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Procedia CIRP 26 (2015) 653 657

Available online at www.sciencedirect.com

2212-8271 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of Assembly Technology and Factory Management/Technische Universitt Berlin.

doi:10.1016/j.procir.2014.07.046

ScienceDirect

12th Global Conference on Sustainable Manufacturing

Manufacturing Capability, Manufacturing Strategy and Performance

Of Indonesia Automotive Component Manufacturer

Rahmat Nurcahyoa*, Alan Dwi Wibowo

b

aIndustrial Engineering Department, Universitas Indonesia, Kampus UI Depok, 16424, IndonesiabAgro-Industrial Technology Department, Universitas Lambung Mangkurat, Banjarbaru, 70714, Indonesia

* Corresponding author. Tel.: +62 21 7888805; fax:+62 21 78885656.E-mail address:[email protected]

Abstract

Automotive industry is believed to be the pioneer in pushing the growth of manufacturing industry in Indonesia. By implementing

manufacturing strategy, the industry will get its performance improved. Automotive component manufacturer is an important part of

automotive industry. This research discusses the relationship between influential variables with performance in Indonesia automotive

component manufacturer namely manufacturing capability and manufacturing strategy. A model is developed and data are collected fromautomotive component manufacturers around Jakarta area. Data is processed using Structural Equation Modeling (SEM). The result shows that

manufacturing capability significantly influences manufacturing strategy while manufacturing strategy also significantly influences

performance of Indonesia automotive component manufacturer.

2014 The Authors. Published by Elsevier B.V.


Keywords:Manufacturing Capability, Manufacturing Strategy, Performance, Automotive Component Manufacturer

1. Introduction

Indonesia's economic growth is influenced by three

dominant industries, namely agriculture, mining, and

manufacturing. Manufacturing industry plays an important

role in contributing Indonesia's economic growth towards the

National Gross Domestic Product, employment, and exports.

Manufacturing industry contribution to National Gross

Domestic Product was 20,8% in 2013. However, despite

growing in a positive trend, growth of manufacturing industry

tends to become slow. The growth was increasing in 2009-

2011 from 2,56% to 6,83%. Unfortunately, the grwoth is

stagnant in 2011-2013 (Q1) such as 6,83%, 6,32%, 6,69% [1].

This growth is below the average target of Indonesia Medium

Term Development Plan. One of the manufacturing sub-

sectors, namely automotive industry, is a pioneer in the

growth of manufacturing industry. The growth of automotiveindustry could have an impact on increasing the national

economy.

Automotive industry is a global industry so Indonesia

automotive industry must compete with automotive industry

of other countries in order to growth. However Indonesia

became the third country of automotive investment destination

in the ASEAN region after Thailand and Malaysia. In order to

compete with automotive industry of other countries, the

Indonesia automotive industry must improve their

competitiveness. On the other hand, growing of Indonesia

automotive market is followed by growing number of import

of automotive components, especially in 2010 [2]. It shows

the competitiveness of Indonesia component manufacturer is

relatively low.

In automotive industry, component manufacturers supply

nearly 70% of components in an automobile [3,4]. Component

manufacturers play a vital role in automotive manufacturers.

Many research about automotive industry have been

conducted but research focus on component manufacturers is

very limited [4], especially related with the performance.

To increase competitiveness in manufacturing industry,many scholars have shown the influence of manufacturing

strategy [5,6,7,8]. Manufacturing strategy will lead better

performance that improve competitiveness. One of the

paradigm in manufacturing strategy is competition through

2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.org/licenses/by-nc-nd/4.0/).


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654 Rahmat Nurcahyo and Alan Dwi Wibowo / Procedia CIRP 26 (2015) 653 657

manufacturing capability [26]. It means that manufacturing

capability have positive influence to manufacturing strategy. It

is interesting to know how the relationship between

manufacturing capability, manufacturing strategy and

performance of Indonesia component manufacturers. It is

expected that this research result will be useful for Indonesia

component manufacturers to improve their competitiveness to

support the growth of Indonesia automotive industry.

