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Jurnal Indonesia Sosial Teknologi: p–ISSN: 2723 - 6609

e-ISSN : 2745-5254

Vol. 2, No. 8 Agustus 2021

THE IMPLEMENTATION OF LEAN MANUFACTURING AND INDUSTRIAL

TECHNOLOGY 4.0 ON BUSINESS PERFORMANCE

Siti Maemunah

, Raden Ardano Pasha

Postgraduate Directorate, Trisakti Institute of Transportation and Logistics,

Jakarta, Indonesia

, Universitas Trisakti

Email: [email protected]

Abstract

The problem of this reasearch has entered the industrial revolution 4.0 (4IR), then

strategy implementation need to be done through 4IR road map. Specific aspect

which has effect to Industry 4.0 Technology Adoption are technology intensity, strict

capital & human resources investment, therefore performances measurement which

consider whole organization become more relevant. The purpose of this research was

to test moderation effect of Industry 4.0 Technology process-related and

product/service-related into the lean manufacturing implementation to business

performance. The methodology of this research is using PLS-SEM approach with

SMART-PLS software. Measurement of variable datas were obtained through 1-5

Likert scale survey which was distributed to PT.XYZ employee. The finding of this

research show those Industry 4.0 Technology process-related and product/service-

related were not moderating implementation of lean manufacturing to business

performance in PT. XYZ. The limitation in this study were only for PT. XYZ

operational scope and were using data from Aug – Nov 2020. Conclusion of this

research can not be generalized to other research object or to describe PT. XYZ in

another time.

Keyword: industry 4.0 technology; lean manufacturing, business performance,

process-related, product/service-related.

Preliminary

The Ministry of Industry (2019) the manufacturing industry is a sector that forms

the backbone of the economy in Indonesia. The manufacturing industry in Indonesia

contributes 20 percent of the total National Gross Domestic Product (GDP). Indonesia is

ranked 5th among the G-20 member countries. When compared with the average GDP

value of countries around the world which is only 15 percent, the manufacturing industry

in Indonesia is quite good. In 2018, the manufacturing industry also became the largest

contributor to state revenue in the form of taxes amounting to IDR 363.60 trillion or a

total of 30 percent of total tax revenue.

Ministry of Industry (2018) Indonesia has entered the fourth industrial revolution

or industrial revolution 4.0 (4IR), it is necessary to implement a strategy through the 4IR

road map in Indonesia which involves stakeholders in the manufacturing industry. The

aim is to provide a clear direction for the movement of Indonesian industry in the future.

The Implementation Of Lean Manufacturing and Industrial Technology 4.0 On Business 
Performance 
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(Tortorella, Giglio, & Van Dun, 2019) the concept of industry 4.0 has not been 
fully understood by practitioners, but some researchers (Rüßmann et al., 2015) agreed to 
define industry 4.0  as  an industry that involves a set of  digital technologies  such as 
embedded systems, wireless sensor networks, 3D printing, cloud computing, & big data 
that have been developed before the year (Wan, Cai, & Zhou, 2015). 2011 (Lasi, Fettke, 
Kemper, Feld, & Hoffmann, 2014) explain the implementation of industry 4.0 related to 
digital  elements  that  monitor  and  control  physical  devices,  sensors,  information  & 
communication technologies (ICT), and the Internet of Things (IoT). 
(Tortorella & Fettermann, 2018) defines that industry 4.0 represents an industry 
characterized by interconnectivity between machines, systems and smart products, as well 
as interrelated solutions. (Tortorella et al., 2019) classifies industrial technology 4.0 into 
two  directions,  namely  process-related  technology  and  product  /  service-related 
technology. 
Lean  manufacturing  (LM)  or  lean  production  (LP)  is  a  common  practice  in 
industry, especially the manufacturing industry. (Shah & Ward, 2003) explained that LM 
practice focuses on reducing activities that do not provide added value and at the same 
time improving productivity and quality from the customer's point of view. The practice 
of LM has been started since the 3.0 industrial revolution along with the presence of the 
Just In Time (JIT) practice promoted by Toyota. (Al Haraisa, 2017) explains the effect of 
implementing  JIT  which  consists  of  equipment  layout,  suppliers  quality,  setup  time 
reduction and pull production which has a positive effect on operational excellence in 
industrial companies in Jordan. 
Previous research related to industrial technology 4.0, (Tortorella et al., 2019) 
regarding  the  adoption  of  industrial  technology  4.0  as  a  moderating  effect  of  LM 
implementation  and  its  impact on  the  operational  performance (OP)  improvement of 
companies in Brazil. The results of this study found a partial relationship between the 
aspects of industrial technology adoption 4.0. Research related to LM (Nawanir, 2016) 
examined  the  effect  of  LM  implementation  on  business  performance  (BP)  in 
manufacturing  companies  in  Indonesia.  The  study  found  that  overall  LM  practices 
contributed significantly to improving business performance as measured by profitability, 
sales, and customer satisfaction. 
(Nawanir, 2016) examined the influence of LM and its positive impact on business 
performance. (Tortorella et al., 2019) examined the relationship between LM variables 
and moderation of industrial technology adoption 4.0 to OP. Research related to LM and 
its impact on OP and business performance has been widely studied, but the measurement 
of LM application on company business performance with moderation in the adoption of 
industrial technology 4.0 is still very little. (Anderl, 2014), one of the specific aspects that 
affect  the  adoption  of  industrial  technology  4.0  into  manufacturing  companies  in 
developing countries is low technology intensity, tight investment in capital & human 
resources. 
Performance measurements in terms of the overall organization such as profit, 
sales and customer satisfaction are more relevant when compared to measurements from 

