A methodology for strategic sourcing

European Journal of Operational Research 154 (2004) 236–250

www.elsevier.com/locate/dsw

O.R. Applications

A methodology for strategic sourcing

Srinivas Talluri *, Ram Narasimhan

Department of Marketing and Supply Chain Management, Eli Broad College of Business, Michigan State University,

N370 North Business Complex, East Lansing, MI 48824, USA

Received 16 May 2001; accepted 29 July 2002

Abstract

Strategic sourcing is critical for firms practicing the principles of supply chain management. It specifically deals with

managing the supply base in an effective manner by identifying and selecting suppliers for strategic long-term part-

nerships, involving in supplier development initiatives by effectively allocating resources to enhance supplier perfor-

mance, providing benchmarks and continuous feedback to suppliers, and in some cases involving in supplier pruning

activities. Currently, the methodologies in practice for strategic sourcing have mostly been subjective in nature with few

objective decision models focused at supplier evaluation, which are also not devoid of limitations. This paper proposes

an objective framework for effective supplier sourcing, which considers multiple strategic and operational factors in the

evaluation process. Suppliers are categorized into groups based on performance, which assists managers in identifying

candidates for strategic long-term partnerships, supplier development programs, and pruning. In addition, this research

investigates the differences among supplier groups in proposing possible improvement strategies for ineffectively per-

forming suppliers. Also, we demonstrate the methodological richness of our framework when compared to some of the

traditional methods proposed and utilized for supplier evaluation purposes. The supplier data utilized in the study is

obtained from a large multinational corporation in the telecommunications industry.

� 2002 Elsevier B.V. All rights reserved.

Keywords: Nonparametric efficiency analysis; Purchasing; Strategic sourcing

1. Introduction

Strategic sourcing is a critical challenge faced by

many firms involved in the latest innovations

of supply chain management. With the recent

emphasis on just-in-time (JIT) manufacturing phi-losophy, strategic sourcing that establishes a long-

* Corresponding author. Tel.: +1-517-3536381; fax: +1-517-

4321112.

E-mail addresses: [email protected] (S. Talluri), nara-

[email protected] (R. Narasimhan).

0377-2217/$ - see front matter � 2002 Elsevier B.V. All rights reserv

doi:10.1016/S0377-2217(02)00649-5

term relationship with suppliers has become even

more important and vital for enhancing organi-

zational performance. Also, in today�s dynamic

environment strategic relationship with suppliers is

a key ingredient to the success of a supply chain.

Strategic sourcing decisions must not be solelybased on operational metrics such as cost, quality,

and delivery, but also incorporate strategic di-

mensions and capabilities of suppliers such as

emphasis on quality management practices, pro-

cess capabilities, management practices, design

and development capabilities, and cost reduction

capabilities into the decision-making process.

ed.

mail to: [email protected]

S. Talluri, R. Narasimhan / European Journal of Operational Research 154 (2004) 236–250 237

These supplier attributes provide information to a

firm�s managers on the infrastructure and practices

employed by the suppliers, which are key elements

for long-term strategic relationships (SR). It is well

established in the strategic supplier evaluation lit-

erature that sourcing decisions significantly impactvarious aspects of a product such as cost, design,

manufacturability, and quality (Burt, 1984; Bur-

ton, 1988). Other research that emphasizes the

importance of supplier evaluation includes works

by Banker and Khosla (1995) and Dobler et al.

(1990). Banker and Khosla (1995) have identified

the supplier evaluation and justification problem

as an important one in operations management.While several methods have been proposed and

utilized for evaluation and selection of suppliers,

they have limitations including: evaluation solely

based on operational metrics without the consid-

eration of strategic capabilities, simple weighted

scoring methods based on subjective assessments,

inappropriate or arbitrary methods utilized to

derive factor weights, and lack of relative evalua-tion across various suppliers. We now expand on

each of these limitations.

While operational metrics such as price, quality

and delivery are important and critical in evalu-

ating suppliers, strategic evaluation of suppliers

leading to a long-term relationship requires con-

sideration of supplier capabilities and practices.

This is important because as a firm�s productsevolve over time it is critical to form relationships

with suppliers that can effectively meet the chang-

ing requirements from the perspective of new prod-

uct development, design, manufacturing processes

and manufacturing capability, at lower costs. Such

suppliers are more likely in the long run to have

the infrastructure and organizational capabilities

in place to effectively meet the changing demandsof the buying firms. For example, it has been

suggested in the literature that quality manage-

ment practices with strategic implications such as

total quality management, zero defects, process

improvement, statistical process control, and con-

tinuous process improvement lead to tangible im-

provements in quality and cost reduction (De Ron,

1998; Lederer and Rhee, 1995; Tham, 1988). Sim-ilarly, design based practices that encompass ini-

tiatives such as design for manufacturability,

modularity, product redesign, concurrent engi-

neering, and standardization have also been asso-

ciated with cost reduction and better delivery

performance (Koulamas, 1992; Tummala et al.,

1997; Coughlan and Wood, 1992). Thus, it is im-

portant to consider these factors in supplier eval-uation decisions.

Several techniques utilized for evaluation of

suppliers assign importance weights to various

supplier evaluation factors in a subjective and/or

arbitrary manner. As the complexity of the deci-

sion-making process increases in terms of factors

and alternatives considered, it is increasingly dif-

ficult to assign a consistent set of weights. Finally,relative evaluation methods that compare suppli-

ers and identify potential reasons for differences in

supplier performance have not been fully explored

in the literature. The primary advantage associated

with relative evaluation methods is that they allow

for grouping suppliers based on performance,

which provides useful insights to management in

identifying benchmarks for ineffective suppliers,and assists in decisions relating to supplier devel-

opment initiatives (SDI) and programs.