2. Theoritical Background

Capabilities are modes of behavior that an organization is

able to perform in order to support its strategy [8]. There are

many different types of capabilities exposed by scholars but

can be categorised to manufacturing capabilities and

functional capabilities. This research will use manufacturing

capability as variable that divided into four factors, namely

cost, delivery, flexibility, and quality.

Manufacturing strategy is common in the manufacturing

industry. The literature on operations strategy and

manufacturing strategy has focused extensively on the

competitive priorities that act as strategic capabilities whichcan help organizations create, develop, and maintain

competitive advantage [9]. Competitive priorities are defined

as the dimensions that a firms production system must

possess to support the demands of the markets in which the

firm wishes to compete [10]. The six criteria which act

competitive priorities are quality, cost, delivery, flexibility,

customer focus, and know-how [11]. However it seems that

there is general agreement among authors and researchers

[12,13] that the major competitive priorities comprise the

following dimensions: flexibility, cost, quality, and delivery.

According to Thompson [14], manufacturing performance

is the firm's fitness for the future. The size of the company's

performance helped the measurement of adaptability to

changing competitive environment [15]. Meanwhile, the

definition of companys business performance is the level of

achievement as measured in the form of performance

outcomes [16]. In this research, the company's performance is

measured in two dimensions, namely manufacturing

performance and business performance. Manufacturing

performance includes elements of cost reduction, quality,

delivery, and flexibility [17], while elements of business

performance including market share and sales levels [5,18].

3. Research Method

This research was carried out on existing automotive

companies in Indonesia especially around Jakarta area.

Automotive industry consists of automobile industry and

motorcycle industry. In 2010-2012 member of The IndonesiaAutomotive (Automobile) Industries is 41 companies while

member of Indonesian Motorcycles Industry Association is 6

companies. Based on this data, then an investigation was

conducted to develop data of automotive component

manufacturers in Indonesia.

A research model comprised the variables was developed.

The number of samples is 200 companies. This number is

considered appropriate particularly in terms of overall

suitability measure with chi-square probabilistic ratio for data

processing with Structure Equation Model (SEM)[19]. The

questionaire as research instruments was also developed based

on the research model. The questionaire related to

manufacturing capabilities and manufacturing strategy consist

of 4 main variables namely cost, delivery, flexibility and

quality, while manufacturing performance questionaire is

consist of two main variables namely manufacturingperformances and business performances.

The data from respondents were processed using ANOVA

testing and by Structural Equation Modeling (SEM). Main

data processing consists of analysis of measurement model,

simplification of latent variable by calculating Latent Variable

Score (LVS), experiment result of Confirmatory Factor

Analysis (CFA) and stuctural model testing.

Structural equation modeling (SEM) is a powerful, general

purpose tool for statistical analysis and modeling of

interactions between observed and unobserved (latent)

variables [20], with the typical goal of testing causal

relationships among variables.

SEM deals with measured and latent variables. A measured

variable is a variable that can be observed directly and ismeasurable. Measured variables are also known as observed

variables, indicators or manifest variables. A latent variable is

a variable that cannot be observed directly and must be

inferred from measurable variables. Latent variables are

implied by the covariance among two or more measured

variables. In this research, the variables are Manufacturing

Capability (KapMan), Manufacturing Strategy (StratMan),

and Manufacturing Performance (Kinerja). There are two

hypotheses used in this research, H1 and H2. H1 represent

that manufacturing strategy influence performance

significantly and H2 represent that capability manufacturing

influence manufacturing strategy significantly.

SEM also provides various fit statistics to assess

evaluating models. The fit statistics can be classified into two

representative categories: absolute fit indices and

incremental fit indices [21]. Absolute fit indices represent the

extent to which the hypothesized model fit the collected data.

The goodness-of-fit (GFI), the root mean square of

approximation (RMSEA), and the standardized residual

(SRMR) are all measures of absolute fit [21].

A value for the RMSEA of .06 or less and of the PNFI of

.60 or greater indicates close model fit [22]. Moreover, [23]

contend that RMSEA values less than .08 indicate an

acceptability of the fit value. A value of the CFI of .90 or

greater is also an indication of good model fit [24].

