Measuring supply chain managementpractices

Ana Beatriz Lopes de Sousa Jabbour, Alceu Gomes Alves Filho,Adriana Backx Noronha Viana and Charbel Jose Chiappetta Jabbour

Summary

Purpose – This paper aims to perform an empirical investigation about the constructs and indicators of

the supply chain management practices framework.

Design/methodology/approach – The measuring framework proposed is based on a survey that was

carried out on 107 Brazilian companies. Statistical techniques were employed to verify, validate, and test

the reliability of the constructs and their indicators. To validate this framework principal component

analysis and structural equation modeling techniques were used.

Findings – In general, previous studies suggest six constructs for measuring the supply chain

management practices framework. However, in this study a framework was achieved with four

constructs of supply chain management practices, namely, supply chain (SC) integration for production

planning and control (PPC) support, information sharing about products and targeting strategies,

strategic relationship with customer and supplier, and support customer order. This framework has

adequate levels of validity and reliability.

Research limitations/implications – The main limitation of this study was that only a small sample of

companies in a single sector and country were surveyed, and therefore there needs to be further

research considering the special conditions in other countries.

Originality/value – This study investigated statistically set indicators to discuss the topic ‘‘supply chain

management practices’’. The framework obtained has good quality of validity and reliability indicators.

Thus, an alternative framework has been added to measure supply chain management practices, which

is currently a popular topic in the supply chain mainstream literature. Both defined constructs and the

validated indicators can be used in other studies on supply chain management.

Keywords Supply chain management, Working practices, Performance measurement (quality), Brazil

Paper type Research paper

1. Introduction

Supply chain management (SCM) is an integrated approach beginning with planning and

control of materials, logistics, services, and information stream from suppliers to

manufacturers or service providers to the end client; it represents a most important

change in business management practices (Fantazy et al., 2010). SCM is one of the most

effective ways for firms to improve their performance (Ou et al., 2010). With the purpose of

managing the supply chain actions for realizing improvement in enterprise performance, it is

necessary to improve the planning and management of activities such as materials

planning, inventory management, capacity planning, and logistics (Chandra and Kumar,

2000) with suppliers and clients.

Currently, the topics that can be considered essential to research suggestions in SCM

include: supply chain coordination, distribution and transport, inventory, order management,

planning and optimization, supply chain integration, reverse logistics, supply chain

information, supplier and vender selection, and green SCM (Hu et al., 2010).

PAGE 18 j MEASURING BUSINESS EXCELLENCE j VOL. 15 NO. 2 2011, pp. 18-31, Q Emerald Group Publishing Limited, ISSN 1368-3047 DOI 10.1108/13683041111131592

Ana Beatriz Lopes de

Sousa Jabbour is Assistant

Professor in the Department

of Industrial Engineering,

Sao Paulo State University,

Sao Paulo, Brazil.

Alceu Gomes Alves Filho is

Full Professor in Industrial

Engineering in the

Department of Industrial

Engineering, Federal

University of Sao Carlos,

Sao Paulo, Brazil.

Adriana Backx Noronha

Viana is Associate

Professor in Business

Administration and

Charbel Jose Chiappetta

Jabbour is Assistant

Professor of Business

Administration, both at The

University of Sao Paulo

Business School – Ribeirao

Preto (FEA-RP/USP),

Sao Paulo, Brazil.

While interest in SCM is increasing day-by-day, there is no consensus about the conceptual

and methodological research bases of SCM, generating gaps in the state-of-the-art of this

research field (Burgess et al., 2006). It is impossible to develop sound SCM theory without

acceptable frameworks and definitions of terms (Stock and Boyer, 2009). In addition, the

lack of a comprehensive view of SCM practices and the lack of a reliable measure of the

concept have constrained guidelines to the practice of SCM and further research on the

topic (Li et al., 2005).

For this reason, the validation of SCM practices issue has been attracting the attention of

researchers. For example, Li et al. (2005) conceptualize, develop, and validate dimensions

of SCM practices constructs. Nonetheless, there are no unanimities in determining the set of

indicators that can adequately address the topic ‘‘Supply Chain Management Practices’’.