This paper proposes a methodology for strate-

gic sourcing that addresses the aforementioned

issues. The methodology utilizes a combination of

traditional and advanced data envelopment anal-

ysis (DEA) models in estimating the efficiencies of

alternative suppliers, and the variability in theirefficiency scores. Nonparametric statistical tech-

niques are utilized in identifying homogenous

groups of suppliers based on their efficiency scores,

which assist management in selecting suppliers

for strategic partnerships, SDI, and supply base

rationalization decisions. Inter-group differences

with respect to various factors are identified in

order to assist in benchmarking and process im-provement efforts.

In summary, some of the questions our meth-

odology addresses, which current supplier evalua-

tion techniques do not comprehensively answer,

are:

• Which suppliers to consider for strategic part-

nerships?• Which suppliers must be a part of supplier de-

velopment initiatives?

238 S. Talluri, R. Narasimhan / European Journal of Operational Research 154 (2004) 236–250

• Which suppliers must be pruned from the sup-

ply base?

• How can ineffective suppliers improve their per-

formance? Against whom should they bench-

mark?• How can firms effectively allocate resources to

supplier improvement programs?

While our paper proposes a new methodology

for evaluating suppliers, the primary focus of our

study is on managerial implications and usefulness

of the results in addressing strategic sourcing is-

sues faced by companies.

2. Literature review

Supplier evaluation is one of the most widely

researched areas in purchasing with methodologies

ranging from conceptual to empirical and model-

ing streams. It is beyond the scope of this paper todiscuss all these works in detail. Since our frame-

work is primarily related to the modeling area, we

mainly limit our discussion to quantitative models

proposed for supplier evaluation.

Empirical work in supplier evaluation dates

back to 1960s. Dickson (1966) conducted a study

that investigated the importance of supplier eval-

uation criteria for industrial purchasing managers.The study concluded that cost, quality, and de-

livery performance were the three most impor-

tant criteria in supplier evaluation. Other relevant

works in this area emphasized the strategic im-

portance of supplier evaluation and the relative

importance and tradeoffs among cost, quality, and

delivery (Hahn et al., 1983; Jackson, 1983; Kralijic,

1983; Browning et al., 1983; Ansari and Modar-ress, 1986; Treleven, 1987; Burton, 1988; Bernard,

1989; Benton and Krajeski, 1990, and Ellram,

1990). Other researchers that specifically addressed

issues relating to the relative importance of various

supplier attributes include Monczka et al. (1981),

Moriarity (1983), Woodside and Vyas (1987),

Chapman and Carter (1990), Tullous and Munson

(1991), and Weber et al. (1991). Based on a reviewof 74 articles on supplier evaluation, Weber et al.

(1991) concluded that quality was the most im-

portant factor followed by delivery performance

and cost in supplier evaluation. It is evident from

these studies that multiple factors need to be in-

corporated into the supplier evaluation process

and that it should not be solely based on a single

criterion such as cost. However, these works have

not developed decision models for supplier evalu-ation.

2.1. Supplier evaluation techniques

In a comprehensive review of supplier selection

methods, Weber et al. (1991) reported that 47 of

the 74 articles in the review utilized multiple cri-

teria. Some of the traditional multi-criteria ap-proaches have utilized factors such as cost, quality,

and delivery, which have become increasingly im-

portant with the emphasis on JIT manufacturing

philosophy (Chapman, 1989; Chapman and Car-

ter, 1990). However, these measures are primarily

at the operational level.

Table 1 depicts the supplier evaluation tech-

niques by methodological area. Several of thesetechniques have utilized multiple supplier criteria

in the evaluation process. However, some of the

issues with many of these techniques include lack

of objective methods for assigning factor weights,

lack of relative comparison of alternative suppliers

for facilitating benchmarking and SDI, minimal

emphasis on strategic level capabilities or prac-

tices, and not addressing issues and reasons relat-ing to ineffective supplier performance.

Application of DEA as a tool for strategic

sourcing of suppliers has been limited. To date

there have been few works that have applied this

tool for supplier evaluation purposes. Kleinsorge

et al. (1992) utilized DEA as a tool for perfor-

mance monitoring of a single supplier over time.

However, their work did not address issues relat-ing to strategic supplier selection or benchmark-

ing. Two articles by Weber and Desai (1996) and

Weber et al. (1998) have addressed the issue of

supplier selection and negotiation using DEA.

However, the supplier metrics utilized by them

were strictly operational ones. Also, their analysis

is based on a traditional DEA model, which has

certain limitations as discussed in the next sectionof the paper. Narasimhan et al. (2001) have ap-

plied DEA for strategic evaluation of suppliers by

Table 1

Vendor evaluation techniques

Evaluation technique Authors

Weighted linear models Lamberson et al. (1976), Timmerman (1986)

Linear programming Pan (1989), Turner (1988)

Mixed integer programming Weber and Current (1993)

Grouping methods Hinkle et al. (1969)

Analytic hierarchy process Barbarosoglu and Yazgac (1997), Hill and Nydick (1992), Narasimhan (1983)

Matrix method Gregory (1986)

Multi-objective programming Weber and Ellram (1993)

Total cost of ownership Ellram (1995)

Human judgment models Patton (1996)

Principal component analysis Petroni and Braglia (2000)

DEA Narasimhan et al. (2001), Weber and Desai (1996), Weber et al. (1998)

Interpretive structural modeling Mandal and Deshmukh (1994)

Statistical analysis Mummalaneni et al. (1996)

Discrete choice analysis experiments Verma and Pullman (1998)

Neural networks Siying et al. (1997)


considering various factors at both strategic andoperational levels. While their approach provided

some useful insights into supplier evaluation and

rationalization, they were also limited by the tra-

ditional DEA model evaluations. Also, their work

has not investigated the reasons behind the dif-

ferences in efficiency scores of suppliers, and thus

did not delve into supplier improvement strategies.

