e2025055

Prévia do material em texto

Multidiscip. Sci. J. (2025) 7:e2025055 
 
Received: March 27, 2024 | Accepted: July 9, 2024 
 
RESEARCH ARTICLE 
Published Online: August 1, 2024 
https://doi.org/10.31893/multiscience.2025055 
 
 
Can industry 4.0 work together with lean? An 
investigation from Malaysian lean manufacturing 
sector 
 
 
 
Nur Ain Qistina Muhammad Shafeea | Effendi Mohamada | Sayed Kushairi Sayed Nordina | 
Mohamad Soufhwee Abd Rahmana | Muhammad Khairul Hamizan Mohamad Zaidia | Teruaki Itob | 
Mohd Hamdi Abd Shukorc
 
 
 
 
aFaculty of Industry and Manufacturing Technology and Engineering, Universiti Teknikal Malaysia Melaka, Malaysia. 
bFaculty of Computer Science and Systems Engineering, Okayama Prefectural University, Japan. 
cDepartment of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia. 
 
 
 
 
1. Introduction 
 
Several businesses do not account for the strategic applicability of lean manufacturing (LM) techniques. Combined with 
the rising intricacies of the current operational supply chain scenarios, several businesses consider LM approaches inadequate 
for the current and future global competitive landscape (Ciccullo et al., 2018). Furthermore, the unexpected disruption in 2020 
increased the speed of technological advancements, prompting manufacturing enterprises to deeply integrate digitalization, 
cloud computing, robotics, and autonomous operations (Mrugalska and Wyrwicka 2017). This prerequisite is leading to the 
digital and physical worlds converging into the Industry 4.0 (I4.0) concept and its incorporation with LM. I4.0 and LM can 
improve the use of LM to determine and reduce waste and change how production is conducted (Bittencourt et al., 2020). 
Smart factories digitally interconnect to meet the needs of global enterprises. This approach holds the power to impact one of 
the most significant changes that humanity has witnessed (Osterrieder et al., 2020). Therefore, this work aimed to answer the 
following research questions: 
 
 How do I4.0 and real-world LM execution correlate presently? 
 Which LM techniques are predicted to be most strongly coupled with I4.0 technology enablers? 
 Is the LM domain practically aligned for implementing I4.0 approaches for future technological improvements? 
 
 
 
1.1. Research Background 
Abstract An examination of Industry 4.0 (I4.0) and its impact on lean manufacturing (LM) is not yet detailed, properly 
evaluated or validated. Notably, there is a lack of recent studies that evaluated I4.0 technology mapping and prioritization 
in the LM industrial context in Malaysia. The study aims to evaluate how I4.0 technology enablers influence the use of lean 
tools. Moreover, this study evaluates the correlation between company type and I4.0 implementation maturity, familiarity, 
and sources exhibiting an inclination to advance skills. The study evaluated 208 respondents belonging to several LM 
enterprises in Malaysia. Self-administered questionnaires were used and association analysis, based on the independence 
test, was used to assess the consensus based on the devised hypothesis. The outcomes were obtained using 
correspondence assessment of the asymptotic significant correlation coefficient (p) in which to draw conclusions using the 
p determined during hypothesis evaluation, a 0.05 significance level indicates all null hypotheses must be accepted. 
Moreover, the three elements; I4.0, LMP, and LMSs are significant with 0.897, 0.980, and 0.876 values, respectively. The 
outcomes indicate a strongly positive correlation between I4.0 and LM system (LMS). This observation is expected to help 
Malaysian LM practitioners to anticipate the future lean landscape based on the boundless innovation provided by these 
Technologies as the anticipated outcome is a strong positive correlation between I4.0 and LM systems (LMS). This finding 
would empower Malaysian LM practitioners to anticipate the future of lean manufacturing, shaped by the boundless 
innovation potential of I4.0 technologies. By embracing this convergence, companies can unlock a future of enhanced 
productivity driven by autonomous operations and data-driven decision making. Therefore, using lean in the context of 
I4.0 is a promising combination for improving future productivity through autonomous operations. 
 
