modularization in automotive
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Production, Manufacturing and Logistics
Matching product architecture with supply chain design
Bimal Nepal a,⇑, Leslie Monplaisir b, Oluwafemi Famuyiwa c
a Industrial Distribution Program, Texas A&M University, College Station, TX, USAb Department of Industrial & Systems Engineering, Wayne State University, Detroit, MI, USAc Schneider Logistics Corporation, Green Bay, WI, USA
a r t i c l e i n f o
Article history:
Received 5 November 2010
Accepted 25 July 2011
Available online 12 August 2011
Keywords:
Product architecture
Supply chain design
Modular strategy
Product development
a b s t r a c t
Product architecture is typically established in the early stages of the product development (PD) cycle.
Depending on the type of architecture selected, product design, manufacturing processes, and ultimately
supply chain configuration are all significantly affected. Therefore, it is important to integrate product
architecture decisions with manufacturing and supply chain decisions during the early stage of the prod-
uct development. In this paper, we present a multi-objective optimization framework for matching prod-
uct architecture strategy to supply chain design. In contrast to the existing operations management
literature, we incorporate the compatibility between the supply chain partners into our model to ensure
the long term viability of the supply chain. Since much of the supplier related information may be very
subjective in nature during the early stages of PD, we use fuzzy logic to compute the compatibility index
of a supplier. The optimization model is formulated as a weighted goal programming (GP) model with
two objectives: minimization of total supply chain costs, and maximization of total supply chain compat-
ibility index. The GP model is solved by using genetic algorithm. We present case examples for two dif-
ferent products to demonstrate the model’s efficacy, and present several managerial implications that
evolved from this study.
2011 Elsevier B.V. All rights reserved.
1. Introduction
Globalization and increasing customer demand for greater
product variety has forced many firms to move away from the tra-
ditional world of mass manufacturing to the world of mass cus-
tomization. To achieve the agility and flexibility needed for mass
customization, industries are adapting and improving their prod-
uct design and development processes to better able to accommo-
date the rapidly changing needs of their customers. To bring a
product through its entire process—from conceptual stage to the
customer’s door—requires a very complex series of decisions re-
lated to product development (PD), production/manufacturing,
and supply chain management (SCM). This has traditionally been
a sequential process that suffered from two major deficiencies
(Gunasekaran, 1998). First, it is slow because parallel processing
opportunities are often missed. Secondly, it leads to sub-optimal
solutions because each stage can make, at best, sequential,
locally-optimal choices. Simultaneous engineering (SE), however,
is a paradigmaimed at eliminating such flaws as found in the serial
method. SE dictates that product and process decisions are made in
parallel as often as possible, and that production considerations are
incorporated into the early stages of product design (Fine, 1998;
Fine et al., 2005). However, SE does complicate the design problem
because it requires a simultaneous optimization of a more complex
objective with a larger set of constraints (Wu and O’Grady, 1999).
As noted in the prior literature (Ulrich, 1995; Fisher, 1997; Fine,
1998; Graves and Willems, 2005; Huang et al., 2005), manufactur-
ing process-related decisions such as manufacturing lead time or
time to market, setups and changeover ; and supply chain decisions,
like supplier selection and inventory placement decisions, are depen-
dent on the structure of the endproduct. For example, it is reported
that product and process design influences 80% of manufacturing
costs, 50% of quality issues, 50% of order lead-time, and 50% of
business complexity (Child et al., 1991). Change in product struc-
ture influences the dynamics of supply chains (Verdouw et al.,
2010). Specifically, the following effects can be seen on supply
chain network as a result of modularization: (1) outsourcing and
transfer of more components to suppliers; (2) consolidation of
first-tier suppliers into mega-suppliers; this then qualifies them
to manage the development and production of a ever-larger sets
of components as modules (Takeishi and Fujimoto, 2001); (3)
reorganization of value creation activities where some former
first-tier-suppliers become value-added second-tier suppliers
(Doran, 2003); (4) suppliers become more powerful and increase
their bargaining power because of the larger role as a full service
supplier; and (5) formation of more strategic alliances/partner-
ships between OEMs and their suppliers.
0377-2217/$ - see front matter 2011 Elsevier B.V. All rights reserved.doi:10.1016/j.ejor.2011.07.041
⇑ Corresponding author. Tel.: +1 979 845 2230; fax: +1 979 845 4980.
E-mail address: [email protected] (B. Nepal).
European Journal of Operational Research 216 (2012) 312–325
Contents lists available at SciVerse ScienceDirect
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Certainly, ours is not the first attempt to address product devel-
opment and supply chain issues simultaneously, and researchers
are examining these topics with great interest because of the
increasingly high stakes to the manufacturing sectors. Lamothe
et al. (2006) developed a mixed integer programming model to se-
lect product variants by minimizing the total supply chain costs.
Designing of product platforms concurrently with the supply chain
configurations has also been studied using linear optimization
techniques (Zhang et al., 2008). Jiao et al. (2007) proposed a sys-
tem-wide, holistic view of product family and supply chain design.
A number of papers have been published recently addressing the
issues of integrating PD decisions with SC decisions. Through case
study based exploratory research, Pero et al. (2010) have found
that the performance of supply chain depends upon the matching
between PD and SC design decisions. Chiu and Okudan (2011) pro-
posed an integrative methodology to combine design for assembly
and SC configuration during PD. Likewise, Ulku and Schmidt (2011)
compared the matching between the level of product modularity
and supply chain configuration from the PD standpoint. The
authors have focused primarily on whether product development
should be done internally or through collaboration with supplier
based on the product architecture and the level of OEM-supplier
relationship. However, the prior studies on PD and SCM have not
numerically examined the effect of product architecture (PA) on
supply chain (SC) design. In this paper, we extend the works of
Graves and Willems (2003, 2005) on supply chain configuration
by integrating product architectural design with SC design.
Furthermore, research shows that majority of SC partnerships
fail in their first year due to poor compatibility between the part-
ners (Bruner and Spekman, 1998; Duysters et al., 1998; Famuyiwa
et al., 2008). We believe that raising these issues early, during the
product development stage, can provide greater cost saving oppor-
tunities for manufacturing firms by helping the firms make in-
formed strategic decisions. Therefore, objective of this paper is to
address the challenges of developing a decision support tool for
designing an effective supply chain network that optimally
matches the product design structure. In particular, we use a mul-ti-objective optimization framework to answer following ques-
tions: (1) What are the optimal supply chain configurations for
integral and modular architecture? (2) How do we address strate-
gic alignment and supplier compatibility issues into supply chain
configuration decision making? (3) What are the benefits of mod-
ular architecture with respect to supply chain performances? (4)
What kind of managerial implication or knowledge can be created
out of this exercise?
Section 2 presents a brief overview of recent literature that ad-
dresses linking product design and supply chain configuration.
Section 3 describes the proposed multi-objective optimization
framework to match product architecture strategies with supply
chain decisions. In Section 4, we provide two illustrative examples
to demonstrate the efficacy of the proposed framework. Manage-
rial implications evolved from this study are listed in Section 5. Fi-
nally, Section 6 presents conclusions and directions for future
work.