In a more recent paper, Talluri and Narasimhan(in press) developed a vendor evaluation model

that effectively considers performance variabil-

ity issues, but their approach only incorporated

operational measures into the decision-making

process.

The robustness of our methodology over exist-

ing DEA models is that we utilize a combination

of methods that effectively discriminates amongsuppliers and avoids some of the pitfalls associated

with the traditional DEA models. Also, our ap-

proach utilizes robust statistical methods for in-

vestigating the differences among suppliers and

providing recommendations for improvement. We

now provide an introduction to the DEA models

utilized in our analysis.

3. DEA models

In this section we provide a brief description ofthe DEA models utilized in our study. The DEA

models include the basic Charnes, Cooper, andRhodes (CCR) and the aggressive cross-efficiency

models. For more details on the model develop-

ment the readers are encouraged to refer to the

actual works in this area.

3.1. CCR DEA model

It is well known in productivity literature thatDEA is a multi-factor analysis tool that measures

the relative efficiencies of a set of decision-making

units. It effectively considers multiple input and

output factors in evaluating the efficiency scores.

In the context of our current study, the input and

output factors correspond to supplier capabilities

and performance metrics, respectively. Based on

the notion of Doyle and Green (1994), the effi-ciency measure utilized in DEA is best defined by

Eq. (1):

Eks ¼P

y OsyvkyPx Isxukx

ð1Þ

where (Eks) is the efficiency or productivity mea-

sure of supplier s, using the weights of test supplier

k; (Osy) is the value of output y for supplier s; (Isx)is the value for input x of supplier s; (vky) is the

weight assigned to supplier k for output y; and(ukx) is the weight assigned to supplier k for input x.

In the ratio DEA model proposed by Charnes

et al. (1978), which is also referred to as the CCR


model, each test supplier k selects optimal weights

for capabilities (inputs) and performance metrics

(outputs) in achieving the highest possible effi-

ciency score subject to the restrictions that these

weights prevent the set of suppliers s from

achieving an efficiency score of greater than 1. TheCCR model is presented in expression (2) below:

maximize Ekk ¼P

yOkyvkyP

xIkxukx

subject to :Eks 6 1 8 Suppliers sukx; vky P 0

ð2Þ

The conversion of (2) into a linear programming

problem is shown below in (3).

maximize Ekk ¼PyOkyvky

subject to :Eks 6 1 8 Suppliers sPxIkxukx ¼ 1

ukx; vky P 0

ð3Þ

This conversion is performed by equating the de-

nominator of the efficiency ratio in (2) to a value of

1, represented by the constraintP

x Ikxukx ¼ 1.

The result of problem (3) is an optimal effi-

ciency score (E�kk), which does not exceed a value of

1. If E�kk ¼ 1 and the corresponding slack variables

are 0 then supplier k is considered to be efficient. If

E�kk < 1, then supplier k does not lie on the efficient

frontier and is dominated by at least one other

supplier or a linear combination of suppliers.

Problem (3) is executed s times in evaluating the

efficiency scores of all the suppliers.

3.2. Cross-efficiency models

Cross-efficiency models are primarily utilized toovercome the unrestricted weight flexibility prob-

lem of the CCR model. The CCR model allows

DMUs to emphasize relatively few inputs and

outputs in achieving a high efficiency score while

ignoring other important factors. Sexton et al.

(1986) introduced the concept of cross-efficiencies

and the cross-efficiency matrix (CEM) in DEA. In

the context of the current paper, the CEM pro-vides information on the efficiency of a specific

supplier with the optimal weighting schemes de-

termined for other suppliers. In the CEM, the ele-

ment in kth row and the sth column represents the

efficiency measure of supplier s when evaluated

against the optimal weights of supplier kðEksÞ.Each of the columns of the CEM is then averaged

to get a mean cross-efficiency score for each sup-plier. The suppliers can be ranked based on these

mean scores. Thus, the CEM provides a mech-

anism for effectively differentiating among the

suppliers.

One issue that may arise in utilizing the cross-

efficiency scores is the uniqueness of the input and

output factor weights obtained from the CCR

model used in their evaluation. This makes thecross-efficiency analysis arbitrary and limits its

applicability. To overcome this potential limita-

tion, a formulation developed by Doyle and Green

(1994) may be used for cross-efficiency evaluations

and development of a CEM. This formulation,

shown as (4), generates a unique set of weights.

