International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12

line decor
  
line decor

 Volume 10, Issue 3 (March 2023), Pages: 143-156

----------------------------------------------

 Original Research Paper

Implementing Industry 4.0 and lean practices for business performance in manufacturing: Case of Malaysia

 Author(s): 

 Yenn Harn Ooi 1, *, Tan Ching Ng 1, 2, Wen Chiet Cheong 1, 3

 Affiliation(s):

 1Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Selangor, Malaysia
 2Centre for Business and Management, Universiti Tunku Abdul Rahman, Selangor, Malaysia
 3Centre for Photonics and Advanced Materials Research, Universiti Tunku Abdul Rahman, Selangor, Malaysia

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-4138-4250

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2023.03.019

 Abstract:

Manufacturing industries had embraced the trend of conceiving a robust manufacturing system and enhancing business performance with the implementation of Industry 4.0 digital technologies and lean manufacturing practices. Despite multiple studies being conducted to identify the correlation between Industry 4.0 digital technologies, lean manufacturing practices, and business performance, ambiguous and conflicting statements are often being debated among researchers. Hence, this study aims to provide empirical evidence gathered from Malaysian manufacturing industries using questionnaires to investigate and model their correlation and explore the mediating influence of Industry 4.0 digital technologies on lean manufacturing practices and business performance using PLS-SEM. Consequently, the findings from 124 respondents were compared with prior studies and revealed that both Lean Manufacturing Practices and Industry 4.0 Digital Technologies are positively correlated with one another, and they positively influence business performance, which findings are coherent with prior studies and fortifying the urgency of implementing both concepts for business performance enhancement. Moreover, this study successfully revealed that Industry 4.0 digital technologies mediate lean manufacturing practices and business performance proving the importance of Industry 4.0 to solving Lean’s limitation, which is not studied in prior studies. In addition, the framework in this study is more practical in providing appropriate theoretical and managerial insights for future action and works due to its medium predictive power associated. In a nutshell, this study effectively implies the substantial roles and reinforced the pragmatisms of implementing both lean manufacturing practices and Industry 4.0 digital technologies concurrently for business excellence.

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Industry 4.0, Lean manufacturing, Business performance, PLS-SEM, Higher-order construct

 Article History: Received 6 September 2022, Received in revised form 19 December 2022, Accepted 20 December 2022

 Acknowledgment 

The authors would like to thank the opportunity to acknowledge and thank Universiti Tunku Abdul Rahman (UTAR) for funding the work under UTAR Research Fund (UTARRF) 2021 Cycle 1 (Vote No. 6550/1N02). In addition, we thank the reviewers and associate editor for their comments which significantly improved this article.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Ooi YH, Ng TC, and Cheong WC (2023). Implementing Industry 4.0 and lean practices for business performance in manufacturing: Case of Malaysia. International Journal of Advanced and Applied Sciences, 10(3): 143-156

 Permanent Link to this page

 Figures

 Fig. 1 

 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 

----------------------------------------------    

 References (87)