2. Related literature
Product architecture decisions are far-reaching. They influence
decisions throughout the entire lifecycle—design, production, dis-
tribution, service, and retirement—of the product (Ulrich, 1995).
According to Dahmus et al. (2001) a modularization strategy en-
abled Volkswagen to save about $1.7 billion annually on develop-
ment and production costs. Modularization is perceived as one of
the strategies to simplify process and improve the operational per-
formance (Miltenburg, 2003). It enables original equipment mak-ers (OEMs) to transfer or outsource production, and sometimes
the development of modules, to key suppliers. Fixson (2005) noted
that individual product architecture characteristics, such as degree
of commonality, nature of interactions, and interfaces between
components may constrain strategic decisions such as postpone-
ment or late customization. He suggests that these characteristics
also affect operational decisions in the supply chain domain such
as service level, delivery schedule, and resources planning as
shown in Fig. 1. Building on earlier work, Fixson and Park (2008)
have investigated effects of increasing the integrality of product
architecture on the structure of the supply chains as a whole. More
recently, empirical studies have been conducted to establish the
relationships among the SC variables with product design variables
(Pero et al., 2010).
The commercial success of a product depends not just on its de-
sign and technical performance, but also on the performance of the
firm’s supply chain in supporting the production of the product,
especially when the demand process is uncertain. In this regard,
Graves and Willems (2000) first developed an optimization model
to determine the safety stock level at various nodes of a multi-
stage supply chain. The initial model was based on the assumption
Fig. 1. Interaction between product architecture characteristics decisions in product, process and supply chain domains. Adapted from Fixson (2005).
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of stationary demand process; however, the authors have recently
proposed a two-stage dynamic model to handle the non-stationary
demands (Graves and Willems, 2008). They also introduced the
term ‘‘supply chain configuration’’ by combining inventory place-
ment decisions with supply options selection decision and pre-
sented optimization models for supply chain configuration of
new products (Graves and Willems, 2003, 2005). In addition to
supply chain costs, Bachlaus et al. (2008) have presented a multi-
objective model considering volume flexibility and plat flexibility.
The authors used a hybrid Taguchi-particle swarm optimization
technique to solve the model. Yadav et al. (2009) used algorithm
portfolio approach to compare the computational performance of
five methods to solve a total supply chain costs minimization prob-
lemduring supply chain configuration. The other recent works that
have been published on supply chain configuration are limited to
supply chain design issues and do not link them with product de-
sign structure. Nonetheless, these studies cover a variety of issues
in supply chain network configuration. The sample topics include
determining the optimal manufacturing scheduling (Framinan
and Ruiz, 2010), inventory coordination policy (Toktas-Palut and
Ulengin (2011)), managing complexity due to multiple options at
the assembly chain nodes (Wang et al., 2010), data management
and coordinated decisions making in PD and supply chains design
( Jafari et al., 2009; Lee et al., 2009; Luh et al., 2010; Framinan and
Ruiz, 2010; Garcia et al., 2010), inventory location and tasks alloca-
tion (Gumus et al., 2009; Wang et al., 2011), and consideration
product lifecycle and reverse flows into the supply chain configu-
ration decision (Salema et al., 2010).
A number of studies have focused on modeling supply chain
decision in conjunction with product design and manufacturing
process design decisions. Fine et al. (2005) developed a goal pro-
gramming model to optimize product fidelity and cost objectives.
Feng et al. (2001) jointly considered component tolerances and
supplier selection decisions. Huang et al. (2005) developed a math-
ematical model to study the interdependency of supply chain con-
figuration and product development decisions with respect to
product variety and commonality. Fixson (2005) and Fixson andPark (2008) presented a comprehensive overview of how product
architecture significantly influences all domains of product devel-
opment decisions, manufacturing, and supply chain issues. Jiao
et al. (2009) proposed a ‘‘factory loading application problem
(FLAP)’’ and ‘‘constraint satisfaction’’ approaches to address the is-
sues of coordinating product, process, and supply chain decisions.
Further, ElMaraghy and Mahmoudi (2009) have incorporated cur-
rency exchange rate in determining total supply chain costs while
selecting the optimal modular design. In a recent study by Chiu and
Okudan (2011), the authors presented an integrated optimization
model for supplier selection decisions by combining with manu-
facturing process selection decisions during product development.
Several other studies have focused on designing a supply chain
for mass customization. Based on their empirical research findings,Salvador et al. (2004) argued that the degrees of freedom in choos-
ing product features directly influenced configuration decisions for
both the product architecture and the supply chain. Huang et al.
(2007) employed a game theoretic approach to configure both
the product family and the supply chain for mass customization.
Other important studies that integrated supply chain decisions
and mass customization include an assemble-to-order case study
on automotive wire harness using a simulated annealing heuristic
(Cunha et al., 2007), and response time reduction using queuing
model (Vidyarthi et al., 2009). Process flexibility in supply chains
is central for achieving the objectives of being lean, agile, and able
to do mass customization. A flexible manufacturing systems capa-
bility is helping Japanese automakers such as Toyota, Nissan, and
Mitsubishi to incorporate the ever-changing market demand intotheir production plans (Tomino et al., 2009).To assess the flexibility
of multi-stage supply chains, Graves and Tomlin (2003) have
developed a numerical measure of flexibility. Moreover, a supply
chain can have different levels of responsiveness at different nodes,
depending upon the configuration of individual nodes (Reichhart
and Holweg, 2007).
Product architecture has also been studied in the context of
buyer–supplier relationships. Sako and Murray (1999) suggest
two different roles to address modularity in the supply chain:
the ‘‘integrator’’ and the ‘‘modularizer.’’ In the integrator role, the
OEM retains module control, while in the ‘‘modularizer’’ role, the
OEM transfers module control to first-tier suppliers that possess
the capabilities required to provide modular solutions. Camuffo
(2000) examines the implications of modularization in design,
manufacturing, and organization of the automotive supply chain.
Fine et al. (2005) suggest that product architecture and supply
chain architecture should be aligned along the integrality-modu-
larity spectrum. They suggest, ‘‘members of the modular supply
chain are highly dispersed geographically and culturally, with
few close organizational ties and modest electronic connectivity’’;
whereas the integral supply chain is ‘‘one in which the members of
the chain are in close proximity with each other, where proximity
can be measured along the four dimensions of geography, organi-
zation, culture, or electronic connectivity.’’ Ulku and Schmidt
(2011) have found analytically that supplier relationship and prod-
uct architectural design are interdependent. However, as men-
tioned earlier, to our knowledge, there are not any mathematical
models availablein the prior literature that quantifies the influence
of PA design on SC configuration. Another noted gap is the lack of
consideration of compatibility of partners into the SCM decisions.
This paper attempts to address these issues through a multi-
objective optimization model.