minimizePy

vkyPs 6¼k

Osy

!;

subject to : Px

ukxPs 6¼k

Isx

!¼ 1;P

yOkyvky � E�

kk

PxIkxukx ¼ 0

Eks 6 1 8 Suppliers s 6¼ kukx; vky P 0

ð4Þ

The above formulation has a primary goal of

obtaining a maximum CCR efficiency score for

supplier k (the test unit) and a secondary goal of

determining a set of weights that minimize theother suppliers� aggregate output, as defined by the

objective function. The test unit k is defined as an

average unit whose efficiency is minimized. This

model has been defined as an aggressive formula-

tion. The data required in (4) includes the optimal

efficiency scores (E�kk) from the CCR model, as

shown by the second constraint set. Thus, a key

advantage of the aggressive model utilized incross-efficiency analysis is that units emphasize on

their strengths, which are the weaknesses of their

competitors. Thus, a unit with high mean cross-

efficiency score can be considered as a superior

performer because it is excelling across many

Table 2

Key differences between the CCR and cross-efficiency models

CCR model Cross-efficiency model

Allows for the selection of weights in an unrestricted

manner resulting in some units to achieve a high

relative efficiency score by emphasizing on relatively

few inputs and outputs

Computes the efficiency of each unit with respect to the optimal weights of

other units for a more comprehensive peer evaluation. This allows in

effectively differentiating between good overall performers and niche

performers

The model is run one time for each unit in obtaining

the relative efficiency scores

The model is run one time for each unit for determining the input and

output weights that not only maintain its CCR efficiency score but also

minimize the efficiency scores of all other units. These weights are utilized

in deriving a mean cross-efficiency score

Supply BaseEfficiencyEvaluation

Identificationof Supplier

Groups

Identificationof Group Diff. on

Inputs and Outputs

ManagerialDecisions

on SR, SDI, Pruning

Capabilities (Inputs)

Performance Metrics (Outputs)

Performance Feedbackand Resource AllocationDecisions

Fig. 1. A framework for strategic sourcing.


dimensions. The key differences and the relative

advantages of the cross-efficiency models over the

CCR model are summarized in Table 2.

The second reason for utilizing the cross-effi-

ciency analysis in our study is to identify vari-

ability in the efficiency scores of a supplier whenevaluated against the optimal weights of its peers.

This facilitates identifying homogenous groups of

suppliers for strategic relationships, SDI, and

pruning decisions for the buying firm.

The third reason for utilizing the cross-effi-

ciency analysis is associated with the limitations in

our sample size. Since small sample size exacer-

bates the unrestricted weight flexibility problem inDEA (Boussofiane et al., 1991), we utilize cross-

evaluations to discriminate better among the sup-

pliers.

It is important to note that other methods de-

veloped in the literature such as reconstructing

virtual input and output combinations from expert

consultation proposed by Thanassoulis and Allen

(1998), and the bootstrap procedure for replicatinginput–output combinations by Simar and Wilson

(1998) can be utilized as alternative approaches for

improving the discriminatory power of the DEA

models.

4. Methodology for strategic sourcing

Fig. 1 depicts the framework utilized for stra-

tegic sourcing. The first step involves the identifi-

cation of the suppliers to be evaluated followed by

data collection. Data collection can be performed

through questionnaires and/or site visits to candi-

date suppliers. The efficiency evaluations are per-

formed by obtaining data on capabilities (inputs)

and performance metrics (outputs) of suppliers

being evaluated. The following section provides

details on the data acquisition process. In general,

any resource can be utilized as a possible input

measure and outputs can encompass activity/per-

formance measures.

The next step involves the evaluation of effi-ciency scores and ranking of suppliers based on

DEA models. Subsequently, the supplier groups

are identified by a nonparametric procedure,

which effectively incorporates variability in effi-

ciency measures into the evaluation process. This

step is performed by utilizing a procedure pro-

posed by Talluri et al. (2000) in categorizing sup-

pliers into groups based on their cross-efficiencies.The next part of the framework addresses the

managerial decisions associated with supplier eval-

uation that can be handled through our analysis.


Here, we stress the identification of suppliers for

SR, SDI, and pruning, which assists managers in

decisions relating to partnerships and effective al-

location of resources to various SDI programs.

Also, we identify the differences among the sup-

plier groups in terms of capabilities and perfor-mance metrics, and provide feedback to ineffective

supplier groups regarding the necessary improve-

ments across various dimensions.

5. Strategic sourcing: An illustrative case

Our study was carried out in a large multi-national telecommunications company, which we

refer to as Company X throughout the paper in

order to maintain anonymity. Company X is a

global leader in design, production, and marketing

of communication systems. It operates production

plants, research and development facilities, and

distribution systems on a global basis. The critical

objectives of the company in procurement andsupply management include:

• improving the quality of purchased products/

services;

• reducing lead-time and improving on-time de-

livery;

• developing long-term relationships with key

suppliers;• securing global competitive pricing.

In order to achieve these objectives, Company

X has placed emphasis on supplier rationalization

by evaluating and developing suppliers for long-

term SR, providing continuous feedback for im-

proving performance, achieving excellence across

multiple competitive dimensions, and decreasingsupply based by pruning inefficient suppliers. As

discussed earlier, our framework specifically ad-

dresses these issues.

The factor selection and data acquisition pro-

cess was initiated by first defining the relevant

input and output dimensions to be utilized in the

efficiency analysis. This was accomplished in focus

group sessions with the management of CompanyX. Due to the decentralized nature of Company

X�s supply management system, these focus group

sessions required had to be carefully planned. A

series of meetings were conducted in order to

identify the specific product-line to be examined

and the input and output dimensions to be used in

DEA. It was decided that the data gathering ef-

forts must be performed as objectively as possiblewhile ensuring convenience of data collection.

Also, in order to ensure full participation, it was

decided early in the study that the time and effort

required to collect data from suppliers and buyers,

must be kept to an acceptable minimum.