Keywords: industry 4.0, lean manufacturing, manufacturing performance, Malaysian company, correspondence analysis 
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LM may essentially be considered a philosophy, where the definition and guiding processes are its constituents. 
Consequently, its implementation is considered practical for different business domains globally. The beginning of the Malaysia 
Japan Automotive Industries Cooperation (MAJAICO) initiative was the first recorded lean transformation for local 
manufacturing businesses in the Malaysian manufacturing industry; this transformation was reported in 2006 (Techakanont, 
2015). 
The I4.0 approach allows lean to remain a critical and binding element to ensure the transformation where LM aspects 
such as profitability, efficiency, and productivity improve as a result of rapidly scaled production volumes, interacting machines, 
operational digitalization, automation, data analytics, and rapid data-based decisions. Lucato et al. (2019) suggested that 
integrating LM with I4.0 is associated with operating costs dropping by 40% over ten years. 
LM enterprises must presently implement advanced I4.0 technologies within their entire workflows. Assimilation suffers 
because of the integration aspects and their novelty. Notably, the two alignment approaches better target efficiency as a 
shared trait. 
Moreover, academicians are aligned in that LM emphasizes individuals, processes, and gradual improvement ideologies, 
while I4.0 emphasizes using digital technologies to solve the problems that present-day businesses face Saabye et al. (2020), 
Kerin et al. (2019), and Sun et al. (2023). In 2011, I4.0 was commercially available amidst competitive global markets; however, 
LM was faced with concerns regarding the evaluation of complex problems concerning the existing manufacturing ecosystem. 
This discussion provides motivation for this work, where the direct impact of I4.0 is evaluated in the context of LM 
systems (LMSs). Moreover, the mediating influence of LM performance (LMP) is also evaluated. The following hypotheses are 
therefore proposed: 
 
H1: I4.0 has a significant effect on LMSs. 
H2: I4.0 has a significant effect on LMP. 
H3: LMP significantly affects the indirect correlation between I4.0 and LMSs. 
 
2. Research Methodology 
 
2.1. Instrument development 
 
The research questionnaire was in English and was prepared in three phases to ensure the validity and reliability of the 
measures. Initially, eight experts were consulted for the research instrument; they comprised five senior scholars from the 
manufacturing and industrial engineering domain and three engineers from the LM domain. The consultations were held to 
minimize ambiguity, improve clarity, and make measurement more appropriate. These experts performed a subjective 
validation to ensure that the chosen measures were appropriate for the primary concerns of this work. The subsequent phase 
comprised a pilot study involving 57 postgraduates pursuing master’s degrees in industrial engineering. The pilot study 
outcomes were used to calculate a Cronbach’s alpha () of 0.762, which established that the internal consistency was 
acceptable. 
 
2.2. Data collection 
 
To ensure the precision and accuracy of the data collection, this study’s scope emphasized Malaysia’s manufacturing 
sector. The UteM Centre of GraduateEmployability and the UTeM Centre of Industry Collaboration database provided updated 
official data indicating 997 registered manufacturing businesses employing 400 engineers and managers. The following 
equations (1) and (2) were used to compute the sample size: 
 
x=𝑍 (
𝐶
100
)
2
𝑟(100 − 𝑟) 
 
n = N 
𝑥
((𝑁−1)𝐸2+𝑥)
 
 
Where N denotes the total number of manufacturing enterprises, r denotes the response fraction (50% - conservative 
approach), Z(c/100) denotes the confidence level cutoff (95%), E denotes the error margin (assumed to be 9%), and n denotes 
the sample size. Hence, the sample comprised 303 engineers and supervisors. The error margin and confidence interval for this 
study were set at 10% and 95%, respectively. 
Subsequently, a cross-sectional email survey was distributed to numerous industrial enterprises across Malaysia to 
collect the data for the study. The food and beverage, automotive, construction, electrical, metal manufacturing, and plastic 
processing industries were included. The members of the sample were provided a link to the online survey. Participation was 
voluntary, and coauthors across Malaysia followed up regularly using text messages. The email comprised a cover note 
suggesting that participants should respond only if they were well versed with I4.0 approaches and LMSs and had one or more 
(1) 
(2) 
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years of experience working in manufacturing organisations. The participants were provided four months to submit their 
responses to the online questionnaire. 
 
2.3. Analytical tools and techniques 
 
Statistical evaluation was conducted on the gathered survey responses after taking the individual averages for every 
item with multiple responses. Consequently, the sample comprising 303 individuals was reduced to 208 overall respondents. 
Furthermore, to assess the nonresponse bias concerning early and delayed responses, the Kolmogorov‒Smirnov two 
independent sample test was used to determine statistical information regarding 165 responses gathered during the initial 
eight weeks where the questionnaire was first sent and two reminders were given. After this, the rate of receiving surveys 
decreased significantly, requiring three additional reminders during the remaining timeframe in the survey period from January 
to June 2023. 
Consequently, the hypothesis evaluation framework was validated by performing correlation relationship (CR) and 
confirmatory factor analysis (CFA) analyses to determine the correspondence relationships. The evaluation was based on 
factors such as the mean, standard deviation, factor loading (FL), kurtosis, and skewness. Additionally, the chi-square Pearson 
(p) metric was used for CR to determine the relationship degree considering parameters belonging to the same category. This 
value determines the strength of the relationship. 
Pudovkin (1999) indicated the use of the goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative 
fit index (CFI), nonnormed fit index (NFI), and root mean square error of approximation (RMSEA) (Sun, 2021). Other scholars 
(Rahman et al. (2020), Plock et al. (2022), and Tortorella et al. (2018)) also agree with the use of these metrics, suggesting that 
the assessment of the statistical misalignment concerning practical data and model estimates can be performed using these 
parameters. These findings also verify the theoretical framework concerning a study target or effect. The tests were performed 
using the IBM® Statistical Package for the Social Sciences (SPSS)® 26. 
 
