A Fuzzy Based Risk Evaluation Model for Industry 4.0 Transition Process

  • Murat ColakEmail author
  • Ihsan Kaya
  • Melike Erdogan
Conference paper
Part of the Lecture Notes in Management and Industrial Engineering book series (LNMIE)


The concept of industry 4.0 is a critical topic that has been addressed by many studies recently as well as the business community. However, there are not many studies on the risk assessment of industry 4.0 transition process. In this paper, it is aimed to identify the risks that companies may face in the industry 4.0 transition process and to suggest a methodology for prioritization of these risks. We applied to expert opinions to address all numerical and verbal factors and used a fuzzy multicriteria decision-making (MCDM) methodology in order to determine the most and the least critical risks. For this aim, hesitant fuzzy sets (HFSs) and interval type-2 fuzzy sets (IT2FSs) have been utilized together to obtain the best results that are closer to the reality. Finally, risks have been prioritized for companies in the transition process to Industry 4.0.


Hesitant fuzzy sets Industry 4.0 Multi-criteria decision-making Risk management Type-2 fuzzy 


  1. Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20, 87–96.MathSciNetCrossRefGoogle Scholar
  2. Çevik Onar, S., Öztayşi, B., & Kahraman, C. (2014). Strategic decision selection using hesitant fuzzy TOPSIS and interval type-2 fuzzy AHP: A case study. International Journal of Computational Intelligence Systems, 7, 1002–1021.CrossRefGoogle Scholar
  3. Chen, S. M., & Lee, L. W. (2010). Fuzzy multi attributes group decision-making based on the interval type-2 TOPSIS method. Expert Systems with Applications, 37, 2790–2798.CrossRefGoogle Scholar
  4. Çolak, M., & Kaya, İ. (2017). Prioritization of renewable energy alternatives by using an integrated fuzzy MCDM model: A real case application for Turkey. Renewable and Sustainable Energy Reviews, 80, 840–853.CrossRefGoogle Scholar
  5. Erdoğan, M., & Kaya, İ. (2016). A combined fuzzy approach to determine the best region for a nuclear power plant in Turkey. Applied Soft Computing, 39, 84–93.CrossRefGoogle Scholar
  6. Giannetti, C., & Ransing, R. S. (2016). Risk based uncertainty quantification to improve robustness of manufacturing operations. Computers & Industrial Engineering, 101, 70–80.CrossRefGoogle Scholar
  7. Kahraman, C., Öztayşi, B., Uçal Sarı, İ., & Turanoğlu, E. (2014). Fuzzy analytic hierarchy process with interval type-2 fuzzy sets. Knowledge-Based Systems, 59, 48–57.CrossRefGoogle Scholar
  8. Kılıç, M., & Kaya, İ. (2015). Investment project evaluation by a decision making methodology based on type-2 fuzzy sets. Applied Soft Computing, 27, 399–410.CrossRefGoogle Scholar
  9. Liao, H., & Xu, Z. (2013). A VIKOR-based method for hesitant fuzzy multi-criteria decision making. Fuzzy Optimization and Decision Making, 12, 373–392.MathSciNetCrossRefGoogle Scholar
  10. Long, F., Zeiler, P., & Bertsche, B. (2017). Modelling the flexibility of production systems in industry 4.0 for analysing their productivity and availability with high-level Petri nets. IFAC-PapersOnLine, 50(1), 5680–5687.CrossRefGoogle Scholar
  11. Macurová, P., Ludvik, L., & Žwaková, M. (2017). The driving factors, risks and barriers of the industry 4.0 concept. Journal of Applied Economic Sciences, 12(7), 2003–2011.Google Scholar
  12. Mendel, J. M., John, R. I., & Liu, F. (2006). Interval type-2 fuzzy logic systems made simple. IEEE Transactions on Fuzzy Systems, 14, 808–821.CrossRefGoogle Scholar
  13. Niesen, T., Houy, C., Fettke, P., & Loos, P. (2016). Towards an integrative big data analysis framework for data-driven risk management in industry 4.0. In 49th Hawaii International Conference on System Sciences (HICSS), Koloa (pp. 5065–5074), January 5–8.Google Scholar
  14. Papa, M., Kaselautzke, D., Radinger, T., & Stuja, K. (2017). Development of a safety industry 4.0 production environment. Annals of DAAAM & Proceedings, 28, 981–987.CrossRefGoogle Scholar
  15. Pereira, T., Barreto, L., & Amaral, A. (2017). Network and information security challenges within industry 4.0 paradigm. Procedia Manufacturing, 13, 1253–1260.CrossRefGoogle Scholar
  16. Preuveneers, D., Joosen, W., & Ilie-Zudor, E. (2017). Identity management for cyber-physical production workflows and individualized manufacturing in industry 4.0. In Proceedings of the 32nd Annual ACM Symposium on Applied Computing, Marrakesh (pp. 1452–1455), April 4–6.Google Scholar
  17. Rajnai, Z., & Kocsis, I. (2017). Labor market risks of industry 4.0, digitization, robots and AI. In IEEE 15th International Symposium on Intelligent Systems and Informatics, Subotica (pp. 000343–000346), September 14–16.Google Scholar
  18. Riel, A., & Flatscher, M. (2017). A design process approach to strategic production planning for industry 4.0. In European Conference on Software Process Improvement (pp. 323–333). Cham: Springer.Google Scholar
  19. Saturno, M., Ramos, L. F. P., Polato, F., Deschamps, F., & Loures, E. F. R. L. (2017). Evaluation of interoperability between automation systems using multi-criteria methods. Procedia Manufacturing, 11, 1837–1845.CrossRefGoogle Scholar
  20. 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), 1515–1532.CrossRefGoogle Scholar
  21. Švingerová, M., & Melichar, M. (2017). Evaluation of process risks in industry 4.0 environment. Annals of DAAAM & Proceedings, 28, 1021–1029.CrossRefGoogle Scholar
  22. Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25, 529–539.zbMATHGoogle Scholar
  23. Tupa, J., Simota, J., & Steiner, F. (2017). Aspects of risk management implementation for industry 4.0. Procedia Manufacturing, 11, 1223–1230.CrossRefGoogle Scholar
  24. Veza, I., Mladineo, M., & Gjeldum, N. (2015). Managing innovative production network of smart factories. IFAC-PapersOnLine, 48(3), 555–560.CrossRefGoogle Scholar
  25. Xia, M., & Xu, Z. (2011). Hesitant fuzzy information aggregation in decision making. International Journal of Approximate Reasoning, 52, 395–407.MathSciNetCrossRefGoogle Scholar
  26. Xu, Z., & Xia, M. (2011). Distance and similarity measures for hesitant fuzzy sets. Information Sciences, 181, 2128–2138.MathSciNetCrossRefGoogle Scholar
  27. Zhang, N., & Wei, G. (2013). Extension of VIKOR method for decision making problem based on hesitant fuzzy set. Applied Mathematical Modelling, 37, 4938–4947.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Kocaeli UniversityKocaeliTurkey
  2. 2.Yildiz Technical UniversityIstanbulTurkey
  3. 3.Duzce UniversityDuzceTurkey

Personalised recommendations