After the identification of the input and output

dimensions, we developed two separate question-

naires––one to assess supplier capabilities (com-prising the input dimensions of DEA) and the

other to assess supplier performance (comprising

the output dimensions of DEA). The question-

naires utilized multiple items to measure the input

and output dimensions. The individual items were

measured on a binary scale (yes/no responses) to

afford maximum objectivity and accuracy of sur-

vey responses. The questions were carefully wor-ded so that the responses of the suppliers could be

easily verified for accuracy. In addition, the bi-

nary scale used obviated the need for suppliers to

be making judgments regarding their capability

allowing for distortion of input data. The design

of the questionnaire and the binary scale were

conscious design choices made by us in the data

collection phase of the study to ensure a highdegree of reliability of input data. It should be

noted that the alternative of conducting a de-

tailed audit of suppliers by a team from Company

X was rejected as both time consuming and re-

quiring an inordinate amount of effort. The

questionnaires were reviewed by Company X�smanagement and revised to reflect their comments

and suggestions.The Supplier Capability Questionnaire was sent

out to the suppliers and the Supplier Performance

Assessment Questionnaire was sent out to the pur-

chasing staff of Company X. The returned ques-

tionnaires were sent to the project staff for data

coding, entry, and analysis.

To test the hypothesis that the questionnaires

might have contained difficult or ambiguousquestions, an analysis of responses to individual

items was carried out by examining the proportion


of sample with missing data on individual items.

The analysis showed that there was no evidence to

support the hypothesis, confirming that the ques-

tionnaire was acceptable (i.e., not difficult to fill

out) as a data collection instrument. The following

section describes the questionnaires and subse-quent sections discuss the data analysis and man-

agerial implications of the study.

5.1. Supplier capability questionnaire

Items on the Supplier Capability Questionnaire

were grouped into the following categories, which

are utilized as the inputs in the DEA model eval-uations:

• quality management practices and systems

(QMP);

• documentation and self-audit (SA);

• process/manufacturing capability (PMC);

• management of the firm (MGT);

• design and development capabilities (DD);• cost reduction capability (CR).

These six categories were measured with a

composite score between 0 and 1. The score was

computed as the proportion of �yes� answers to

individual questionnaire items in the category.

�Blank� and �not applicable� responses were not

considered in the calculation of the proportion ofthe �yes� responses.

5.2. Supplier performance assessment questionnaire

Items on the Supplier Performance Assessment

Questionnaire were grouped into the following

categories, constituting the outputs:

• quality;

• price;

• delivery;

• cost reduction performance (CRP);

• other.

The above categories were also measured with a

composite score between 0 and 1. To evaluate thescore, the proportion of �yes� answers were identi-

fied in each category to provide an �objective�

measure of the variables in the category. Table 3

shows the scaled composite scores for the input

and output variables for the 23 suppliers. While we

have utilized the actual composite scores in the

DEA evaluations, in order to maintain confiden-

tiality of the data we have scaled it by dividingeach measure by a factor.

For the categories in which subjective questions

were included, the answers to the questions were

normalized to a value between 0 and 1, and then

combined with the responses to the �objective�measures on items belonging to the category. This

was performed by taking a weighted average of the

�subjective� and �objective� measures, with 0.4 and0.6, respectively, as weights for the two, based on

the managerial input from Company X.

6. Data analysis

6.1. DEA results

First we determined the CCR efficiency scores

of the 23 suppliers with respect to the six capa-

bilities (inputs) and five performance metrics

(outputs). The scaled supplier input and output

data is shown in Table 3. CCR model identified

suppliers 2, 3, 4, 6, 7, 10, 12, 15, 20, 22, and 23 to

be efficient with a score of 1.000, and the other 12

suppliers are inefficient with scores of less than1.000. These results are shown in Table 3 under the

heading ‘‘CCR Eff’’. Since firm�s objectives of

linearity in inputs and outputs and the implicit

technology assumptions are not justified in the

selection of the CCR model, we have tested the

sensitivity of the results using the Banker, Char-

nes, and Cooper (BCC) model (Banker et al.,

1984), which works under the assumption ofvariable returns to scale. While the BCC model

identified 12 suppliers to be efficient and the rest

inefficient, the CCR model identified 11 of those 12

suppliers to be efficient and the remaining suppli-

ers inefficient. Also, we utilized the Mann–Whit-

ney test (nonparametric t-test) to test for the

differences in mean efficiency scores between the

CCR and BCC model results, and failed to rejectthe null hypotheses at a p-value 6 0.05 indicating

that the differences are statistically insignificant.

Table 3

Scaled supplier data with inputs and outputs and efficiency scores

Supplier # QMP SA PMC MGT DD CR Quality Price Delivery CRP Other CCR

Eff.

X-Eff.

mean

Standard

deviation

1 0.9662 0.9742 1.0385 1.0808 1.1417 0.7839 0.6211 0.8922 0.1284 1.2107 0.6359 0.602 0.427 0.129

2 0.7054 1.0438 0.7500 0.8782 0.0000 0.8750 0.6932 0.8922 0.3855 0.0000 0.3179 1.000 0.412 0.288

3 0.5611 0.8947 0.7789 0.7205 0.8372 0.7404 1.0205 0.4341 1.5420 0.0000 1.2719 1.000 0.536 0.326

4 1.1272 1.0438 0.9520 0.9607 0.9661 1.1402 1.6639 1.1333 1.5420 1.2107 1.8019 1.000 0.752 0.243

5 1.1272 1.0438 1.1251 1.0808 1.2560 1.2115 0.9983 1.3503 1.1565 1.2107 0.9540 0.855 0.615 0.207