3. Findings and Discussion 
 
This study targeted 897 manufacturing firms in the electric and electrical, automotive, plastic processing, food and 
beverages, metal production, construction, and other industries, representing 28.8%, 23.1%, 17.8%, 6.7%, 6.3%, 4.3%, and 
13%, respectively. Hence, the final sample was 303 based on 208 respondents, indicating a 68.6% success rate. Concerning 
their awareness of implementing I4.0, 8.7%, 91.3%, and 0% of employees answered yes, maybe, and no, respectively. Primary 
information sources comprised 76%, 12.5%, 6.7%, and 4.8% of professional consultation, self-discovery, peer sharing, and 
national news, respectively. Notably, most respondents agreed with the need to enhance their skills—87% yes, 13% maybe, 
and 0% no. The chosen sample had variations based on the position or designation—47.6% engineers, 32.7% senior engineers, 
14.4% managers, and 5.3% executive board members. 
Several survey responses were averaged to determine one value representing every item. All such averaged values were 
subjected to statistical assessment. Hence, 110 manufacturing enterprises composed the effective study sample. Accounting 
for the limited resources and data gathering efforts to assess a subject comprising the association of I4.0 technologies, LMSs, 
and LMP, the 110-large sample is statistically adequate for the following analyses: CFA, correlation value (CV), and hypothesis 
modeling. 
 
3.1. Nonbias response 
 
The collected responses were segregated into two categories: early and delayed responses. There were 165 respondents 
in the early response category—questionnaires obtained during the initial eight weeks—while the delayed response category 
comprised 43 questionnaires obtained during the final five weeks of the survey. The disparities concerning the mean values 
for all constructs were evaluated using the Kolmogorov‒Smirnov test (Wallace & Mellor, 1988). The outcomes suggested that 
the two categories exhibited no significant differences. This suggests a lack of nonresponse bias since all the values were within 
the 0.50-1.00 range (Table 1). 
 
3.2. Measurement model 
 
3.2.1. Correlation relationship (CR) 
 
The correlation concerning the three aspects of this research, I4.0, LMSs, and LMP, is initially highlighted using 
asymptotic significance. The assessment employs p to indicate whether the hypothesis is accepted at the 0.05 significance 
level. Furthermore, p > 0.05 suggests insufficient evidence to reject the null hypothesis (HO) and accept the other hypothesis 
(H1). Nevertheless, p itself does not conclude for or against a hypothesis. Instead, it acts as evidence in favor of or against the 
hypothesis. P > 0.05 is typically considered to indicate statistical significance, while: 
 
 
 
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Table 1 Nongiasignificant Response. 
Variables Most Extreme Difference Kolmogorov‒Smirnov 
Statistic 
Asymp. Sig. 
(2-tailed) Absolute Positive Negative 
5S 0.166 0.166 -0.048 0.941 0.338 
JIT 0.233 0.000 -0.233 0.320 0.611 
Kaizen 0.190 0.000 -0.190 0.079 0.195 
Kanban 0.128 0.044 -0.128 0.726 0.667 
KPI 0.194 0.018 -0.194 0.100 0.569 
OEE 0.095 0.650 -0.096 0.542 0.876 
PDCA 0.109 0.109 -0.130 0.870 0.675 
Poka-Yoke 0.152 0.120 -0.180 0.852 0.453 
TPM 0.108 0.107 -0.176 0.756 0.348 
Grouping variables: 1-early respondents (N=165), 2-late respondents (N=43) 
 
p > 0.05 = HO is accepted, H1 is rejected 
 
pused to draw conclusions using the p determined during hypothesis evaluation. A 0.05 significance level 
indicates that all null hypotheses must be accepted. Moreover, the three elements, i.e., I4.0, LMP, and LMS, are significant with 
values of 0.897, 0.980, and 0.876, respectively. 
 
