MANAGEMENT SCIENCE
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Vol. 57, No. 5, May 2011, pp. 897–914
issn 0025-1909 eissn 1526-5501 11 5705 0897
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doi 10.1287/mnsc.1110.1326
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Retail Channel Structure Impact on Strategic
Engineering Product Design
Nathan Williams
Shell Energy North America, Spokane, Washington 99201, nathan.n.williams@shell.com
P. K. Kannan
Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742,
pkannan@rhsmith.umd.edu
Shapour Azarm
Department of Mechanical Engineering, University of Maryland, College Park, Maryland 20742,
azarm@umd.edu
W
e examine, in a strategic setting, the broad issue of how retail channel structures—retail monopoly versus
retail duopoly—impact a manufacturer’s optimal new product design, both in terms of engineering design
specifications as well as manufacturer and retailer profits. Our strategic framework enables manufacturers in
specific contexts to anticipate the reactions of the retailers and competitive manufacturers to new designs in
terms of the retail and wholesale pricing and to understand how different channel structures and channel
strategies (such as an exclusive channel strategy) impact the engineering design of the new product, conditional
on consumer preference distributions and competitor product attributes. Based on a simple numerical and a
power tool design example, we illustrate how the insight from the framework translates to design guidelines;
specifically, understanding which designs are optimal under differing channel structure conditions, and which
design variables need precise targeting given their profit sensitivity.
Key words: new product design; engineering design; research and development; retail channels; marketing;
game theory; genetic algorithms; latent class models; structural models
History: Received September 30, 2007; accepted December 22, 2010, by Christoph Loch, R&D and product
development. Published online in Articles in Advance April 1, 2011.
1.
Introduction
methodologies that consider cross-disciplinary impact
and synergies (Morgan et al. 2001). In the engineering
design literature, the methodologies developed have
been improvements in engineering design aspects and
assume that the manufacturer or producer interacts
directly with the consumer in the marketplace (e.g.,
Li and Azarm 2000, Wassenaar and Chen 2003, Luo
et al. 2005, Besharati et al. 2006, Luo 2011). These
approaches rely on the estimation of customer utility for high-level product attributes that are the result
of engineering design decisions. These high-level
product attributes are translated into market share
and profit with implications focusing on competitive
draw or market expansion using a discrete choice
model and generally a cost model (e.g., Ramdas and
Sawhney 2001).
With the emerging dominance and clout of retailers (Cappo 2003), recent approaches have started
examining and accounting for the impact of retail
channels in manufacturers’ new product design
in addition to customer preferences. For example,
from an analytical viewpoint, a number of game
theoretic frameworks have been developed to understand strategic interactions in retail environments
The product development process has been defined
as the transformation of a market opportunity into a
product available for sale and involving disciplines
of marketing, operations management, organizational
management, and engineering design with each discipline focusing on critical decisions (Krishnan and
Ulrich 2001). These critical product decisions are ultimately realized as product attributes and features
that are important to the market. The realization that
the decision for many of these attributes and features is made early in the design stage and cannot
be changed significantly later in product development to enhance the marketability of the product
or its economic success has led to cross-disciplinary
approaches in many of these related fields (Ulrich
and Eppinger 2004). To that end, many approaches
have been developed in extant literature to collect and
integrate customer preferences in the early stages of
design, aiming to provide the manufacturer flexibility
in designing products that are market focused. Some
of these approaches focus on the information sharing and coordination aspects across disciplines (e.g.,
Terwiesch et al. 2002); others propose specific design
897
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898
Williams, Kannan, and Azarm: Retail Channel Structure Impact on Strategic Engineering Product Design
(monopolies—Dewan et al. 2003; duopolies—Savin
and Terwiesch 2005, Klastorin and Tsai 2004). From
an empirical viewpoint, Luo et al. (2007) examined
the issue of how manufacturers can design new products strategically in a specific retail monopoly setting in order to maximize manufacturer profits, and
Shiau and Michalek (2009) examined vertical Nash
structures with symmetric competition at the retail
and manufacturer level. Williams et al. (2008) analyzed how manufacturers can increase the acceptance
of their new products by a retail monopolist through
a mix of design options and slotting allowances.
In this paper, we examine, in a strategic setting,
the important issue of how retail channel structures itself (retail monopoly versus retail duopoly)
and the specific channel strategy (e.g., exclusive or
nonexclusive) impact of a manufacturer’s optimal
new product design, both in terms of engineering
design specifications as well as manufacturer and
retailer profits. Specifically, we propose a framework
that enables manufacturers, in specific institutional
settings and under certain market assumptions, to
anticipate the reactions of the retailers and competitive manufacturers to new designs in terms of the
retail and wholesale pricing under different structures and channel strategies. In such a setting, the
framework allows the manufacturer to determine the
quality (the specific attributes) of the new product
that the retailer(s) would prefer most to carry in
their assortment given the specific distribution of consumer preferences and competitive product attributes.
Based on numerical examples, including an application to power tool design, we illustrate how the
insight from the framework—the increased competition in duopoly/oligopoly retail settings allows manufacturers to target the design preferences of the more
price sensitive consumer segments more profitably as
compared to the retail monopoly case—translates to
specific design guidelines. Collectively, our paper provides important insights and testable hypotheses into
how the downstream channel structure and the exclusive/nonexclusive strategy followed by a manufacturer will shape the optimal design characteristics of a
new product as well as manufacturer/retailer profits,
as the following overview highlights.
In evaluating a new product to carry in their assortment, retailers typically use revenue per square foot
or revenue per shelf space as metrics (in addition
to customer preferences). The inclusion of the new
product has to lead to an increase in overall category
profit. For example, Home Depot will only carry in
their assortment the 5 out of 20 available drills that
generate the greatest revenue for the drill category.
Additionally, in the presence of retail competition,
this assortment composition is particularly important
in maximizing the retailer’s market share vis-à-vis
Management Science 57(5), pp. 897–914, © 2011 INFORMS
their competition. Given these considerations and the
fact that the retailers’ shelf space is limited, manufacturers have to strategically consider the attributes
and features of their product vis-à-vis the assortment
(i.e., competing manufacturers’ product features and
attributes) the retailers carry. This must all be done at
the early design stage so as to maximize the chances
of the new product being carried by one or more
retailers and being successful in the market.
In considering the powerful retailers and their
interactions, and the competitive manufacturers’
products and their designs, a manufacturer cannot
afford to take a “myopic” perspective in the design
decisions by considering only its technical design and
its impact on the customers. Given that engineering
design decisions determine product cost and attribute
positioning at the foundation of the development process, it is logical to conclude that engineering design
decisions of a manufacturer are transmitted to competitors and retailers as strategies to which they are
forced to counteract. For example, just as a manufacturer considers retailers’ assortment, profit criteria,
and competitors’ existing products in designing a new
product, other competitors may anticipate this strategy and make their own move to influence the retailers. They might, for example, reduce their wholesale
prices to the retailers to make the retailer margins
more attractive. Retailers, on the other hand, may
also consider such dynamics in new product offerings
and wholesale prices in making their own assortment decisions considering the extent of retail competition. Thus, “games” in the marketplace calls for the
manufacturers to be “strategic” in their design decisions. This is a key assumption of our approach that
has considerable support in practice (Montgomery
et al. 2005).
Using the strategic approach proposed in this
paper, a designer would be able to develop a scenario that if a product is designed with engineering design variables x, which result in customer level
product attributes y, then an equilibrium price vector P would appear in the retail environment as a
result of strategic interactions by competing retailers
and manufacturers. The retail price vector P determines the market shares m and manufacturer profitability of the design that the manufacturer wants
to maximize. Thus, using our approach, a manufacturer is able to determine the optimal new product
design under different structures characterizing the
retail channel, specifically, how the optimal engineering design variables x and manufacturer and retailer
profits vary under different channel structure scenarios. Additionally, in the case of retail duopoly structures, our strategic approach enables consideration of
the specific channel strategy early in the design process to determine the optimal new product design. It
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is important to note that our approach is appropriate in specific institutional settings where the model
assumptions we make are valid. In market settings
that are different, our framework can be used as a
heuristic to develop hypotheses that could be tested
using other approaches.