3. Proposed framework to match PA with supply chain design
We propose a three-step process to match the PA with SC de-
sign. These three tasks include: (1) selection of product architec-ture, (2) evaluation of potential suppliers, and (3) optimal
configuration of supply chain. As mentioned earlier, our approach
is different from prior studies in that it integrates supply chain de-
sign decisions with product architectural decisions, and provides a
quantitative framework to study the sensitivity or impact of one
(product development) decision on the other (supply chain) deci-
sions. Further, since the product architecture strategy and the cor-
responding supply chain configuration both are established during
the early stage of the product development cycle, the information
available on potential suppliers is generally ambiguous, and key
decisions are made based only on subjective evaluation criteria.
Therefore, we propose a fuzzy-logic based model to compute the
compatibility index of each supplier based on subjective informa-
tion available through subject matter experts (SME) interviews.
3.1. Task I: Select architectural strategies and the corresponding supply
chain networks
The first task involves the selection of potential product archi-
tecture strategies the firm might adopt using one or more of the
modular product design methodologies presented in literature
(e.g., see Nepal, 2005; Nepal et al., 2005). Through scenario analysis
or using multiple methods, one can arrive at multiple modular
strategies. These architectural strategies are then used to map
the supply chain networks to determine the optimal supply chain
configuration for each architectural strategy. A generic bill of mate-
rials (GBOM) ( Jiao et al., 1998) is used to represent modular rela-tionship for a product. For example, let’s assume a product X
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with five components that are arranged into three modules as
shown in Fig. 2. Component 5 is a module by itself.
Once the product GBOM is developed, the next step is to devel-
op the corresponding supply chain network. For modeling pur-
poses, the supply chain network is represented as a multi-stage
network. The stages or nodes of the network represent suppliers
of materials, and the directed arcs represent the demand and
supply relationships (i.e., flow of materials). For example, since
module 1 in product X consists of components 1 and 2, the stages
of components 1 and 2 are connected directly to that of module 1
in the supply chain network. Fig. 3 shows the corresponding supply
chain network for product X .
Two sets of stages can be observed from the supply chain
network. One set includes the most upstream stages that perform
procurement of raw materials and do not have any further prede-
cessors. The second set includes the assembly stages where two or
more components are combined together.
3.2. Task II: Evaluate the compatibility of potential members of supply
chain
Once the supply chain network is developed, the second task in-
volves identifying alternatives available for each node of the sup-
ply chain network and collecting the necessary information on
each one of them. The decision maker(s) collect information on
the production cost, lead-time, and compatibility index of each
alternative identified. Since we adopted the guaranteed service-
time model (Graves and Willems, 2000) in this research, we as-
sumed that the production cost and lead-time of each alternative
are deterministic and known.
In this research, we consider three key factors (namely, struc-
tural, managerial, and financial) to compute the computability in-
dex. In order to improve the precision of computation, the three
main factors are further divided into 12 sub-factors. The sub-fac-
tors are structural (cultural alignment, communication and infor-
mation sharing, and coordination and cooperation); managerial
(managerial trust and commitment, compatibility in strategic
goals, conflict management techniques); and financial (profit mar-
gin, return on investment, bond rating). Since the information
regarding these sub-factors is largely qualitative in nature, we
use rule-based fuzzy logic to aggregate the sub-factors into three
indexes such as structural alignment index (SI ), managerial alignment
index (MI ), and financial alignment index (FI ). Lastly, the overall
compatibility index is determined as shown below:
Compatibility index ðbiÞ forasupplieri
¼ w1 SI i þ w2 MI i þ w3 FI i: ð1Þ
It may be important to note that information on the degree of com-
patibility of each alternative to the supply chain is obtained throughsubject matter expert (SME) interviews and then fed into a fuzzy-lo-
gic based model in order to compute an overall compatibility index.
More details on fuzzy logic model can be found in Famuyiwa et al.
(2008).
3.3. Task III: Match product architecture to supply chain configuration
In this task, a multi-objective mathematical model is developed
to determine the optimal supply chain configuration for each prod-
uct architecture strategy. It is modeled as a weighted goal pro-
gramming (GP) model (Ignizio, 1976), and solved by using
genetic algorithm (GA) because of the non-linearity of the resulting
model. The formulation of the GP model is as follows.
Notationsc i production cost at stage iT i production time at stage ibi compatibility index at stage iOi supplier option is selected for stage iC iOi
production cost for stage i with option O i
T iOi production time for stage i with option Oi
biOi compatibility index for stage i with option Oi
U i inventory coverage period for stage iLi replenishment lead-time for stage iS out i guaranteed output service of stage iS in j guaranteed input service time for stage j by its predecessor
stage
Product X
Module 1 Module 2
Component 1 Component 2 Component 3 Component 4 Component 5
Fig. 2. Generic bill of materials (GBOM) showing module relationship for product X .
1
2
8
7
6
3
4
5
Component 5
Component 1
Component 2
Component 3
Component 4
Module 1
Module 2
Product X
1
2
8
7
6
3
4
5
Component 5
Component 1
Component 2
Component 3
Component 4
Module 1
Module 2
Product X
Stages 1, 2, 3, 4, 5 = procurement stagesStages 6 and 7 = assembly stages
Stage 8 = end stage
Fig. 3. Corresponding supply chain network diagram of product X .
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ai service level at stage iW i cumulative cost of work in process items at stage iC i cumulative cost of finished items at stage ihi inventory holding cost per unit at stage iH time interval of interest to the decision-makerl, r mean and standard deviation of customer demand at the
end stage of the supply chain respectively AOH i ¼
12kili þ airi ffiffiffiffiffiU ip average on-hand inventory level at stage i
WIP i = liT i average pipeline/working inventory (WIP) at stage ihi{(C i AOH i) + (W i WIP i)} + (H c i li) total cost of goods sold
and inventory carrying costs at stage i
Pi2N
hifðC i AOH iÞ þ ðW i WIP iÞgþðH c i liÞ
total supply chain cost
(TSCC)Pi2N bi
total compatibility index across the supply chain (TCI)
3.3.1. Formulation of goal programming (GP) model
Goal programming model is a popular technique that is used in
simultaneous optimization of multiple objectives. In this study, the
objectives of the GP model are to minimize the total costs of supply
chain (TCSC) and to maximize the total supply chain compatibility
index (SCCI) among the supply chain members. That is,
MinimizeXi2N
hiððC i AOH iÞ þ ðW i WIP iÞ½ þ ðH c i liÞ
( )
and MaximizeXi; j2 A
bij
( ) ð2Þ
In this paper, we use a weighted GP model that is formulated as fol-
lows. Let kTSCC denote the optimal cost value when the TSCC model
is solved as single objective (known as target value for cost), kCI de-
note the optimal compatibility index value when the compatibility
model is solved as single objective (known as target value for com-
patibility). Our objective here is to minimize the deviations from
these target values. Mathematically, let wTSCC and DTSCC denote
the weight and deviation of the total supply chain cost from its tar-
get value, and wCI andDCI denote the weight and deviation compat-
ibility index from its target value. Then the weighted goal
programming formulation of the multi-objective problem is given
as:
Minimize wTSCC
DTSCC
kTSCC
þ wCI
DCI
kCI
ð3Þ
Subject to:
i:
Xi2N
½hiððC i AOH iÞ þ ðW i WIP i þ ðH c i liÞ
( ) DTSCC 6 kTSCC
ðTSCC GoalÞ; ð4Þ
ii:Xi; j2 A
bij( )
þ DCI P kCI ðCI GoalÞ; ð5Þ
iii:XOi2S ðiÞ
T iOi yiOi
( ) T i ¼ 0; i 2 N ; ð6Þ
iv:
XOi2S ðiÞ
C iOi yiOi
( ) c i ¼ 0; i 2 N ; ð7Þ
v: S ini þ T i S out i P 0; i 2 N ; ð8Þ
vi:Xi2N
yiOibiO j
( ) bi ¼ 0; i 2 N ; ð9Þ
vii:XOi2S ðiÞ
yiOi¼ 1; i 2 N ; ð10Þ
viii: S out i ;Oi;bij P 0; i 2 N ; ð11Þ
ix: yiOiis binary for i 2 N and O i;
where, DTSCC; DCI; S out i and yiOi
are decision variables. In this case,
S out i denotes the guaranteed service time by node i to downstream
node(s) and y iOiis an integer decision variable that represents the
option selected at node i. constraints (i) and (ii), referred to as soft
constraints. They ensure that the deviations of both total supply
chain cost and compatibility are not greater than their target values.
