Defining SMEs’ 4.0 Readiness Indicators
Source abstract
Abstract
Industry 4.0 revolution offers smart manufacturing; it systematically incorporates production technology and advanced operation management. Adopting these high-state strategies can increase production efficiency, reduce energy consumption, and decrease manufacturer costs. Simultaneously, small and medium-sized enterprises (SMEs) were the backbone of economic growth and development. They still lack both the knowledge and decision-making to verify this high-stage technology’s performance and implementation. Therefore, the research aims to define the readiness indicators to assess and support SMEs toward Industry 4.0. The research begins with found aspects that influence the SME 4.0 readiness by using Bibliometric techniques. The result shows the aspects which were the most occurrences such as the Industrial Internet, Cloud Manufacturing, Collaborative Robot, Business Model, and Digital Transformation. They were then grouped into five dimensions by using the visualization of similarities (VOS) techniques: (1) Organizational Resilience, (2) Infrastructure System, (3) Manufacturing System, (4) Data Transformation, and (5) Digital Technology. Cronbach’s alpha then validated the composite dimensions at a 0.926 level of reliability and a significant positive correlation. After that, the indicators were defined from the dimension and aspects approach. Finally, the indicators were pilot tested by small enterprises. It appeared that 23 indicators could support SMEs 4.0 readiness indication and decision-making in the context of Industry 4.0.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Transformation in Industry · Economic and Technological Innovation · Big Data and Business Intelligence
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate Program in Industrial Engineering, Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
Excellent Center in Logistics and Supply Chain Management, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
Funding
European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie
References (97)
Show source-provided references
1. Industry 4.0 as policy-driven discourse to institutionalize innovation systems in manufacturing10.1016/j.techfore.2018.02.012 ↗
2. Defining and assessing industry 4.0 maturity levels—Case of the defence sector10.1080/09537287.2018.1503355 ↗
3. Intelligent Manufacturing in the Context of Industry 4.0: A Review10.1016/J.ENG.2017.05.015 ↗
4. Shrouf, F., Ordieres, J., and Miragliotta, G. (2014, January 9–12). Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm. Proceedings of the 2014 IEEE International Conference on Industrial Engineering and Engineering Management, Bandar Sunway, Malaysia.10.1109/IEEM.2014.7058728 ↗
5. Lean Automation enabled by Industry 4.0 Technologies10.1016/j.ifacol.2015.06.359 ↗
6. A review of the meanings and the implications of the Industry 4.0 concept10.1016/j.promfg.2017.09.032 ↗
7. Smart products development approaches for Industry 4.010.1016/j.promfg.2017.09.035 ↗
8. Sopadang, A., Chonsawat, N., and Ramingwong, S. (2020). Smart SME 4.0 Implementation Toolkit, Springer Science and Business Media LLC.10.1007/978-3-030-25425-4_10 ↗
9. Chonsawat, N., and Sopadang, A. (2019, January 5–7). The development of the maturity model to evaluate the smart SMEs 4.0 readiness. Proceedings of the International Conference on Industrial Engineering and Operations Management, JW Marriott Hotel Bangkok, Bangkok, Thailand. Available online: http://www.ieomsociety.org/ieom2019/papers/97.pdf.
10. Birkel, H., Veile, J.W., Müller, J.M., Hartmann, E., and Voigt, K.-I. (2019). Development of a Risk Framework for Industry 4.0 in the Context of Sustainability for Established Manufacturers. Sustainability., 11.10.3390/su11020384 ↗
11. Past, present and future of Industry 4.0—A systematic literature review and research agenda proposal10.1080/00207543.2017.1308576 ↗
12. Lichtblau, K., Stich, V., Bertenrath, R., Blum, M., Bleider, M., Millack, A., and Schröter, M. (2015). Industrie 4.0-Readiness, Impuls-Stiftung des VDMA.
13. Agca, O., Gibson, J., Godsell, J., Ignatius, J., Davies, C.W., and Xu, O. (2017). An Industry 4 Readiness Assessment Tool, WMG-The University of Warwick.
