Readiness of Enterprises in Czech Republic to Implement Industry 4.0: Index of Industry 4.0
Source abstract
Abstract
Industry 4.0 includes digital process transformation, information technology (IT) development, mobile devices, learning software, automation, and robotics, as well as intelligent sensors to collect large datasets, store, analyze, and use them in business, including simulation, virtual reality, and digital twins. The aim of the paper is to characterize the readiness of the enterprise to use Industry 4.0. In the research, a questionnaire survey was carried out on a sample of 276 enterprises mainly from the manufacturing industry. Using explorative factor analysis, the index of Industry 4.0 (VPi4) was designed to determine the level of Industry 4.0 implementation in the enterprises. The results were further verified by a statistical analysis, using Mann–Whitney test and correlation coefficients. The results indicate that the VPi4 index was consistent in terms of distribution when comparing the results on the verification sample. Its results correlate with the subjective perception of the enterprises, and different levels of the index reflect the difference in technological intensity of the industry. The VPi4 index enables the enterprises to determine their own level of current state of readiness for Industry 4.0, to better prioritize business development. The proposed solution categorizes Industry 4.0 components into a useful theoretical framework. Further research offers the possibility of applying the index in other sectors, its relation to the size of enterprises, and updating with respect to new trends in information technology.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Transformation in Industry · Big Data and Business Intelligence · Business and Economic Development
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Management, Faculty of Economics, University of South Bohemia in Ceske Budejovice, Studentska 13, 370 05 Ceske Budejovice, Czech Republic
References (100+)
Show source-provided references
1. Machine-to-Machine Communication10.1109/MS.2014.87 ↗
2. Kelkar, O. (2019, November 11). Studie Industrie 4.0—Eine Standortbestimmung der Automobil-und Fertigungsindustrie. Mieschke Hofmann und Partner (MHP), Ludwigsburg, Germany. Available online: https://www.mhp.com/fileadmin/mhp.de/assets/studien/MHP-Studie_Industrie4.0_V1.0.pdf.
3. (2019, November 08). Pricewaterhouse Coopers PwC-und Strategy&-Studie: Industrie 4.0 hat Hohes Nutzenpotenzial für Deutsche Unternehmen. Available online: https://www.pwc.de/de/digitale-transformation/pwc-studie-industrie-4-0-steht-vor-dem-durchbruch.html.
4. Dörfler, M. (2019, November 11). Industrie 4.0 ist im Mittelstand Noch Nicht Angekommen. Available online: https://www.marktundmittelstand.de/404/.
5. Industrial revolution—Industry 4.0: Are German manufacturing SMEs the first victims of this revolution?
6. Aspects of Risk Management Implementation for Industry 4.010.1016/j.promfg.2017.07.248 ↗
7. Intelligent Manufacturing in the Context of Industry 4.0: A Review10.1016/J.ENG.2017.05.015 ↗
8. RFID enabled manufacturing: Fundamentals, methodology and applications10.1504/IJASM.2006.008860 ↗
9. Mansoor, K., Ghani, A., Chaudhry, S.A., Shamshirband, S., Ghayyur, S.A.K., and Mosavi, A. (2019). Securing IoT-Based RFID Systems: A Robust Authentication Protocol Using Symmetric Cryptography. Sensors, 19.10.20944/preprints201907.0298.v1 ↗
10. Mitsuishi, M., Ueda, K., and Kimura, F. (2008). Smart Factory—A Step towards the Next Generation of Manufacturing. Proceedings of the Manufacturing Systems and Technologies for the New Frontier, Tokyo, Japan, 26–28 May 2008, Springer.10.1007/978-1-84800-267-8 ↗
11. Tan, P., Wu, H., Li, P., and Xu, H. (2018). Teaching Management System with Applications of RFID and IoT Technology. Educ. Sci., 8.10.3390/educsci8010026 ↗
12. Reis, M., and Gins, G. (2017). Industrial Process Monitoring in the Big Data/Industry 4.0 Era: From Detection, to Diagnosis, to Prognosis. Processes, 5.10.3390/pr5030035 ↗
13. Auer, M.E., and Zutin, D.G. (2018). Remote Control of Large Manufacturing Plants Using Core Elements of Industry 4.0. Proceedings of the Online Engineering & Internet of Things, Columbia University, New York, USA 15–17 March 2017, Springer International Publishing.10.1007/978-3-319-64352-6 ↗
14. Big Data: The Management Revolution
15. Mayer-Schönberger, V., and Cukier, K. (2014). Big Data, Computer Press.
16. Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon10.1080/1369118X.2012.678878 ↗
17. Laney, D. (2001, February 06). 3D Data Management: Controlling Data Volume, Velocity, and Variety. Available online: http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf.