Constraints (iii) and (iv) apply to production time and production
cost corresponding to the option selected at each node. Once a sup-
plier option is selected, the production lead-time and production
cost of the node are set to the corresponding production lead-time
and production cost of the option selected. Constraint (v) ensures
that the inventory coverage time is non-negative since back-order
is not allowed. Constraint (vi) ensures that once a supplier option
is selected, the compatibility index of the node is set to the corre-
sponding value of the compatibility index of the supplier selected.
Constraint (vii) ensures that only one supplier is selected at each
node (single sourcing). The final two constraints represent the char-
acteristics of the decision variables. Lastly, N indicates the number
of nodes in the supply chain.
4. Examples
We use two case studies to demonstrate an application of the
proposed framework. The first case study is taken from heavy
industry, and is applied to bulldozer assembly and manufacturing
as presented in Graves and Willems (2003). The second case study
is applied to an automotive climate control system. The reasons for
selecting these two case studies are as follows: first, we felt it was
important to test the universality of the framework by selecting
case studies from two different industries; second, we wanted to
replicate the analysis given in Graves and Willems (2003) and
check whether the proposed approach improves the results in
terms of supply chain compatibility and stability. In other words,
we used Graves and Willems’ results as baseline values for com-
paring our results.
4.1. Bulldozer case study
The bulldozer supply chain is a good example of a heavy indus-
try supply chain. At a high level, components of a bulldozer can be
combined into 14 major groups: frame assembly, case, brake, drive,
plant carrier, platform, fender, roll-over, transmission, engine, fan,
bogie assembly, pin assembly, and track-roller frame.
The supply chain setting of the bulldozer case study is same as
that in Graves and Willems (2003). The average daily demand is set
at 5 and the daily standard deviation is 3. Furthermore, the de-
mand bound is equal to the 95th percentile of demand, so that
the safety factor is equal to 1.645. The following assumptions were
made for the purpose of analysis: the bulldozer manufacturing
company uses annual demand for configuring the supply chain,there are 260 work days per year, and the company applies an an-
nual holding cost rate of 30% when calculating the inventory costs.
4.1.1. Task I: Select PA strategies and the corresponding supply chain
networks
Here, two product architecture strategies (integral and modular
architectures) are selected for the bulldozer example. For demon-
stration purpose, we assumed that in an integral architecture envi-
ronment the 14 major groups of components would be arranged
into three modules as shown in Fig. 4(a). On the other hand, for
a modular architecture case, the main assembly module would
be further broken down into three additional modules (see
Fig. 4(b)).
Fig. 5(a) and (b) shows the supply chain network diagrams forboth the integral and modular architecture respectively.
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4.1.2. Task II: Compute compatibility index and costs of supply chain
members
Upon selection of candidate architectural designs and their cor-
responding supply chain network, the next step is to quantify the
compatibility and costs related to supply chain management of
each alternative available at each node. There are two types of
nodes in the bulldozer supply chain: procurement, which repre-
sent the procurement of components from outside of the supply
chain, and assembly, which represent where one or more compo-nents are combined together in the process. For testing purposes,
two alternatives are considered for each node. If the node is a pro-
curement stage, the first alternative represents the standard supply
option (that is, the existing procurement arrangement). The second
option represents a consignment option where the supplier is
responsible for providing immediate delivery to the bulldozer line.
Similarly, for the assembly node, the first option represents the
standard manufacturing method while the second option repre-
sents an expedited alternative that corresponds to a supplier
who has invested in process improvement efforts in order to de-
crease its supply lead-time.
The production costs and lead-times data for the modular de-
sign are obtained from Graves and Willems (2003). It is worth not-
ing here that the modular design data are aggregated to create thecorresponding integral design data, where applicable. For example,
chassis–platform, common-assembly, dressed-out engine, and
main-assembly production cost data in the modular design are
added together to provide production cost data for the main-
assembly in the integral design; thus, $28,420 ($4320 + $8000 +
$4100 + $12,000 = $28,420) for the standard option and $28,795
($4395 + $8075 + $4175 + $12,150 = $28,795) for the expedited op-
tion. Table 1 presents production costs and the associated produc-
tion lead-times for each alternative available for integral design.
To obtain data on the compatibility drivers for each alternativeat each node, it is assumed that the ratings of the compatibility
drivers for the low-cost alternative range uniformly in [1–8] and
that of high-cost alternative ranges uniformly in [3–10]. Therefore,
for the low-cost supply alternative, the value of the rating for each
compatibility driver is calculated as U(1–8), while that of the high-
cost supply alternative is calculated as U(3–10), where U(a, b) is a
discrete uniform distribution in the range of [a, b]. Table A.1 (see
appendix) shows the compatibility data and corresponding index
of all the potential supply chain members for the given bulldozer
case study.
