measuring supply

Measuring Supply Chain Management Practices

___________________________________________________________________________

Ana Beatriz Lopes de Sousa Jabbour (First Author) – Author for Contact

She has a Ph.D. in Industrial Engineering from The Federal University of Sao Carlos – UFSCar,

Brazil. She is Assistant Professor in Sao Paulo State University. Her research interests include supply

chain management and operations management.

Address: UNESP-FEB – Departamento de Engenharia de Produção. Avenida Eng. Carrijo Coube,

S/N, Bauru, SP-Brazil CEP 17033360.

E-mail: [email protected]

__________________________________________________________________________________

Alceu Gomes Alves Filho (Second Author)

He has a Ph.D. in Industrial Engineering from The University of Sao Paulo – USP, Brazil. He is Full

Professor on Industrial Engineering in The Federal University of Sao Carlos – UFSCar (São Carlos).

His research interests include supply chain management and operations management.

Address: UFSCar - Campus São Carlos, Rodovia Washington Luís, (SP-310), KM 235, São Carlos -

São Paulo – Brazil, CEP 13565-905.


__________________________________________________________________________________

Adriana Backx Noronha Viana (Third Author)

She has a Ph.D. in Engineering from The Campinas State University – Unicamp, Brazil. She is

Associated Professor on Business Administration in The University of São Paulo Business School –

Ribeirão Preto (FEA-RP/USP). Her research interests include applied statistics and business

administration.

Address: FEA-RP/USP, Avenida Bandeirantes, 3900, Monte Alegre, Ribeirão Preto, São Paulo,

Brazil, CEP 14040-900.


___________________________________________________________________________

Charbel José Chiappetta Jabbour (Fourth Author)

He has a Ph.D. in Industrial Engineering from The University of Sao Paulo – USP, Brazil. He is

Assistant Professor on Business Administration in The University of São Paulo Business School –

Ribeirão Preto (FEA-RP/USP). His research interests include business administration, environmental

management in companies and operations management.

Address: FEA-RP/USP, Avenida Bandeirantes, 3900, Monte Alegre, Ribeirão Preto, São Paulo,

Brazil, CEP 14040-900.


[email protected]

Measuring Supply Chain Management Practices

Abstract

Purpose – This paper aims to do 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 its indicators.

To validate this framework we used Principal Component Analysis and Structural

Equation Modeling techniques.

Findings – In general, previous studies suggest 6 constructs for measuring the supply

chain management practices framework. However, in this study we achieved a

framework with 4 constructs of supply chain management practices, namely, (1) SC

(Supply Chain) integration for PPC (production planning and control) support, (2)

information sharing about products and targeting strategies, (3) strategic relationship

with customer and supplier, and (4) 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, we add an alternative framework 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 (SCM); SCM practices; Measuring framework;

Brazil.

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 manage 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, Yang

and Huang, 2010).

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 (Burgees et al., 2006). It is impossible to develop sound

SCM theory without acceptable frameworks and definitions of terms (Stock and

Boyle, 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 supply chain management 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 Bealieu (2010), Bayraktar et al. (2009), Hsu et al. (2009),

Robb et al. (2008), Chow et al. (2008), Koh et al. (2007), Zhou and Benton Jr (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 supply chain management 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: (1)

activities; (2) benefits; and (3) constituents/components. The first theme of SCM

definitions, 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, Li et al., 2006

and 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 6 constructs: (a) supply chain integration, (b) information sharing,

(c) supply chain characteristics, (d) customer service management, (e) geographical

proximity, (f) 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: (a)

supply chain performance, (b) product differentiation, (c) lead time management, (d)

postponement and customization, (e) inventory and cost management, (f) bullwhip

effects, (g) information sharing and coordination, (h) buyer-seller relationship, (i)

retail strategy and (j) 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 and Li et al., 2006).

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

(supply chain planning, 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 = not implemented, 7 = 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: (1) customer and supplier

management, (2) supply chain features, (3) communication and speed, and (4)

information sharing.

Robb et al. (2008) considered 4 constructs in their research: (a) customer

relationships, (b) supplier relationships, (c) e-commerce, and (d) enterprise software.

They used a seven- point Likert scale.

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: (1) increase suppliers’ just in time capabilities, (b)

participating in sourcing decisions, (c) geographical proximity of suppliers, (d) formal

information sharing agreements, (e) improving the integration of activities, (f)

searching for new ways for integration, (g) communicating future strategic needs, (h)

on-time delivery and (i) reducing response time.

Halley and Beaulieu (2010) used 4 constructs (nesting, collaboration, financial

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

scale.

Table 1 summarizes the theoretical studies of constructs pointed out in this

section.