Studies performed by Halley and Beaulieu (2010), Bayraktar et al. (2009), Hsu et al. (2009),

Robb et al. (2008), Chow et al. (2008), Koh et al. (2007), Zhou and Benton (2007), Wong et al.

(2005), Tan et al. (2002) and Tan (2002) pointed out different types of indicators and

constructs used.

Therefore, studying SCM practices can contribute to finding a better understanding about

SCM. Hence, this paper aims to do an empirical investigation of the constructs and

indicators of the SCM practices framework.

The following sections include a brief literature review on SCM practices (section 2),

methodological procedures (section 3), analyses and discussions (section 4), and finally,

conclusions (section 5).

2. Literature background

A high level of confusion has occurred amongst supply chain scholars during the past

decades by the several SCM definitions that have been proposed in the literature (Stock and

Boyer, 2009). Three key subjects emerged from the various definitions: activities, benefits,

and constituents/components. The first theme of SCMdefinitions, activities, contains the flow

of materials and information, and networks of relationships, focusing on both internal (within

the organization) and external (outside the organization). Second, the benefits resulting from

effective implementation of SCM strategies are to add value and increase customer

satisfaction. Third, the components or constituent parts of SCM; what organizations,

functions and processes involve the supply chain (Stock et al., 2010).

SCM practices are defined as the set of activities undertaken by an organization to promote

effective management of its supply chain (Li et al., 2005, 2006; Koh et al., 2007); as the

approaches applied in integration, managing and coordination of supply, demand and

relationships in order to satisfy clients in effective way (Wong et al., 2005); as tangible

activities/technologies that have a relevant role in the collaboration of a focal firm with its

suppliers and/or clients (Vaart and Donk, 2008); and as the approach to involve suppliers in

decision making, encouraging information, sharing and looking for new ways to integrate

upstream activities. As a consequence, it involves developing customer contacts by

customer feedback to integrate the downstream activities and delivering orders directly to

customers (Chow et al., 2008). In this sense, studying SCM practices supports the view

theory regarding SCM.

Relevant initiatives to identify and validate SCM practices have been reported, but it is worth

noting that there is no pattern in defining and adopting indicators and constructs for SCM

practices.

Tan et al. (2002) and Tan (2002) identified 24 SCM practices from previous studies and

formed six constructs:

1. supply chain integration;

2. information sharing;

3. supply chain characteristics;

VOL. 15 NO. 2 2011 jMEASURING BUSINESS EXCELLENCEj PAGE 19

4. customer service management;

5. geographical proximity; and

6. JIT capability.

They used a five-point Likert scale to measure the importance of these practices.

Wong et al. (2005) proposed like indicators of SCM practices in their study:

B supply chain performance;

B product differentiation;

B lead time management;

B postponement and customization;

B inventory and cost management;

B bullwhip effects;

B information sharing and coordination;

B buyer-seller relationship;

B retail strategy; and

B SCM initiatives.

They used a case study and the practices based on the toy industry.

Six distinctive constructs of SCM practices emerged, including strategic supplier

partnership, customer relationship, information sharing, information quality, internal lean

practices and postponement. All the items were measured on a five-point scale (Li et al.,

2005, 2006).

Zhou and Benton (2007) consider three constructs of supply chain practices (supply chain

planning, just-in-time (JIT) production, and delivery practice), because they have been

shown to be closely related to delivery performance. Each statement required responses

based on a seven-point Likert scale (1 ¼ not implemented, 7 ¼ extensively implemented).

A list of SCM constructs used in previous literature regarding the SCM practices is relying on

the extant literature. Koh et al. (2007) and Bayraktar’s et al. (2009) studies identify a set of 12

SCM practices: close partnership with suppliers, close partnership with customers, just in

time supply, strategic planning, supply chain benchmarking, few suppliers, holding safety

stock, e-procurement, outsourcing, subcontracting, 3PL, many suppliers. Items were

measured on five-point scales ranging from 1 (not at all implemented) to 5 (fully

implemented). Koh et al. (2007) measuring two constructs and Bayraktar et al. (2009)

measuring three constructs.