18. Berman, J.J. (2013). Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information, Elsevier, Morgan Kaufmann.
19. Extracting value from chaos
20. Critical analysis of Big Data challenges and analytical methods10.1016/j.jbusres.2016.08.001 ↗
21. Big Data for Internet of Things: A Survey10.1016/j.future.2018.04.053 ↗
22. From cloud computing to cloud manufacturing10.1016/j.rcim.2011.07.002 ↗
23. Big data analytics: A survey10.1186/s40537-015-0030-3 ↗
24. What does Industry 4.0 mean to Supply Chain?10.1016/j.promfg.2017.09.191 ↗
25. Ertürk, M.A., Aydın, M.A., Büyükakkaşlar, M.T., and Evirgen, H. (2019). A Survey on LoRaWAN Architecture, Protocol and Technologies. Future Internet, 11.10.3390/fi11100216 ↗
26. Impact of 5G Technologies on Industry 4.010.1007/s11277-018-5615-7 ↗
27. Leber, J. (2019, November 08). General Electric Pitches an Industrial Internet. Available online: https://www.technologyreview.com/s/507831/general-electric-pitches-an-industrial-internet/.
28. Floyer, D. (2019, November 08). Defining and Sizing the Industrial Internet—Wikibon. Available online: http://wikibon.org/wiki/v/Defining_and_Sizing_the_Industrial_Internet#Constraints_on_Adoption_of_the_Industrial_Internet.
29. Fuertes, W., Reyes, F., Valladares, P., Tapia, F., Toulkeridis, T., and Pérez, E. (2017). An Integral Model to Provide Reactive and Proactive Services in an Academic CSIRT Based on Business Intelligence. Systems, 5.10.3390/systems5040052 ↗
30. Automating ETL processes using the domain-specific modeling approach10.1007/s10257-016-0325-8 ↗
31. Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R.H., Konwinski, A., Lee, G., Patterson, D.A., Rabkin, A., and Zaharia, M. (2009). Above the Clouds: A Berkeley View of Cloud Computing, Electrical Engineering and Computer Sciences University of California.10.1145/1721654.1721672 ↗
32. Buyya, R. (2009, January 19–21). Market-Oriented Cloud Computing: Vision, Hype, and Reality of Delivering Computing as the 5th Utility. Proceedings of the 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, Shanghai, China.10.1109/CCGRID.2009.97 ↗
33. A break in the cloud: Towards a cloud definition10.1145/1496091.1496100 ↗
34. McKinsey and Co (2009). Clearing the Air on Cloud Computing, McKinsey and Co.
35. Jamsa, D.K. (2012). Cloud Computing: SaaS, PaaS, IaaS, Virtualization, Business Models, Mobile, Security and More, Jones & Bartlett Learning. [1st ed.].
36. Mell, P., and Grance, T. (2008). Perspectives on Cloud Computing and Standards, National Institute of Standards and Technology.
37. Luo, Y. (2010). A Solution of Manufacturing Resources Sharing in Cloud Computing Environment. Proceedings of the Cooperative Design, Visualization, and Engineering, Hangzhou, China, 21–24 October 2018, Springer.10.1007/978-3-030-00560-3 ↗
38. Trakadas, P., Nomikos, N., Michailidis, E.T., Zahariadis, T., Facca, F.M., Breitgand, D., Rizou, S., Masip, X., and Gkonis, P. (2019). Hybrid Clouds for Data-Intensive, 5G-Enabled IoT Applications: An Overview, Key Issues and Relevant Architecture. Sensors, 19.10.3390/s19163591 ↗
