Assessing Lean 4.0 for Industry 4.0 Readiness Using PLS-SEM towards Sustainable Manufacturing Supply Chain
Abstract
:1. Introduction
2. Literature Review
3. Theoretical Framework and Hypothesis Development
- (a)
- Soft and hard L4.0 practices association and their influence on I4.0 readiness.
- (i)
- The role of soft L4.0 practices on hard L4.0 practices.
- (ii)
- The mediating role of hard L4.0 practices.
- (iii)
- Relationship between hard L4.0 practices and I4.0 readiness.
4. Methods
5. Data Analysis
- (i)
- Descriptive statistics.
- (b)
- Assessment of measurement model
- (i)
- Reliability and convergent validity measures.
- (a)
- Discriminant validity
- (b)
- Assessment of structural model.
- (c)
- Mediation analysis
- (d)
- Artificial neural network (ANN) analysis
6. Discussion
7. Research Limitations and Future Research Directions
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type of Lean Practice | Name of the Lean Practices | Description | References |
---|---|---|---|
Soft | Continuous improvement | Through incremental and ground-breaking advancements, it is the continuous enhancement of goods, services, or procedures. | [25,49,50,51] |
Soft | Top management leadership | A person’s, a group’s, or an organization’s ability to “lead” other individuals, teams, or entire organizations via influence or direction. | [25,50,52] |
Soft | Total employee involvement | It promotes increased involvement of team members, employees, and individual contributors in organizational problem-solving, planning, and decision-making processes. | [50,52] |
Soft | Supplier development and partnership | It propagates partnering with long-term external organizations to help internal processes. | [25,50,53] |
Soft | Organizational culture | The full collection of attitudes, values, and beliefs that a corporation holds, as well as how they influence how its employees act. | [49,50,51] |
Soft | Training employees | It refers to the ongoing initiatives taken by a business to improve employee performance. | [50,51,54,55] |
Soft | Customer focus | It cultivates a workplace culture devoted to raising customer satisfaction and establishing enduring relationships with them. | [56] |
Soft | Customer relationship management | It consists of the techniques, strategies, and tools used by organizations in handling and analyzing customers. | [50,53] |
Soft | Worker empowerment | The firms’ act of giving workers some degree of autonomy and control over their daily tasks. | [57] |
Soft | Multi-skilling development | It is the ability to perform multiple tasks at once. | [58] |
Soft | Small-group problem solving | It uses the consensus of the stakeholders who participate in decision-making to find the problem’s solution. | [50,51,55] |