A five-point interval rating scale system was used by Chow et al. (2008) with 5 equaling the

highest extent or degree. The constructs were:

B customer and supplier management;

B supply chain features;

B communication and speed; and

B information sharing.

Robb et al. (2008) considered four constructs in their research:

1. customer relationships;

2. supplier relationships;

3. e-commerce; and

4. enterprise software.

They used a seven-point Likert scale.

PAGE 20 jMEASURING BUSINESS EXCELLENCEj VOL. 15 NO. 2 2011

In research performed by Hsu et al. (2009), respondents were asked to indicate on a

five-point Likert scale (1 – low, 5 – high), the importance of each practice in their firm. The

indicators were:

B increase suppliers’ just in time capabilities;

B participating in sourcing decisions;

B geographical proximity of suppliers;

B formal information sharing agreements;

B improving the integration of activities;

B searching for new ways for integration;

B communicating future strategic needs;

B on-time delivery; and

B reducing response time.

Halley and Beaulieu (2010) used four constructs (nesting, collaboration, financial

incorporation, and distancing) along with 13 indicators from the five-point Likert scale.

Table I summarizes the theoretical studies of constructs pointed out in this section.

Table II shows the constructs, the indicators and conceptual meaning used in this paper to

measure and validate the SCM practices framework. The selection of constructs and

indicators was based on research to reconcile the concepts of SCM (Stock et al., 2010;

Chandra and Kumar, 2000), in which they considered the necessity to manage, plan and

control production and inventory, i.e. the flow of information and materials; the definition of

SCM practices (Wong et al., 2005; Chow et al., 2008), the managed integration and

coordination of supply, demand and relationships; and the most commonly found constructs

and the indicators (Bayraktar et al., 2009; Robb et al., 2008; Chow et al., 2008; Koh et al.,

2007; Zhou and Benton, 2007; Li et al., 2006, 2005; Tan et al., 2002; Tan, 2002). Taking this

into consideration, the constructs considered were: supply chain integration, information

sharing, customer service management, customer relationship, supplier relationship and

postponement.

3. Methodology

3.1 Survey design

In order to assess the construct of the SCM practices, a questionnaire was developed from a

review of literature (Table II), and the respondents were asked to evaluate each question in

terms of the level of implementation of each specific practice in their company. A five-point

Likert scale (1 – non-implemented and 5 – totally implemented) was adopted because there

are many researches uses the same method.

Prior to this, a pre-test was given to professionals in the SC over a 65-day period, and from

this pre-test some necessary adjustments were done to the questionnaire in order to make

the questions clearer. The questionnaire was sent out to 532 different companies via

personalized e-mails. In all, 107 companies responded (response rate of 20.11 percent)

over a 44-day period (from July to September 2009). The invitation only e-mails were sent to

each of the 532 companies up to three times each.

The participating companies were classified according to the position they occupy in their

supply chain as follows: raw material supplier, component supplier, assembly company,

distributor, or retailer. The companies were also classified according to their size (micro

company, small company, medium company, and large company) and the predominant

bargaining power in their major supply chain.

The data were collected through an e-survey (internet-based survey) conducted with one

respondent from each company (from different areas, such as marketing, operations,

supply, and sales departments) of several different segments of the Brazilian

VOL. 15 NO. 2 2011 jMEASURING BUSINESS EXCELLENCEj PAGE 21

Table I Summaries of the theoretical studies of constructs pointed out in this research

Author(s) Research objective Construct Context Scale

Tan (2002) The first objective was to derive aset of SCM practices and comparehow practitioners ranked thesepractices to enhance competitiveposition. The second objective wasto identify and compare the majorconcerns in implementing asuccessful SCM program. Finally,the third objective attempted toidentify the practices and theconcerns associated withsuccessful supply chains

Supply chain integrationInformation sharingSupply chain characteristicCustomer servicemanagementGeographical proximityJIT capability

Different industries Five-pointLikert

Tan et al. (2002) The article described a surveyeffort to study contemporary supplychain management and supplierevaluation practices. This alsorelated these practices to firmperformance