With respect to the extant literature in the product development area, our area of focus—downstream
channel structure, strategies, and product design
decisions—has seen limited research (see Krishnan
and Ulrich 2001, Krishnan and Loch 2005). Whereas
early work has focused on upstream channels in
explaining product variety in the market as a function of supply chain structure (Randall and Ulrich
2001) and in exploring the impact of supply chain
configuration/coordination in a manufacturer–retailer
competitive setting on product design/product variety in the market (Subramanian and Kapuscinski
2002, Subramanian et al. 2008), only recently have
Luo et al. (2007), Williams et al. (2008), and Shiau
and Michalek (2009) incorporated downstream channel considerations in an empirical setting to determine optimal product designs. However, these papers
consider the design issue either only in the retail
monopoly setting or in an analytical setting. Our
approach focuses on understanding (in addition to
customer-facing attributes and price) the variations
in engineering design attributes and, more importantly, physical feasibility along with related costs
within multiple channel structures (retail monopoly
versus retail duopoly—identical versus differentiated)
and channel cooperation strategies (exclusive versus
nonexclusive strategies). In addition, our approach
provides insights into which design variables need
to be right on target and where variations in design
variables can be tolerated under different channel
structures.
In §2, we provide an overview of our proposed
framework along with model assumptions and justifications. In §3, we discuss the numerical examples and
analysis that provide an illustration of our methodology, along with the different cases and results.
We highlight the managerial implications and the
challenges in generalizing the approach in §4. We
conclude in §5 by presenting the contributions, limitations, and extensions of our approach and directions
for future work.
2.
Market Structure and Proposed
Framework
The product market that we consider is one characterized by manufacturers reaching out to customers
indirectly through a retail channel consisting of powerful retailers (monopoly or duopoly). The manufacturers differentiate themselves with strong brands
in a mature market and compete with other man-
Figure 1
Strategic Design Framework
Marketing
design
considerations
Customer
product
celection
mi
P1i
P2i
P2i
P1i
WA
x?
WB
WC
WD
Strategic
design
considerations
Retailer
competition
Manufacturer
competition
Engineering
design
considerations
ufacturers for retail shelf space. When they introduce new products, they set wholesale prices for the
retailers who choose to either carry the product or
not carry the product. Retailers set their own retail
prices, which along with the wholesale price is taken
into account for the carry or carry-not decision. This
product market is characteristic of many consumer
durables that are engineered and marketed to customers through retailers (e.g., power tools, household
appliances, electronics, etc.). The multilevel strategic
design framework is shown in Figure 1 for a retailer
duopoly (which can be simplified to the monopoly
channel by removing one retailer) with four manufacturers and four consumer segments. Table 1 provides
the nomenclature we use in presenting the multitiered
design framework.
The product design problem will be analyzed from
the perspective of the manufacturer firm (i.e., the perspective of a product designer in the firm—bottom
level in Figure 1) who is interested in maximizing
profit and can be described as follows. In a competitive market of i products, manufacturer A (the
focal manufacturer) designs a candidate product with
engineering design variables x that must take into
account the strategic response of other manufacturers. We assume that the other manufacturers B, C,
and D have only the strategic move of altering their
wholesale prices WB , WC , and WD , respectively. This
is a standard assumption (Luo et al. 2007) because
other responses in attributes are difficult to achieve
in the short term. For manufacturers to set wholesale
prices, they must know the effect on market share,
which can only be determined after retailers set their
retail prices (e.g., Pri = P1i P2i PRi ; middle level in
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Table 1
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Nomenclature
Engineering design variables (e.g., current, diameter,
length, etc.)
Customer level product attributesa (e.g., weight,
amp rating, etc.)
Intermediate level variables (attributes not evaluated
by customers)
Engineering constraints
Product index for an n product assortment
Production cost of the ith product ($)a
Wholesale price ($) of the ith producta
Utility for attribute j of product i in segment k a
Total product utility for a product i in segment k a
Market share of the ith product in segment k (%)a
Market segment size (%)
Overall market share of the ith product (%)a
Market size (units)
Retailer index for R retailers
Retail price ($) of the ith producta of retailer r
Profit of Retailer r on product i ($)a
Profit of manufacturer on product i ($)
a
x
y
z
gx ≤ b
i = 1 n
Ci
Wi
uijk
Uik
mik
Sk
mi
N
r = 1 R
Pr i
r i
i
Functions of engineering design variables.
Figure 1). We assume that both retailers and manufacturers are fully informed about customer preferences
(top level in Figure 1), which is a valid assumption
in mature markets (e.g., Villas-Boas and Zhao 2005).
The market provides feedback to the retailers’ actions
in the form of product market shares mi . The retailers choose their retail prices to maximize profits in
the monopoly case or to reach price equilibrium in
the duopoly/oligopoly case. Considering the retail
price responses at the retail level, manufacturers can
determine equilibrium wholesale prices. Given price
equilibrium at the two levels (manufacturer and retail
levels) the manufacturer is able to determine the efficacy of any candidate design x. The focal manufacturer can, thus, perform a strategic scenario analysis
with retail profits and manufacturer profits as outcomes given any design candidate. Thus, the framework provides a rich and realistic environment for
evaluating engineering design decisions because it
accounts for the power of retailers and the strategic
responses available to competitors. We expand on the
links between engineering design, strategy, and marketing in the next subsection.
2.1. From Product Design to Market Share
Before discussing the strategic interactions in any
design evaluation, we present the mapping process for turning engineering design x into product
attributes y, which are then used to determine market share mi . This process is depicted in Figure 2
for a hand-held power tool—a right angle drill. We
assume that market information—competition and
their offerings—is already available, through shelf
surveys of assortments at the retailers. In the short
term, we assume that the product attributes (except
price) of competitor products, y, are fixed (e.g., the
weight of competitor product). Each power tool’s
attributes in the assortment are recorded as the existing competitor’s product attributes that are critical to
the positioning of any new design. Customer preference data can be in the form of survey data or choicebased conjoint data. The customer preference data is
analyzed using finite mixture estimation techniques
to identify distinct latent class segments to capture
the heterogeneity in customer preferences.1 This latent
class conjoint estimation along with the shelf survey allows our design approach to search for gaps
in the competitive landscape that are weak in terms
of competitive offerings as well as find customer segments whose preferences are currently underserved.
The integration of this information with a bottom-up
translation of engineering designs into customer relevant product attributes is presented in Figure 2.
The design process starts with an instance of design
variables x, which are then transformed to product
attributes y for the focal manufacturer through appropriate engineering computations and determination
of intermediate variables z, which are frequently used
for constraints (see the bottom two blocks in Figure 2). For example, the weight of product is calculated from the density and volume of its constituent
components. Similarly, power and torque of a product will be functions of gear ratios, current, and voltage. Engineering constraints such as gear stresses,
heat flux, armature velocity, and others are calculated
at this point to determine if the candidate design
is feasible before proceeding to market share estimate determination. Design variables and engineering functions (constraints or attribute functions) need
not be continuous as we will employ a genetic algorithm (Deb 2001) to find optimal designs. It is worth
noting that the marketing and engineering should
collaborate (e.g., Morgan et al. 2001) to determine
which product attributes and intermediate variables
are most relevant in investigating for optimization
(i.e., they must matter to customers or affect the production cost). For example, marketing communicates
to engineering that weight is one of the important
evaluation criteria to customers and should be an output of the design model. Similarly, if engineering and
manufacturing have determined that revolutions per
minute (RPM) is an important driver of cost in the
past because of higher stresses and heat dissipation
requirements, it should be communicated to marketing for inclusion in the conjoint study in an appropriate way. Even if customers place little value on
such attributes, this knowledge will be important to
the overall design optimization because designers can
1
Alternatively, a hierarchical Bayes conjoint estimation can also be
used to capture heterogeneity.
Williams, Kannan, and Azarm: Retail Channel Structure Impact on Strategic Engineering Product Design
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Figure 2
Product Design to Market Share
Compute market share (latent class model)
Uik =
j∈i
ujk
expUik i=1 expUik + Unc
mik = n
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Conjoint
analysis
mi =
Sk × mik
k
u ijk
Customer
preferences (u jk )
Table 3
Shelf survey
Competitor
attributes
Table 2
Convert to attribute utilities
uijk = f (y)
y
Engineering computations
Total mass (kg)
Attribute computations y = f (x)
Mt = Ma + Ms + Other components
Girth G (m)
G = 2(Ro + 0.004 (m))
Amp rating (amps)
I (amps)
Intermediate computations z = f (x)
Armature diameter lr (m) lr = 2(Ro – t – lgap )
Stator mass Ms = ((Ro)2 – (Ro – t)2) · L · s
2
Armature mass Ma = ( · lr )/(4 · L · s )
Density of steel s
Constraint computations g(x) ≤ b
Armature and stator wire turns Nc , Ns integers
Length to diameter ratio
L / Ro ≤ 5
Nc · I
Armature heat flux Ks (A/m) Ks = · l ≤ 10,000
r
Ns · I
K
=
Stator heat flux Ks (A/m)
s (l + t) ≤ 10,000
r
x
Right angle grinder design variables (x)
Current I (amps)
Armature turns Nc (no. of turns)
Stator turns Ns (no. of turns)
Stator outer radius R o (m)
Pinion pitch diameter Dp (m)
Switch type
Gear ratio r
Stator thickness t (m)
Stack length L (m)
Air gap lgap (m)
Tool body
Stator
relax preconceived notions for minimum RPM values
(a constraint) and possibly reduce production costs
without affecting overall product performance and
utility. Thus, an early concurrent consideration of all
the relevant criteria (engineering design and customer
preference) by the product development team gives a
significant advantage in avoiding the costly mistake
of performing customer studies that do not contain
all of the relevant attributes that are cost or performance drivers in the engineering model (see Loch and
Terwiesch 1998).