Hard | Total quality management | It is a strategic move by the organization to involve everyone from entry-level employees to its highest-ranking executives to focus on quality improvement and ensure customer satisfaction. | [50,53] |
Hard | Total productive maintenance | It is a strategic move to involve workers and staff in maintenance-related activities to enhance production. | [50,53] |
Hard | Just-in-time delivery by the supplier | It is an inventory management strategy in which suppliers only deliver products as needed. | [50,53] |
Hard | Production scheduling and systemization | It is a systematic approach to concerting production plans into a production schedule for flawless production. | [50,55] |
Hard | Statistical process control | It involves the use of statistical techniques to track and manage the quality level of the production process. | [25,50,51,55] |
Hard | Kanban | It helps track the production and order management of components and materials. | [25,50,55] |
Hard | Setup time reduction | An arrangement to speed up the process transition while switching to new manufacturing. | [25,50,55] |
Hard | Equipment layout for continuous flow | It is a systematic arrangement for equipment to enable continued product flow. | [50,55] |
Hard | Autonomous maintenance | It aims to provide more responsibility to the operator and permits preventive maintenance tasks | [50] |
Hard | Lean manufacturing Practices | It is an approach that focuses on reducing waste in production systems while also increasing productivity. | [50,59] |
Variable | Item | Frequency | Percentage (%) |
---|---|---|---|
Gender | Male | 128 | 0.582 |
Female | 92 | 0.418 | |
Firm size based on employee strength | Micro (1–4) | 53 | 0.241 |
Small (5–99) | 72 | 0.327 | |
Medium (100–499) | 95 | 0.432 | |
Establishment Years | <5 | 41 | 0.186 |
>5 <10 | 86 | 0.391 | |
>10 years | 93 | 0.423 | |
Industry type | Casting Machining | 46 | 0.209 |
Gear manufacturing | 30 | 0.136 | |
Machines manufacturers | 31 | 0.141 | |
Surgical parts manufacturers | 63 | 0.286 | |
Automotive parts manufacturers | 19 | 0.086 | |
Electrical parts manufacturers | 14 | 0.064 | |
Other | 17 | 0.077 |
Constructs | N | Mean | Kurtosis | Skewness |
---|---|---|---|---|
Top management leadership (TML) | 220 | 3.5582 | 0.056 | −0.325 |
Customer focus (CF) | 220 | 3.6805 | 0.354 | −0.040 |
Employee training and learning (ETL) | 220 | 3.5260 | 0.377 | 0.080 |
Total productive maintenance (TPM) | 220 | 3.8778 | −0.168 | −0.082 |
Statistical process control (SPC) | 220 | 3.8812 | 0.069 | −0.342 |
Advanced manufacturing technologies (AMT) | 220 | 3.8145 | 0.133 | −0.329 |
Operational readiness (OR) | 220 | 3.8407 | 0.015 | −0.229 |
Managerial readiness (MR) | 220 | 3.8856 | 0.085 | −0.402 |
Technological readiness (TR) | 220 | 3.6636 | 0.328 | −0.448 |