14. A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises10.1016/j.procir.2016.07.040 ↗
15. Leyh, C., Schäffer, T., Bley, K., and Forstenhäusler, S. (2016, January 11–14). SIMMI 4.0—A Maturity Model for Classifying the Enterprise-wide IT and Software Landscape Focusing on Industry 4.0. Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, Gdansk, Poland. Available online: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7733413.10.15439/2016F478 ↗
16. Development of a Monitoring System for Implementation of Industrial Digitalization and Automation using 143 Key Performance Indicators10.1016/j.procir.2020.03.012 ↗
17. Durana, P., Zauskova, A., Vagner, L., and Zadnanova, S. (2020). Earnings Drivers of Slovak Manufacturers: Efficiency Assessment of Innovation Management. Appl. Sci., 10.10.3390/app10124251 ↗
18. A framework for developing a domain specific business intelligence maturity model: Application to healthcare10.1016/j.ijinfomgt.2015.01.011 ↗
19. Discriminant analysis of technology adoption behavior: A case of internet technologies in small businesses
20. Viharos, Z.J., Soós, S., Nick, G.A., Várgedő, T., and Beregi, R.J. (2017, January 6–7). Non-Comparative, Industry 4.0 Readiness Evaluation for Manufacturing Enterprises. Proceedings of the 15th IMEKO TC10 Workshop on Technical Diagnostics Technical Diagnostics in Cyber-Physical Era, Budapest, Hungary. Available online: http://eprints.sztaki.hu/9238/1/Viharos_181_3257460_ny.pdf.10.21014/acta_imeko.v7i1.571 ↗
21. Gökalp, E., Şener, U., and Eren, P.E. (2017). Development of an Assessment Model for Industry 4.0: Industry 4.0-MM. Proceedings of the Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health, Springer Science and Business Media LLC.10.1007/978-3-319-67383-7_10 ↗
22. Mobilizing SMEs Towards Industrie 4.0-enabled Smart Products10.1016/j.procir.2017.03.346 ↗
23. Is Your Company Ready for a Digital Future?
24. Strategy, not technology, drives digital transformation: Becoming a digitally mature enterprise: Findings from the 2015 Digital Business Global Executive Study and Research Project
25. Analysing the factors influencing clean technology adoption: A study of the Spanish pulp and paper industry10.1002/bse.426 ↗
26. The Adoption of New Technology: Conceptual Model and Application10.4067/S0718-27242010000400008 ↗
27. Advanced Manufacturing Technology Adoption in SMEs: An Integrative Model10.4067/S0718-27242012000400009 ↗
28. Developing a Fuzzy Group AHP Model for Prioritizing the Factors Affecting Success of High-Tech SME’s in Iran: A Case Study10.1016/j.sbspro.2012.09.163 ↗
29. Analyzing the effects of technological, organizational and competition factors on Web knowledge exchange in SMEs10.1016/j.tele.2014.08.003 ↗
30. A Research on Determining Innovation Factors for SMEs10.1016/j.sbspro.2014.09.032 ↗
31. Frmanzah The Effect of Dynamic Capability to Technology Adoption and its Determinant Factors for Improving Firm’s Performance; Toward a Conceptual Model10.1016/j.sbspro.2015.10.168 ↗
32. Analysis of factors that influence the ICT adoption by SMEs in Colombia10.3926/ic.726 ↗
33. Organisational factors affecting cloud computing adoption in small and medium enterprises (SMEs) in service sector10.1016/j.procs.2017.11.126 ↗
34. Examining the critical success factors of cloud computing adoption in the MSMEs by using ISM model10.1016/j.hitech.2017.10.004 ↗
35. Identifying key performance factors for sustainability development of SMEs—Integrating QFD and fuzzy MADM methods10.1016/j.jclepro.2017.05.063 ↗
36. Blatz, F., Bulander, R., and Dietel, M. (2018, January 17–20). Maturity Model of Digitization for SMEs. Proceedings of the 2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Stuttgart, Germany.10.1109/ICE.2018.8436251 ↗
37. Human resources training: A bibliometric analysis10.1016/j.jbusres.2019.02.026 ↗
38. Critical factors for the successful implementation of Industry 4.0: A review and future research direction
39. Identification of critical success factors, risks and opportunities of Industry 4.0 in SMEs10.1080/00207543.2019.1636323 ↗
40. Gajdzik, B., Grabowska, S., Saniuk, S., and Wieczorek, T. (2020). Sustainable Development and Industry 4.0: A Bibliometric Analysis Identifying Key Scientific Problems of the Sustainable Industry 4.0. Energies, 13.10.3390/en13164254 ↗