  1. Ante G, Facchini F, Mossa G, and Digiesi S (2018). Developing a key performance indicators tree for lean and smart production systems. IFAC-PapersOnLine, 51(11): 13-18. https://doi.org/10.1016/j.ifacol.2018.08.227   [Google Scholar]
  2. Bagozzi RP and Yi Y (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16: 74-94. https://doi.org/10.1007/BF02723327   [Google Scholar]
  3. Becker JM, Klein K, and Wetzels M (2012). Hierarchical latent variable models in PLS-SEM: Guidelines for using reflective-formative type models. Long Range Planning, 45(5-6): 359-394. https://doi.org/10.1016/j.lrp.2012.10.001   [Google Scholar]
  4. Becker JM, Ringle CM, Sarstedt M, and Völckner F (2015). How collinearity affects mixture regression results. Marketing Letters, 26(4): 643-659. https://doi.org/10.1007/s11002-014-9299-9   [Google Scholar]
  5. Bédard-Maltais PO (2017). Industry 4.0: The new industrial revolution: Are Canadian manufacturers ready? Business Development Bank of Canada, Toronto, Canada.   [Google Scholar]
  6. Bhamu J and Sangwan KS (2014). Lean manufacturing: Literature review and research issues. International Journal of Operations and Production Management, 34(7): 876-940. https://doi.org/10.1108/IJOPM-08-2012-0315   [Google Scholar]
  7. Bittencourt VL, Alves AC, and Leão CP (2019). Lean thinking contributions for Industry 4.0: A systematic literature review. IFAC-PapersOnLine, 52(13): 904-909. https://doi.org/10.1016/j.ifacol.2019.11.310   [Google Scholar]
  8. Bittencourt VL, Alves AC, and Leão CP (2021). Industry 4.0 triggered by lean thinking: Insights from a systematic literature review. International Journal of Production Research, 59(5): 1496-1510. https://doi.org/10.1080/00207543.2020.1832274   [Google Scholar]
  9. Bortolotti T, Romano P, and Nicoletti B (2009). Lean first, then automate: An integrated model for process improvement in pure service-providing companies. In the IFIP International Conference on Advances in Production Management Systems, Springer, Heidelberg, Germany: 579-586. https://doi.org/10.1007/978-3-642-16358-6_72   [Google Scholar]
  10. Buer SV, Semini M, Strandhagen JO, and Sgarbossa F (2021). The complementary effect of lean manufacturing and digitalization on operational performance. International Journal of Production Research, 59(7): 1976-1992. https://doi.org/10.1080/00207543.2020.1790684   [Google Scholar]
  11. Buer SV, Strandhagen JO, and Chan FT (2018). The link between industry 4.0 and lean manufacturing: Mapping current research and establishing a research agenda. International Journal of Production Research, 56(8): 2924-2940. https://doi.org/10.1080/00207543.2018.1442945   [Google Scholar]
  12. Byrne BM (2013). Structural equation modeling with EQS: Basic concepts, applications, and programming. Routledge, New York, USA. https://doi.org/10.4324/9780203807644   [Google Scholar]
  13. Byrne BM (2016). Structural equation modeling with Amos: Basic concepts, applications, and programming. Routledge, New York, USA. https://doi.org/10.4324/9781315757421   [Google Scholar]
  14. Cheah JH, Ting H, Ramayah T, Memon MA, Cham TH, and Ciavolino E (2019). A comparison of five reflective–formative estimation approaches: Reconsideration and recommendations for tourism research. Quality and Quantity, 53(3): 1421-1458. https://doi.org/10.1007/s11135-018-0821-7   [Google Scholar]
  15. Cohen J (1988). Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associates, New York, USA.   [Google Scholar]
  16. Davies R, Coole T, and Smith A (2017). Review of socio-technical considerations to ensure successful implementation of Industry 4.0. Procedia Manufacturing, 11: 1288-1295. https://doi.org/10.1016/j.promfg.2017.07.256   [Google Scholar]
  17. Dombrowski U, Richter T, and Krenkel P (2017). Interdependencies of industry 4.0 and lean production systems: A use cases analysis. Procedia Manufacturing, 11: 1061-1068. https://doi.org/10.1016/j.promfg.2017.07.217   [Google Scholar]
