Factors Influencing the Selection of Human Capital Compatible with the Fourth Generation Industrial Revolution Using Fuzzy DEMATEL Method
Subject Areas : New theories and models of human resource training and development
mohammad amin aeiny
1
(1. PhD Student, Department of Management, Faculty of Economics and Administrative Sciences, Lorestan University, Khorramabad, Iran. )
Keywords: Fourth Generation Industry, Fuzzy Dimatel, Knowledge-Based Companies.,
Abstract :
The present study has a three-dimensional purpose, which is: First, to identify new criteria for selecting employees in the fourth generation industrial environment. Second; Prioritize and identify causal relationships between these criteria. Third, contribute to the operations management literature by focusing on the recruitment process and supporting human resource activities according to industry-related criteria of the fourth generation. The type of research is applied and descriptive-causal research method and has been done in knowledge-based companies with advanced technology. The statistical population consisted of 5 experts from the relevant companies who were responsible for making changes in order to adapt to the fourth generation industrial revolution in their company. The fuzzy DEMATEL approach has been used to conduct the research. Based on the findings, eleven critical factors influencing the selection of labor in the fourth generation industrial environment were identified, the most important of which, in order of priority, include; Ability to deal with complexity and problem solving, thinking about overlapping processes, and flexibility to adapt to new plans and work environments. In addition, the results indicate the existence of causal factors in descending order, including: information technology and technology production, awareness of information technology security and information protection, the ability to resolve work crises and how to combine relevant knowledge and use it in a job or They are a special process. Outcome factors also include; Flexibility to adapt to new plans and work environments, high analytics and perceptual skills, ability to communicate with new intermediaries, ability to deal with complexity and problem solving, thinking about overlapping processes, continuous interdisciplinary learning and collaboration, and confidence in new technologies and readiness for change Are.
1. Arntz, M., Gregory T., & Zierahn, U. (2016). The risk of automation for jobs in OECD countries: A
2. Brettel, M., Friederichen, N., Keller, M. and Rosenberg, M. (2014), “How virtualization, decentralization and network building change the manufacturing landscape: an Industry 4 Perspective”, International Journal of Mechanical, Industrial Science and Engineering, Vol. 8 No. 1, pp. 37-44.
3. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: work, progress, and prosperity in a time of brilliant technologies. New York: W. W. Norton & Company.
4. Chang, B., Chang, C-W., Wu, H. (2011), Fuzzy DEMATEL method for developing supplier selection criteria. Expert Systems with Applications, 38: 1850-1858.
5. Dijkman, R.M., Sprenkels, B., Peeters, T. and Janssen, A. (2015), “Business models for the internet of things”, International Journal of Information Management, Vol. 35 No. 6, pp. 672-678.
6. Dombrowski, U. and Wagner, T. (2014), “Mental strain as field of action in the 4th industrial revolution”, Procedia CIRP, Vol. 17, pp. 100-105.
7. Ford, M. (2015). Rise of the robots technology and the threat of a jobless future. New York: Basic Books.
8. Frey, C. B., & Osborne, M. A. (2013). The future of employment: How susceptible are jobs to computerization? Oxford Martin School. Oxford, UK.
9. Gehrke, L., Kühn, A.T., Rule, D., Moore, P., Bellmann, C., Siemes, S. and Standley, M. (2015), “A discussion of qualifications and skills in the factory of the future: a German and American perspective”, VDI/ASME Industry, Vol. 4, pp.1-28.
10. Hirschi, A. (2017). The Fourth Industrial Revolution: Issues and Implications for Career Research and Practice. Career Development Quarterly.
11. Kazancoglu,Yigit., Ozkan-Ozen, Yesim Deniz., (2018) "Analyzing Workforce 4.0 in the Fourth Industrial Revolution and proposing a road map from operations management perspective with fuzzy DEMATEL", Journal of Enterprise Information Management, https://doi.org/10.1108/ JEIM-01-2017-0015.
12. Kolberg, D. and Zühlke. D. (2015), “Lean automation enabled by industry 4 technologies”, IFACPapersOnline, Vol. 48 No. 3, pp. 1870-1875.
13. Lasi, H., Fettke, P., Kemper, H.G., Feld, T. and Hoffmann, M. (2014), “Industry 4”, Business & Information Systems Engineering, Vol. 6 No. 4, pp. 239.
14. Lorenz, M., Rüssmann, M., Strack, R., Lasse Lueth, K. and Bolle, M. (2015), Man and Machine in Industry 4: How Will Technology Transform the Industrial Workforce Through 2025? Consulting Group, Boston, MA, September, available at: http://imagesrc.bcg.com/Images/BCG_Man_and_Machine_in_Industry_4_0_Sep_ 015_tcm9-61676.
15. Lee, E.A. (2008), Cyber Physical Systems: Design Challenges, IEEE, Berkley, CA.
16. Lee, J., Bagheri, B. and Kao, H.A. (2015), “A cyber-physical systems architecture for industry 4-based manufacturing systems”, Manufacturing Letters, Vol. 3, pp. 18-23.
17. Lin, C.J., Wu, W.W. (2008). A causal analytical method for group decision-making under Fuzzy Environment. Expert Systems with Applications, 34 (1): 205-213.
18. Mosterman, P.J. and Zander, J. (2016), “Industry 4 as a cyber-physical system study”, Software & Systems Modeling, Vol. 15 No. 1, pp. 17-29.
19. Opricovic, S. and Tzeng, G.-H. (2003), “Defuzzification within a multicriteria decision model”, International Journal of Uncertaintry, Fuziness and Knowledge-Based Systems, Vol. 11 No. 5, pp. 635-652.
20. Romero, D., Bernus, P., Noran, O., Stahre, J. and Fast-Berglund, Å. (2016), “The Operator 4: Human Cyber-Physical Systems & Adaptive Automation towards Human-Automation Symbiosis Work Systems”, IFIP International Conference on Advances in Production Management Systems, Springer, Cham, pp. 677-686.
21. Schuh, G., Potente, T., Wesch-Potente, C., Weber, A.R. and Prote, J. (2014), “Collaboration mechanisms to increase productivity in the context of industrie 4”, Procedia CIRP, Vol. 19, pp. 51-56.
22. Shieh J.I, Wu H.H, Huang K.K. (2010) A dematel method in identifying key success factors of hospital service quality.Knowledge-Based Systems; 23: 277–282.
23. Shafiei rodpashty, Meysam,. Emami, Seyed Morteza,. Malekshahi, Fatemeh (2013) Cardiovascular Diseases Using Fuzzy dematel Technique. Magazine Financial Engineering and Securities Management, issue number sixteen. (in Persian)
24. Schwab, K. (2016). The fourth industrial revolution. New York: Crown Business.
25. Toro, C., Barandiaran, I., and Posada, J. (2015), “A perspective on Knowledge Based and Intelligent Systems Implementation in Industrie 4”, Procedia Computer Science, Vol. 60, pp. 362-370.
26. Qin, J., Liu, Y. and Grosvenor, R. (2016), “A categorical framework of manufacturing for industry 4 and beyond”, Procedia CIRP, Vol. 52, pp. 173-178.
27. Wu, W.W. and Lee, Y.T. (2007), “Developing global managers’ competencies using the fuzzy dematel method”, Expert Systems with Applications, Vol. 32 No. 2, pp. 499-507.
28. Yang, J.L. and Tzeng, G.H. (2011), “An integrated MCDM technique combined with dematel for a novel cluster-weighted with ANP method”, Expert Systems with Applications, Vol. 38 No. 3, pp. 1417-1424.