41. Trends and Future Directions in Sustainable Development10.1002/sd.1687 ↗
42. The Literature of Bibliometrics, Scientometrics, and Informetrics
43. Statistical bibliography or bibliometrics?
44. A bibliometric analysis of research on Big Data analytics for business and management10.1108/MD-07-2018-0754 ↗
45. Software survey: VOSviewer, a computer program for bibliometric mapping10.1007/s11192-009-0146-3 ↗
46. Van Eck, N.J., and Waltman, L. (2020, April 01). Text Mining and Visualization Using VOSviewer. Available online: https://arxiv.org/pdf/1109.2058.pdf.
47. Durana, P., Valaskova, K., Vagner, L., Zadnanova, S., Podhorska, I., and Siekelova, A. (2020). Disclosure of Strategic Managers’ Factotum: Behavioral Incentives of Innovative Business. Int. J. Financ. Stud., 8.10.3390/ijfs8010017 ↗
48. A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS10.1002/asi.21421 ↗
49. Van Eck, N.J., Waltman, L., Van Raan, A.F.J., Klautz, R.J.M., and Peul, W.C. (2013). Citation Analysis May Severely Underestimate the Impact of Clinical Research as Compared to Basic Research. PLoS ONE, 8.10.1371/journal.pone.0062395 ↗
50. A new methodology for constructing a publication-level classification system of science10.1002/asi.22748 ↗
51. A smart local moving algorithm for large-scale modularity-based community detection10.1140/epjb/e2013-40829-0 ↗
52. Coefficient alpha and the internal structure of tests10.1007/BF02310555 ↗
53. Maturity model for enterprise interoperability
54. Haseeb, M., Hussain, H.I., Ślusarczyk, B., and Jermsittiparsert, K. (2019). Industry 4.0: A Solution towards Technology Challenges of Sustainable Business Performance. Soc. Sci., 8.10.3390/socsci8050154 ↗
55. SPICE: Software Process Improvement and Capability Determination10.1007/BF00403764 ↗
56. How to normalize cooccurrence data? An analysis of some well-known similarity measures10.1002/asi.21075 ↗
57. Sustainable Industrial Value Creation: Benefits and Challenges of Industry 4.010.1142/S1363919617400151 ↗
58. Braccini, A.M., and Margherita, E.G. (2018). Exploring Organizational Sustainability of Industry 4.0 under the Triple Bottom Line: The Case of a Manufacturing Company. Sustainability, 11.10.3390/su11010036 ↗
59. Business model innovation in small- and medium-sized enterprises10.1108/JMTM-01-2018-0008 ↗
60. Opportunities of Sustainable Manufacturing in Industry 4.010.1016/j.procir.2016.01.129 ↗
61. A critical investigation of Industry 4.0 in manufacturing: Theoretical operationalisation framework10.1080/09537287.2018.1424960 ↗
62. Sustainable value co-creation in business networks10.1016/j.indmarman.2015.05.018 ↗
63. Industry 4.0: A survey on technologies, applications and open research issues
64. Networked, Smart, and Responsive Devices in Industry 4.0 Manufacturing Systems10.22381/EMFM15320203 ↗
65. Industry 4.0 and the current status as well as future prospects on logistics10.1016/j.compind.2017.04.002 ↗
66. Müller, J.M., Buliga, O., and Voigt, K.-I. (2020). The role of absorptive capacity and innovation strategy in the design of industry 4.0 business Models-A comparison between SMEs and large enterprises. Eur. Manag. J., 1–11.10.1016/j.emj.2020.01.002 ↗
67. Fundamental Theories and Key Technologies for Smart and Optimal Manufacturing in the Process Industry10.1016/J.ENG.2017.02.011 ↗
68. How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective
69. Sustainable value creation in SMEs: A case study10.1108/17542731311286423 ↗
70. Interdependencies of Industrie 4.0 & Lean Production Systems: A Use Cases Analysis10.1016/j.promfg.2017.07.217 ↗