4.1.3. Task III: Match modular strategy to supply chain configuration
Once the data is collected on production costs, production lead-
times, and compatibility index ratings for all alternatives in thesupply chain network, the next task is to match each architectural
a) Integral architecture b) Modular architecture
Frame assembly
• Case
• Brake
• Drive
• Plant carrier
• Platform
• Fender
• Transmission
• Brake & Drive
• Engine
• Fan
Main Assembly Module
• Boggie Assembly
• Pin Assembly
Suspension Module• Track Roller Frame
• Frame assembly
• Case
• Brake
• Drive
• Plant carrier
• Platform
• Fender
• Transmission
• Brake & Drive
• Engine
• Fan
Main Assembly Module
• Boggie Assembly
• Pin Assembly
Suspension Module• Track Roller Frame
• Boggie Assembly
• Pin Assembly
Suspension Module
Dressed out
Engine Module
Chassis/Platform
Module
•Platform
•Fender
•Rollover
Common
Subassembly
Module
•Frame assembly
•Case
•Transmission
•Brake
•Drive
•Plant carrier
•Engine
•Fan
•Track Roller Frame• Boggie Assembly
• Pin Assembly
Suspension Module
Dressed out
Engine Module
Chassis/Platform
Module
•Platform
•Fender
•Rollover
Chassis/Platform
Module
•Platform
•Fender
•Rollover
Common
Subassembly
Module
•Frame assembly
•Case
•Transmission
•Brake
•Drive
•Plant carrier
•Engine
•Fan
•Track Roller Frame
Fig. 4. Modular structures of bulldozer.
a) Integral architecture b) Modular architecture
Final
Assembly
Track
Roller
Frame
Main
Assembly
Platform
Fender
Roll Over
Frame
Assembly
Case
Frame
and Case
Transmission
Plant
Carrier Engine
Fan
Boggie
Assembly
Suspension
Brake
Drive
Plant
Carrier
Brake and
Drive
Engine
Fan Pin Assembly
Final
Assembly
Track
Roller
Frame
Main
Assembly
Platform
Fender
Roll Over
Frame
Assembly
Case
Frame
and Case
Transmission
Brake
Drive
Plant
Carrier
Brake and
Drive
Engine
Fan
Dressed OutEngine
Pin
Assembly
Boggie
Assembly
Suspension
Common
Subassembly
Chassis/Platform
Fig. 5. Supply chain network for bulldozer. Adapted from Graves and Willems (2003).
B. Nepal et al. / European Journal of Operational Research 216 (2012) 312–325 317
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strategy to its corresponding optimal supply chain configuration.
For this purpose, the goal programming model was solved using
genetic algorithm. Tables 2 and 3 show the results of numerical
measures of the supply chain for integral and modular architec-
tural strategies respectively.
4.1.4. Discussion of results
As shown in Table 3, in the majority of the bulldozer supply
chain stages for a modular architectural design, option two hasbeen selected. While it is more expensive than option one, option
twohas a lower production lead-time andhigher compatibility rat-
ings for all stages because its modularity increases the degree of
dependency, based on relative costs of inputs, between supply
chain nodes. Since more expensive alternatives are selected in
the modular design case, the value of the cost of goods sold (COGS)
is slightly higher for the modular design ($ 95,498,000) than inte-
gral design ($95,205,500). However, because of the corresponding
lower production lead-times for the options selected in modular
design, the inventory carrying cost in the modular design is about20% lower than in the integral design ($1,381,503 versus
Table 1
Lead-times and costs for bulldozer case study (Graves and Willems, 2003).
Modular Integral Modular Integral
Stage Alternative Lead time
(days)
Cost
($)
Lead time
(days)
Cost
($)
Stage Alternative Lead time
(days)
Cost
($)
Lead time
(days)
Cost
($)
Frame assembly Standard 19 605 19 605 Engine Standard 7 4500 7 4500
Consignment 0 622 0 622 Consignment 0 4547 0 4547
Case Standard 15 2200 15 2200 Fans Standard 12 650 12 650Consignment 0 2250 0 2250 Consignment 0 662 0 662
Brake group Standard 8 3850 8 3850 Chassis/platform Standard 7 4320 NA NA
Consignment 0 3896 0 3896 Expedite 2 4395 NA NA
Drive group Standard 9 1550 9 1550 Common
subassembly
Standard 5 8000 NA NA
Consignment 0 1571 0 1571 Expedite 2 8075 NA NA
Plant carrier Standard 9 155 9 155 Dressed-out
engine
Standard 10 4100 NA NA
Consignment 0 157 0 157 Expedite 3 4175 NA NA
Platform group Standard 6 725 6 725 Boggie assembly Standard 11 575 11 575
Consignment 0 732 0 732 Consignment 0 584 0 584
Fender group Standard 9 900 9 900 Pin assembly Standard 35 90 35 90
Consignment 0 912 0 912 Consignment 0 95 0 95
Roll over group Standard 8 1150 8 1150 Track roller
frame
Standard 10 3000 10 3000
Consignment 0 1164 0 1164 Consignment 0 3045 0 3045
Case and frame Standard 16 1500 16 1500 Main assembly Standard 8 12,000 8 28,420
Expedite 4 1575 4 1575 Expedite 2 12,150 2 28,795
Transmission Standard 15 7450 15 7450 Suspension
Group
Standard 7 3600 7 3600
Consignment 0 7618 0 7618 Expedite 2 3675 2 3675
Final drive and
brake
Standard 6 3680 6 3680 Final assembly Standard 4 8000 4 8000
Expedite 2 3755 2 3755 Expedite 1 83,000 1 83,000
Table 2
Results of optimal supply chain configuration for integral architecture of a bulldozer.
Stage Alternative
selected
Guaranteed
service time (days)
AOH cost WIP cost Total inventory
carrying cost
COGS TSCC
Frame assembly 2 0 $0 $0 $0 $808,600 $808,600
Case 1 0 $12,614 $49,500 $62,114 $2,860,000 $2,922,114Brake 1 0 $16,120 $46,200 $62,320 $5,005,000 $5,067,320
Drive 2 0 $0 $0 $0 $2,042,300 $2,042,300
Plant carrier 2 0 $0 $0 $0 $204,100 $204,100
Platform 1 6 $0 $6,525 $6,525 $942,500 $949,025
Fender 1 7 $1,884 $12,150 $14,034 $1,170,000 $1,184,034
Roll over 1 7 $1,702 $13,800 $15,502 $1,495,000 $1,510,502
Frame and case 1 7 $19,194 $85,728 $104,922 $1,950,00 $2,054,922
Transmission 2 0 $0 $0 $0 $9,903,400 $9,903,400
Brake and drive 1 6 $0 $66,762 $66,762 $4,784,000 $4,850,762
Engine 2 0 $0 $0 $0 $5,911,100 $5,911,100
Fan 1 7 $2,152 $11,700 $13,852 $845,000 $858,852
Chassis/platfom
Common assembly
Dressed out engine
Boggie assembly 1 0 $2,823 $9,488 $12,311 $747,500 $759,811
Pin assembly 2 0 $0 $0 $0 $123,500 $123,500
Track roller 1 10 $0 $45,000 $45,000 $3,900,000 $3,945,000
Main assembly 2 12 $0 $326,760 $326,760 $37,433,500 $37,760,260
Suspension 1 7 $0 $25,935 $25,935 $4,680,000 $4,705,935
Final assembly 1 0 $569,820 $415,410 $985,230 $10,400,000 $11,385,230
Total $626,309 $1,114,958 $1,741,267 $95,205,500 $96,946,767
318 B. Nepal et al. / European Journal of Operational Research 216 (2012) 312–325
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$1,741,267). Figs. 6 and 7 depict graphical representations of the
optimal supply chain configurations for integral and modular strat-
egies respectively.
Table 4 gives a summary of the results optimal configuration for
both cases.
