types become indistinguishable, thus the manufacturer never uses any flexibility. By continuity,
we know that for any γ, the manufacturer never uses any flexibility when ∆ is sufficiently small,
namely y1i = x1∗i , i= a, b. In this case, (8)-(10) can be simplified as
Π1c
.= max
x1c≥0{−(c+m+ s)x1
c + γ2Π1m(µH , µH , x
1c) + 2γ(1− γ)Π1
m(µH , µL, x1c) + (1− γ)2Π1
m(µL, µL, x1c)},
Π1m(µa, µb, x
1c).= pE[min{D, (x1
c +µa−µb)/2}|µ= µa] + pE[min{D, (x1c +µb−µa)/2}|µ= µb].
Similarly, for a sufficiently small ∆, (11)-(13) can be simplified as
Π0m
.= max
x0m≥0{−(c+m+ s)x0
m + γ2Π0s(µH , µH , x
0m) + 2γ(1− γ)Π0
s(µH , µL, x0m) + (1− γ)2Π0
s(µL, µL, x0m)},
Π0s(µA, µB, x
0m)
.= pE[min{D, (x0
m +µA−µB)/2}|µ= µA] + pE[min{D, (x0m +µB −µA)/2}|µ= µB].
These formulations are identical. Therefore, we conclude that for a sufficiently small ∆, thus the
manufacturer is indifferent toward reshoring. �
31
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