Constructs | Items | Loadings (>0.70) * | VIF (<5) ** | Reliability | Average Variance Extracted (AVE) (≥0.50) ** | |
---|---|---|---|---|---|---|
Cronbach’s Alpha (≥0.70) ** | rho_A | |||||
Top management leadership (TML) | TML1 | 0.723 | 1.278 | 0.705 | 0.715 | 0.622 |
TML2 | 0.848 | 3.045 | ||||
TML3 | 0.813 | 2.779 | ||||
Customer focus (CF) | CF1 | 0.815 | 1.766 | 0.714 | 0.743 | 0.633 |
CF2 | 0.833 | 2.086 | ||||
CF3 | 0.852 | 2.223 | ||||
CF4 | 0.767 | 1.350 | ||||
Employee training and learning (ETL) | ETL1 | 0.898 | 2.181 | 0.848 | 0.854 | 0.687 |
ETL 2 | 0.910 | 2.309 | ||||
ETL 3 | 0.816 | 2.487 | ||||
ETL 4 | 0.789 | 2.021 | ||||
Total productive maintenance (TPM) | TPM1 | 0.917 | 2.99 | 0.761 | 0.765 | 0.678 |
TPM2 | 0.796 | 1.812 | ||||
TPM3 | 0.944 | 2.962 | ||||
Statistical process control (SPC) | SPC1 | 0.819 | 1.808 | 0.719 | 0.730 | 0.642 |
SPC2 | 0.830 | 1.996 | ||||
Advanced manufacturing technologies (AMT) | AMT1 | 0.914 | 1.575 | 0.747 | 0.748 | 0.664 |
AMT2 | 0.876 | 1.570 | ||||
AMT3 | 0.783 | 1.845 | ||||
Operational readiness (OR) | OR1 | 0.823 | 2.370 | 0.866 | 0.867 | 0.713 |
OR2 | 0.782 | 2.372 | ||||
OR3 | 0.783 | 2.243 | ||||
OR4 | 0.904 | 1.760 | ||||
Managerial readiness (MR) | MR1 | 0.862 | 2.560 | 0.865 | 0.937 | 0.695 |
MR2 | 0.885 | 2.907 | ||||
MR3 | 0.809 | 2.096 | ||||
MR4 | 0.858 | 2.557 | ||||
Technological readiness (TR) | TR1 | 0.759 | 1.371 | 0.779 | 0.783 | 0.602 |
TR2 | 0.871 | 1.764 | ||||
TR3 | 0.908 | 2.100 | ||||
TR4 | 0.728 | 2.805 |
Latent Construct | TML(1) | CF(2) | ETL(3) | TPM(4) | SPC(5) | AMT(6) | OR(7) | MR(8) | TR(9) |
---|---|---|---|---|---|---|---|---|---|
TML(1) | |||||||||
CF(2) | 0.547 | ||||||||
ETL(3) | 0.539 | 0.662 | |||||||
TPM(4) | 0.812 | 0.597 | 0.507 | ||||||
SPC(5) | 0.624 | 0.609 | 0.475 | 0.684 | |||||
AMT(6) | 0.7 | 0.712 | 0.536 | 0.789 | 0.843 | ||||
OR(7) | 0.586 | 0.631 | 0.459 | 0.704 | 0.764 | 0.829 | |||
MR(8) | 0.083 | 0.076 | 0.029 | 0.124 | 0.058 | 0.099 | 0.182 | ||
TR(9) | 0.572 | 0.609 | 0.434 | 0.678 | 0.726 | 0.821 | 0.827 | 0.112 |
Relation | β | t Value | f2 | CI [2.05%–97.5%] | Decision |
---|---|---|---|---|---|
H1a: TML→TPM | 0.624 | 29.162 | 0.062 | 0.582–0.665 | accepted |
H1b: TML→SPC | 0.34 | 13.852 | 0.571 | 0.292–0.387 | accepted |
H1c: TML→AMT | 0.344 | 13.858 | 0.299 | 0.296–0.393 | accepted |
H2a: CI→TPM | 0.134 | 5.343 | 0.126 | 0.086–0.186 | accepted |
H2b: CI→SPC | 0.295 | 10.471 | 0.326 | 0.24–0.35 | accepted |
H2c: CI→AMT | 0.348 | 12.639 | 0.203 | 0.294–0.401 | accepted |
H3a: CRM→TPM | 0.036 | 1.675 | 0.001 | −0.077–0.006 | accepted |
H3b: CRM→SPC | 0.083 | 3.012 | 0.031 | 0.03–0.138 | accepted |
H3c: CRM→AMT | 0.088 | 3.629 | 0.012 | 0.041–0.137 | accepted |
H5a: TPM→OR | 0.147 | 6.71 | 0.084 | 0.105–0.19 | accepted |
H5b: TPM→MR | 0.055 | 1.671 | 0.049 | −0.013–0.112 | rejected |
