Automation Configuration Evaluation in Adaptive Assembly Systems Based on Worker Satisfaction and Costs

  • Peter BurggräfEmail author
  • Matthias Dannapfel
  • Tobias Adlon
  • Aaron Riegauf
  • Jessica Schmied
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 959)


Nowadays requirements and challenges for production systems have led to an increasing importance of flexible assembly processes. Arising technologies in the context of Industry 4.0 provide previously unknown possibilities for human-oriented automation. To exploit those possibilities effectively, the research project A4BLUE aims to develop and evaluate sustainable and adaptive workplaces that are human-centered and best automated for dynamic environments. As part of the research project, a methodology is developed to determine the optimal automation configuration in relation to context and situation. The present work provides an overview of the methodological approach, implemented as a software prototype within the A4BLUE Adaptive Framework. The methodology takes into account the aspects of worker satisfaction as well as economic factors by adding a systematic, process-related logic to validated approaches. In the present paper, the cost-based module of the methodology is outlined based on process characteristics and manufacturing targets of assembly systems.


Assembly Production planning Adaptive automation Cost evaluation Level of automation Worker satisfaction 



The research work has been conducted by the Laboratory of Machine Tools and Production Engineering (WZL) and the chair of Production Engineering of E-Mobility Components (PEM) at RWTH Aachen University within the research project A4BLUE, funded by the European Commission within the HORIZON 2020 program; Grant Agreement No. 723 828.


  1. 1.
    Kern, W., Rusitschka, F., Kopytynski, W., Keckl, S., Bauernhansl, T.: Alternatives to automobile assembly line production in the automotive industry. In: The 23rd International Conference on Production Research (2015)Google Scholar
  2. 2.
    Wagner, W.: Fabrikplanung für die standortübergreifende Kostensenkung bei marktnaher Produktion. Utz, München (2006)Google Scholar
  3. 3.
    Chryssolouris, G.: Manufacturing Systems: Theory and practice. Springer, New York (2006)Google Scholar
  4. 4.
    Frohm, J., Lindström, V., Winroth, M., Stahre, J.: The industry’s view on automation in manufacturing. IFAC Proc. Vol. 39(4), 453–458 (2006)CrossRefGoogle Scholar
  5. 5.
    Endsley, M.: Level of automation: integrating humans and automated systems. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 41, pp. 200–204 (1997)CrossRefGoogle Scholar
  6. 6.
    Parasuraman, R., Sheridan, T.B., Wickens, C.D.: A model for types and levels of human interaction with automation. IEEE Trans. Syst. Man Cybern. - Part A: Syst. Hum. 30(3), 286–297 (2000)CrossRefGoogle Scholar
  7. 7.
    Fasth, A.: Quantifying levels of automation: to enable competitive assembly systems. Dissertation, Chalmers University of Technology (2012)Google Scholar
  8. 8.
    Bakotić, D.: Relationship between job satisfaction and organisational performance. Econ. Res.-Ekonomska Istraživanja 29(1), 118–130 (2015)CrossRefGoogle Scholar
  9. 9.
    Gorlach, I., Wessel, O.: Optimal level of automation in the automotive industry. Eng. Lett. 16, 141–149 (2008)Google Scholar
  10. 10.
    Boothroyd, G., Dewhurst, P., Knight, W.A.: Product Design for Manufacture and Assembly. CRC Press, Hoboken (2011)Google Scholar
  11. 11.
    Lotter, B.: Wirtschaftliche Montage: Handbuch für Elektrogerätebau und Feinwerktechnik. VDI-Verlag, Düsseldorf (1986)Google Scholar
  12. 12.
    Ross, P.: Bestimmung des wirtschaftlichen Automatisierungsgrades von Montageprozessen in der frühen Phase der Montageplanung. Dissertation, Technische Universität München (2002)Google Scholar
  13. 13.
    Co, H.C., Eddy Patuwo, B., Hu, M.Y.: The human factor in advanced manufacturing technology adoption. Int. J. Oper. Prod. Manag. 18(1), 87–106 (1998)CrossRefGoogle Scholar
  14. 14.
    Owusu-Ansah, C.M., Mprah, R.K.: The impact of library automation on the job satisfaction of library staff. Eur. J. Bus. Soc. Sci. 3(9), 100–113 (2014)Google Scholar
  15. 15.
    Friedrichs, J.: Methoden empirischer Sozialforschung. VS Verlag für Sozialwissenschaften, Opladen (1990)CrossRefGoogle Scholar
  16. 16.
    Nachreiner, F.: Grundlagen naturwissenschaftlicher Methodik in der Arbeitswissenschaft. In: Volpert, W., Müller, T. (eds.): Handbuch Arbeitswissenschaft, pp. 82–87. Schäffer-Poeschel, Stuttgart (1997)Google Scholar
  17. 17.
    Diekmann, A.: Empirische Sozialforschung: Grundlagen, Methoden, Anwendungen. Rowohlt, Reinbek bei Hamburg (1995)Google Scholar
  18. 18.
    Geyer, G.: Entwicklung problemspezifischer Verfahrensketten in der Montage. C. Hanser, München (1991)Google Scholar
  19. 19.
    Annett, J.: Hierarchical Task Analysis (HTA). In: Stanton, N., Hedge, A., Brookhuis, K., Salas, E., Hendick, H. (eds.) Handbook of Human Factors and Ergonomics Methods. CRC Press, Boca Raton (2005). 33-1-33-7Google Scholar
  20. 20.
    Frohm, J., Lindström, V., Winroth, M., Stahre, J.: Levels of automation in manufacturing. Egonomia – Int. J. Ergonomics Hum. Factors 30(3), 28 (2008)Google Scholar
  21. 21.
    Frohm, J.: Levels of automation in production systems. Chalmers University of Technology, Göteborg (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Peter Burggräf
    • 1
    • 2
    Email author
  • Matthias Dannapfel
    • 1
  • Tobias Adlon
    • 1
  • Aaron Riegauf
    • 3
  • Jessica Schmied
    • 3
  1. 1.Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen UniversityAachenGermany
  2. 2.Chair of International Production Engineering and Management of University of SiegenSiegenGermany
  3. 3.Chair of Production Engineering of E-Mobility Components (PEM) of RWTH Aachen UniversityAachenGermany

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