39. Zavoral, P. (2019, November 11). ICT Revue. Available online: //ictrevue.ihned.cz/.
40. Cyber threats confronting the digital built environment: Common data environment vulnerabilities and block chain deterrence10.1108/ECAM-03-2018-0101 ↗
41. Managing RFID Sensors Networks with a General Purpose RFID Middleware10.3390/s120607719 ↗
42. Aghenta, L.O., and Iqbal, M.T. (2019). Low-Cost, Open Source IoT-Based SCADA System Design Using Thinger.IO and ESP32 Thing. Electronics, 8.10.3390/electronics8080822 ↗
43. Wang, C., Chen, X., Soliman, A.-H.A., and Zhu, Z. (2018). RFID Based Manufacturing Process of Cloud MES. Future Internet, 10.10.3390/fi10110104 ↗
44. Industry 4.0 Concept: Background and Overview10.3991/ijim.v11i5.7072 ↗
45. Digital Object Memory Based Monitoring Solutions in Manufacturing Processes10.1016/j.proeng.2014.03.011 ↗
46. Context Related Information Provision in Industry 4.0 Environments10.1016/j.promfg.2017.07.181 ↗
47. The Readiness of ERP Systems for the Factory of the Future10.1016/j.procs.2015.08.598 ↗
48. Schlank, R. (1982). Dynamic Memory: A Theory of Reminding and Learning in Computers and People, Cambridge University Press.
49. Systematic literature review of machine learning based software development effort estimation models10.1016/j.infsof.2011.09.002 ↗
50. Enhanced e-Learning Experience using Case based Reasoning Methodology
51. Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines10.1016/j.jneumeth.2015.01.022 ↗
52. Sun, X., Chen, C., Wang, L., Kang, H., Shen, Y., and Chen, Q. (2019). Hybrid Optimization Algorithm for Bayesian Network Structure Learning. Information, 10.10.3390/info10100294 ↗
53. Comparison of partial least square regression, support vector machine, and deep-learning techniques for estimating soil salinity from hyperspectral data10.1117/1.JRS.12.022204 ↗
54. Yu, J., Sun, W., Huang, H., Wang, W., Wang, Y., and Hu, Y. (2019). Crack Sensitivity Control of Nickel-Based Laser Coating Based on Genetic Algorithm and Neural Network. Coatings, 9.10.3390/coatings9110728 ↗
55. López, R., González Gurrola, L., Trujillo, L., Prieto, O., Ramírez, G., Posada, A., Juárez-Smith, P., and Méndez, L. (2018). How Am I Driving? Using Genetic Programming to Generate Scoring Functions for Urban Driving Behavior. Math. Comput. Appl., 23.10.3390/mca23020019 ↗
56. Forecasting International Tourism Demand Using a Non-Linear Autoregressive Neural Network and Genetic Programming10.3390/forecast1010007 ↗
57. Seismic vulnerability assessment using association rule learning: Application to the city of Constantine, Algeria10.1007/s11069-016-2739-5 ↗
58. Constrained dynamic rule induction learning10.1016/j.eswa.2016.06.041 ↗
59. Teaching–learning based optimization algorithm based fuzzy-PID controller for automatic generation control of multi-area power system10.1016/j.asoc.2014.11.027 ↗
60. Advanced design-driven approaches for an Industry 4.0 framework: The human-centred dimension of the digital industrial revolution
61. The rise of the robot worker10.1109/MSPEC.2012.6309254 ↗
62. QB Robotics (2019, November 08). Saphari—Safe and Autonomous Physical Human-Aware Robot Interaction. Available online: https://qbrobotics.com/projects/saphari-safe-autonomous-physical-human-aware-robot-interaction/.