Once product attribute variables y are determined
from intermediate engineering design computations,
one can estimate the utility of each attribute y (with
a piecewise interpolation of utility values assigned to
attribute levels, if needed) based on the conjoint analysis estimates for each segment by summing the util-
Rotor
Bevel gears
ities ujk , in segment k, of all attributes j that appear
in product i resulting in
Uik =
j∈i
ujk (1)
The same procedure is repeated for each of the n competing products in the assortment. We estimate the
segment share of product i in segment k as follows
while taking into account the utility of the no-choice
(or no-purchase) option Unc :
expUik expU
ik + Unc
i=1
mik = n
(2)
The total market share (%) of a given product i is computed by summing over the segment size Sk (%) estimated using the finite mixture estimation techniques
Williams, Kannan, and Azarm: Retail Channel Structure Impact on Strategic Engineering Product Design
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(Sawtooth 2001) and the market share within each
segment mik (%) as
mi = Sk mik (3)
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k
Given that retailers have increasingly consolidated
power and control of the retail channel (i.e., access
to consumers), evaluating the manufacturer’s design
in the context of the effect it has on retailer profit is
an important consideration, even with our primary
objective being to maximize the manufacturer profit.
Clearly, if the manufacturer is concerned with a possibility of being denied shelf space by the retailer, he or
she would prefer to select a design that is much more
profitable for the retailer than the existing assortment it carries. At the same time the manufacturer’s
profit and the retailer’s profit are competing objectives so a manufacturer would benefit from being able
to choose from an optimal set of designs with respect
to each of these objectives. The formulation presented
in this paper is such an approach to setting the manufacturer’s design strategy given a specific channel
structure. As such, in addition to maximizing manufacturer profit we add a constraint to our formulation where the manufacturer also wishes to increase
retailer profitability so as to ensure market access.
Thus, the focal manufacturer’s objective (when facing
a monopolist retailer) can be stated as
max i = mi Wi − Ci xi
s.t. gx ≤ b
n
i=1
inew ≥
n
i=1
iold
(4)
Ci mi = f x
The focal manufacturer’s profit i is maximized by
altering engineering design variables x so as to satisfy
engineering constraints gx ≤ b, realizing that market
share mi is largely a function of y and therefore x.
In addition to focusing on optimizing retailer profit
we constrain the design search space to only those
designs that improve the retailer’s category profit (i.e.,
inew ≥ iold .2 Production costs Ci can be modeled
as a function of the engineering design variables x
or can be estimated from product attributes y, like
those shown in Figure 2 (U.S. Department of Defense
1999, Scanlan 2002). We have chosen to use the latter
approach in our application, which is based on historical prices of products in a category. The formulation
presented above may appear simple until one consid2
This channel profit constraint is deterministic although a stochastic
or “chance constrained” approach has been developed for a singleobjective model in previous work (Williams et al. 2008). However,
they do not consider this in a strategic context, that is, they do not
consider the (re-) actions of other manufacturers or retailers.
ers that market share is also significantly dependent
on retail price Pi (as shown in Figure 2), which is also
dependent upon the wholesale price Wi . The prices
Wi and Pi are not under the manufacturer’s control
because of competitor manufacturer and retailer reactions, which require us to develop a more nuanced
framework that endogenize price equilibrium models.
An equilibrium framework that analytically captures the interactions of Figure 1 is presented in Figure 3 for both monopoly (one retailer) and duopoly
(two retailers) channels with an oligopoly of manufacturers competing with the focal manufacturer. The
framework incorporates the layers of strategic pricing moves available to competitors under the classical
game theory assumptions (Osbourne and Rubinstein
1994), assuming that players are rational and fully
informed of each other’s possible strategic moves (i.e.,
perfect information).
2.2. Equilibrium Framework
The focal manufacturer (manufacturer A) develops a
new product A in the assortment i = 1 n, which
has engineering design variables x (bottom layer, Figure 3) with an objective to maximize profit. In the
short term (one quarter to one year) the competing manufacturers will be unable to change product
designs because of the manufacturing line and supply contract modifications that would be necessary.
However, they can alter their wholesale prices and
do so under the assumption that their competitors
will attempt to make a “best response” to any Wi
decision (manufacturer layer, Figure 3). The retailers
also select retail prices Pri that will maximize their
profit under a “best” response assumption from their
competitive retailer (in the case of a duopoly retail
channel) or simply maximize profit (in the case of
the monopoly channel). The retail prices and product
designs affect each consumer segment depending on
its specific preference structure, which the finite mixture latent class model estimates based on the conjoint
analysis. These determine the segment sizes and the
market shares (top layer, Figure 3).
The strategic aspects of retailers and manufacturers
selecting retail and wholesale prices based on Nash
equilibria makes the problem of optimizing product design computationally intensive. Following Luo
et al. (2007), we solve the layered equilibrium conditions as a nested algorithm where the pricing level
optimization is the selection of retail prices P using
the Nash equilibrium of profit for retailers. The setting of wholesale prices is also based on the concept of Nash equilibrium. Simultaneously (with retail
price setting), we minimize the sum of the squares
of the first derivatives of manufacturer profit functions with respect to wholesale prices Wi to solve the
manufacturer first-order conditions (FOC) in the middle layer of Figure 3. This structure creates a vertical
Williams, Kannan, and Azarm: Retail Channel Structure Impact on Strategic Engineering Product Design
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Figure 3
Equilibrium Framework
Monopoly channel
Duopoly channel
Market share analysis: MSA2
Market share analysis: MSA1
—For each product
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—For each product
expUi mik = N
i=1 expUi mi = Sk · mik
expU mrik = R n ri
r=1
i=1 expUri mri = Sk · mrik
k
k
mi
P1i
mri
Pricing level: PL2
Pricing level: PL1

11
P11
12
P12
1
W1
2
W2
mi
11

 P11


 1
W1
1
W1
1n
=0
P1n
n
=0
Wn
···
Wi
mri
x
max i = mi Wi − Ci i=1
Wi
x
s.t. gx ≤ b
n
i=1
iold
Nash equilibrium for the manufacturers and retailers in setting prices. The details of the optimization
methodology are provided in the online appendix
(provided in the e-companion),3 where we also provide proofs that optimal engineering design is dependent on the channel structure.
i=1
3.1.
Impact of Channel Structure on Engineering
Design: A Simple Numerical Example
Let us assume we focus on a single design variable
x (say, product life expressed in number of years).
3
An electronic companion to this paper is available as part of the online version that can be found at http://mansci.journal.informs.org/.
rinew ≥
n
i=1
riold
Ci mi = f x
Consider the manufacturer profit maximization problem of Equation (4) determining the optimal xi of
product i, with the overall market share mi being the
sum of market shares from each retailer r, mri . Then
Equation (4) without the retailer constraint can be
rewritten as
max i =
Numerical Examples and Analyses
In this section, we provide two numerical illustrations to highlight how channel structure impacts optimal engineering design. The first example involves a
very simple design problem to highlight the intuition
behind different optimal designs for different structures and how they are related to customer preferences. The second example is a power tool design
problem based on some customer preferences and
firm and market data.
n
∀r
Ci mi = f x
3.