Siti Maemunah, Raden Ardano Pasha

1434 Jurnal Indonesia Sosial Teknologi, Vol. 2, No. 8, Agustus 2021

the operational level alone. Therefore, this study examines the impact of LM

implementation moderated by the adoption of industry 4.0 technology on business

performance (profitability, sales, & customer satisfaction).

DeLoitte University Press (2016) explains that at the beginning of the 21st

century, industry 4.0 connects the internet of things (IOT) with manufacturing techniques

to allow the system to share information, analyze and use it as an intelligent execution

guide. It also combines various cutting-edge technologies such as additive manufacturing,

robotics, artificial intelligence and all cognitive technologies, cutting edge materials, and

augmented reality.

Research Methodology

Testing on the outer model aims to measure how well each indicator is related to

reflect and explain latent variables. Things that need to be considered are the validity and

reliability of the data generated from the research instruments. Testing the validity in this

study using convergent validity and discriminant validity, while for reliability using

indicators of reliability and internal consistency reliability.

Convergent validity is done by measuring the average variance extracted (AVE).

There are three discriminant validity measurements, namely Fornell-Larcker criterion,

cross loading, and heterotrait-Monotrait (HTMT) ratio. Indicator reliability is based on

measuring indicators of outer loading, while internal consistency reliability uses

Cronbach's alpha and composite reliability.

Inner model testing is used to measure the level of suitability or feasibility of data

in a modeling using test (GoF). The GoF test on the outer model can be done through 2

approaches, namely measuring the relationship between variables (R2) or predictive

relevance (Q2). The coefficient of determination explains the proportion of variability Y

described by the least square X regression. The coefficient of determination is determined

by the value of R2.

Discussion of Research Results

The discussion of the research results includes the results of the validity test,

reliability test, GoF test and hypothesis testing. Target respondents at PT. XYZ totaled

60 employees, but only 55 employees filled out the questionnaire, so the data used became

n = 55. Based on the level of position, respondents who had staff positions were 39

respondents (70.9%), supervisors were 8 respondents (14.5%), managers were 5

respondents (9.1%), deputy general managers were 3 respondents (5, 5%), while for the

General Manager level, no one filled out the quisoner.

Based on shop, respondents who work in shop casting are 11 respondents (20%),

shop welding is 8 respondents (14.5%), shop painting is 12 respondents (21.8%), shop

machining is 9 respondents (16.4 %), and shop assembling as many as 15 respondents

(27.3%).