63. A Skill-based Robot Co-worker for Industrial Maintenance Tasks10.1016/j.promfg.2017.07.141 ↗
64. Industry 4.0: A review on industrial automation and robotic
65. Using artificial neural networks for the prediction of dimensional error on inclined surfaces manufactured by ball-end milling10.1007/s00170-015-7543-y ↗
66. Meng, Z., Wu, Z., and Gray, J. (2017). A Collaboration-Oriented M2M Messaging Mechanism for the Collaborative Automation between Machines in Future Industrial Networks. Sensors, 17.10.3390/s17112694 ↗
67. Chen, M. (2012). Machine-to-Machine Communications: Architectures, Standards and Applications. KSII Trans. Internet Inf. Syst.10.3837/tiis.2012.02.002 ↗
68. Ali, A., Shah, G.A., and Arshad, J. (2019). Energy Efficient Resource Allocation for M2M Devices in 5G. Sensors, 19.10.3390/s19081830 ↗
69. Hacia una ingraestructura de fabricación flexible, conectada e integrable en redes 5G
70. Cloud computing for ubiquitous computing on M2M and IoT environment mobile application10.1007/s10586-016-0573-x ↗
71. Pilloni, V. (2018). How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0. Future Internet, 10.10.3390/fi10030024 ↗
72. Veber, J. (2016). Management Inovací, Management Press.
73. Burian, P. (2014). Internet Inteligentních Aktivit, Grada.
74. Supply chain collaboration: Impact on collaborative advantage and firm performance10.1016/j.jom.2010.12.008 ↗
75. Digital twin-based process reuse and evaluation approach for smart process planning10.1007/s00170-018-2748-5 ↗
76. Forecast Information Sharing for Managing Supply Chains in the Big Data Era: Recent Development and Future Research10.1142/S0217595917400012 ↗
77. Quick response in fashion supply chains with dual information updating10.3934/jimo.2006.2.255 ↗
78. Willingness to share information in a supply chain: A partnership-data-process perspective10.1016/j.im.2011.10.003 ↗
79. Contracting to Assure Supply: How to Share Demand Forecasts in a Supply Chain10.1287/mnsc.47.5.629.10486 ↗
80. Imapct of pos data sharing on supply chain management: An experimental study10.1111/j.1937-5956.2003.tb00194.x ↗
81. The Supply Chain Becomes the Demand Chain10.1111/jbl.12037 ↗
82. Demand chain management-integrating marketing and supply chain management10.1016/j.indmarman.2005.10.003 ↗
83. Product lifecycle data sharing and visualisation: Web-based approaches10.1007/s00170-008-1503-8 ↗
84. An Internet-enabled integrated system for co-design and concurrent engineering10.1016/j.compind.2003.10.010 ↗
85. A cooperative agent modelling approach for process planning10.1016/S0166-3615(99)00012-3 ↗
86. Design and implementation of a collaborative engineering information system for allied concurrent engineering10.1080/095119200130027 ↗
87. A cyber physical system (CPS) approach for safe human-robot collaboration in a shared workplace10.1016/j.rcim.2018.10.003 ↗
88. Self-cleaning surfaces—Virtual realities10.1038/nmat856 ↗
89. Vogel-Heuser, B., Bauernhansl, T., and ten Hompel, M. (2016). Sichere Industrie-4.0-Plattformen auf Basis von Community-Clouds. Handbuch Industrie 4.0: Produktion, Automatisierung und Logistik, Springer.10.1007/978-3-662-53254-6 ↗
90. Burdea, C.G., and Coiffet, P. (2003). Virtual Reality Technology, John Wiley & Sons.10.1162/105474603322955950 ↗
91. Defining virtual reality—dimensions determining telepresence10.1111/j.1460-2466.1992.tb00812.x ↗
92. Virtual reality software package for implementing motor learning and rehabilitation experiments10.1007/s10055-017-0323-2 ↗
93. Yin, X., Fan, X., Yang, X., Qiu, S., and Zhang, Z. (2019). An Automatic Marker—Object Offset Calibration Method for Precise 3D Augmented Reality Registration in Industrial Applications. Appl. Sci., 9.10.3390/app9204464 ↗
94. Jurášková, O. (2012). Velký Slovník Marketingových Komunikací, Grada Publishing Inc.
95. Toward a Digital Twin for real-time geometry assurance in individualized production10.1016/j.cirp.2017.04.038 ↗
96. About the Importance of Autonomy and Digital Twins for the Future of Manufacturing10.1016/j.ifacol.2015.06.141 ↗
97. Digital Twins the Convergence of Multimedia Technologies10.1109/MMUL.2018.023121167 ↗
98. Digital twins10.1177/2399808318796416 ↗
99. Pilot production environments driven by digital twins
100. Digital twin-based smart production management and control framework for the complex product assembly shop-floor10.1007/s00170-018-1617-6 ↗