2
W2
x
s.t. gx ≤ b
inew ≥
2
W2

1n
 0
P1n 
=

0
n 
···
Wn
n
=0
Wn
···
max i = mi Wi − Ci x
12
P12
Engineering design level: EDL2
Engineering design level: EDL1
n
Pri
xi
R
r=1
mri Wi − Ci (5)
s.t. gx ≤ b
Ci mi = f x
Expressing the market shares derived for product i
from each retailer using a logit expression and the cost
function to be a quadratic function of x with coefficient c and intercept C, the above maximization problem can be written (dropping the subscript i) as the
sum of profits derived from each retailer r:
max =
x
N
R
W −cx −12 +x−cexpbx −P/+U r=1
expbx −P/+U +Utot +Unc
(6)
where N is the total market size. The designer/
manufacturer’s margin is the difference in wholesale
Williams, Kannan, and Azarm: Retail Channel Structure Impact on Strategic Engineering Product Design
904
Table 2
Management Science 57(5), pp. 897–914, © 2011 INFORMS
Example Problem Settings
Figure 4
Competitor specific parameters
Focal product
Competitor
6.5
5
6
6
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Common parameters
C
N
U (no choice)
B
a
1/3
1,000,000 units
−1.1
Focal manufacturer profit
U (segment 1)
U a (segment 2)
18
16
14
12
10
8
6
Segment 1 (50%)
Segment 2 (50%)
10
1/5
12
8/15
U does not contain x (quality) or price utility.
price W and the quadratic cost function with coefficient c and intercept of C. Utility increases linearly in
x at the rate of b and utility decreases linearly in retail
price P at a rate of 1/. We consider one other competing product in the market place and between zero
and three retailers so profits are summed across the
index r as well as segments k, which is suppressed
(see Equations (2) and (3)). The total utility of competing product in this case is represented by Utot , and
Unc is the utility of the no-choice option. The competitor has a design of x = 9 and identical cost structure
to the focal manufacturer (see Table 2).
Even with such a simple problem, an infinite number of analysis scenarios are possible, so we focus on
realistic problem parameters that were robust in converging to pricing equilibria as shown in Figure 3.
We analyze four simple cases: (1) no retailer
(i.e., the manufacturers sell directly to market);
(2) monopolist retailer; (3) duopolist retailers; and
(4) a three-member oligopoly of retailers. Prices for
the competing manufacturer and the retailers are calculated using a nested optimization using FOC for
retailers and manufacturers as shown in Figure 3.
The no-retailer case is important because if one is to
believe that channel structure is important then we
should see a difference between optimal design without retailers and those taking into account retail pricing. The cases are exhaustively analyzed for design
optimality by parameterizing x between 2 and 15. Our
aim is to see how optimal design changes according
to market structure and examine why that is so.
One can see in Figure 4 that the optimal design
varies markedly by virtue of the channel structure.
The optimal design (i.e., product life, which is a measure of design quality) for the monopoly case xm∗ = 78
is significantly lower than the other three cases. From
there the duopoly structure has the next lowest optimal design xd∗ = 84, followed by the three retailer
oligopoly xo∗ = 10 and finally when retail competition is neglected we have the highest quality design
4
5
6
7
8
9
10
11
12
13
14
Design variable x
Monopoly
Duopoly
Oligopoly
No retailer
∗
xnr
= 109. The difference between the monopoly
case and the competitive retail case is the most striking and is largely explained by the equilibrium pricing as shown in Figure 5.
Clearly, in the monopolist case, equilibrium retailer
markup increases significantly as quality is increased
and the monopolist retailer prices the products much
higher in general (an average of $133 for all products
and $103 for the optimal product). The higher monopolist markup and prices are anticipated (see, e.g.,
Andersen et al. 1992) because of lack of a strong nochoice option. In contrast, average and optimal retail
prices for the duopoly across all retailers (average
$120, optimal $89) and oligopoly ($118, $98) cases are
much lower, which is also consistent with expectations as retail competition rises.
In examining Equation (6), costs increase quadratically in quality (left side of numerator) and utility
increases linearly in quality so one would expect a
trade-off to exist. The design variable equilibrium for
the monopoly is reached much quicker (lower) in the
parametric study because the comparative utility with
the no-choice option is reached much more quickly
with the higher markup from the retailer. At this
point, subsequent increases in quality add insufficient
utility to overcome the subsequent increase in price
Figure 5
Average Equilibrium Retailer Markup as a Function of
Focal Design
45
Focal manufacturer profit
a
Channel Structure Effect on Optimal Design
Monopoly
Duopoly
Oligopoly
40
35
30
25
20
15
4
6
8
10
Design variable x
12
14
Williams, Kannan, and Azarm: Retail Channel Structure Impact on Strategic Engineering Product Design
Management Science 57(5), pp. 897–914, © 2011 INFORMS
Figure 6
Pricing Change with Respect to x
–4
Focal manufacturer profit
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–5
–6
xnr*
–7
–8
–9
xm*
–10
xd*
Monopoly
Duopoly
Oligopoly
No retailer
–11
–12
–13
xo*
–14
4
5
6
7
8
9
10
11
12
13
14
Design variable x
and loss of utility. We can see this by analyzing the
inner focal manufacturer component of Equation (6).
If we assume wholesale margins (first parenthetical
in numerator of Equation (6)) have reached an equilibrium, changes in profitability can only be accomplished by the focal manufacturer changing x in the
second numerator parenthetical in Equation (6): x +
Pr / + U . Let A = bx + Pr / + U ; the following identity should be true at equilibrium when a
solution is focused largely on one segment: dA/dx =
b − 1/dPr /dx = 0 if we take an economic interpretation that the gain in utility for quality should
be equal to the loss of utility for cost at equilibrium.
Thus, the design equilibrium for a given strategic case
is realized when the utility coefficient b is equal to
the change in price with respect to quality dPr /dx
after being scaled by the price scaling factor . We
numerically observe that each of the strategic cases
converged to a slope similar to b for segment 2 (see
Figure 6). The slope for the monopoly and duopoly
cases (change in Pr / with respect to x) is nearly equal
to 8/15 at the optimal design that equates to the second segment’s quality sensitivity (b).
The monopolist case, not surprisingly, converges to
a design/price equilibrium that equates to the second
segment’s price elasticity where consumers are least
price sensitive. The second segment is the least sensitive to price increases and yet places more value on
design improvements by virtue of the coefficient and b, respectively (see Table 2). Clearly, the monopolist is less interested in increasing quality because
maximal profits can be made off of the second segment with very little penetration into the first segment
(less than 17%). The remainder of the cases also have
low penetrations in segment 1 (relative to segment 2),
which were 30%, 22%, and 19% for duopoly, oligopoly,
and no-retailer, respectively. These cases converge to
slopes of 0.5 to 0.7, which is closer to the b attribute of
segment 2 than that of segment 1 as expected.
This numerical study suggests that retail pricing in
the monopolist case will have significant effects on
optimal design. Specifically, the monopolist retailer
905
has incentive to select products to take advantage
of its strategic position, so it forces the focal manufacturer to produce lower/medium quality and price
it high enough to skim the market. As more retailers enter the market prices get more competitive.
As the monopolist retailer relinquishes control to a
duopoly/oligopoly, the focal manufacturer starts to
focus on increasing quality to capture market share
from the higher-quality competitor as the price utility of all products increases. As quality increases, the
products with lower prices reach more of the lowend, price-sensitive consumers. It is also worth noting
that as more retailers are added to the channel the
difference between the no-retail and oligopoly structures start decreasing. So, if the number of retailers is
very high (fragmented channel) then manufacturers
can totally disregard the impact of retailers in their
design as that impact will be very minimal (it is as if
there were no retailers).
The results we discuss in this example are consistent with the analytical result (in Online Appendix B)
that product quality tends to be higher, given the
preferences of the consumer segments, and prices
decrease with retail competition. Additionally, optimal designs are shown to be dependent on channel
structure as shown analytically in Online Appendix B.
Somewhat specific to this numerical example (i.e.,
both consumer segments value higher quality) quality
increases with retail competition, as a focal product
design gets closer to consumer segment preferences
given the underlying settings and assumptions (e.g.,
that costs are a function of design and utility is mildly
increasing in quality for both segments). Costs as a
function of production run volumes, and higher-order
utility functions may yield different results but one
can make the firm conclusion that monopoly and
duopoly retail pricing can affect design significantly—
retail competition tends to increase product qualities, lowers prices, and allow offerings to reach the
more price-sensitive consumers—and so the market
structure should be taken into consideration by the
designers. If the number of segments increase, the
larger of the more price-sensitive segments’ preferences will have a greater impact on the optimal
design. It should also be noted that consistent with
the numerical nature of the previous example, the
observations are only locally valid though consistent
with extant economic/game theoretic literature and
the analytical results in Online Appendix B. These
results are functions of many parameters (segment
sizes, cost coefficient, price sensitivity, quality sensitivity of segments, and the utility of no choice, etc.)
and so are valid within close vicinities of these parameter values and thus cannot be generalized when one
or more parameters change significantly.
