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

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http://dx.doi.org/10.1016/j.ejor.2011.07.041

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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).

B. Nepal et al. / European Journal of Operational Research 216 (2012) 312–325 313



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

314 B. Nepal et al. / European Journal of Operational Research 216 (2012) 312–325



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 .




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.




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).




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


Plant carrier Standard 9 155 9 155 Dressed-out

engine

Standard 10 4100 NA NA


Platform group Standard 6 725 6 725 Boggie assembly Standard 11 575 11 575


Fender group Standard 9 900 9 900 Pin assembly Standard 35 90 35 90


Roll over group Standard 8 1150 8 1150 Track roller

frame

Standard 10 3000 10 3000


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




$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.




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




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!






Front End Module

Radiator, Condenser

Engine Fan

Big HVAC Module!






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


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


Radiator/Condenser

ModuleRadiator, Condenser

Fig. 8. Current and modular architecture of an automotive climate control system.




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




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




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http://imvp.mit.edu/papers


http://dx.doi.org/10.1115/1.4001082

http://dx.doi.org/10.1115/1.4001082



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