  18. Ejsmont K and Gładysz B (2020). Lean industry 4.0-Wastes versus technology framework. In The 10th International Conference on Engineering, Project, and Production Management, Springer, Singapore, Singapore: 537-546. https://doi.org/10.1007/978-981-15-1910-9_44   [Google Scholar]
  19. Faul F, Erdfelder E, Buchner A, and Lang AG (2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4): 1149-1160. https://doi.org/10.3758/BRM.41.4.1149   [Google Scholar] PMid:19897823
  20. Faul F, Erdfelder E, Lang AG, and Buchner A (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2): 175-191. https://doi.org/10.3758/BF03193146   [Google Scholar] PMid:17695343
  21. Fornell C and Larcker DF (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1): 39-50. https://doi.org/10.1177/002224378101800104   [Google Scholar]
  22. Garza-Reyes JA (2015). Lean and green–A systematic review of the state of the art literature. Journal of Cleaner Production, 102: 18-29. https://doi.org/10.1016/j.jclepro.2015.04.064   [Google Scholar]
  23. Geisser S (1974). A predictive approach to the random effect model. Biometrika, 61(1): 101-107. https://doi.org/10.1093/biomet/61.1.101   [Google Scholar]
  24. Ghobakhloo M (2018). The future of manufacturing industry: A strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management, 29(6): 910-936. https://doi.org/10.1108/JMTM-02-2018-0057   [Google Scholar]
  25. Ghobakhloo M and Ching NT (2019). Adoption of digital technologies of smart manufacturing in SMEs. Journal of Industrial Information Integration, 16: 100107. https://doi.org/10.1016/j.jii.2019.100107   [Google Scholar]
  26. Ghobakhloo M and Fathi M (2019). Corporate survival in industry 4.0 era: The enabling role of lean-digitized manufacturing. Journal of Manufacturing Technology Management, 31(1): 1-30. https://doi.org/10.1108/JMTM-11-2018-0417   [Google Scholar]
  27. Hair JF, Black WC, Babin BJ, and Anderson RE (2009). Multivariate data analysis. 7th Edition, Prentice Hall, Upper Saddle River, USA.   [Google Scholar]
  28. Hair JF, Hult GTM, Ringle CM, and Sarstedt M (2016). A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications, Thousand Oaks, USA.   [Google Scholar]
  29. Hair JF, Matthews LM, Matthews RL, and Sarstedt M (2017a). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2): 107-123. https://doi.org/10.1504/IJMDA.2017.10008574   [Google Scholar]
  30. Hair JF, Sarstedt M, and Ringle CM (2019). Rethinking some of the rethinking of partial least squares. European Journal of Marketing, 53(4): 566-584. https://doi.org/10.1108/EJM-10-2018-0665   [Google Scholar]
  31. Hair JF, Sarstedt M, Ringle CM, and Gudergan SP (2017b). Advanced issues in partial least squares structural equation modeling. SAGE Publications, Mobile, USA. https://doi.org/10.1007/978-3-319-05542-8_15-1   [Google Scholar]
  32. Imran M, Hameed WU, and Haque AU (2018). Influence of industry 4.0 on the production and service sectors in Pakistan: Evidence from textile and logistics industries. Social Sciences, 7(12): 246. https://doi.org/10.3390/socsci7120246   [Google Scholar]
  33. Kamble S, Gunasekaran A, and Dhone NC (2020). Industry 4.0 and lean manufacturing practices for sustainable organisational performance in Indian manufacturing companies. International Journal of Production Research, 58(5): 1319-1337. https://doi.org/10.1080/00207543.2019.1630772   [Google Scholar]
  34. Kline RB (2015). Principles and practice of structural equation modeling. Guilford Press, New York, USA.   [Google Scholar]
  35. Klingenberg CO, Borges MAV, and do Vale Antunes JrJA (2022). Industry 4.0: What makes it a revolution? A historical framework to understand the phenomenon. Technology in Society, 70: 102009. https://doi.org/10.1016/j.techsoc.2022.102009   [Google Scholar]