Williams, Kannan, and Azarm: Retail Channel Structure Impact on Strategic Engineering Product Design
906
Table 3
Management Science 57(5), pp. 897–914, © 2011 INFORMS
Example Assortment at a Retailer
Table 4
Utility Estimates for Four Latent Segments
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Segment
Tool
Brand
Amps
Life (hrs)
Switch
Girth (in)
Weight (lbs)
A
W
8.00
80
Paddle
2
6.00
B
X
12.00
110
Trigger
4
16.00
C
Y
6.00
150
Side
2
5.00
D
Z
9.00
110
Side
2.5
6.00
3.2. A Power Tool Design Example
In this section, we present a practical engineering
design example using four cases of varying retail
channel structures to show the effect of taking into
account the channel’s competitive structure on optimal engineering design based on an actual productmarket. We consider four manufacturers (A–D) and
two retailers (1 and 2) (see Table 3). The channel structure cases are as follows:
Case 1: Retailer Monopoly/Manufacturer Oligopoly.
In this case, given the one retailer, strategic interactions occur only at the manufacturer level in setting
wholesale prices that impact the engineering optimization. This is the baseline case that extends the
prior work in the retail monopoly setting (Luo et al.
2007) to include engineering design optimization.
Case 2: Retailer Duopoly/Manufacturer Oligopoly—
Identical Retailers. In this case, each retailer carries
the same assortment and consumers across all segments are indifferent as to which retailer to buy
from, so the retailers compete only on price. This
case is more complex than Case 1 because it also
has strategic interactions at the retailer level. The
formulation for setting retailer prices takes into
account wholesale prices as before but now is formulated as an equilibrium optimization where retail
prices are adjusted to minimize the square of the
first derivatives of the duopoly retailers’ profits with
respect to price. Although somewhat unrealistic, such
a case should demonstrate downward pressure on
retail prices and therefore wholesale prices relative to
the monopoly case. This will also serve as a baseline
to examine Case 3 and Case 4 results.
Case 3: Retailer Duopoly/Manufacturer Oligopoly—
Differentiated Retailers. This case is more realistic
because it accounts for the differentiated preference
of consumers for the retailers themselves. For example, Lowe’s targets female customers with wider,
brighter isles and a greater emphasis on decorating.
A conjoint study can easily include samples where
product i is offered at retailer r and then assess the
value that consumers place on the “retailer attribute.”
In Table 4 we show that three out of four customer
segments prefer one retailer over the other.
Case 4: Retailer Duopoly/Manufacturer Oligopoly—
Exclusive Retailer Strategy. This models the case
Share (%)
Brand
W
X
Y
Z
Price
$60.00
Slope u/$
Amps
6.0
9.0
12.0
Life (hrs)
80.0
110.0
150.0
Switch type
Paddle
TopSlider
SideSlider
Trigger
Girth
Small (1.5 in)
Large (4 in)
Weight
16 lbs
9 lbs
6 lbs
Retailer
One
Two
1
2
3
4
36.40
26.62
13.17
23.81
01
01
01
01
22
−24
−15
17
−05
02
08
−05
01
01
01
01
05
11
01
−16
238
−006
01
283
0001 −0045
005
212
0002 −0035
009
187
0001 −004
01
0001
13
01
−14
01
01
01
05
−14
10
01
01
01
−15
−07
21
006
01
01
−05
−24
28
01
01
02
−09
13
−04
01
01
01
−01
−05
06
01
00
01
−47
−58
105
008
003
004
08
07
−15
01
01
01
04
−10
24
−18
01
01
01
01
03
−07
−01
04
01
01
00
01
−33
−30
25
39
005
004
005
004
−07
04
06
−03
01
01
00
01
05
−05
01
01
07
−07
01
01
15
−15
003
003
24
−24
01
01
−23
05
18
00
01
01
08
12
−20
00
00
00
−15
05
10
002
001
004
15
05
−20
01
00
01
05
−05
01
01
07
−07
01
01
−15
15
005
005
14
−14
01
01
005
006
01
006
−02
−02
12
−08
01
01
01
01
where manufacturers and retailers seek exclusive
channel relationships (Moner-Coloques 2006) as a
means to secure access to market (manufacturer’s
perspective) and as a means to differentiate an
assortment for greater profits (retailer’s perspective).
We model this arrangement in a manner similar to
Case 2 except that the focal manufacturer decides
to go to the market through only one of the two
identical retailers as an exclusive retailer strategy.
This approach where one retailer is allowed to fulfill
all demand has been shown to theoretically improve
profits for both parties in the exclusive channel
(Tsay and Agrawal 2004). The retailer chosen for the
exclusive relationship will carry the new product
offered by the manufacturer as long as its profits
improve relative to the original assortment just as in
the previous cases. The competing retailer not chosen
for exclusivity with our manufacturer simply offers
the original assortment.
We apply Cases 1–4 to a previously developed
design problem. Although the channel structure of
the case is characterized by a differentiated duopoly,
we analyze an assumed monopoly structure (with the
more dominant of the two retailers as the monopolist), an identical duopoly, and the possibility of the
manufacturer going into an exclusive channel contract
Williams, Kannan, and Azarm: Retail Channel Structure Impact on Strategic Engineering Product Design
907
Management Science 57(5), pp. 897–914, © 2011 INFORMS
Figure 7
Optimization Formulations
Monopoly
Duopoly
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x Wi
i=1
i
s.t. gx ≤ b
max x Wi
n
i=1
Ci mi = f x
inew ≥
max i = mi Wi − Ci x Wi
x Wi
n
Exclusive
n
i=1
iold
2 n
r=1 i=1
x Wi
max ri
s.t. gx ≤ b
x Wi
∀r
n
i=1
Ci mi = f x
with one of the retailers. The monopoly case forms
the baseline to examine the impact of duopoly, and
the identical duopoly is used as a baseline to compare
the exclusive channel contract. These comparisons
provide better insights into how the channel structure
impacts design decisions, which is quite useful for
the focal manufacturer as the channel structures do
vary across different categories of tool manufacturers.
A detailed engineering design structure and conjoint
data (Williams et al. 2008) were available for common
small angle grinders and an ideal candidate application for the case studies as they are typically sold in a
strong retailer channel environment. A brief shelf survey of the channel controlling retailers would reveal
an assortment similar to that shown in Table 3.
For the four strategic cases considered, our model
replaces tool A with a new product when the channel
constraint is met in Equation (4). The assortment is
the same for the retailers under the monopolist and
duopolist cases in that they carry the new product
and products B–D. For the fourth (exclusive retailer
channel) we assume that retailer 1 carries the new
product and that the competing retailer carries the
existing product (i.e., product A from Table 3).
3.2.1. Estimating Number of Segments and Market Shares. The latent class segmentation module
(Sawtooth 2001) of the Sawtooth Software Market
Research Tools (SMRT) was used to perform a conjoint analysis for small angle grinders (Figure 7) reanalyzing the customer response data from Williams
et al. (2008) treating price as a continuous attribute
and the other attributes as discrete levels. The results
of the conjoint analysis are tabulated in Table 4. Each
segment has an estimate of utility mean () and standard deviation (#) for several possible alternatives of
product attributes. The utilities are normalized using
Sawtooth Software. For the application we assume
that prices are set by players (retailers and manufacturers) based upon the mean value for attribute utilities. A linear interpolation of utilities from Table 4
is used for any product attribute along with Equations (1)–(3) to determine the segment and market
share of any given design.
inew ≥
n
i=1
iold
n
i=1
1inew
s.t. gx ≤ b
n
i=1
1inew ≥
n
i=1
1iold
Ci mi = f x
3.2.2. Engineering Model. An engineering model
is necessary to produce feasible designs that generate
product attributes that are evaluated at the customer
level on the basis of the finite mixture latent class
model estimates. These attributes will impact production costs as well as the strategic pricing of any product. We employ a detailed engineering model that
provides direct links from design variables to customer conjoint attributes in Table 4. The components
that are the greatest drivers for tool cost are shown
in Figure 2 with the associated engineering design
variables at the bottom of the figure. One can see
that the rotor and bevel gear assembly slip inside of
the stator that is then encased in the plastic body
of the tool. The engineering design variables shown
are evaluated to ensure feasibility as well as calculate
product attributes (weight and girth) prior to utility
conversion and ultimately calculation of market share
as explained in §2.1 (see Williams et al. 2008 for all
computations; also included in the online appendix).