By selecting a modular design, a firm can outsource more of its
production, leading to further cost savings if such outsourcing can
be achieved at lower costs. While the final decision on the selection
of supply chain configuration lies with management, the optimiza-
tion framework provides a hands-on, objective process for making
an informed decision.
4.1.5. Sensitivity analysis of supplier development program
Research conducted by Krause (1997) shows that supplier per-
formance improved significantly in all dimensions as a result of a
supplier development program (SDP). The authors report esti-
Table 3
Results of optimal supply chain configuration for modular architecture of a bulldozer.
Stage Alternative
selected
Guaranteed
service time (days)
AOH cost WIP cost Total inventory
carrying cost
COGS TSCC
Frame assembly 2 0 $0 $0 $0 $808,600 $808,600
Case 2 0 $0 $0 $0 $2,925,000 $2,925,000
Brake 2 0 $0 $0 $0 $5,064,800 $5,064,800
Drive 2 0 $0 $0 $0 $2,042,300 $2,042,300
Plant carrier 2 0 $0 $0 $0 $204,100 $204,100Platform 2 0 $0 $0 $0 $951,600 $951,600
Fender 2 0 $0 $0 $0 $1,185,600 $1,185,600
Roll over 2 0 $0 $0 $0 $1,513,200 $1,513,200
Frame and Case 2 3 $6,583 $21,957 $28,540 $2,047,500 $2,076,040
Transmission 2 0 $0 $0 $0 $9,903,400 $9,903,400
Brake and drive 2 2 $0 $22,505 $22,505 $4,881,500 $4,904,005
Engine 2 0 $0 $0 $0 $5,911,100 $5,911,100
Fan 2 0 $0 $0 $0 $860,600 $860,600
Chassis/platfom 1 5 $14,923 $52,164 $67,087 $5,616,000 $5,683,087
Common assembly 2 5 $0 $76,445 $76,445 $10,500,000 $10,573,945
Dressed out engine 2 3 $0 $32,834 $32,834 $5,430,000 $5,460,334
Boggie assembly 2 0 $0 $0 $0 $759,200 $759,200
Pin assembly 2 0 $0 $0 $0 $123,500 $123,500
Track roller 1 7 $7,692 $45,000 $52,692 $3,900,000 $3,952,692
Main assembly 2 7 $0 $156,320 $156,320 $15,800,000 $15,951,320
Suspension 1 7 $0 $26,030 $26,030 $4,680,000 $4,706,030
Final assembly 1 0 $502,290 $416,760 $919,050 $10,400,000 $11,319,050Total $531,488 $850,015 $1,381,503 $95,508,000 $96,879,503
Final Assembly
Track Roller
Frame
Main Assembly
Platform
Fender
Roll Over
Frame
Assembly
Case
Frame
and Case
Transmission
Brake
Drive
Plant
Carrier
Brake
and Drive
Engine
Fan
Pin
Assembly
Boggie
Assembly
Suspension
1
6525
1
14034
2
0 1
45000
1
985230
2
326760
1
62114
1
62320
2
0
2
0
1
15502
1
104922
2
0
1
66762
2
0
1
13852
1
12311
1
25935
2
0
Alternative Selec ted
Inventory Level
Legend
Fig. 6. Optimal supply chain configuration for bulldozer with integral architecture.
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mated changes due to supplier development in the reduction in
number of incoming defects (by 75.91%), improvement in percent
on-time delivery (by 75.91%), reduction in cycle time from order
placement to receipt, inclusively (by 15.8) days, and an increase
in percent orders received complete (by 78.34%). In this study,
we argue that an implementation of a supplier development pro-
gram will improve the compatibility measures (bi) of suppliers be-
cause the improvements in the above mentioned attributes have apositive influence on compatibility input factors. Thus, for sensitiv-
ity analysis, we assume a scenario where the standard suppliers’
compatibility (option-1) indexes are increased by certain percent-
ages, either through providing better information/educational
training to foreign suppliers or by more aggressive negotiations.
For simplicity, we also assumed that this will be part of a regular
supply management function of a company’s purchasing depart-
ment, hence it will not add any significant cost. Table 5 shows
the compatibility index sensitivity analysis on supply chain perfor-
mance, thereby also impacting the architectural strategy and sup-
ply chain configuration.
Sensitivity analysis results have shown that the total supply
chain costs have decreased with an increase in compatibility index.
However, there is no direct or linear correlation between the totalsupply chain costs and the total compatibility index of the suppli-
ers. It may be due to the fact that the total supply chain cost is a
function of COGS and inventory cost, which mainly depends on
lead time. Secondly, lead times of standard suppliers tend to be
longer than consignment options, but because of standard suppli-
ers’ lower prices, the model might have selected the standard op-tion. Further, we also observed that the integral architectural
design approach selected a large number of standard supply op-
tions compared to modular architecture. As a result, the total sup-
ply chain costs and total compatibility index are both higher for
integral architecture than those for modular architecture.
4.1.6. Comparison of Graves and Willems’s approach and the proposed
approach
The results of the proposed multi-objective optimization model
were compared with those of the Graves and Willems’ (2003) sin-
gle objective model for supply chain configuration. We used their
approach as base case in this study. Table 6 summarizes the results
from optimizing the supply chain configuration based on a single
objective (total supply chain cost) and compares the results tothose for a solution based on multiple objectives (total supply
chain cost and compatibility) model as presented in the research
work.
Although the multi-objective supply chain configuration mod-
el increases the COGS by $690,300 relative to the single-objec-
tive model, because the higher production cost options have
lower production lead-times, there is a reduction of $338,797
in the total inventory cost in the supply chain network. This still
leaves an overall increase in supply chain cost of $351,503, but
with the added benefits of more stability in the supply chain
relationship due to the selection of more compatible suppliers
in the multi-objective model, which in turn allows the deci-
sion-maker to directly ascertain the cost trade-offs involved in
achieving compatibility of supply options selected in the supplychain.
Final
Assembly
Track Roller
Frame
Main
Assembly
Platform
Fender
Roll Over
Frame
Assembly
Case
Frame and
Case
Transmission
Brake
Drive
PlantCarrier
Brake and
Drive
Engine
Fan
Dressed Out
Engine
Pin
Assembly
Boggie
Assembly
Suspension
Common
Subassembly
Chassis/
Platform
2
1
1
1
2
2
2
2
2
2
2
2
2
2
2
22
1
2
2
2
2
0
0
0
0
0
0
0
0
28540
0
0
0
22505
76445
67087
52692
156320
0
0
919050
2603032834
Alternative Selected
Inventory Level
Legend
Fig. 7. Optimal supply chain configuration for bulldozer with modular architecture.
Table 4
SC performance measures for integral vs. modular architectures of a bulldozer.