  36. Kock N and Lynn G (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7): 1-40. https://doi.org/10.17705/1jais.00302   [Google Scholar]
  37. Kolberg D and Zühlke D (2015). Lean automation enabled by industry 4.0 technologies. IFAC-PapersOnLine, 48(3): 1870-1875. https://doi.org/10.1016/j.ifacol.2015.06.359   [Google Scholar]
  38. Kolberg D, Knobloch J, and Zühlke D (2017). Towards a lean automation interface for workstations. International Journal of Production Research, 55(10): 2845-2856. https://doi.org/10.1080/00207543.2016.1223384   [Google Scholar]
  39. Kusiak A (2019). Fundamentals of smart manufacturing: A multi-thread perspective. Annual Reviews in Control, 47: 214-220. https://doi.org/10.1016/j.arcontrol.2019.02.001   [Google Scholar]
  40. Lee MX, Lee YC, and Chou CJ (2017). Essential implications of the digital transformation in industry 4.0. Journal of Scientific and Industrial Research, 76(8): 465-467.   [Google Scholar]
  41. Liker JK (2004). The Toyota way: 14 management principles from the world's greatest manufacturer. McGraw-Hill Education, New York, USA.   [Google Scholar]
  42. Ma J, Wang Q, and Zhao Z (2017). SLAE–CPS: Smart lean automation engine enabled by cyber-physical systems technologies. Sensors, 17(7): 1500. https://doi.org/10.3390/s17071500   [Google Scholar] PMid:28657577 PMCid:PMC5539867
  43. Mardia KV (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3): 519-530. https://doi.org/10.1093/biomet/57.3.519   [Google Scholar]
  44. Mayr A, Weigelt M, Kühl A, Grimm S, Erll A, Potzel M, and Franke J (2018). Lean 4.0-A conceptual conjunction of lean management and Industry 4.0. Procedia CIRP, 72: 622-628. https://doi.org/10.1016/j.procir.2018.03.292   [Google Scholar]
  45. MITI (2018). Industry 4WRD: National policy on industry 4.0. Ministry of International Trade and Industry, Kuala Lumpur, Malaysia.   [Google Scholar]
  46. Mohamed M (2018). Challenges and benefits of industry 4.0: An overview. International Journal of Supply and Operations Management, 5(3): 256-265.   [Google Scholar]
  47. Mrugalska B and Wyrwicka MK (2017). Towards lean production in industry 4.0. Procedia Engineering, 182: 466-473. https://doi.org/10.1016/j.proeng.2017.03.135   [Google Scholar]
  48. Nawanir G (2016). The effect of lean manufacturing on operations performance and business performance in manufacturing companies in Indonesia. Universiti Utara Malaysia, Kedah, Malaysia.   [Google Scholar]
  49. Ng TC and Ghobakhloo M (2018). What determines lean manufacturing implementation? A CB-SEM model. Economies, 6(1): 9. https://doi.org/10.3390/economies6010009   [Google Scholar]
  50. Nicoletti B (2013). Lean and automate manufacturing and logistics. In the IFIP International Conference on Advances in Production Management Systems, Springer, Heidelberg, Germany: 278-285. https://doi.org/10.1007/978-3-642-41263-9_34   [Google Scholar]
  51. Nunnally JC and Bernstein IH (1995). Psychometric theory. McGraw-Hill, New York, USA.   [Google Scholar]
  52. Ohno T (2019). Toyota production system: Beyond large-scale production. Productivity Press, Tokyo, Japan. https://doi.org/10.4324/9780429273018   [Google Scholar]
  53. Pereira AC, Dinis-Carvalho J, Alves AC, and Arezes P (2019). How Industry 4.0 can enhance lean practices. FME Transactions, 47(4): 810-822. https://doi.org/10.5937/fmet1904810P   [Google Scholar]
  54. Podsakoff PM, MacKenzie SB, Lee JY, and Podsakoff NP (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5): 879-903. https://doi.org/10.1037/0021-9010.88.5.879   [Google Scholar] PMid:14516251
  55. Powell D, Romero D, Gaiardelli P, Cimini C, and Cavalieri S (2018). Towards digital lean cyber-physical production systems: Industry 4.0 technologies as enablers of leaner production. In the IFIP International Conference on Advances in Production Management Systems, Springer, Seoul, Korea: 353-362. https://doi.org/10.1007/978-3-319-99707-0_44   [Google Scholar]