Stator and armature mass make up a large component of the motor mass Mm presented in Figure 2
that is then used to compute the designs’ utility
from Table 4 using piecewise linear interpolation. Like
any engineering problem it is necessary to satisfy
constraints to ensure that the product will operate
safely under a variety of conditions as well as be
durable and reliable. These are shown in detail in the
online appendix, Table E1, and ensure that the design
selection process does not produce motors that will
exceed constraints such as heat flux limits or the recommended slenderness ratio L/Ro . These constraints
are evaluated before any pricing algorithm is run as
developed in §2.2. Thus, we are able to reject infeasible engineering designs prior to the computation cost
of reaching wholesale and retail prices.
Production cost is a critical component in our
strategic framework because an increase in cost puts
upward pressure on wholesale and retail prices for
any competitor. We model cost Ci as a function of
product attributes that takes the form presented in
Equation (10):
Ci = &0 + &1 I + &2 I · V /Mt + e
(7)
Williams, Kannan, and Azarm: Retail Channel Structure Impact on Strategic Engineering Product Design
where &1 and &2 are the multiple regression coefficients with estimates of 3.6160 and 0.1865, respectively; the estimate of intercept &0 is found to be
−29.294; I is the current of the tool in amps; and
IV /Mt is the power to weight ratio in watts/kg,
where V is voltage and Mt is the total weight. For a
detailed development of the multiple-regression cost
model, see Williams et al. (2008). Finally, the example is implemented within our framework whereby
engineering design variables (Figure 2) are optimized
while taking into account engineering constraints and
production costs (Equation (10)) all within the strategic pricing topology presented in Figure 3.
3.2.3. Analysis Details. We used Matlab’s Genetic
Algorithm and Direct Search Toolbox (MathWorks
2007) to develop a multiobjective genetic algorithm
to simultaneously optimize focal manufacturer profit
(manufacturer A) and retailer profit. Although our
focus (Equation (4)) is to maximize the focal manufacturer’s profit while ensuring that retailer makes
at least as much profit as he was making with the
existing assortment, an extension to finding the Pareto
solutions for manufacturer and retailer profits helps to
understand design impacts within a channel better, as
we show subsequently. Additionally, one could argue
that the increase in the retailer’s profitability above
the prior assortment profit would strengthen the manufacturer’s case to obtain shelf space. Thus, we formulate the manufacturer’s decision as two objectives:
(1) maximizing his own profit, and (2) maximizing
the channel partner’s profit (monopolist, duopolist,
or exclusive retailer). This can be described mathematically by adding the retailer profit maximization
objective to the engineering design level (Figure 3).
(In the retail duopoly, retail profits are the sum of the
two retailers.) Three optimization formulations that
we evaluate at the engineering design level are shown
in Figure 7.
A nondominated sorting algorithm (Deb 2001) is
employed to find a Pareto frontier for each strategic
situation (monopoly, duopoly with identical retailers,
and duopoly with differing retailers, and the exclusive retailer). The inner optimizations for retail price
setting and wholesale price setting are strictly quasiconcave for monopoly and duopoly price setting (see
the proofs in Online Appendix B) and as such are
amenable to gradient based optimizers such as Matlab’s fmincon (MathWorks 2007). The computational
issues involved in our methodology are discussed at
length in Online Appendix C.
3.3. Results of Numerical Analysis
The results focus on manufacturer A’s design strategy
in developing a new design to replace the existing
product A design in the market under different
channel structures. We assume that the competitor
Management Science 57(5), pp. 897–914, © 2011 INFORMS
products remain in the market with their existing
attributes but possibly changing wholesale prices,
which is taken into account in developing the new
design scenarios using strategic analyses. Because our
numerical results are a function of (a) the relative utilities of consumers in the different segments, (b) the
competitive product attributes and positioning in the
market, and (c) competitor actions, we graphically
provide the impact of these interactions by considering changes in design attributes as functions manufacturer and retail profits under the different channel
structures and strategies.
3.3.1. Impact of Channel Structures: Monopoly,
Identical Duopoly, and Differentiated Duopoly. The
optimal design solution set of each of the strategic
cases presented in Figure 8 are unique and are a
function of the specific channel structure. All designs
have a high probability of acceptance by the retailers because they satisfy the profitability improvement constraint from Figure 7, but, of course, those
designs that provide higher retailer profitability provide an extra incentive for the retailers to carry the
product (i.e., designs M1, ID1, DD1). The Pareto frontier (rather than analyzing a single design) is interesting in that manufacturers can analyze a trade-off
between retailer incentives and their own. Strategies
can even be developed from this information. For
example, under the monopolist setting the manufacturer might propose a $5 million slotting allowance
to the retailer to carry M3 instead of M1. Clearly, the
manufacturer is expected to be better off under that
choice than designing M1. Similarly, the design ID3,
although is Pareto optimal, is an exceedingly poor
choice for the manufacturer if we do not consider the
retailer profit as an objective. In this case the manufacturer can select design ID∗ with relatively little
loss in profit but a significant improvement in retailer
profitability. Figure 8 also provides those designs that
provide significant profits to the manufacturer while
Figure 8
Comparison of Strategic Case Results for Focal Tool
420
400 M1
Increasi
M2
ng amp
380
Retailer profit ($M)
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908
360
rating
M3
ID1
340
DD2
ID2
320
300
DD1
260
240
220
125
DD3
Increasi
ng amp
280
rating
Monopoly retailer
Identical duopoly
Differentiated duopoly
135
145
ID3
155
165
Manufacturer profit ($M)
175
185
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ensuring that the retailer profits are high (designs M2,
ID2, and DD2). These designs can be considered as
“optimal” designs under each channel structure and
are included in the table in Online Appendix C.
The monopoly Pareto frontier spans the lower
profit regions for manufacturers—both duopoly cases
have solutions acceptable to the retailers that have
higher profits than the monopoly solution with the
greatest manufacturer profit. In addition, monopoly
retailer profits exceed profits of either of the duopoly
cases, which is consistent with the extant literature
(Anderson et al. 1992) given that the monopolist does
not have competitive pressure to shift prices lower. In
Figures 8–10 duopoly profits are the sum of the two
retailers’ profits.
The increased price competition in the two duopoly
retailer cases (identical and differentiated) allows for
the possibility of greater manufacturer profits. Retail
prices are significantly lower for the duopoly cases as
expected (Andersen et al. 1992; and as in the previous
numerical example) which increases the overall market penetration and profitability for the focal manufacturer by reducing the number of consumers who
do not participate in the market (choosing no-choice
option) and focusing on the more price-sensitive consumers and their preferences. To reduce these retail
prices for the duopoly cases the optimization feedback from the retailers suggests that lower production cost and lower wholesale prices are preferable
(even though the best assortment fit lies in the high
performance/high retail price strategy as evidenced
by the monopoly results). The lower production cost
has important implications for the optimal designs.
Specifically, they drive down the current rating. This
is precisely what we see in Figure 9, which shows that
the duopoly case designs tend to be characterized by
lower current rating and weights as compared to the
monopoly case. For example, none of the competitor products (Table 3) can compare on amp rating to
the monopoly designs that reach 14.5+ amps nor the
extremely low performing solution at 4.5 amps (see
Figure 9, bottom panels). In addition to a high current
rating, the focal manufacturer has chosen to largely
pursue designs in the 2.5 in to 3 in girth range and
10 to 14 lb range for the powerful monopolist models, which is significantly smaller in girth and lighter
than the competing powerful product (product B) and
larger/heavier than all of the rest of the competing
products.
However, these results are also easy to explain
when one examines the customer preferences in
Table 4. From Table 4 we can infer that segments 2
and 4, which together make up 50% of the market, prefer heavier and more powerful tools. They
are also not as price sensitive as segment 1. Therefore, in the monopoly case, the retailer prefers to
909
carry a high performance, more expensive assortment
of tools with higher margins and focus on the relatively price-insensitive and quality-sensitive segments
to skim the market (the segments make up 50% of
the market). Hence, the optimal design for the focal
manufacturer tends to be the heavier and higher price
tool. However, when the channel structure changes to
duopoly and price competition starts to overwhelm
the other attributes as competition forces prices lower,
the more price-sensitive segment 1 (with a market
size of 36%) suddenly becomes more important to
the retailers competing for marker shares and profits
as more consumers participate in the market moving away from the no-choice option lured by lower
prices. Given this fact, the focal manufacturer starts
to focus on designs that are lower in prices, lower in
power (amperage), and lighter in weight (see Table 4)
catering to the needs of the more price-sensitive segments. This is precisely what we observe in Figure 9, where the duopoly solutions tend to align more
with the more price-sensitive segment 1 preferences.