H5c: TPM→TR | 0.113 | 5.034 | 0.042 | 0.069–0.156 | accepted |
H6a: SPC→OR | 0.212 | 7.32 | 0.076 | 0.153–0.267 | accepted |
H6b: SPC→MR | 0.045 | 0.975 | 0.192 | −0.051–0.132 | rejected |
H6c: SPC→TR | 0.238 | 7.815 | 0.032 | 0.179–0.298 | accepted |
H7a: AMT→OR | 0.466 | 15.382 | 0.015 | 0.408–0.526 | accepted |
H7b: AMT→MR | −0.003 | 0.062 | 0.562 | −0.115–0.1 | rejected |
H7c: AMT→TR | 0.425 | 13.901 | 0.390 | 0.366–0.484 | accepted |
Hypotheses | Relation | Β | t Value | p Value | CI [2.05%–97.5%] | Decision |
---|---|---|---|---|---|---|
H4a: | TML→TPM→OR | 0.091 | 6.591 | 0 | 0.065–0.119 | accept |
TML→TPM→MR | 0.034 | 1.662 | 0.097 | −0.008–0.071 | reject | |
TML→TPM→TR | 0.070 | 4.972 | 0 | 0.043–0.098 | accept | |
CF→TPM→OR | 0.020 | 4.023 | 0 | 0.011–0.030 | accept | |
CF→TPM→MR | 0.007 | 1.536 | 0.125 | −0.002–0.017 | reject | |
CF→TPM→TR | 0.015 | 3.440 | 0.001 | 0.008–0.025 | accept | |
ETL→TPM→OR | −0.005 | 1.609 | 0.108 | −0.012–0.001 | reject | |
ETL→TPM→MR | −0.002 | 1.048 | 0.295 | −0.006–0.001 | reject | |
ETL→TPM→TR | −0.004 | 1.577 | 0.115 | −0.009–0.001 | reject | |
H4b: | TML→SPC→OR | 0.072 | 6.678 | 0 | 0.051–0.093 | accept |
TML→SPC→MR | 0.015 | 0.970 | 0.332 | −0.017–0.045 | reject | |
TMLQ→SPC→TR | 0.081 | 6.868 | 0 | 0.059–0.104 | accept | |
CF→SPC→OR | 0.062 | 5.615 | 0 | 0.042–0.085 | accept | |
CF→SPC→MR | 0.013 | 0.963 | 0.336 | −0.014–0.040 | reject | |
CF→SPC→TR | 0.070 | 5.874 | 0 | 0.048–0.094 | accept | |
ETL→SPC→OR | 0.018 | 2.806 | 0.005 | 0.006–0.031 | accept | |
ETL→SPC→MR | 0.004 | 0.878 | 0.380 | −0.004–0.013 | reject | |
ETL→SPC→TR | 0.020 | 2.812 | 0.005 | 0.007–0.034 | accept | |
H4c: | TML→AMT→OR | 0.160 | 10.51 | 0 | 0.131–0.191 | accept |
TML→AMT→MR | −0.001 | 0.062 | 0.951 | −0.039–0.035 | reject | |
TML→AMT→TR | 0.146 | 9.775 | 0 | 0.117–0.177 | accept | |
CF→AMT→OR | 0.162 | 9.241 | 0 | 0.129–0.198 | accept | |
CF→AMT→MR | −0.001 | 0.062 | 0.951 | −0.040–0.035 | reject | |
CF→AMT→TR | 0.148 | 8.88 | 0 | 0.117–0.181 | accept | |
ETL→AMT→OR | 0.041 | 3.556 | 0 | 0.019–0.064 | accept | |
ETL→AMT→MR | 0 | 0.059 | 0.953 | −0.011–0.009 | reject | |
ETL→AMT→TR | 0.038 | 3.567 | 0 | 0.018–0.059 | accept |
Network | RMSE (Training) | RMSE (Testing) | AMT | CF | TPM | ETL | SPC | TML |
---|---|---|---|---|---|---|---|---|
1 | 0.700 | 0.712 | 0.324 | 0.173 | 0.171 | 1 | 0.649 | 0.384 |
2 | 0.705 | 0.667 | 0.549 | 0.42 | 1 | 0.816 | 0.882 | 0.521 |
3 | 0.698 | 0.649 | 0.338 | 0.442 | 0.821 | 1 | 0.969 | 0.697 |
4 | 0.703 | 0.699 | 0.448 | 0.232 | 0.156 | 1 | 0.921 | 0.093 |
5 | 0.696 | 0.699 | 0.478 | 0.508 | 0.729 | 1 | 0.645 | 0.822 |
6 | 0.701 | 0.764 | 0.214 | 0.27 | 0.329 | 1 | 0.396 | 0.639 |
7 | 0.703 | 0.721 | 0.214 | 0.846 | 0.477 | 1 | 0.952 | 0.427 |
8 | 0.705 | 0.678 | 0.717 | 0.141 | 0.748 | 1 | 0.094 | 0.351 |
9 | 0.699 | 0.732 | 1 | 0.26 | 0.578 | 0.824 | 0.395 | 0.653 |