6 0.9877 1.0438 0.9376 1.0808 1.0466 0.9422 1.0426 1.3263 1.7990 2.4214 1.2719 1.000 0.810 0.171

7 0.8051 0.8351 1.0385 0.9607 1.2560 1.0768 1.2201 1.2056 0.7710 2.4214 1.2719 1.000 0.821 0.207

8 1.1809 1.0438 1.1251 1.0208 1.0627 1.0096 0.8429 1.1333 0.6424 1.2107 0.8479 0.723 0.523 0.156

9 1.2346 1.0438 1.1251 1.0808 1.2560 1.1442 0.6433 0.8922 0.3855 0.0000 0.5299 0.562 0.316 0.201

10 0.5904 1.0438 0.6058 0.7629 0.5796 0.4038 1.4419 0.4341 1.4135 0.0000 1.2719 1.000 0.578 0.369

11 0.8642 0.8118 0.8182 0.9536 0.9661 0.8076 0.4215 0.8922 1.0279 0.0000 0.8479 0.805 0.459 0.275

12 0.6441 0.8351 1.0227 1.0208 0.9661 1.0768 1.0205 1.3263 0.7710 1.2107 0.7418 1.000 0.722 0.252

13 1.2346 1.0438 1.1251 1.0808 1.2560 1.2115 0.5546 1.1092 1.0279 1.2107 1.1660 0.773 0.518 0.175

14 1.0662 1.0438 1.1251 1.0808 1.1593 1.2115 0.8208 0.8922 0.8994 1.2107 0.8479 0.609 0.479 0.115

15 1.0100 1.0438 0.8654 1.0208 0.7322 0.6815 1.2423 1.5674 1.4135 2.4214 1.2719 1.000 0.906 0.16

16 0.8978 0.9742 1.0385 1.0208 0.9420 0.8076 1.0205 0.8922 0.3855 0.0000 0.4240 0.764 0.392 0.255

17 1.1272 0.9742 1.0385 1.0208 1.2560 1.0768 1.0205 0.8681 0.7710 0.0000 0.5299 0.702 0.398 0.242

18 1.1809 1.0438 1.1251 1.0808 1.2560 1.2115 1.2201 0.2411 0.0000 0.0000 0.4240 0.733 0.193 0.204

19 1.0735 1.0438 1.1251 0.9007 1.1593 0.9422 1.1647 0.8922 1.4135 1.2107 1.0599 0.904 0.596 0.159

20 1.0735 1.0438 1.1251 1.0808 0.6762 1.1442 0.8429 1.0550 1.4135 1.2107 1.4839 1.000 0.618 0.193

21 1.2346 1.0438 1.1251 1.0133 1.2560 1.2115 0.7764 0.8922 1.0279 0.0000 0.9540 0.658 0.405 0.232

22 1.2346 1.0438 0.9520 1.0808 1.0466 1.2115 1.4642 1.3263 1.7990 2.4214 1.4839 1.000 0.817 0.186

23 1.0735 1.0438 1.0385 1.0172 0.8695 1.0768 1.2423 1.3503 1.2849 2.4214 1.5900 1.000 0.813 0.168

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This indicates that our results are not very sensi-

tive to model changes. In order to further test the

sensitivity of these results, we have evaluated the

supplier efficiency scores based on the free disposal

hull (FDH) model proposed by Tulkens (1993),

but the model did not show effective discrimina-tion among the suppliers. This may be a result of

small sample size in the current application. It is

also important to note in these comparisons that

the ratio efficiency measure shown in (1) does not

apply to the BCC and FDH models.

Since the CCR model has certain limitations as

discussed earlier, we utilize the Doyle and Green

(1994) aggressive cross-efficiency model for a morecomplete evaluation of the supplier performance.

The mean cross-efficiency scores of the suppliers

identified from the weights obtained from the

Doyle and Green formulation are shown in Table

3 under the heading ‘‘X-Eff Mean’’. Based on the

mean cross-efficiency scores it can be concluded

that supplier 15 is the best performer with a score

of 0.906, and supplier 18 is the worst performerwith a score of 0.193. It is interesting to note that

supplier 2, which is efficient based on the CCR

model evaluations, is ranked very low based on

cross-efficiency evaluations with a mean score of

only 0.412. In fact some of the CCR inefficient

suppliers such as 1, 5, 8, 11, 13, 14, and 19 are

better performers than supplier 2 based on cross-

efficiency evaluations. Supplier 2 is a typical caseof a ‘‘false- positive’’ or niche performer. Also,

supplier 5, with a CCR efficiency score of 0.855,

achieved a cross-efficiency mean score of 0.615,

which is higher than the CCR efficient suppliers

that include 2, 3, and 10. These types of insights

and differentiation among suppliers are not pos-

sible when using the CCR model alone, which

demonstrates the strength of cross-efficiency eval-uations as a more comprehensive technique for

efficiency evaluation. It is essential for the deci-

sion-maker to consider these issues in supplier

rationalization in order to avoid making a Type II

error in the selection process.

6.2. Identifying homogenous groups of suppliers

Since the traditional cross-efficiency analysis is

primarily based on the mean scores and does not

take into consideration the variability in the effi-

ciency scores, the ranking obtained from the

analysis may not be the best. In fact it can be

concluded from Table 3 that mean scores alone

may not be appropriate in ranking suppliers be-

cause while certain suppliers, such as 3 and 8, haveclose to the same mean scores their variability in

terms of cross-efficiencies are quite different. Thus,

we utilized the method proposed by Talluri et al.