The insight that emerges from this analysis is similar to the one from the numerical analysis in §3.1—
under the monopolist condition, the retailer targets
the price-insensitive segment to make more profits—
this results in higher prices than otherwise for the
consumers targeted and depending on quality preferences for this segment and the location of competitive products, the segment may or may not get the
quality they most prefer. Under duopoly with more
price competition, the retailer has an incentive to target the more price-sensitive segments with products
they prefer the most. In this case, it is the segment 1
preferences that determine the optimal design to a
great extent.
The plots in Figure 9 also show that the tool
attributes affect the players (retailers or focal manufacturer) differently depending upon the strategic case. The most drastic example is the effect of
increased current rating on the duopoly cases. Even
minor increases in current appear to drive manufacturer profitability sharply lower. This is because
higher values of current are farther away from the
ideal values for segment 1 and also lead to higher
manufacturing costs. Thus, increased cost causes the
manufacturer to lose profits in the environment of stiff
price competition. A weaker trend can be seen in the
decreased manufacturer profits associated with heavier tools for the duopoly cases. The opposite is true
for the monopoly case where the monopoly retailer is
able to encourage the design of the tool that fits his
or her assortment (heavier and more powerful) without regard to price competition. Just as importantly
optimal profits of all parties are relatively insensitive
to girth and some strategies are distinctly absent for
design considerations. For example there are no large
Williams, Kannan, and Azarm: Retail Channel Structure Impact on Strategic Engineering Product Design
910
420
175
Retailer profit ($M)
Manufacturer profit ($M)
Focal Tool Attribute Correlations with Manufacturer and Retailer Profit
165
155
145
135
125
370
320
170
220
8.5
10.5
14.5
12.5
8.5
10.5
Weight (lbm)
14.5
450
180
Retailer profit ($M)
Manufacturer profit ($M)
12.5
Weight (lbm)
190
170
160
150
140
130
120
400
350
300
250
200
1.5
2.0
2.5
3.0
3.5
1.5
2.0
Girth (in)
2.5
3.0
3.5
Girth (in)
190
450
180
Retailer profit ($M)
Manufacturer profit ($M)
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Figure 9
Management Science 57(5), pp. 897–914, © 2011 INFORMS
170
160
150
140
130
400
350
300
Monopoly retailer
Identical duopoly
250
Differentiated duopoly
200
120
4.5
6.5
8.5
10.5
12.5
14.5
Current (A)
girth tools (above three inches in diameter) and no
tools that are simultaneously light with low amperage. All tools are at least medium in weight, which
implies that under no circumstances is the increased
cost of higher power to weight ratios sufficiently offset by increased utility for higher-power, lower-weight
tools. (Table D1 in the online appendix provides the
details of the designs shown in Figure 8 by selecting a
design from each end of the strategic case Pareto frontiers and also the optimal for each strategic case.)
The insights from these results are very instructive.
Under a monopoly structure, retailer steers the new
design to align with the segment that is least price
sensitive and, in this case, the segment that prefers
higher power and heavier tools. Under duopoly the
assortments cover a wider spectrum of market preferences and the more price-sensitive segments. Thus, in
our power tool design example, the optimal designs
4.5
6.5
8.5
10.5
12.5
14.5
Current (A)
for the manufacturer under duopoly include light
tools with low amperage as the manufacturer would
be best served in designing cost reductions into the
manufacturing process rather than increasing amperage (i.e., production cost is very important under
these more price-sensitive environments). Given the
distribution of customer preferences, overall manufacturer profits are extremely sensitive to current rating
and somewhat sensitive to weight, and only smaller
girth products were found acceptable. This provides
designers specific targets for improvements in manufacturing and suggestions for successful designs.
What if the manufacturer does not take into account
the channel structure in such design decisions? Figure 10 provides the scenario of how designs developed under the assumption of a specific channel
structure would fare if the retail structures were different. It also shows the performance of the opti-
Williams, Kannan, and Azarm: Retail Channel Structure Impact on Strategic Engineering Product Design
911
Management Science 57(5), pp. 897–914, © 2011 INFORMS
Figure 10
Profit Performance of Designs Under Alternative Channel
Structures
Figure 11
200
450
350
300
250
200
150
Heavy tools, high amp rating, > 15
180
Retailer profit ($M)
Retailer profit ($M)
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190
Monopoly retailer
ID designs in monopolist objective NR-M
No retailer in ID objective
Identical duopoly
Monopolist designs in ID objective
No retailer in monopolist objective
400
Exclusive Relationship vs. Identical Duopoly
NR-ID
100
ER2
ER1
170
ID2
160
ID1
150
Light tools, low amp rating, 5–7
140
130
50
120
0
0
20
40
60
80
100
120
140
160
180
200
Manufacturer profit ($M)
110
110
Exclusive retailer
Identical duopoly
120
130
ID3
140
150
160
170
180
Manufacturer profit ($M)
mal design solution derived under the no-retailer case
(that is, disregarding the retailers and just considering the manufacturer competition) under a monopoly
channel structure and duopoly channel structure.
(The monopoly retailer and identical duopoly positions are the same as in Figure 8 to provide a basis
for comparison.) It is clear that no-retailer solution
(NR-M and NR-ID in Figure 10) is suboptimal under
both scenarios. Only one design is presented for the
NR situation because only a single optimal exists
under the single-objective formulation.
Not surprisingly, when the optimal identical
duopoly (ID) designs are evaluated in the monopolist retailer objective function, they are suboptimal in
comparison. This is largely due to a complete change
in design focus for the manufacturer from segment 4
(dropping from 90% of its sales coming from segment 4 to less than 40%) to increasing segment 1 share
from 25% to 50%. Segment 4 prefers high amperage designs whereas segment 1 prefers low and we
noted that the monopoly designs tended to be high
in amperage. The monopolist can afford to assign the
focal manufacturer to this segment to extract the most
profit because the remaining tools (B–D) fit the other
segments relatively well and prices can be high all
around because of the only competition being from
the no-choice option, quite similar to the situation
observed in the first numerical example. The monopolist retailer designs in the ID setting are even less
successful as the high-amp, high-weight monopolist
designs have too high of production costs to be competitive. The increased competition from two retailers
(no longer just competition from no-choice) more than
compensates for any added utility from the higher
amperage and thus these designs are not selected for
retention in the genetic algorithm solution set. This
analysis further highlights the need to consider retail
channel structure in design decisions.
3.3.2. Analysis of the Exclusive Case/Strategy.
We analyze the exclusive retail channel strategy that is
common in the power tool industry where, for example, Home Depot exclusively sells Husky hand tools
and Ryobi power tools (Han Shih 2005). To set up
the exclusive retail channel we selected one of the
two retailers as a “channel partner” who has exclusive rights to carry the new tool (tool A) along
with tools B–D. The remaining retailer or “competing
retailer” in the subsequent figures carries the original assortment (tools B–E). Tool E is the design in
place prior to optimization. We selected retailer 1 as a
fixed choice for the exclusive relationship. The exclusive channel (Case 4) is compared to the most relevant
example from the previous cases, duopoly with identical retailers (Case 2) in Figure 9.
In Figure 11 we observe that the under the exclusive
channel strategy the channel partner (retailer selected
to carry the optimized tool) benefits greatly as the
profits for the entire Pareto set dominate those of
the nonexclusive case. In contrast, the manufacturer
loses the possibility of achieving the highest profit.
The manufacturer gives up $5–$40 million whereas
the retail partner gains between $25 and $75 million in an exclusive relationship. If the manufacturer
was already contemplating a design that maximizes
retailer profit (left side of identical duopoly in Figure 11) and his chance of acceptance, the exclusive
relationship may be worthwhile to pursue because it
significantly increases retailer profits. This illustration
explains the existence of exclusive retail relationships.
Given that the retailer’s still have strategic dominance
in this situation because of their control of market
access, one can consider the exclusive offering as an
incentive to a retailer to obtain shelf space similar
to a slotting allowance. For a cash-strapped or riskaverse manufacturer the exclusive offering may be
much more attractive than a slotting allowance. This
risk is because of the high outflow of cash for a slotting allowance in the present period with no guarantee of sales volume. The profits of the model are
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912
Williams, Kannan, and Azarm: Retail Channel Structure Impact on Strategic Engineering Product Design
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still a prediction whereas a slotting allowance is an
immediate deterministic outflow of cash.