Cost category Integral Modular
Cost of goods sold (COGS) $95,205,500 $95,498,000
Inventory cost (INVC) $1,741,267 $1,381,503Total supply chain cost $96,946,767 $96,879,503
Contribution of COGS to TSCC 98.20% 98.57%
Contribution of INVC to TSCC 1.80% 1.43%
Total compatibility index 9.15 14.06
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4.2. Automotive climate control system
As a second case study, the proposed framework for matching a
product architecture strategy with a supply chain design was val-
idated through application to an automotive climate control sys-
tem. The automotive climate control system is used for cooling
and heating of the passenger compartment in automobiles. A typ-
ical automotive climate control system consists of the following 16
major components: air controls, refrigeration controls, sensors,
heater hoses, command distribution, radiator, engine fan, con-
denser, compressor, accumulator, evaporator core, heater core,
blower motor, blower controller, evaporator case, and actuator.
The data for this case study was collected from a tier-1 automotive
supplier located in Michigan (US). In order to protect the confiden-
tiality of the company, the data was masked. We followed the
same steps to determine optimal supply chain configuration for cli-
mate control systems as in the earlier bulldozer supply chain
study.
In this case, two types of architectures are selected for further
investigation. The first one is based on the current architecture of
the automotive climate control system. We refer to the current
architecture as integral architecture, and the other, proposed by
Nepal (2005), as modular architecture. Fig. 8 illustrates both inte-
gral and modular architectures for the automotive climate con-trol system. Notice that the current/integral architecture has
only four sub-assemblies which can be treated as four modules
as shown in Fig. 8(a). The first, a large HVAC module, consists
of eleven components: air-control, refrigerant-controls, com-
mand-distribution, sensors, blower-controller, accumulator, evap-
orator-case, actuator, heater-core, blower-motor, and evaporator-
core. The second, front-end module, consists of a radiator, con-
denser, and engine, while the third and fourth modules consist
of compressor and heater hoses, respectively. In comparison,
the new modular architecture has six modules as shown in
Fig. 8(b).
4.2.1. Evaluation of potential members of the climate control supply
chain
Data on the potential members of the supply chain was col-
lected through interviews with SMEs, senior climate control engi-
neers with experience in various departments of climate control.
For each stage of the supply chain, each expert was asked to iden-
tify potential suppliers and provide production costs and produc-
tion lead-times for those suppliers. Similar to Bulldozer case
study, here also for each state two types of suppliers were identi-
fied which are classified as suppliers 1 and 2. Supplier 1 was gen-
erally more expensive but offered lower production lead-times,
and Supplier 2, who offered lower production costs but with longer
production lead-times. The experts from automotive industry were
asked to evaluate each alternative supplier for computing a com-
patibility index. The total climate control experience of the experts
involved in the data collection was over 35 years.
4.2.2. Matching climate control architectural strategy to supply chain
configuration
For the purposes of supply chain configuration, we assume that
climate control will have a mean daily demand of 500 and a stan-
dard deviation of 100. We also assumed that the company uses an-
nual demand for configuring its supply chain, and that there are
260 working days per year. The service level for each stage of the
supply chain is set to 95%, so that the safety factor at each stage
is equal to 1.645. By employing the goal programming model and
solving that using a genetic algorithm, we can optimize the supply
chain network for the given climate control system. Figs. 9 and 10
also show the graphical representation of optimal supply chain
configurations along with the inventory placement level for both
current and optimal architectures, respectively.
4.2.3. Discussion of results
Table 7 summarizes the results from optimizing the supply
chain configuration for existing and optimal modular designs. As
Table 5
SC performance measures of baseline scenario versus SDP case for bulldozer case study.
Cost
Category
Baseline scenario 10% 20% 30% 50%
Integral Modular Integral Modular Integral Modular Integral Modular Integral Modular
Inventory
cost
$1,789,099 $1,696,595 $1,821,112 $1,540,692 $1,993,572 $1,412,148 $2,034,461 $1,412,148 $2,034,461 $1,487,746
COGS $95,205,500 $95,240,600 $95,171,700 $95,338,100 $94,953,300 $94,953,30 $94,892,200 $95,885,400 $94,953,300 $95,435,600
Total SC
cost
$96,994,599 $96,937,195 $96,992,812 $96,878,792 $96,878,792 $96,871,148 $96,926,661 $96,987,778 $96,946,872$96,871 $96,923,346
Total C.
index
14.6 14.61 15.6 14.5 16.3 14.6 17.0 15.1 19.6 15.4
a) Current integral architecture b) new modular architecture
Compressor Heater
Hoses
Big HVAC Module!
Air Control, Refrigerant Controls,
Command Distribution, Sensors,
Blower Controller, Accumulator,
Evaporator Case, Actuator,
Heater Core, Blower Motor, Evaporator Core
Front End Module
Radiator, Condenser
Engine FanCompressor
Heater
Hoses
Big HVAC Module!
Air Control, Refrigerant Controls,
Command Distribution, Sensors,
Blower Controller, Accumulator,
Evaporator Case, Actuator,
Heater Core, Blower Motor, Evaporator Core
Front End Module
Radiator, Condenser
Engine Fan
Big HVAC Module!
Air Control, Refrigerant Controls,
Command Distribution, Sensors,
Blower Controller, Accumulator,
Evaporator Case, Actuator,
Heater Core, Blower Motor, Evaporator Core
Front End Module
Radiator, Condenser
Engine Fan
Controls/Sensors Module
Air Control, Refrigerant Controls, Sensors,
Command Distribution, Blower Controller
HVAC ModuleEvaporator Case, Accumulator,
Heater Core, Blower Motor,
Evaporator Core, Actuator
Heater
Hoses
Engine FanCompressor
Radiator/Condenser
ModuleRadiator, Condenser
Controls/Sensors Module
Air Control, Refrigerant Controls, Sensors,
Command Distribution, Blower Controller
HVAC ModuleEvaporator Case, Accumulator,
Heater Core, Blower Motor,
Evaporator Core, Actuator
Heater
Hoses
Engine FanCompressor
Radiator/Condenser
ModuleRadiator, Condenser
Fig. 8. Current and modular architecture of an automotive climate control system.
B. Nepal et al. / European Journal of Operational Research 216 (2012) 312–325 321
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shown below, the optimal modular architecture creates an annual
savings of $1,001,814 in total supply chain costs, primarily because
this architecture allows the company to source part of their
production to module suppliers who have lower production costs.
Furthermore, the optimal modular architecture offers lower inven-
tory costs ($5,820,813) compared to the current architecture
($4,998,999). Therefore, it is highly recommended that the currentintegral automotive climate control system architecture be
replaced with the optimal modular architecture.
5. Managerial implications
Based on the results of the analysis performed, our research
team made a few observations. First, the greater the number of
modules present in the supply chain network, the higher the com-
patibility ratings required in the supply chain (see Fig. 11) because
the modularity increases the degree of dependency (based on rel-
ative values) between nodes in the supply chain. Therefore, more
alternatives with higher compatibility ratings will be selected in
modular design.