10 | 0.698 | 0.708 | 0.496 | 0.399 | 0.454 | 1 | 0.723 | 0.699 |
Mean | 0.701 | 0.703 | 0.478 | 0.369 | 0.546 | 0.964 | 0.663 | 0.529 |
SD | 0.003 | 0.033 | ||||||
IMP | 50% | 38% | 57% | 100% | 69% | 55% |
Network | RMSE (Training) | RMSE (Testing) | AMT | CF | TPM | ETL | SPC | TML |
---|---|---|---|---|---|---|---|---|
1 | 0.497 | 0.486 | 1 | 0.201 | 0.247 | 0.147 | 0.47 | 0.169 |
2 | 0.486 | 0.475 | 1 | 0.313 | 0.309 | 0.139 | 0.43 | 0.159 |
3 | 0.485 | 0.451 | 1 | 0.436 | 0.392 | 0.09 | 0.479 | 0.168 |
4 | 0.480 | 0.538 | 1 | 0.596 | 0.568 | 0.453 | 0.667 | 0.364 |
5 | 0.496 | 0.449 | 1 | 0.268 | 0.462 | 0.12 | 0.644 | 0.278 |
6 | 0.492 | 0.446 | 1 | 0.221 | 0.215 | 0.133 | 0.27 | 0.224 |
7 | 0.482 | 0.461 | 1 | 0.292 | 0.284 | 0.032 | 0.407 | 0.084 |
8 | 0.500 | 0.494 | 1 | 0.292 | 0.213 | 0.032 | 0.364 | 0.167 |
9 | 0.483 | 0.491 | 1 | 0.428 | 0.456 | 0.142 | 0.602 | 0.255 |
10 | 0.493 | 0.443 | 1 | 0.295 | 0.52 | 0.165 | 0.513 | 0.281 |
Mean | 0.489 | 0.473 | 1 | 0.3342 | 0.3666 | 0.1453 | 0.4846 | 0.2149 |
SD | 0.007 | 0.030 | ||||||
IMP | 100% | 33% | 37% | 15% | 48% | 21% |
Network | RMSE (Training) | RMSE (Testing) | AMT | CF | TPM | ETL | SPC | TML |
---|---|---|---|---|---|---|---|---|
1 | 0.520 | 0.485 | 1 | 0.262 | 0.107 | 0.09 | 0.692 | 0.316 |
2 | 0.513 | 0.497 | 1 | 0.356 | 0.122 | 0.132 | 0.407 | 0.098 |
3 | 0.507 | 0.458 | 1 | 0.328 | 0.224 | 0.101 | 0.371 | 0.141 |
4 | 0.507 | 0.498 | 1 | 0.524 | 0.354 | 0.211 | 0.558 | 0.276 |
5 | 0.517 | 0.541 | 1 | 0.628 | 0.399 | 0.319 | 0.779 | 0.246 |
6 | 0.524 | 0.506 | 1 | 0.616 | 0.113 | 0.069 | 0.743 | 0.296 |
7 | 0.510 | 0.497 | 1 | 0.354 | 0.245 | 0.155 | 0.508 | 0.099 |
8 | 0.511 | 0.476 | 1 | 0.362 | 0.28 | 0.165 | 0.738 | 0.203 |
9 | 0.537 | 0.531 | 0.599 | 0.699 | 0.665 | 0.35 | 1 | 0.318 |
10 | 0.515 | 0.511 | 1 | 0.421 | 0.3 | 0.135 | 0.57 | 0.195 |
Mean | 0.516 | 0.500 | 0.960 | 0.455 | 0.281 | 0.173 | 0.637 | 0.219 |
SD | 0.009 | 0.025 | ||||||
IMP | 100% | 47% | 29% | 18% | 66% | 23% |
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Qureshi, K.M.; Mewada, B.G.; Kaur, S.; Qureshi, M.R.N.M. Assessing Lean 4.0 for Industry 4.0 Readiness Using PLS-SEM towards Sustainable Manufacturing Supply Chain. Sustainability 2023, 15, 3950. https://doi.org/10.3390/su15053950
Qureshi KM, Mewada BG, Kaur S, Qureshi MRNM. Assessing Lean 4.0 for Industry 4.0 Readiness Using PLS-SEM towards Sustainable Manufacturing Supply Chain. Sustainability. 2023; 15(5):3950. https://doi.org/10.3390/su15053950
Chicago/Turabian StyleQureshi, Karishma M., Bhavesh G. Mewada, Sumeet Kaur, and Mohamed Rafik Noor Mohamed Qureshi. 2023. "Assessing Lean 4.0 for Industry 4.0 Readiness Using PLS-SEM towards Sustainable Manufacturing Supply Chain" Sustainability 15, no. 5: 3950. https://doi.org/10.3390/su15053950