Table 2 Hypothesis test results. 
 Hypothesis Path Coefficient (p) Sig. Conclusion 
 I4.0-LM Systems 0.897 
*** 
Dependence 
 I4.0-LM Performance 0.980 Dependence 
 LM Performance-I4.0-LM Systems 0.876 Dependence 
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Table 3 Confirmatory Factor Analysis (CFA) for I4.0, LM Systems, and LM Performance. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 1 Relationship Model Based on the Factor Loading Value. 
Instrument Details 
Factors Loadings Mean Std. Deviation Skewness Kurtosis I4.0 
Our organizations implement; 
Additive Manufacturing 0.833 3.733 1.325 -0.674 -0.869 
Cyber Physical System 0.820 3.727 1.326 -0.717 -0.847 
Smart Manufacturing 0.733 3.689 1.261 -0.728 -0.577 
Artificial Intelligence 0.685 3.444 0.791 -1.696 -0.527 
Big Data 0.935 4.866 1.204 -0.067 3.628 
Simulation 0.905 4.487 0.785 -0.171 2.295 
Cloud Computing 0.835 3.834 1.216 -0.703 -0.756 
Augmented Reality 0.882 3.754 1.197 -0.786 -0.337 
Internet of Things 0.998 4.575 0.694 -0.004 5.627 
Automation 0.854 3.706 1.233 -0.709 -0.554 
Systems Integration 0.821 3.963 1.114 -1.964 -0.017 
LM systems 
Our organizations implement; 
 
Kaizen 0.789 3.522 0.532 -0.231 -0.321 
Kanban 0.765 3.214 0.783 -0.323 0.042 
PDCA 0.643 3.252 0.671 -0.321 0.341 
OEE 0.891 3.524 1.453 -0.046 2.341 
5S 0.876 3.523 0.897 -0.214 -0.341 
JIT 0.756 3.135 0.732 -0.152 0.121 
Jidoka 0.745 3.512 0.712 -0.321 0.901 
TQM 0.934 4.165 1.867 -0.021 2.123 
KPI 0.835 3.421 0.783 -0.321 -0.341 
VSM 0.982 4.231 1.781 -0.003 3.234 
TPM 0.761 3.452 0.321 -0.671 -0.231 
LM Performance 
Our organizations notice; 
 
Employee Involvement 0.876 3.546 0.918 0.143 -0.321 
Cost Reduced 0.765 3.765 0.831 0.121 -0.112 
Waste Reduced 0.987 4.453 1.398 0.091 3.211 
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4. Conclusion 
 
This work focused mainly on evaluating the direct effect of I4.0 technologies and LMSs on LMP, indicating a robust 
association between the three aspects. Moreover, the study results offer several valid theories concerning LMSs, I4.0, and LMP 
in the context of Malaysian manufacturing. 
Such theories significantly support decision-makers in properly deploying I4.0 and LMSs. This work highlights the 
criticality of active participation by supply chain stakeholders by highlighting the importance of incorporating social, economic, 
and environmental success at the enterprise level. Notably, this research revealed that for numerous technologies, BD had the 
highest score, followed by SM and IoT. Future works should evaluate the level of integration for the two elements, focusing 
specifically on small and medium manufacturing businesses. Moreover, sample size expansion to consider aspects such as 
evaluating the effects of other mediating parameters such as local business processes and the circular economy is another 
research suggestion. This differentiated approach endeavours to offer a detailed understanding that facilitates the 
advancement of the Malaysian economy. 
 
Acknowledgments 
 
The authors fully acknowledge the Ministry of Higher Education (MOHE) and Universiti Teknikal Malaysia Melaka 
(UTeM) for their approval, which made this important research viable and effective. Finally, all of the authors are grateful to 
the industrial members for their help in terms of sharing ideas and suggestions for improving the study design. 
 
Ethical considerations 
 
The practice of maintaining great ethical considerations was paramount throughout this study. Hence, this study adhered to 
rigorous ethical research practices. Informed consent was a cornerstone of the data collection process. Every participant 
received a detailed information sheet explaining the study’s objective, data usage and confidentiality protocols. This 
transparency empowered participants to complete the form and ensured their voluntary engagement. Consequently, every 
participant retained the right to withdraw from the study at any point. Finally, to safeguard privacy, all collected data were 
anonymized by removing any personally identified information (PII) before the analysis process. An anonymized dataset 
strengthened the research’s ethical foundation and fostered trust within this Malaysian LM community. 
 
Conflict of interest 
 
In the acknowledgment of full transparency and high upholding of great ethical research standards, every author declares that 
no conflicts of interest exist in any relation that occurs within this whole study. Neither funding sourced nor collaboration with 
Malaysian LM Components influenced the design, conduct, analysis, or interpretation of the results. Furthermore, no authors 
possess personal biases toward specific I4.0 technologies or LM philosophies that could have skewed the study outcome. These 
commitments are related to the study’s credibility, which fosters trust among the academic community. 
 
Funding 
 
This research was supported by the Ministry of Higher Education (MOHE) through a fundamental research grant scheme 
(FRGS/1/2020/TK0/UTEM/02/42). 
 
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