PLEASE, TAKE IN TABLE 1

Table 2 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 and 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 and

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

Jr., 2007, Li et al, 2006, Li et al., 2005, Tan et al., 2002 and 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 2), and the respondents were asked to

evaluate each question in terms of the level of implementation of each specific practice

in their company. A 5-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%) over a 44 day period (from July to September 2009). The invitation only

emails were sent to each of the 532 companies up to 3 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 collect 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 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), emails 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 GDP, which is also the same selection criterion used by Law et

al. (2009).

Table 3 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).

PLEASE, TAKE IN 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 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 which can explain the variance value of 67.547%. 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 anti-image higher than 0.6. We also checked the

commonalities for each variable (Hair Jr et al., 2005).

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

Principal Component Analysis was analyzed (4 factors) by verifying: (a) the adequacy

of sample for each individual factor by the KMO test, (b) Cronbach's Alpha of each

factor, (c) the eigenvalue of each factor, where they were extracted factors with

eigenvalues greater than 1, and (d) an accumulated explained variance. The high alphas,

the high KMO, accumulated explained variance and eigenvalues show partial evidences

that the scales are adequate. Table 4 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, Kahai and Piovoso, 2009).

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

of average variance extracted, composite reliability, and communality (Table 4). 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 4 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 5) 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 4)


In Table 6, the bold diagonal representing the square root of the average

variance extracted exceeded the off-diagonal elements in the construct correlation

matrix. 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.”

Factor 3 is comprised of 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”.

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 this 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 6 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: (a) SC integration for PPC support, (b) information sharing

about products and targeting strategies, (c) strategic relationship with customer and

supplier and (d) support customer order.

Halley and Bealieu (2010) said that supply chain practices were used more

intensively with clients than with suppliers. Olhager and Sellding (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 (1) the testing and

measurement of indicators of SCM practices to obtain appropriate values of reliability

and validity, which may indentify indicators and latent variables (constructs) to

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

SCM practices, (2) 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 Bealieu (2010), and (3) 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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Figure 1 – Created framework based on Principal Component Analysis.

Author (s) Research Objective Construct Context Scale

Tan (2002)

Tan et al.

(2002)

The first objective was to derive a set of

SCM practices and compare how

practitioners ranked these practices to

enhance competitive position. The second

objective was to identify and compare the

major concerns in implementing a

successful SCM program. Finally, the

third objective attempted to identify the

practices and the concerns associated

with successful supply chains.

The article described a survey effort to

study contemporary supply chain

management and supplier evaluation

practices. This also related these practices

to firm performance

� Supply chain

integration

� Information sharing

� Supply chain

characteristic

� Customer service

management

� Geographical

proximity

� JIT capability

Different

industries

5-point

Likert

Wong et al.

(2005)

The study explored SCM practices of toy

supply chains, and revealed their practical

and theoretical gaps.

� None Toy industry -

Retail (volatile

demand)

None

Li et al.

(2005)

Li et al.

The purpose of research was to develop

and validate a parsimonious measurement

instrument for SCM practices.

The purpose of study therefore to

� Strategic supplier

partnership

� Customer

relationship


� Information quality

Different

industries

5-point

Likert

(2006) empirically test a framework identifying

the relationships among SCM practices,

competitive advantage and organizational

performance.

� Internal lean practices

� Postponement

Zhou and

Benton Jr

(2007)

The purpose of study was to investigate

(1) the relationship between information

sharing and supply chain practice, (2) the

influence of supply chain dynamism on

information sharing and supply chain

practice, and (3) the impact of

information sharing and supply chain

practice on delivery performance.

� Supply chain plan

� JIT production

� Delivery practices

Different

industries

7-point

Likert

Koh et al.

(2007)

The purpose of study was to determine

the underlying dimensions of SCM

practices and to empirically test a

framework identifying the relationships

among SCM practices, operational

performance and SCM-related

organizational performance with special

emphasis on small and medium size

enterprises (SMEs) in Turkey.

� Strategic

collaboration and

lean practices

� Outsourcing and

multi-suppliers

SME companies

from Turkey

5-point

Likert

Bayraktar et

al. (2009)

Study sought to determine the underlying

dimensions of SCM and IS practices.

Next, empirically test a framework

identifying the causal links among SCM

and IS practices, SCM and IS related

inhibitors operational performance.

� Strategic

collaboration and

lean practices

� Suppliers selection

practices

� Procurement

practices

SME companies

from Turkey

5-point

Likert

Chow et al.

(2008)

Through structural equation modeling

critical components of supply chain

management were found to have

considerable effects on organizational

performance.