4. Result and Discussion

By the ANOVA testing, we can see whether there aredifferences among the three latent variables (KapMan,

StratMan, and Kinerja). The test is evaluated from the three

profiles of respondents, based on age, division and position of

respondents.

We can conclude that for latent variable in Manufacturing

Capabilities (KapMan), Manufacturing Strategy (StratMan),

Manufacturing Performance (Kinerja) in almost all groups of

respondents profile shows the results of no difference.

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The next step is validity and reliability testing, to test the

measurement model latent variable towards the study of each

variable appropriate research model. Validity and reliability

testing of data that is visible from the value of the

Standardized Loading Factor (SLF) reflects whether the

observed variables have been measured Latent Variable

research and reliable or not. And viewing of Goodness of Fit

Index (GOFI) from output Lisrel juga also can reflect whetherthe existing data, to support the research model that has been

previously set or not.

Something similar is also done throughout Latent Variable

research model, where all path diagrams Latent Variable LVS

has been calculated, can be seen further in the model test

results Confirmatory Factor Analysis (CFA) Model Research.

CFA test model study was conducted to determine the

Latent Variable anywhere that is valid and can be further

processed to test the structural model. If processed

simultaneously, which can be seen in Fig. 1 as follows.

Chi-Square = 9.86, df =6, P-Va lue = 0.13058, RMSEA = 0.062

Fig. 1. Result of path diagram Confirmatory Factor Analysis (CFA)

When testing a model for fit, the complete fit of the model

as well as the individual parameters should be examined. One

of the more popular fit indices is the Goodness of Fit Index

(GOFI), which can loosely be considered as a measure of the

proportion of variance and covariance that the proposed

model is able to explain [25]. Then the indicator value of

GOFI Latent Variable Test Result CFA can be seen in Table

1.

Next step is analysis of model structural. This objective of

this analysis is to see whether the research hypothesis

acceptable or not. This is done by analyzing the processing of

the T-value (t value) to test hypotheses of significance to the

research model. T-Value is the measure of the statistical

significance of an independent variable b in explaining the

dependent variable y.

Table 1. Goodness Of Fit Index (GOFI) Latent Variable test result ofCFA

GOFI IndicatorGOFI Value

Calculation

Standar

Value for

Suitability

Conclusion

Root Mean Square

Error of

Approximation(RMSEA)

0.062 < 0.08 Suitable

Normed Fit Index

(NFI)0.97 > 0.90 Suitable

Non-Normed Fit Index

(NNFI)0.97 > 0.90 Suitable

Comparative Fit Index(CFI)

0.99 > 0.90 Suitable

IFI 0.99 > 0.90 Suitable

RFI 0.92 > 0.90 Suitable

Std. RMR 0.045 < 0.05 Suitable

Goodness of Fit Index

(GFI)0.98 > 0.90 Suitable

Adjusted Goodness of

Fit Index (AGFI)0.93 > 0.90 Suitable

Hypothesis accepted if the absolute number of t> 1.96 witha coefficient in accordance with the proposed research

hypotheses (positive or negative) where previously measured

first value Standardized Factor Loading research model

shown in Fig. 2 below.

Besides calculating t value (T Value), we calculate

Standardized Loading Factor coefficient from structural

model research which shown ini Fig. 3 below.

To see how far the data support the model, then used the

indicator value by GOFI for all latent variables structural

model. The calculation results are listed in Table 2 as follows.

Table 2. Goodness Of Fit Index (GOFI) Structural Model Research

GOFI

Indicator

GOFI Value

Calculation

Standar Value for

SuitabilityConclusion

RMSEA 0.059 < 0.08 Suitable

NFI 0.96 > 0.90 Suitable

NNFI 0.97 > 0.90 Suitable

CFI 0.99 > 0.90 Suitable

IFI 0.99 > 0.90 Suitable

RFI 0.99 > 0.90 Suitable

Std.

