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Operator 4.0 and Cognitive Ergonomics

  • Mohamad Fallaha
  • Zeki Murat CinarEmail author
  • Orhan Korhan
  • Qasim Zeeshan
Conference paper
  • 28 Downloads
Part of the Lecture Notes in Management and Industrial Engineering book series (LNMIE)

Abstract

Industry 4.0 requires a paradigm shift from the traditional manufacturing practices and work environment to a dynamic workplace where humans and machines must work together as a human cyber-physical system for increased productivity and flexibility. It necessitates novel interactions between operators and machines, consequently leading to the transformation of a traditional operator to Operator 4.0 or Smart Operator. Improvement in operators and engineers’ cognitive skills is imminent to adapt to Industry 4.0 working environment. Wearable technology, sensors, or virtual reality equipment enhances cognitive capabilities of the Operator 4.0. Thus cognitive skills of the smart operators are required more rather than the physical strength. This paper presents a review of the recent developments in cognitive ergonomics of Smart Operator or Operator 4.0 in the context of Industry 4.0.

Keywords

Industry 4.0 Operator 4.0 Cognitive Ergonomics Human intelligence Cyber-Physical Systems (CPS) Human factors Artificial Intelligence (AI) 

References

  1. Badri A, Boudreau-Trudel B, Souissi AS (2018) Occupational health and safety in the Industry 4.0 era: a cause for major concern? Saf Sci 109:403–411CrossRefGoogle Scholar
  2. Belkadi F, Dhuieb MA, Aguado JV, Laroche F, Bernard A, Chinesta F (2019) Intelligent assistant system as a context-aware decision-making support for the workers of the future. Comput Ind Eng.  https://doi.org/10.1016/j.cie.2019.02.046CrossRefGoogle Scholar
  3. Benešová A, Tupa J (2017) Requirements for education and qualification of people in Industry 4.0. Procedia Manuf 11:2195–2202CrossRefGoogle Scholar
  4. Birtel M, Mohr F, Hermann J, Bertram P, Ruskowski M (2018) Requirements for a human-centered condition monitoring in modular production environments. IFAC-PapersOnLine 51(11):909–914CrossRefGoogle Scholar
  5. Bligård L-O, Osvalder A-L (2014) CCPE: methodology for a combined evaluation of cognitive and physical ergonomics in the interaction between human and machine. Hum Factors Ergon Manuf Serv Ind 24(6):685–711.  https://doi.org/10.1002/hfm.20512CrossRefGoogle Scholar
  6. Dröder K, Bobka P, Germann T, Gabriel F, Dietrich F (2018) A machine learning-enhanced digital twin approach for human-robot-collaboration. Procedia CIRP 76:187–192CrossRefGoogle Scholar
  7. Jakobs E-M, Digmayer C, Vogelsang S, Servos M (2017) Not ready for Industry 4.0: usability of CAx systems. Paper presented at the international conference on applied human factors and ergonomicsGoogle Scholar
  8. Kaasinen E, Liinasuo M, Schmalfuß F, Koskinen H, Aromaa S, Heikkilä P, Honka A, Mach S, Malm T (2018) A worker-centric design and evaluation framework for Operator 4.0 solutions that support work well-being. Paper presented at the IFIP working conference on human work interaction designGoogle Scholar
  9. Kim IJ (2016) Cognitive ergonomics and its role for industry safety enhancements. J Ergon 6(4):01–17.  https://doi.org/10.4172/2165-7556.1000e158CrossRefGoogle Scholar
  10. Kinzel H (2017) Industry 4.0–where does this leave the human factor? J Urban Culture Res 15:70–83Google Scholar
  11. Kopka B, Żytniewski M (2014) The system ergonomics and usability as measurement of the software agent impact to the organization. In: Proceedings of advances in ergonomics in design, usability and special populations, pp 21–34Google Scholar