71. Sustainable Industrial Value Creation in SMEs: A Comparison between Industry 4.0 and Made in China 202510.1007/s40684-018-0056-z ↗
72. Vrchota, J., Volek, T., and Novotná, M. (2019). Factors Introducing Industry 4.0 to SMES. Soc. Sci., 8.10.3390/socsci8050130 ↗
73. Corporate survival in Industry 4.0 era: The enabling role of lean-digitized manufacturing10.1108/JMTM-11-2018-0417 ↗
74. Transformation strategies for the supply chain: The impact of industry 4.0 and digital transformation10.1080/16258312.2020.1716633 ↗
75. Smart manufacturing: Characteristics, technologies and enabling factors10.1177/0954405417736547 ↗
76. Lessons learned from Industry 4.0 implementation in the German manufacturing industry10.1108/JMTM-08-2018-0270 ↗
77. A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements10.1016/j.cie.2018.11.030 ↗
78. Industry 4.0 Readiness Assessment for South African Industries. South African
79. The ‘how’ of benefits management for digital technology: From engineering to asset management10.1016/j.autcon.2019.102930 ↗
80. New state standards of higher education for training of digital personnel in the conditions of Industry 4.010.1108/OTH-07-2019-0043 ↗
81. Internet of things for smart ports: Technologies and challenges10.1109/MIM.2018.8278808 ↗
82. Complexity-based task allocation in human-robot collaborative assembly10.1108/IR-11-2018-0231 ↗
83. A simulation-based approach for plant layout design and production planning10.1007/s12652-018-0687-5 ↗
84. (2018). Inclusive Robotics for a Better Society. Biosyst. Biorobotics, 25.
85. Industry 4.0 based process data analytics platform: A waste-to-energy plant case study10.1016/j.ijepes.2019.105508 ↗
86. An Integrated Industrial Ethernet Solution for the Implementation of Smart Factory10.1109/ACCESS.2017.2770180 ↗
87. A Blockchain Tokenizer for Industrial IOT trustless applications10.1016/j.future.2019.12.020 ↗
88. Enhancing smart shop floor management with ubiquitous augmented reality10.1080/00207543.2019.1629667 ↗
89. An HMM and polynomial regression based approach for remaining useful life and health state estimation of cutting tools10.1016/j.cie.2018.05.017 ↗
90. Big Data Strategy
91. The adoption stages (Evaluation, Adoption, and Routinisation) of ERP systems with business analytics functionality in the context of farms10.1016/j.compag.2018.11.028 ↗
92. Benhimane, S., Najafi, H., Grundmann, M., Genc, Y., Navab, N., and Malis, E. (2008, January 22–25). Real-Time Object Detection and Tracking for Industrial Applications. Proceedings of the Third International Conference on Computer Vision Theory and Applications, Funchal, Madeira, Portugal.
93. Failure Mode Identification and Prioritization Using FMECA: A Study on Computer Numerical Control Lathe for Predictive Maintenance10.1007/s11668-019-00717-8 ↗
94. Dalmarco, G., and Barros, A.C. (2018). Adoption of Industry 4.0 Technologies in Supply Chains. Contributions to Management Science, Springer Science and Business Media LLC.10.1007/978-3-319-74304-2_14 ↗
95. Müller, J.M., Kiel, D., and Voigt, K.-I. (2018). What Drives the Implementation of Industry 4.0? The Role of Opportunities and Challenges in the Context of Sustainability. Sustainability, 10.10.3390/su10010247 ↗
96. Development of a Scaling Factor Identification Method Using Design of Experiments for Product-Family-Based Product and Process Design10.1081/QEN-100108690 ↗
97. Tippayawong, K.Y., Niyomyat, N., Sopadang, A., and Ramingwong, S. (2016). Factors Affecting Green Supply Chain Operational Performance of the Thai Auto Parts Industry. Sustainability, 8.10.3390/su8111161 ↗