Reliability in this study considers indicator reliability using indicators of outer

loading and internal consistency reliability using Cronbach's alpha and composite

The Implementation Of Lean Manufacturing and Industrial Technology 4.0 On Business 
Performance 
Jurnal Indonesia Sosial Teknologi, Vol. 2, No. 8, Agustus 2021                                            1435 
reliability. Based on the results of the calculation of 1st order construct, the indicator's 
outer loading on each latent variable shows that there are several values that do not meet 
the criteria value> 0.6 (Hair, Hult, Ringle, Sarstedt, & Thiele, 2017). Indicators that do 
not meet the criteria are not used in the next stage. 
(Sarstedt, Ringle, & Hair, 2014) discriminant validity refers to the extent to which 
constructs actually differ from one another empirically, also measuring the degree of 
difference between overlapping constructs. Discriminant validity can be measured using 
the fornell-larcker criterion, cross-loading, or HTMT method. Based on the test results, 
the fornell-larcker criterion on  the 1st  order  construct and 2nd  order construct of  all 
variables was declared valid and sufficient to meet the discriminant validity requirements. 
Testing the feasibility of the model through GoF considers the GoF value with the 
input coefficient of determination (R2). Based on the results of the calculation of the R2 
value  in  table  15.,  the  simultaneous  effect  of  LM  to  PROF  is  0.042  (4.2%),  the 
simultaneous effect of LM to SALE is 0.204 (20.4%), and the simultaneous effect of LM 
to CUST is 0.111 ( 11.1%). This value shows that R2 for LM against the dependent 
variable SALE is categorized as weak, while the dependent variables PROF and CUST 
are outside the recommended value category. Based on this value, the effect of LM on 
SALE at PT. XYZ is the biggest and can be explained by LM at 20.4%. 
The results of hypothesis testing show that none of the interactions of industrial 
technology 4.0 process-related (H1;  H3; H5)  moderate the application of  LM to BP. 
(Tortorella et al., 2019) moderation by industrial technology 4.0 process-related to OP is 
the practice of LM low setup. The low setup aspect in LM practices in this study lies in 
the QS variable (Nawanir, 2016). Outer loading on the QS variable in the 2nd order 
construct shows values that do not meet the criteria so they are eliminated in hypothesis 
testing. PT. XYZ does not fully practice QS in its operations. Therefore the results of 
hypothesis testing are in line with  (Tortorella et al., 2019) that apart from low setup 
practices, industrial 4.0  process-related  technology does not  moderate  OP.  (Nawanir, 
Teong, & Othman, 2013) Partially OP is a mediating variable for the relationship between 
LM and BP. 
The results of hypothesis testing show that none of the interactions of industrial 
technology 4.0 product / service-related (H2; H4; H6) moderate the application of LM to 
BP. (Tortorella et al., 2019) moderation by industrial technology 4.0 product / service-
related to OP is the practice of LM flow. (Tortorella et al., 2019) practice flow includes 
improvement through planned layout arrangements according to product families and 
balancing work station cycle time. This flow practice is actually in line with CL practice 
involved in hypothesis testing. The difference between (Tortorella et al., 2019) and this 
study occurs due to the industrial 4.0 product / service technology involved, IND6 (3D 
printing) and IND7 (simulation) which are industrial technology 4.0 which is less closely 
related to the manufacturing process of PT. XYZ. 
PT.  XYZ  produces  mass  production  automotive  components,  3D  printing  or 
additive manufacturing is not suitable for mass production because of the low processing 
speed in making one item. Therefore, IND6 (3d printing) technology investment is not 

Siti Maemunah, Raden Ardano Pasha

1436 Jurnal Indonesia Sosial Teknologi, Vol. 2, No. 8, Agustus 2021

suitable for strengthening LM practices at PT. XYZ. (Tortorella & Fettermann, 2018)

virtual models and IoT technology aim to support processes related to product

development and service innovation, so that the use of virtual model technology (IND7)

alone will not strengthen LM practices related to processes.

Conclusion

This study concludes that industry 4.0 process-related & product / service-related

technology does not moderate the implementation of lean manufacturing on business

performance in terms of profitability, sales, and customer satisfaction at PT. XYZ. From

the lean manufacturing side, the factor that affects this is the implementation of lean

manufacturing which is not done completely by PT. XYZ. In terms of industrial

technology 4.0, the influencing factor is the adoption of industrial technology 4.0 PT.

XYZ focuses on technology to improve the design stage rather than the process. Besides

that, the understanding of the practitioners of PT. XYZ on industrial technology 4.0 to

moderate the implementation of lean manufacturing may be another factor.

The Implementation Of Lean Manufacturing and Industrial Technology 4.0 On Business 
Performance 
Jurnal Indonesia Sosial Teknologi, Vol. 2, No. 8, Agustus 2021                                            1437 
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