  12. Lödding H, Riedel R, Thoben K-D, Von Cieminski G, Kiritsis D (2017) Advances in production management systems. The path to intelligent, collaborative and sustainable manufacturing: IFIP WG 5.7 international conference, APMS 2017, Hamburg, Germany, September 3–7, 2017, Proceedings, vol 514. Springer, HeidelbergCrossRefGoogle Scholar
  13. Malik AA, Bilberg A (2018) Digital twins of human robot collaboration in a production setting. Procedia Manuf 17:278–285CrossRefGoogle Scholar
  14. Mehta RK (2016) Integrating physical and cognitive ergonomics. IIE Trans Occup Ergon Hum Factors 4(2–3):83–87.  https://doi.org/10.1080/21577323.2016.1207475CrossRefGoogle Scholar
  15. Moray N, Groeger J, Stanton N (2017) Quantitative modelling in cognitive ergonomics: predicting signals passed at danger. Ergonomics 60(2):206–220.  https://doi.org/10.1080/00140139.2016.1159735CrossRefGoogle Scholar
  16. Nelles J, Kuz S, Mertens A, Schlick CM (2016) Human-centered design of assistance systems for production planning and control: the role of the human in Industry 4.0. Paper presented at the 2016 IEEE international conference on industrial technology (ICIT)Google Scholar
  17. Rabelo RJ, Romero D, Zambiasi SP (2018) Softbots supporting the Operator 4.0 at smart factory environments, vol 536, pp 456–464.  https://doi.org/10.1007/978-3-319-99707-0_57CrossRefGoogle Scholar
  18. Rauch E, Linder C, Dallasega P (2019) Anthropocentric perspective of production before and within Industry 4.0. Comput Ind Eng.  https://doi.org/10.1016/j.cie.2019.01.018CrossRefGoogle Scholar
  19. Romero D, Stahre J, Wuest T, Noran O, Bernus P, Fast-Berglund Å, Gorecky D (2016) Towards an Operator 4.0 typology: a human-centric perspective on the fourth industrial revolution technologies. Paper presented at the proceedings of the international conference on computers and industrial engineering (CIE46), Tianjin, ChinaGoogle Scholar
  20. Romero D, Wuest T, Stahre J, Gorecky D (2017) Social factory architecture: social networking services and production scenarios through the social internet of things, services and people for the social Operator 4.0. Paper presented at the IFIP international conference on advances in production management systemsCrossRefGoogle Scholar
  21. Ruppert T, Jaskó S, Holczinger T, Abonyi J (2018) Enabling technologies for Operator 4.0: a survey. Appl Sci 8(9):1650.  https://doi.org/10.3390/app8091650CrossRefGoogle Scholar
  22. Segura Á, Diez HV, Barandiaran I, Arbelaiz A, Álvarez H, Simões B, Posada J, García-Alonso A, Ugarte R (2018) Visual computing technologies to support the Operator 4.0. Comput Ind Eng.  https://doi.org/10.1016/j.cie.2018.11.060CrossRefGoogle Scholar
  23. Stadnicka D, Litwin P, Antonelli D (2019) Human factor in intelligent manufacturing systems - knowledge acquisition and motivation. Procedia CIRP 79:718–723.  https://doi.org/10.1016/j.procir.2019.02.023CrossRefGoogle Scholar
  24. Valdeza AC, Braunera P, Schaara AK, Holzingerb A, Zieflea M (2015) Reducing complexity with simplicity-usability methods for Industry 4.0. Paper presented at the proceedings 19th triennial congress of the IEAGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mohamad Fallaha
    • 1
  • Zeki Murat Cinar
    • 1
    • 2
    Email author
  • Orhan Korhan
    • 1
  • Qasim Zeeshan
    • 1
    • 2
  1. 1.Industrial Engineering DepartmentEastern Mediterranean University (EMU)FamagustaTurkey
  2. 2.Mechanical Engineering DepartmentEastern Mediterranean University (EMU)FamagustaTurkey

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