(2000), which effectively incorporates the vari-

ability measures in identifying homogenous groups

of suppliers. Talluri et al. (2000) performed this

step by applying a nonparametric statistical test

due to Friedman (Friedman, 1937) on the CEM.They utilized a nonparametric test because the

efficiency scores do not lend themselves to the as-

sumptions of normality.

We conduct the Friedman�s test for the fol-

lowing null and alternative hypotheses:

Ho: The suppliers have identical cross-efficiency

scoresHa: At least one of the suppliers tends to yield lar-

ger cross-efficiency scores than at least one other

supplier

We treated the rows of the CEM as blocks and

the columns as the treatments. The cross-efficiency

scores within each block are ranked by assigning 1

for the lowest, 2 for the second lowest, and so on.Mean ranks are considered in the event that ties

exist. The cross-efficiency matrix based on ranks is

shown in Table 4. The test resulted in a p-value of0.000 thereby rejecting the null hypotheses at an

a ¼ 0:05. Thus, there is sufficient evidence to

conclude that at least one supplier tends to yield

larger cross-efficiency scores than at least one

other supplier.Using the least significant difference tests

(Cononver, 1980) on the ranked transformed data,

all pair-wise comparisons were performed to iden-

tify suppliers that are different. For this case, for a

pair of suppliers to be significantly different, at

a ¼ 0:05, the absolute difference between sums of

their ranks must be greater than 54.95. The results

of the analysis are shown in Table 5. We identifiedthree groups of suppliers based on the overlaps in

the least significant difference tests. It is evident

Table 4

Ranked cross-evaluation scores

Supplier # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

1 11 8 3 14 18 19 21 15 6 2 16 22.5 13 5 22.5 10 9 1 7 12 4 17 20

2 6 21.5 14 19 12 15 17 9 1 21.5 5 21.5 3 7 21.5 13 8 2 10 11 4 16 18

3 2 6 23 18 12 21 11 5 3 22 13 15 8.5 10 19 4 7 1 16.5 16.5 8.5 20 14

4 6 2 21 22.5 10 16 15 9 4 22.5 12 7 14 8 18 3 5 1 13 17 11 19 20

5 6 5 3 16 17 19.5 21 12 4 2 15 22.5 13 8 22.5 7 9 1 11 14 10 19.5 18

6 14 4 8 17 12 22 22 10 2 9 7 18 11 13 22 3 5 1 15 15 6 19 20

7 17 5 5 12.5 12.5 22 23 11 5 5 5 18 10 16 21 5 5 5 14.5 14.5 1 19 20

8 4.5 13 3 16 19 17.5 20 15 6 2 10 21 14 4.5 23 8 7 1 12 11 9 17.5 22

9 10 9 3 16 19 17.5 21 15 4.5 2 14 22.5 13 4.5 22.5 11 7 1 8 12 6 17.5 20

10 8 7 20 21 9 14 15 10 3 23 2 11.5 1 5 22 19 11.5 13 18 6 4 17 16

11 2 3.5 11 17 16 22 18 9.5 3.5 6 15 20 12 8 22 5 7 1 13 14 9.5 22 19

12 10 19 6 14 17 20 21 11 2.5 4 15 23 9 8 22 13 5 1 7 12 2.5 16 18

13 5 2 12 21.5 15 18 21.5 10 3 8 14 17 13 7 21.5 4 6 1 11 16 9 21.5 19

14 5 2 15 20 14 18 22 8 3 10 13 17 12 7 22 4 6 1 11 16 9 22 19

15 18 5 5 14 11 22 20.5 16 5 5 5 15 11 11 23 5 5 5 17 13 5 19 20.5

16 5 7 15 21.5 12 16 21.5 10 2 21.5 3 19 1 6 21.5 14 11 8 13 9 4 18 17

17 6 4 11 22 15 16 22 10 1 17 3 19 2 7 22 13 12 8 14 9 5 20 18

18 4 5 15 23 10 11 22 8.5 3 20 1 19 2 7 17.5 12.5 12.5 16 14 8.5 6 21 17.5

19 2 5 21 21 14 18 13 8 3 21 11 12 9 7 21 4 6 1 17 15 10 21 16

20 5 22.5 17 20 7.5 15 14 10 2 21 11 9 13 6 18 4 3 1 12 22.5 7.5 16 19

21 2 5 19 22 14 20 13 8 3 16 10 12 11 7 22 4 6 1 17 15 9 22 18

22 10 3 9 18 14 21.5 21.5 11 2 8 7 17 13 12 21.5 4 5 1 16 15 6 21.5 19

23 10 2 9 18 11 19 22 14 4 8 6 15 13 12 22 3 5 1 17 16 7 20 22

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Table 5

Supplier groups based on Friedman�s test

Table 6

ANOVA results on inputs and outputs for supplier groups

Factor Type F -value Significance

QMP Input 1.050 0.369

SA Input 0.440 0.650

PMC Input 0.840 0.446

MGT Input 1.110 0.348

DD Input 0.820 0.454

CR Input 0.990 0.389

Quality Output 5.390 0.013

Price Output 11.320 0.001

Delivery Output 5.850 0.010

CRP Output 17.020 0.000

Other Output 7.050 0.005


from this analysis that suppliers 15, 22, 7, 23, 4, 6,and 12 are the best (in the sense of the highest

ranked group of) performers. These are the sup-

pliers that management must consider as poten-

tial candidates for SR. These suppliers are the

stars that are excelling with respect to several input

and output dimensions, i.e., capabilities and per-

formance metrics. From a resource allocation

standpoint, management must primarily invest inimproving integration with these suppliers in terms

of implementing systems such as electronic data

interchange (EDI) and web-based procurement for

effective and rapid transactional ability with the

suppliers. Also, as we discuss later, these suppliers

can serve as potential benchmarks for ineffectively

performing suppliers. In essence, management

must find possible ways of transferring their bestpractices to other suppliers.