The second observation is that the greater the number of nodesthere are, the greater the flexibility of the supply chain will be. In
parallel, the higher the degree of modularity, the higher the num-
ber of nodes in the supply chain. Therefore, a modular architecture
will be more flexible than an integral architecture, which more of-
ten leads to lower total supply chain cost. Third, if a firm can out-
source production of the modules to suppliers at a lower cost, then
a higher modularity will lead to outsourcing of a larger proportion
of the production at lower cost, leading to an overall lower totalsupply chain cost. In this case, it would be necessary to balance
the need to select suppliers with high compatibility ratings, which
can often be more expensive, versus the ability to outsource mod-
ules at lower costs in modular designs.
Managers can apply the proposed framework in four ways. First,
the framework can be a guideline to evaluate different architec-
tural strategic decisions involved in the creation of a new-product
supply chain. For a planned new product, it can help to identify the
modular strategy that will best serve the company’s overall strat-
egy. Second, since the commercial success of a product depends
not just on its design and technical performance but also on the
performance of the firm’s supply chain in supporting production,
product designers must interact intensively during the product
development stage with supply chain professionals to fully grasp
the operational implications of alternative product designs. These
Alternative Selected
Inventory Level
Legend
Final
Assembly
Big HVAC
Module
Air
Control
Refrigerant
Control
Sensors
Blower
Controller
Evaporator
Case
Accumulator
HeaterCore
Engine
Fan
Condenser Front End
Module
Command
Distribution
Heater
Hoses
Compressor
Blower
Motor
Evaporator
Core
Actuator
Radiator
2
47250
2
47250
1
22500
1
181500
2
43200
1
24300
2
47250
1
432000
2
75600
1
341472
2
4200
2
36300
1
632621
1
436258
1
584853
1
23851
1
1848349
1
568977
2
811882
Fig. 9. Current integral supply chain configuration automotive climate control system.
Table 6
Comparison of results of proposed multi-objective optimization approach with single objective approach.
Cost category Results from single objective model (Graves
and Willems, 2003) without
considering compatibility index
Results from multi-objective
with compatibility index
Numerical difference
Cost of goods sold $94,807,700.00 $95,498,000.00 $690,300.00
Total inventory stock cost $1,720,300.00 $1,381,503.00 $338,797.00
Total supply chain cost $96,528,000.00 $96,879,503.00 $351,503.00
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bulldozer supply chain was adopted from Graves and Willems
(2003) and used as baseline for comparison of the proposed model.
The second product, an automotive climate control system, was se-
lected as an extension to Nepal (2005) to compare the supply chain
performance of the proposed modular architecture versus current
integral climate control architecture. Several insights of manage-
rial importance have also been presented in the paper.
There are at least three areas in which this research can be ex-
tended: first, current assumptions of guaranteed service time
should be relaxed and more realistic (stochastic) service time be
considered; second, more factors like sustainability and flexibility
should be considered in addition to cost and compatibility while
making the SC configuration decisions in order to capture the glob-
alized supply market and shorter product lifecycle aspects of 21st
century marketplace; and thirdly, it would be interesting to see
how downstream nodes (distribution channel) can inform the SC
design decisions. Lastly, the sensitivity analysis results of the bull-
dozer case study have shown that total supply chain costs de-
creased with a correlating increase in the compatibility index.
However, there is no direct or linear correlation between the total
supply chain costs and the total compatibility index of the suppli-
ers. This relationship needs further investigation in future. Lastly,
the current study involved two cases. An empirical study involving
a large number of companies can further strengthen the validation
of managerial implications described in the paper.
Appendix A
See Table A.1.
Table A.1
Compatibility index ratings for each alternative in bulldozer supply chain.
Alternative Cultural
alignment
Communication
and information
sharing
Coordination
and Co-
operation
Managerial
trust and
commitment
Compatibility
in strategic
goals
Conflicts
management
techniques
Profit
margin
Return
on
assets
Bond
rating
Compatibility
index
Frameassembly
Standard 1 4 5 4 5 6 1 3 6 0.3750Consignment 5 6 4 10 10 9 7 10 8 0.7568
Case Standard 2 5 5 2 6 2 3 1 6 0.3000
Consignment 6 8 6 5 8 10 5 6 10 0.7000
Brake group Standard 2 6 4 1 1 2 4 5 4 0.3682
Consignment 9 6 4 6 8 6 4 4 4 0.5500
Drive group Standard 3 1 1 4 5 6 3 3 5 0.2872
Consignment 7 5 5 9 4 6 8 7 6 0.7000
Plant carrier Standard 3 1 6 1 2 3 6 6 5 0.3749
Consignment 8 4 9 6 8 9 7 9 7 0.7500
Platform group Standard 4 3 1 2 4 6 1 5 6 0.3249
Consignment 5 9 10 7 9 8 10 5 5 0.7500
Fender group Standard 5 5 3 4 4 6 1 1 3 0.2804
Consignment 9 6 6 9 6 9 10 10 10 0.8517
Roll over group Standard 2 5 1 6 6 5 3 2 1 0.3249Consignment 8 6 6 10 7 10 10 4 6 0.7596
Case and frame Standard 2 4 6 6 1 6 3 4 4 0.4250
Expedite 6 6 10 8 8 10 7 7 10 0.7500
Transmission Standard 3 4 6 4 6 4 2 6 2 0.3750
Consignment 8 5 4 4 6 6 5 7 10 0.6250
Final drive and
brake
Standard 3 6 2 2 3 3 6 3 1 0.2499
Expedite 5 8 4 9 6 4 8 4 9 0.7000
Engine Standard 4 5 4 4 4 6 5 5 1 0.3750
Consignment 9 8 7 4 9 4 9 6 6 0.7500
Engine Standard 5 6 5 2 2 4 4 3 1 0.3000
Consignment 4 10 7 6 10 5 6 7 10 0.7500
Fans Standard 2 3 6 2 3 5 1 2 3 0.2499
Expedite 9 4 8 8 8 9 9 10 7 0.8446
Common
subassembly
Standard 4 4 1 3 5 2 5 2 4 0.3749
Expedite 7 4 5 8 8 10 4 4 8 0.5750
Dressed-out
engine
Standard 2 6 1 1 4 2 4 6 4 0.3749
Expedite 4 6 9 4 6 6 9 6 10 0.6750
Boggie
assembly
Standard 4 1 5 5 4 3 2 4 5 0.4499
Consignment 5 8 6 10 8 8 6 5 9 0.7000
Pin assembly Standard 4 1 4 6 6 3 3 5 2 0.3249
Consignment 9 6 7 8 10 7 5 7 7 0.7500
Track roller
frame
Standard 4 2 4 1 3 3 5 4 4 0.4250
Consignment 9 6 9 10 8 8 5 10 9 0.7500
Main assembly Standard 1 3 5 2 5 1 6 5 2 0.3749
Expedite 4 5 9 7 5 5 10 6 5 0.6750
Suspension
group
Standard 1 4 1 5 5 7 3 1 2 0.3249
Expedite 9 3 4 9 6 5 4 9 4 0.7000
Final assembly Standard 2 5 5 6 6 3 3 2 4 0.3750
Expedite 8 6 9 7 10 8 8 10 7 0.7500
324 B. Nepal et al. / European Journal of Operational Research 216 (2012) 312–325
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