  56. Preacher KJ and Hayes AF (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, and Computers, 36(4): 717-731. https://doi.org/10.3758/BF03206553   [Google Scholar] PMid:15641418
  57. Preacher KJ and Hayes AF (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3): 879-891. https://doi.org/10.3758/BRM.40.3.879   [Google Scholar] PMid:18697684
  58. Prinz C, Kreggenfeld N, and Kuhlenkötter B (2018). Lean meets Industrie 4.0–A practical approach to interlink the method world and cyber-physical world. Procedia Manufacturing, 23: 21-26. https://doi.org/10.1016/j.promfg.2018.03.155   [Google Scholar]
  59. Ramayah TJFH, Cheah J, Chuah F, Ting H, and Memon MA (2018). Partial least squares structural equation modeling (PLS-SEM) using smartPLS 3.0: An updated guide and practical guide to statistical analysis. Pearson, Kuala Lumpur, Malaysia.   [Google Scholar]
  60. Ringle CM, Wende S, and Becker JM (2015). SmartPLS 3. SmartPLS GmbH, Boenningstedt. Journal of Service Science and Management, 10(3): 32-49.   [Google Scholar]
  61. Rojko A (2017). Industry 4.0 concept: Background and overview. International Journal of Interactive Mobile Technologies, 11(5): 77-90. https://doi.org/10.3991/ijim.v11i5.7072   [Google Scholar]
  62. Rossini M, Costa F, Tortorella GL, and Portioli-Staudacher A (2019). The interrelation between Industry 4.0 and lean production: An empirical study on European manufacturers. The International Journal of Advanced Manufacturing Technology, 102(9): 3963-3976. https://doi.org/10.1007/s00170-019-03441-7   [Google Scholar]
  63. Rüttimann BG and Stöckli MT (2016). Lean and Industry 4.0-Twins, partners, or contenders? A due clarification regarding the supposed clash of two production systems. Journal of Service Science and Management, 9(6): 485-500. https://doi.org/10.4236/jssm.2016.96051   [Google Scholar]
  64. Sanders A, Elangeswaran C, and Wulfsberg JP (2016). Industry 4.0 implies lean manufacturing: Research activities in industry 4.0 function as enablers for lean manufacturing. Journal of Industrial Engineering and Management, 9(3): 811-833. https://doi.org/10.3926/jiem.1940   [Google Scholar]
  65. Sanders AK, Subramanian KR, Redlich T, and Wulfsberg JP (2017). Industry 4.0 and lean management–synergy or contradiction? In the IFIP International Conference on Advances in Production Management Systems, Springer, Hamburg, Germany: 341-349. https://doi.org/10.1007/978-3-319-66926-7_39   [Google Scholar]
  66. Santos C, Mehrsai A, Barros AC, Araújo M, and Ares E (2017). Towards Industry 4.0: An overview of European strategic roadmaps. Procedia Manufacturing, 13: 972-979. https://doi.org/10.1016/j.promfg.2017.09.093   [Google Scholar]
  67. Sarstedt M, Hair JrJF, Cheah JH, Becker JM, and Ringle CM (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal, 27(3): 197-211. https://doi.org/10.1016/j.ausmj.2019.05.003   [Google Scholar]
  68. Sarstedt M, Ringle CM, and Hair JF (2022). Partial least squares structural equation modeling. In: Homburg C, Klarmann M, and Vomberg A (Eds.), Handbook of market research: 587-632. Springer, Munich, Germany. https://doi.org/10.1007/978-3-319-57413-4_15   [Google Scholar]
  69. Satoglu S, Ustundag A, Cevikcan E, and Durmusoglu MB (2018). Lean transformation integrated with Industry 4.0 implementation methodology. In the Industrial Engineering in the Industry 4.0 Era, Springer, Vienna, Austria: 97-107. https://doi.org/10.1007/978-3-319-71225-3_9   [Google Scholar]
  70. Schmidt R, Möhring M, Härting RC, Reichstein C, Neumaier P, and Jozinović P (2015). Industry 4.0-potentials for creating smart products: Empirical research results. In the International Conference on Business Information Systems, Springer, Poznan, Poland: 16-27. https://doi.org/10.1007/978-3-319-19027-3_2   [Google Scholar]