Wong et al. (2005) The study explored SCM practicesof toy supply chains, and revealedtheir practical and theoretical gaps

None Toy industry – retail (volatiledemand)

None

Li et al. (2005) The purpose of research was todevelop and validate aparsimonious measurementinstrument for SCM practices

Strategic supplierpartnershipCustomer relationshipInformation sharing

Different industries Five-pointLikert

Li et al. (2006) The purpose of study therefore toempirically test a frameworkidentifying the relationships amongSCM practices, competitiveadvantage and organizationalperformance

Information qualityInternal lean practicesPostponement

Zhou and Benton(2007)

The purpose of study was toinvestigate: the relationshipbetween information sharing andsupply chain practice: the influenceof supply chain dynamism oninformation sharing and supplychain practice; and the impact ofinformation sharing and supplychain practice on deliveryperformance

Supply chain planJIT productionDelivery practices

Different industries Seven-pointLikert

Koh et al. (2007) The purpose of study was todetermine the underlyingdimensions of SCM practices andto empirically test a frameworkidentifying the relationships amongSCM practices, operationalperformance and SCM-relatedorganizational performance withspecial emphasis on small andmedium-sized enterprises (SMEs)in Turkey

Strategic collaboration andlean practicesOutsourcing andmulti-suppliers

SME companies from Turkey Five-pointLikert

Bayraktar et al.(2009)

Study sought to determine theunderlying dimensions of SCM andIS practices. Next, empirically test aframework identifying the causallinks among SCM and IS practices,SCM and IS-related inhibitorsoperational performance

Strategic collaboration andlean practicesSuppliers’ selection practicesProcurement practices

SME companies from Turkey Five-pointLikert

(Continued)

PAGE 22 jMEASURING BUSINESS EXCELLENCEj VOL. 15 NO. 2 2011

electro-electronics sector (industrial automation, electrical and electronic components,

generation, transmission and distribution, informatics, telecommunications, etc.). Based on

a list provided by the Brazilian Electrical and Electronics Industry Association (ABINEE),

e-mails were sent to the listed companies inviting them to participate in the survey by

responding to an online multiple-choice questionnaire. The electronic sector was chosen as

it is one of the most important components of Brazil’s gross domestic product (GDP), which

is also the same selection criterion used by Law et al. (2009).

Table III shows the profile of the respondent companies. It is observed that customers drive

these supply chains, since they have the most bargaining power; that most of the

respondents are small to midsized companies and that the respondent companies in

generally occupy the position of manufacturers in the chains.

3.2 Data analysis

In the first step, the principal component analysis was applied to all indicators. The principal

component analysis was applied to study the interrelationships between the variables based

on the data reduction to explain their relationship, i.e., the way the indicators are combined

to form the constructs of SCM practices. Thus, the principal component analysis divides the

indicators (questions) into groups (factors), summarizing their relationship pattern.

The principal component analysis resulted in a framework with four factors (Figure 1).

Then each of these four factors was analyzed for quality measures. These quality measures

were obtained using the SPPS and SmartPLS software packages. SPSS proved to be useful

for verifying the measures, such as the adequacy of the sample. When applying principal

component analysis (four limiting factors/constructs), we were able to obtain the measures

Table I

Author(s) Research objective Construct Context Scale

Chow et al. (2008) Through structural equationmodeling critical components ofsupply chain management werefound to have considerable effectson organizational performance

Customer and suppliermanagementSupply chain featuresCommunication and speedInformation sharing

Compare US and Taiwanmanufacturing

Five-pointLikert

Robb et al. (2008) The relationship between supplychain/operations practice andoperational/financial performancehas been of interest to academicsand practitioners for many years.The paper proposed anddeveloped a model exploring theseconnections, utilizing data from asurvey of 72 furnituremanufacturers located throughoutChina

Customer relationshipsSupplier relationshipsE-commerceEnterprise software

Furniture manufacturing inChina

Seven-pointLikert

Hsu et al. (2009) The research analyzed the roles ofoperations capability and supplychain management practice on firmperformance

None Different industries Five-pointLikert

Halley andBeaulieu (2010)

The paper described the use ofsupply chain managementpractices and shows that their useis dependent on the nature of thebusiness partners (i.e., upstream ordownstream positioning ofpartnership in chain logistics ofbusinesses studied), business field(i.e., sector of activities in which thebusiness operates), andorganizational size

InterlinkingConsultationSharingDetachment

Different industries Five-pointLikert

VOL. 15 NO. 2 2011 jMEASURING BUSINESS EXCELLENCEj PAGE 23

of quality of the framework using the partial least squares (PLS) statistical technique. The

main results of our analysis are shown below.