RMR0.048 < 0.05 Suitable

GFI 0.98 > 0.90 Suitable

AGFI 0.94 > 0.90 Suitable

Significance test results of the structural model of research,

as reflected by the t value (T-Value) and the Standardized

coefficients associated with the research hypotheses listed in

Table 6. Research hypotheses is acceptable if the absolute

value of t> 1.96, with a coefficient of positive or negative

according to the research hypothesis in the following Table 3.

Quality SML

Delivery SML

Flexibility

Kapman

0.72

0.66

0.69

0.48

0.57

0.53

Quality SML

Cost SML

StratMan0.99

0.57

0.01

0.67

KinManL Kinerja1.000.01

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Chi-Square = 11.01, df =7, P-Value = 0.13615, RMSEA = 0.059

Fig.2. Path diagram of structural model research (T-Value)

Chi-Square = 11.01, df =7, P-Value = 0.13815, RMSEA = 0.059

Fig. 3. Path diagram of structural model research (Standarized)

Table 3. Result of Significance Testing Structural Model Research

Interaction

Latent

Variable

t Value Coefficient Conclusion

(H1)

StratMan

Kinerja

2.40 0.16

StratMan influence

Kinerja significantly,

H1 accepted

(H2) KapMan

StratMan4.70 0.39

KapMan influence

StratMan significantly,

H2 accepted

5. Conclusion

Based on ANOVA testing, we can conclude that for latent

variable in Manufacturing Capabilities (KapMan),

Manufacturing Strategy (StratMan), Manufacturing

Performance (Kinerja) in almost all groups of respondents

profile shows the results of no difference.

Latent Variable Test Results CFA as a whole has a good

fit. It can be concluded that the suitability of variable model

Test Results CFA is good and the data support the model for

these variables. Model Structural Research has overall good

fit. It can be concluded that the suitability of Structural

Research is a good model and the data support the model.

As a final conclusion, all hypotheses can be accepted by

the path : KapManStratMan Kinerja. This path is shown

proving the truth of the hypothesis that manufacturing

capabilities will affect the manufacturing strategy to be

applied in the company, and the priority of the chosen strategy

will also affect the performance of the company.

Manufacturing capability in component manufacturers

such as operators skills and knowledge can practically serve

as the spearhead in manufacturing strategy, because they are

involved directly in the field related to the quality of the

products. If they are high skilled, the quality of the products

can be guaranteed. By guaranteeing the product quality, the

company itself will be able to implement manufacturing

strategy, particularly in the areas of quality.

Manufacturing strategy in component manufacturers such

as delivery, quality and cost strategies implied positive effecton manufacturing performance. Delivery, quality, and cost

strategies are influential in determining manufacturing

performance due to these three things are things that are

considered important in the automotive companies. Delivery

strategy leads to the ability of component manufacturers the

in meeting the appropriate level of automotive company

demand at the time quickly and precisely. Automotive

company tend to provide punishment system like penalty fee

if the delivery is delayed. If the delivery performance is bad

and occurs over and over again, then it is likely that

termination of contracts. However, if the delivery

performance tends is good, then automotive company tend to

give new projects, so that it could increase the company's

business performance to be better developed. Otherfundamental problem often encountered in the component

manufacturers is the component quality issues. When

implementing the company's manufacturing strategy, quality

needs to be set as a top priority strategy. If the price offered

by the company is low, but the quality of goods produced is

not good, still the consumer choice will definitely go for the

company with higher quality products. This explains why

Quality KML

Delivery KML

Flexibility

KML

Kapman

StratMan

Kinerja

10.2

6

8.94

6.54

4.70

2.40Cost SML

Quality SML

KinManL

10.34

6.76

7.84

6.69

0.00

9.70

0.00

Quality KML

Delivery KML

Flexibility KML

0.47

0.59

0.53

Kapman

0.73

0.54

0.58

0.39

1.00

StratMan

Kinerja

0.15

KinManL

0.99

Cost SML

Quality SML

0.57

0.01

0.57

0.01

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these three strategies are considered influential enough to

increase manufacturing performance.

Acknowledgements

The authors acknowledge the support of Universitas

Indonesia through Hibah Riset Awal grant number

DRPM/Hibah Awal/2010/I/3851.

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