  71. Shah R and Ward PT (2003). Lean manufacturing: Context, practice bundles, and performance. Journal of Operations Management, 21(2): 129-149. https://doi.org/10.1016/S0272-6963(02)00108-0   [Google Scholar]
  72. Shah R and Ward PT (2007). Defining and developing measures of lean production. Journal of Operations Management, 25(4): 785-805. https://doi.org/10.1016/j.jom.2007.01.019   [Google Scholar]
  73. Sharma PN, Shmueli G, Sarstedt M, Danks N, and Ray S (2021). Prediction‐oriented model selection in partial least squares path modeling. Decision Sciences, 52(3): 567-607. https://doi.org/10.1111/deci.12329   [Google Scholar]
  74. Shmueli G, Ray S, Estrada JMV, and Chatla SB (2016). The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69(10): 4552-4564. https://doi.org/10.1016/j.jbusres.2016.03.049   [Google Scholar]
  75. Shmueli G, Sarstedt M, Hair JF, Cheah JH, Ting H, Vaithilingam S, and Ringle CM (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11): 2322-2347. https://doi.org/10.1108/EJM-02-2019-0189   [Google Scholar]
  76. Sommer L (2015). Industrial revolution-industry 4.0: Are German manufacturing SMEs the first victims of this revolution? Journal of Industrial Engineering and Management, 8(5): 1512-1532. https://doi.org/10.3926/jiem.1470   [Google Scholar]
  77. Sony M (2018). Industry 4.0 and lean management: A proposed integration model and research propositions. Production and Manufacturing Research, 6(1): 416-432. https://doi.org/10.1080/21693277.2018.1540949   [Google Scholar]
  78. Stone M (1974). Cross‐validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2): 111-133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x   [Google Scholar]
  79. Strandhagen JW, Alfnes E, Strandhagen JO, and Vallandingham LR (2017). The fit of Industry 4.0 applications in manufacturing logistics: A multiple case study. Advances in Manufacturing, 5(4): 344-358. https://doi.org/10.1007/s40436-017-0200-y   [Google Scholar]
  80. Szász L, Demeter K, Rácz BG and Losonci D (2020). Industry 4.0: A review and analysis of contingency and performance effects. Journal of Manufacturing Technology Management, 32(3): 667-694. https://doi.org/10.1108/JMTM-10-2019-0371   [Google Scholar]
  81. Tortorella G, Sawhney R, Jurburg D, de Paula IC, Tlapa D, and Thurer M (2021). Towards the proposition of a lean automation framework: Integrating industry 4.0 into lean production. Journal of Manufacturing Technology Management, 32(3): 593-620. https://doi.org/10.1108/JMTM-01-2019-0032   [Google Scholar]
  82. Tortorella GL and Fettermann D (2018). Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies. International Journal of Production Research, 56(8): 2975-2987. https://doi.org/10.1080/00207543.2017.1391420   [Google Scholar]
  83. Tortorella GL, Giglio R, and Van Dun DH (2019). Industry 4.0 adoption as a moderator of the impact of lean production practices on operational performance improvement. International Journal of Operations and Production Management, 39(6/7/8): 860-886. https://doi.org/10.1108/IJOPM-01-2019-0005   [Google Scholar]
  84. Vogel-Heuser B and Hess D (2016). Guest editorial Industry 4.0–Prerequisites and visions. IEEE Transactions on Automation Science and Engineering, 13(2): 411-413. https://doi.org/10.1109/TASE.2016.2523639   [Google Scholar]
  85. Wagner T, Herrmann C, and Thiede S (2017). Industry 4.0 impacts on lean production systems. Procedia CIRP, 63: 125-131. https://doi.org/10.1016/j.procir.2017.02.041   [Google Scholar]
  86. Womack JP and Jones DT (1997). Lean thinking-Banish waste and create wealth in your corporation. Journal of the Operational Research Society, 48(11): 1148-1148. https://doi.org/10.1038/sj.jors.2600967   [Google Scholar]
  87. Wong KKK (2019). Mastering partial least squares structural equation modeling (PLS-Sem) with Smartpls in 38 Hours. iUniverse, Bloomington, USA.   [Google Scholar]