4. Results and discussions

The first step to data analysis was to calculate the global Cronbach’s alpha of the indicators

of SCM practices. The general value of 0.942 was obtained, which was considered

excellent.

The data reduction of all variables (V1-V21) was performed using the principal component

analysis method with varimax. This procedure resulted in a framework with four factors that

can explain the variance value of 67.547 percent. The global KMO test that verifies the

adequacy of the sample was 0.885, and is considered adequate. In order to refine the

results, the principal component analysis only shows variable loadings higher than 0.5 and

factors with eingenvalues higher than 1 and coefficients of the diagonal of the matrix

Table II The constructs, indicators and theoretical meanings used in this paper to measuring and validate SCM practices

construct

Construct Indicator Meaning Code

Supply chain integration Customer integration Integration of the products development in thedownstream supply chain (customer)

V1

Supplier integration Integration of the products development in theupstream supply chain (suppliers)

V2

Customer involvement in the plans Involvement of the downstream supply chain inproducts/services/marketing plans

V3

Supplier involvement in the plans Involvement of the upstream supply chain inproducts/services/marketing plans

V4

Supplier collaboration demand forecasting Collaboration of the upstream supply chainmembers with demand forecasting

V5

Customer collaboration demand forecasting Collaboration of the downstream supply chainmembers with demand forecasting

V6

Supplier collaboration stock planning Collaboration of the upstream supply chainmembers with stock planning

V7

Customer collaboration stock planning Collaboration of the downstream supply chainmembers with stock planning

V8

Supplier collaboration production planning Collaboration of the upstream supply chainmembers with production planning

V9

Customer collaboration production planning Collaboration of the downstream supply chainmembers with production planning

V10

Creation of multifunctional teams Creation of multifunctional logistics and qualityteams that include members of other teams

V11

Information sharing Cost information sharing customer Formal information sharing about productioncosts with customers

V12

Information sharing product launchingsupplier

Formal information sharing about newproducts launching with suppliers

V13

Participation in customer marketing Participation in the customers’ marketing effort V14Customer future needs Determine customer future needs V15Supplier communication future strategy Communicate suppliers of future strategies V16

Customer servicemanagement

Customer feedback Obtain final customers feedback on servicesadequacy

V17

Customer relationship Customer support new product decision Consult customers to support decisions aboutnew products

V18

Consult customer production programming Consult customers about productionprogramming changes

V19

Supplier relationship Consult supplier production programming Consult suppliers about productionprogramming changes

V20

Supplier support product development Consult customer to support new productsdevelopment

V21

Postponement Assembly near customer Assembly products near final customer V22

PAGE 24 jMEASURING BUSINESS EXCELLENCEj VOL. 15 NO. 2 2011

anti-image higher than 0.6. We also checked the commonalities for each variable (Hair et al.,

2005).

Initially, using the SPSS software, the quality of the proposed model by principal component

analysis was analyzed (four factors) by verifying:

1. the adequacy of sample for each individual factor by the KMO test;

2. Cronbach’s alpha of each factor;

3. the eigenvalue of each factor, where they were extracted factors with eigenvalues greater

than 1; and

4. an accumulated explained variance.