Suppliers 5, 20, 19, 10, 3, 8, 13, 11, 14, 16, 1, 2,

17, and 21 are in the second category. These are the

suppliers that management should consider as

potential candidates for supplier development

programs and initiatives. While these suppliers

have demonstrated potential, they have scope for

further improvement. The exact supplier develop-ment programs to implement will depend on the

areas in which they are weak, which we address

later in this section by identifying the differences

among the three groups of suppliers in terms of

capabilities and performance metrics. Finally, sup-

pliers 9 and 18 are possible candidates for pruning.

6.3. Identifying differences in performance across

supplier groups

From process improvement and supplier de-

velopment perspectives, we further investigated the

reasons for differences in performance across the

three supplier groups. We analyzed the differences

in their capabilities (inputs) and performance

metrics (outputs). The ANOVA results on inputsand outputs for the three groups are shown in

Table 6. It is interesting to note that there is no

significant difference in terms of inputs or capa-

bilities of the supplier groups, i.e., QMP, SA,

PMC, MGT, DD, and CR levels are not statisti-

cally different. However, the performance metrics

Quality, Price, Delivery, CRP, and Other are all

significantly different at an a ¼ 0:05. In order toinvestigate which groups differ we performed the

Duncan�s multiple range tests on output measures.

These results are summarized in Table 7.

Based on results in Table 7, we can conclude that

lower supplier efficiency groups 2 and 3 are ranked

well below group 1 with respect to several output

variables. It also shows that group 3 is the lowest

ranked with respect to all performance metrics oroutputs. It can be seen that group 1 suppliers� per-formance is vastly superior on ‘‘price’’ and ‘‘CRP’’

compared to groups 2 and 3. Group 2 suppliers who

will be the primary targets of SDI programs could

Table 7

Duncan�s multiple comparison results on supplier group dif-

ferences

Factor/

subsets

1 2 3 Level of

signifi-

cance

0.525 (G2)

Quality 0.764 (G1) 0.560 (G3) 0.1

Price 0.730 (G1) 0.499 (G2) 0.313 (G3) 0.05

Delivery 0.490 (G2) 0.100 (G3) 0.05

0.695 (G1)

CRP 0.572 (G1) 0.000 (G3) 0.05

0.167 (G2)

Other 0.706 (G1) 0.472 (G2) 0.250 (G3) 0.1


learn from group 1 suppliers on how to reduce theircosts by effectively implementing cost reduction

programs. It is conceivable that group 1 suppliers

might refuse to divulge their best practices to group

2 firms for fear of intensified competition from them

in the future. This can be mitigated by entering into

strategic partnering agreements with group 1 firms

that essentially make them the principal beneficia-

ries of the buying firm�s competitive performance.In summary, the managerial implications are that

group 2 suppliers must improve with respect to

Quality, Price, CRP, and Other. However, they are

categorized as being in the same subset with group 1

suppliers with respect toDelivery. This is the type of

feedback thatmanagement should provide to group

2 suppliers. Since there are no significant differences

in the inputs across the three groups, it implies thatgroups 2 and 3 have all capabilities in place, but are

poor in executing these capabilities and trans-

forming them into high level of performance. Thus,

these groups must benchmark themselves against

group 1 suppliers and identify ways to execute their

capabilities better. The buying firm�s SDI programs

can be targeted at group 2 suppliers and specific

areas of performance improvement. The knowledgetransfer from group 1 suppliers to group 2 suppliers

can be filtered through the buying firm and can be

kept at a level acceptable to the group 1 suppliers.

7. Conclusions

In this paper, we have proposed a frameworkand methodology for strategic sourcing. We uti-

lized a combination of DEA models for effectively

discriminating supplier performance. We utilized

both strategic capabilities and performance met-

rics in evaluating suppliers. Our analysis yielded a

number of managerial insights that could not have

been possible with traditional supplier evaluationmethods. These include the identification of sup-

pliers for strategic partnerships, deployment of

resources for SDI, identifying the factors on which

ineffective suppliers need to improve on, and se-

lecting targets for improvement.

The principal advantages of our methodology

are that: it simultaneously considers supplier ca-

pabilities and performance metrics in evaluat-ing the efficiency of alternative suppliers; it does

not require the decision-maker to select a priori

weights or preferences for the supplier factors; it

overcomes some of the problems associated with

the traditional DEA models, which include unre-

stricted weight flexibility in selection of input and

output weights in supplier evaluation decisions; it

effectively incorporates efficiency variability mea-sures into the analysis in determining homogenous

groups of suppliers; and identifies the key differ-

ences across the supplier groups in terms of per-

formance.

While we have considered the input side of the

DEA model somewhat comprehensively, the out-

put measures might need further examination. In

addition, it should be pointed that although theinput and output dimensions considered in this

paper are generally useful, they are context spe-

cific. Also, in a specific application of this meth-

odology, if in fact the set of ineffective suppliers is

deemed an unacceptable result by management,

the output dimensions of DEA model must be re-

examined for relevant but missing dimensions,

which might cause them to be ineffective. A re-evaluation of the proposed methodology along

these lines would yield additional insights and lead

to a better approach for strategic sourcing.

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