The characteristics of the designs under exclusive
strategy are similar to those of the monopoly as the
manufacturer’s new product competes against products B–D at retailer 1 and the entire prior assortment
at retailer 2. Notice that segment 4 prefers to shop
at retailer 1 (Table 4) and given that this segment
also prefers heavier, more powerful tools it is not surprising that, under an exclusive channel agreement,
retailer 1 would want a heavier, more powerful tool
from the focal manufacturer so that the retailer can
profit significantly from this segment. The exclusive
case gives the opportunity for the focal retailer and
manufacturer to offer heavy/powerful products much
like the results from the monopoly case (details are
presented in the online appendix). Thus, even though
the duopoly case typically creates downward pressure
on prices it is possible to position one’s design away
from the low-performance/low-cost assortment that
appeals to the price-sensitive segments and, specifically so when an exclusive relationship exists. The last
two columns in Table D1 in the online appendix provide the details of the design under exclusive channel
strategy. These designs tend to be heavy and powerful similar to the designs under the monopoly case.
4.
Discussions and Managerial
Implications
The conclusions from the two examples are highly
consistent with each other. Under monopoly retail,
the retailer adds friction to the market. The retailer
forces the manufacturers to target the price-insensitive
segment to make more profits for themselves at
the expense of the manufacturer and the consumers.
The design, therefore, is dictated by to what extent the
retailer wants to fulfill the preferences of the priceinsensitive segment given his profit motives. In the
numerical example, this results in a high price, lower
or medium quality product, given that a competing
manufacturer already produces at the high end. In the
power tool design example, the monopoly condition
results in a design that caters to the price-insensitive
segment—high amperage and heavier tools.
With increasing competition at the retail level
(duopoly and oligopoly), the market friction induced
by the retailers decrease, and the optimal designs
move in the direction to increase overall consumer
utilities. The retail prices are lower because of competition, the more price-sensitive segments are targeted,
and it is these segment preferences that determine
the optimal design characteristics. In the numerical example, both segment utilities increase monotonically in quality and thus optimal designs under
duopoly and oligopoly tend to be higher in the
(single) quality dimension (thus increasing segment
utilities), whereas being lower in retail prices. In
the power tool design example, the utilities of the
consumer segments do not increase monotonically
in the multiple dimensions of quality, rather each
segment tends to have its ideal designs consistent
with the willingness to pay. Thus, in the power tool
design example, the preferences of the more pricesensitive segments (which get targeted as the structure changes from monopoly to oligopoly) dictate the
optimal designs—lower prices, low amperage, and
lower weight products that increase the overall utilities for these segments, while being moderated by the
location of the competitive products. Under increased
competition, the consumers in both cases are able to
get products that are closer to their ideal points.
What our framework allows is to specifically determine the quality of the new product that the retailers
would prefer most, under different channel structures, for targeting those consumer segments that
maximize their profits—i.e., the specific attributes of
the new product given a specific distribution of consumer preferences, competitive locations, and production costs. In the power tool design example,
under the monopoly structure, the retailer sets an
assortment that covers a portion of the customer
preferences, albeit setting retail prices that are high,
and forcing price-sensitive customers out of the market. Therefore, the focal manufacturer would be better off designing heavier and more powerful tools
that align well with the retailer’s ideal assortment.
On the other hand, the retail price pressure that is
experienced in the duopoly case precludes any such
(heavy/powerful) design by the manufacturer as the
more price-sensitive customers with their preferences
for lower performance tools come into play, unless the
manufacturer decides on an exclusive channel agreement. Under the nonexclusive strategy, the optimal
designs tend to be lighter and less powerful. Additionally, the results provide useful insights into the
sensitivity of profits to the design attributes. Thus,
manufacturer profits are very sensitive to current rating (even a small shift higher reduces profits significantly). On the other hand, weight is not as sensitive
a variable, and only smaller girth products are found
acceptable. This implies that from a design viewpoint,
the manufacturer‘s target on current ratings has to be
very focused with little room for variations, whereas
the attribute girth tends to be small in all acceptable
products. Understanding these degrees of freedom on
the design attributes of the tool early on in the design
process can lead to more acceptable and profitable
designs.
Similarly, if competitive pressures in the duopoly
situation make the manufacturer very apprehensive
of the eventual acceptance of their new product by
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either retailer, a risk-averse manufacturer would be
able to make their offer significantly more attractive to a retailer with an exclusive channel contract
with them. Although the manufacturer is limited to
lower profits under such a contract, the exclusive
relationship provides significant motivation for channel acceptance and may be preferable to choosing a
higher retailer profit under the nonexclusive arrangement. The impact on the design is also quite different
across these two situations as the exclusive strategy
allows the retailer to act as a monopolist and thus the
designs are closer to what we find under monopolistic
conditions.
5.
Conclusions
The main contribution of our paper is (1) to show
that the retail channel structure has a significant
impact on the optimal design of a manufacturer,
and (2) to provide a framework to take into account
the retail channel structure and channel strategies
in developing design guidelines appropriate for specific institutional settings and under certain market conditions. We illustrate our framework and its
usefulness through numerical examples that show
how the insights derived using the framework under
those conditions translate to specific design guidelines. Specifically, it allows us to identify the attributes
of the designs that are optimal under specific channel structure conditions, and the sensitivity of profits to different design variables. In such situations,
the results using our framework are very useful to
managers who are concerned about the acceptance of
their new designs by the retail channel and getting
shelf space. Our methodology provides a Pareto set of
designs with varying manufacturer and retailer profits to choose from, under each channel structure scenario, so that manufacturers can mitigate the risk of
shelf space denial.
It is important to note that we have made many
simplifying assumptions in the process for analytical and numerical tractability. Some assumptions such
as retailer profit constraints (in Equation (4)) or that
the costs are dependent on customer-facing attributes
rather than underlying engineering design choices
(Equation (7)) can be relaxed without impacting the
general nature of the our results. But other assumptions are much more fundamental to the model. We
assume that retailers and manufacturers are hyperrational and prefer equilibrium solutions in the short
term. We also view the new product introduction as a
single-period problem and also do not allow for other
manufacturers to change any dimension other than
price. In a multiperiod problem this is an obvious
move by manufacturers. Thus, although our methodology may hold in markets that are characterized by
913
above market conditions (mature markets, for example, appliances and automobiles), it may not be appropriate for turbulent markets where players could have
different motives (preemptive moves, price cuts, etc)
or where competitors can react quickly with their
own design changes. Thus, caution has to be exercised in deriving very general implications from the
model and the results, given the heuristic nature of
the framework application.
It is also important to note that these results are
conditional on the competitive positioning and the specific distribution of customer preferences in the market. Thus, what we provide is a framework that will
allow managers to examine their situation numerically using the data on competitive positions and customer preferences characterizing their markets and
within the ranges where the analytical results are
valid. If the ranges are exceeded or if the functional
form of demand as a function of product attributes
and prices is much more complex, we can consider
the results as only hypotheses, and thus the general implications of our results could be limited. One
way to test such hypotheses would be to use new
empirical industrial organization models (e.g., Sudhir 2001) to analyze historical data on new product introductions, prices, market shares, and retailer
entries in specific markets to analyze the impact of
retail structure over time on attributes and qualities
of new products introduced. Such models can also
be used to test for the strategic behaviors of the
manufacturers and retailers (vertical Nash or Stackelberg’s leader–follower and deviations from these
setups) (see Kadiyali et al. 2000). Such industry level
analysis can provide insights into whether our framework can be applicable in specific institutional and
industry settings.
With regard to providing specific design recommendations to a focal manufacturer, we view our
research as the first step. For future research, it
would be useful to extend this methodology to take
into account competitor strategies in other attributes
(amperage, weight, advertising, etc.). This will no
doubt be challenging because it involves extending the strategic analysis across multiple dimensions. Additionally, entire product lines face similar
consideration along with competition from a store’s
own brand, which may change the strategic landscape significantly and provide another avenue for
extensions. It is obvious that such extensions will
be very difficult to achieve within a game-theoretic
framework, assuming hyperrational players. One way
to make the results more general is to have more
restrictive assumptions for a theoretical or acrossfirm treatment of such problems (Anderson et al.
1995, Kekre and Srinivasan 1990). Another approach
to retain the practical usefulness of the model while
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considering more realistic scenarios is to use agentbased approaches to analyze competitive moves using
Bayesian learning or dynamic games as alternative
basis (see Hart and Mas-Collel 2000, Miller 2007). An
agent-based approach can facilitate analysis of scenarios where players with different motives and objectives interact, allowing some of the hyperrationality
assumptions and short-term reaction assumptions to
be relaxed.
6.
Electronic Companion
An electronic companion to this paper with relevant
proofs and additional details is available as part of
the online version that can be found at http://mansci
.journal.informs.org/.
Acknowledgments
The work presented here was supported in part by the
National Science Foundation through Grant CMMI0654042.
Such support does not constitute an endorsement by the
funding agency of the opinions expressed in the paper.
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