Table III Profile of the respondents’ companies

%

PositionRaw material supplier 0.9Components supplier 15.9Assembly company 76.6Distributor 4.7Retail 1.9

SizeMicro company 10.3Small company 31.8Medium company 42.1Large company 15.9

Bargain powerOwn suppliers 8.4Own company 10.3Own customers 81.3

Figure 1 Created framework based on principal component analysis

V1

V10

V5

V6

V7

V8

FACTOR 1

SCM Practices

FACTOR 2

FACTOR 3FACTOR 4

V9

V11

V13

V16

V17

V18

V19

V20

V21

V14

V15

V2

V3

V4

V22

V12

VOL. 15 NO. 2 2011 jMEASURING BUSINESS EXCELLENCEj PAGE 25

The high alphas, the high KMO, accumulated explained variance and eigenvalues show

partial evidences that the scales are adequate. Table IV shows the formed factors and their

measures of quality.

Next, the partial least squares (PLS) was used to run scale’s validity and reliability. PLS is a

second-generation structural equation modeling technique and is especially useful when

working with theory in early stages of development. A framework was created containing the

four constructs obtained from the principal component analysis, as explained above. The

aim of this procedure was to test the validity and reliability of the principal component

analysis model. The analyses were conducted using the software SmartPLS 2.03 (Sosik

et al., 2009).

Good quality indicators for the proposed framework have been achieved in terms of average

variance extracted, composite reliability, and communality (Table IV). The loadings of all

indicators on their corresponding constructs reached acceptable levels (over than 0.6). To

reach satisfied reliability and validity, the composite reliability value should be higher than

0.7 while the average variance extracted value should be higher than 0.5. Construct

reliability was assessed using composite reliability. Convergent validity examined the

average variance extracted measure. Table IV shows that all of the values of composite

reliability are higher than 0.7 and all of the values of average variance extracted are higher

than 0.5 (Foltz, 2008).

The cross loading matrix was checked (Table V) and all indicator loadings were located

where planned. A bootstrap of 300 subsamples was used to estimate the statistical

significance of proposed relationships between indicators and constructs (Table IV)

In Table VI, the italicized diagonal representing the square root of the average variance

extracted exceeded the off-diagonal elements in the construct correlation matrix.

Table IV Quality measures for the proposed framework

Factor1 2 3 4

Label SC integration for PPC

support

Information sharing about

products and targeting

strategies

Strategic relationship

with customer and

supplier

Support customer

order

Indicators Supplier collaboration

stock planning (V7)

Supplier collaboration

production planning(V9)

Customer collaboration

stock planning (V8)

Customer collaboration

production planning (V10)

Customer collaboration

demand forecast (V6)

Supplier collaboration

demand forecast (V5)

Customer integration (V1)

Consult customer production

planning (V19)

Consult supplier production

planning (V20)

Decision support new

customer product (V18)

Customer feedback (V17)

Supplier support product

development (V21)

Supplier communication

future strategy (V16)

Information sharing product

launching supplier (V13)

Supplier involvement

plans (V4)

Customer involvement

plans (V3)

Customer marketing

participation (V14)

Customer future needs

(V15)

Supplier integration (V2)

Assembly near

customer (V22)

Cost information

sharing customer

(V12)

Multifunctional team

creation (V11)

Average variance

extracted 0.7214 0.5631 0.6083 0.5893Composite reliability 0.9474 0.8997 0.8853 0.8110Cronbach’s alpha 0.9345 0.8700 0.8375 0.6621Kaiser-Meyer-Olkin

measure of sampling

adequacy 0.848 0.840 0.760 0.655Communality 0.7214 0.5631 0.6083 0.5893Eigenvalue 10.237 1.920 1.395 1.309Accumulated explained

variance (%) 25.741 44.326 58.582 67.547

PAGE 26 jMEASURING BUSINESS EXCELLENCEj VOL. 15 NO. 2 2011

Consistent results were obtained. After testing the quality of the proposed scale, we can

then analyze the best label for each factor. This analysis is intended to ensure content

validity. Content validity refers to the extent to which a measure represents all facets of a

given construct.

The indicators have been taken based on the highest incidences in the definition of SCM, on

the discussion of SCM practices and current research topics (integration, relationship,

information exchange, matching of supply and demand), as explained in section 2.