� Customer and

supplier management

� Supply chain features

� Communication and

speed


Compare US and

Taiwan

manufacturing

5-point

Likert

Rob et al.

(2008)

The relationship between supply

chain/operations practice and

operational/financial performance has

been of interest to academics and

practitioners for many years. The paper

proposed and developed a model

exploring these connections, utilizing

data from a survey of 72 furniture

manufacturers located throughout China

� Customer

relationships

� Supplier relationships

� E-commerce

� Enterprise software

Furniture

manufacturing in

China

7-point

Likert

Hsu et al

(2009)

The research analyzed the roles of

operations capability and supply chain

management practice on firm

performance.

� None Different

industries

5-point

Likert

Halley and

Beaulieu

(2010)

The paper described the use of supply

chain management practices and show

that their use is dependent on the nature

of the business partners (i.e., upstream or

downstream positioning of partnership in

chain logistics of businesses studied),

business field (i.e., sector of activities in

which the business operates), and

organizational size.

� Interlinking

� Consultation

� Sharing

� Detachment

Different

industries

5-point

Likert

Table 1 – Summarize the theoretical studies of constructs pointed out in this research.

Construct Indicator Meaning Code

Customer Integration Integration of the products development in the

downstream supply chain (customer)

V1

Supplier Integration Integration of the products development in the

upstream supply chain (suppliers)

V2

Customer Involvement in the Plans Involvement of the downstream supply chain in

products/services/marketing plans

V3

Supplier Involvement in the Plans Involvement of the upstream supply chain in

products/services/marketing plans

V4

Supplier Collaboration Demand

Forecasting

Collaboration of the upstream supply chain

members with demand forecasting

V5

Supply Chain

Integration

Customer Collaboration Demand

Forecasting

Collaboration of the downstream supply chain

members with demand forecasting

V6

Supplier Collaboration Stock Planning Collaboration of the upstream supply chain

members with stock planning

V7

Customer Collaboration

Stock Planning


members with stock planning

V8

Supplier Collaboration Production

Planning

Collaboration of the upstream supply chain

members with production planning

V9

Customer Collaboration

Production Planning


members with production planning

V10

Creation of Multifunctional Teams Creation of multifunctional logistics and quality

teams that include members of other teams

V11

Cost Information Sharing Customer Formal information sharing about production costs

with customers

V12

Information Sharing

Product Launching

Supplier

Formal information sharing about new products

launching with suppliers

V13

Information

Sharing

Participation in Customer Marketing Participation in the customers’ marketing effort V14

Customer Future Needs Determine customer future needs V15

Supplier Communication Future

Strategy

Communicate suppliers of future strategies V16

Customer Service

Management

Customer Feedback Obtain final customers feedback on services

adequacy

V17

Customer

Relationship

Customer Support New Product

Decision

Consult customers to support decisions about new

products

V18

Consult Customer Production

Programming

Consult customers about production programming

changes

V19

Supplier

Relationship

Consult Supplier Production

Programming

Consult suppliers about production programming

changes

V20

Supplier Support Product Development Consult customer to support new products

development

V21

Postponement Assembly Near Customer Assembly products near final customer V22

Table 2 - Show the constructs, indicators and theoretical meanings used in this paper to measuring and

validate SCM practices construct.

Raw material

supplier

Components

supplier

Assembly

company

Distributor Retail

Position 0.9% 15.9% 76.6% 4.7% 1.9%

Micro company Small company Medium

company

Large company

Size 10.3% 31.8% 42.1% 15.9%

Own suppliers Own company Own customers

Bargain power 8.4% 10.3% 81.3%

Table 3 - Profile of the respondents companies.

F

act

or

1

Fa

cto

r 2

F

act

or

3

Fa

cto

r 4

La

bel

S

C i

nte

gra

tio

n f

or

PP

C

sup

po

rt

Info

rmati

on s

har

ing a

bo

ut

pro

duct

s an

d t

arget

ing

stra

tegie

s

Str

ateg

ic r

elat

ionsh

ip w

ith

cust

om

er a

nd

sup

pli

er

Sup

po

rt c

ust

om

er o

rder

Ind

ica

tors

� S

up

pli

er C

oll

abo

rati

on

Sto

ck P

lan

nin

g (

V7

)

� S

up

pli

er C

oll

abo

rati

on

Pro

duct

ion P

lann

ing(

V9

)

� C

ust

om

er C

oll

abo

rati

on

Sto

ck P

lan

nin

g (

V8

)

� C

ust

om

er C

oll

abo

rati

on

Pro

duct

ion P

lann

ing (

V1

0)

� C

ust

om

er C

oll

abo

rati

on

Dem

and

Fo

reca

st (

V6

)