Four factors were found. Factor 1 combines indicators of the ‘‘SC integration’’ construct and

Factor 2 combines indicators of ‘‘information sharing’’, ‘‘customer service management’’,

‘‘customer relationship’’, and ‘‘supplier relationship’’ constructs. Factor 3 gathers indicators

of ‘‘SC integration’’ and ‘‘information sharing’’ constructs and Factor 4 consists of the

indicators of ‘‘postponement’’, ‘‘information sharing’’, and ‘‘SC integration’’ constructs.

Factor 1 combines the indicators that support the PPC (production planning and control)

of an assembly company. Thus, this factor can be called ‘‘SC integration for PPC

support’’. The customers and suppliers contribute to a better visualization of their

common processes through collaborations with production planning, demand forecast, or

stock planning.

Factor 2 consists of hybrid indicators of different constructs, focusing on information sharing

and strategies such as product development and future strategies, thus, this factor can be

entitled ‘‘information sharing about products and targeting strategies.’’

Table V Variables cross-loadings matrix and level of significance

FactorVariables 1 2 3 4 p value

V1 0.7103 0.5553 0.6710 0.3443 0.0000V2 0.6964 0.6340 0.7799 0.2988 0.0000V3 0.6552 0.4400 0.8127 0.4148 0.0000V4 0.6156 0.5081 0.8662 0.4481 0.0000V5 0.8245 0.5583 0.6874 0.3202 0.0000V6 0.8597 0.5354 0.6477 0.4321 0.0000V7 0.8933 0.6091 0.5706 0.4402 0.0000V8 0.8885 0.5367 0.5475 0.4771 0.0000V9 0.9022 0.6570 0.6143 0.4360 0.0000V10 0.8514 0.5940 0.5526 0.4365 0.0000V11 0.4949 0.4498 0.5302 0.8251 0.0000V12 0.3239 0.4326 0.3144 0.7516 0.0000V13 0.5582 0.7604 0.6307 0.4562 0.0000V14 0.3963 0.5385 0.7384 0.4351 0.0000V15 0.3943 0.5832 0.6908 0.3799 0.0000V16 0.6418 0.8056 0.6152 0.5081 0.0000V17 0.3400 0.6802 0.4340 0.3460 0.0000V18 0.4139 0.6547 0.4277 0.2624 0.0000V19 0.4243 0.7394 0.3875 0.3655 0.0000V20 0.5762 0.8152 0.5197 0.3432 0.0000V21 0.5556 0.7822 0.5642 0.4005 0.0000V22 0.2479 0.2813 0.2506 0.7226 0.0000

Table VI Construct correlation matrix

Factor 1 Factor 2 Factor 3 Factor 4

Factor 1 0.8494Factor 2 0.6819 0.7504Factor 3 0.7208 0.6918 0.7799Factor 4 0.4868 0.5182 0.5030 0.7677

VOL. 15 NO. 2 2011 jMEASURING BUSINESS EXCELLENCEj PAGE 27

Factor 3 comprises indicators of almost all attributes, but those indicators share the required

good and long-term relationship with suppliers and customers in order to adopt each

practice. Therefore, this factor is labeled ‘‘strategic relationship with customer and supplier’’.

Factor 4 gathers indicators that share integration issues with the customer, either by

postponing assembly or by clear cost accountability to facilitate the business between

customer and supplier, i.e. organizing multifunctional teams to facilitate the combined

operations. Therefore, this factor can be called ‘‘support customer order’’.

The factors defined based on the indicators of the constructs of SCM practices make it easy

to understand their interrelationship and enable a better outline of their constructs since they

were summarized. The constructs of practices from literature review were condensed into

four resulting in a better specification of each one based on the indicators involved. For

example, the ‘‘SC integration’’ construct was refined to PPC activities. The ‘‘information

sharing’’ construct was better specified and advanced, and it was labeled ‘‘information

sharing about products and targeting strategies’’. Several other constructs were condensed

into a single one, ‘‘strategic relationship with the customer and supplier’’. The last construct

included only three indicators, and was termed ‘‘support customer order’’.

5. Conclusions

This study aimed to identify a valid framework to measure SCM practices. A survey was

conducted to collect data on the degree of implementation of SCM practices in Brazilian

companies. Using these data, it was possible to perform a statistical analysis, based on

principal component analysis and structural equation modeling, to determine the measures

of sampling adequacy, reliability and validity of the adopted scale. The statistical analysis

demonstrated that the indicators chosen in the literature review and grouped into four

factors/constructs are suitable for the measurement of SCM practices, achieving the

proposed objective for this research.

Li et al. (2005) developed and validated an instrument to measure SCM practices using six

factors to represent the construct of SCM. Based on Li et al. (2005) and other studies, we

have proposed six theoretical latent variables (constructs) and obtained four factors from

principal component analysis. This resulted in improvement and parsimony in

understanding the construct of SCM practices. We started with very general latent

variables (SC integration, information sharing, customer service management, customer

relationship, supplier relationship and postponement), and after the analysis we obtained:

B SC integration for PPC support;

B information sharing about products and targeting strategies;

B strategic relationship with customer and supplier; and

B support customer order.

Halley and Beaulieu (2010) said that supply chain practices were used more intensively with

clients than with suppliers. Olhager and Selldin (2004) identified that to some supply chain

partners, the downstream direction is more often considered dominant than upstream.

According to Mouritsen et al. (2003), the strong tier in the chain tends to influence the actions

of integration with other tiers. The fact that customers are the strong tier in the supply chain of

the companies studied in this research can explain the greater adoption of practices aimed

at integration with customers by assemblers. This argument can explain the use of a factor

(support customer order) with greater emphasis on customer service practices.

Thus, the major contributions of this research are:

B the testing and measurement of indicators of SCM practices to obtain appropriate values

of reliability and validity, which may identify indicators and latent variables (constructs) to

represent SCM practices, and thus support the search for a common understanding of

SCM practices;

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B a study of a specific sector where it is possible to discuss the contextual and contingent

considerations for proposing the latent variables, which represents a gap in the literature,

according to Li et al. (2005), Wong et al. (2005), Jharkharia and Shankar (2006) and

Halley and Beaulieu (2010); and

B even though only one industry was studied, we considered the mainstream of literature

and used generic statistical analysis procedures, and therefore the results may be

applied to any country and any industry, given some refinement or contextualization.

Regarding practical and managerial implications, with the knowledge on indicators of the

construct of SCM practices, supply chain managers are able to conduct research and

benchmarking of the level of adoption of SCM practices with customers and suppliers, and

thus direct efforts to improve performance.

Regarding the social implications, this paper can contribute to a better understanding of

SCM and its management practices. Any improvement in the management of the supply

chain can be targeted for new investment, and consequently the generation of employment

and income.

A major limitation of this study is that the research has focused on companies from a single

sector with a relatively small sample size that has only targeted one country, and therefore

requires further research covering various sectors and taking into consideration the specific

conditions in other countries.

It is recommended that future studies replicate the framework presented here, generating

new ideas and refinements.

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About the authors

Ana Beatriz Lopes de Sousa Jabbour has a PhD in Industrial Engineering from The FederalUniversity of Sao Carlos – UFSCar, Brazil. She is Assistant Professor in Sao Paulo StateUniversity. Her research interests include supply chain management and operationsmanagement. Ana Beatriz Lopes de Sousa Jabbour is the corresponding author and can becontacted at: abjabbour@feb.unesp.br

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Alceu Gomes Alves Filho has a PhD in Industrial Engineering from The University of SaoPaulo – USP, Brazil. He is Full Professor of Industrial Engineering at The Federal University ofSao Carlos – UFSCar (Sao Carlos). His research interests include supply chainmanagement and operations management.

Adriana Backx Noronha Viana has a PhD in Engineering from The Campinas State University– Unicamp, Brazil. She is Associate Professor of Business Administration in The University ofSao Paulo Business School – Ribeirao Preto (FEA-RP/USP). Her research interests includeapplied statistics and business administration.

Charbel Jose Chiappetta Jabbour has a PhD in Industrial Engineering from The University ofSao Paulo – USP, Brazil. He is Assistant Professor of Business Administration in TheUniversity of Sao Paulo Business School – Ribeirao Preto (FEA-RP/USP). His researchinterests include business administration, environmental management in companies andoperations management.

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