� S

up

pli

er C

oll

abo

rati

on

Dem

and

Fo

reca

st (

V5

)

� C

ust

om

er I

nte

gra

tio

n (

V1

)

�

Co

nsu

lt C

ust

om

er P

rod

uct

ion

Pla

nnin

g (

V1

9)

�

Co

nsu

lt S

up

pli

er P

rod

uct

ion

Pla

nnin

g (

V2

0)

�

Dec

isio

n S

up

po

rt N

ew

Cu

sto

mer

Pro

duct

(V

18

)

�

Cu

sto

mer

Fee

db

ack (

V1

7)

�

Sup

pli

er S

up

po

rt P

rod

uct

Dev

elo

pm

ent

(V2

1)

�

Sup

pli

er C

om

mu

nic

atio

n

Futu

re S

trat

egy (

V1

6)

�

Info

rmati

on S

har

ing P

rod

uct

Launchin

g S

up

pli

er (

V1

3)

�

Sup

pli

er I

nvo

lvem

ent

Pla

ns

(V4

)

�

Cu

sto

mer

In

vo

lvem

ent

Pla

ns

(V3

)

�

Cu

sto

mer

Mar

keti

ng

Par

tici

pat

ion (

V1

4)

�

Cu

sto

mer

Futu

re N

eed

s

(V1

5)

�

Sup

pli

er I

nte

gra

tio

n (

V2

)

�

Ass

em

bly

Nea

r

Cu

sto

mer

(V

22

)

�

Co

st I

nfo

rmat

ion

Shar

ing C

ust

om

er (

V1

2)

�

Mult

ifunct

ional

Tea

m

Cre

atio

n (

V1

1)

Av

era

ge

Va

ria

nce

Extr

act

ed

0.7

214

0

.56

31

0

.60

83

0

.58

93

Co

mp

osi

te R

elia

bil

ity

0

.94

74

0

.89

97

0

.88

53

0

.81

10

Cro

nb

ach

’s A

lph

a

0.9

345

0

.87

00

0

.83

75

0

.66

21

Ka

iser

-Mey

er-O

lkin

Mea

sure

of

Sa

mp

lin

g

Ad

equ

acy

0.8

48

0

.84

0

0.7

60

0

.65

5

Co

mm

un

ali

ty

0.7

214

0

.56

31

0

.60

83

0

.58

93

Eig

env

alu

e 1

0.2

37

1

.92

0

1.3

95

1

.30

9

Acc

um

ula

ted

exp

lain

ed

va

ria

nce

2

5.7

41

%

44

.326

%

58

.582

%

67

.547

%

Ta

ble

4 –

Qu

ali

ty m

easu

res

for

the

pro

po

sed

fra

mew

ork

.

Variables Factor 1 Factor 2 Factor 3 Factor 4 p value

V1 0.7103 0.5553 0.6710 0.3443 0.0000

V2 0.6964 0.6340 0.7799 0.2988 0.0000

V3 0.6552 0.4400 0.8127 0.4148 0.0000

V4 0.6156 0.5081 0.8662 0.4481 0.0000

V5 0.8245 0.5583 0.6874 0.3202 0.0000

V6 0.8597 0.5354 0.6477 0.4321 0.0000

V7 0.8933 0.6091 0.5706 0.4402 0.0000

V8 0.8885 0.5367 0.5475 0.4771 0.0000

V9 0.9022 0.6570 0.6143 0.4360 0.0000

V10 0.8514 0.5940 0.5526 0.4365 0.0000

V11 0.4949 0.4498 0.5302 0.8251 0.0000

V12 0.3239 0.4326 0.3144 0.7516 0.0000

V13 0.5582 0.7604 0.6307 0.4562 0.0000

V14 0.3963 0.5385 0.7384 0.4351 0.0000

V15 0.3943 0.5832 0.6908 0.3799 0.0000

V16 0.6418 0.8056 0.6152 0.5081 0.0000

V17 0.3400 0.6802 0.4340 0.3460 0.0000

V18 0.4139 0.6547 0.4277 0.2624 0.0000

V19 0.4243 0.7394 0.3875 0.3655 0.0000

V20 0.5762 0.8152 0.5197 0.3432 0.0000

V21 0.5556 0.7822 0.5642 0.4005 0.0000

V22 0.2479 0.2813 0.2506 0.7226 0.0000

Table 5 – Variables cross loadings matrix and level of significance.

Factor 1 Factor 2 Factor 3 Factor 4

Factor 1 0.8494

Factor 2 0.6819 0.7504

Factor 3 0.7208 0.6918 0.7799

Factor 4 0.4868 0.5182 0.5030 0.7677

Table 6 – Construct correlation matrix.

measuring supply

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