Addressing Uncertainties in Complex Manufacturing Environments: A Multidisciplinary Approach

  • Hitesh DhimanEmail author
  • Daniela Plewe
  • Carsten Röcker
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 793)


With the introduction of intelligent and autonomous systems into factory environments, workplaces where human employees work alongside digital counterparts will become increasingly informational. We develop a generic framework for hypothetical workplaces to investigate how complexities create to uncertainties. Complexity may be explained through the Level of Abstractions used to model a system, and it is encountered in its dynamic form as an alteration of information flow between agents in a phenomenological relationship. Analyzing these systems as ‘information flows’ brings to light the uncertainity(ies) the workers of the future will have to cope with. We develop first concepts that can be used to develop heuristics to manage these uncertainties in complex manufacturing environments. These heuristics may also be useful in creating optimized workplaces that combine the individual abilities of both humans and machines. The framework proposed in this paper may be subject for an empirical validation of these heuristics in the future.


Uncertainties Complexity Human-machine interaction 


  1. 1.
    Broy, M. (ed.): Cyber-Physical Systems. Innovation Durch Software-Intensive Eingebettete Systeme. Springer, Heidelberg (2010)Google Scholar
  2. 2.
    Monostori, L., Váncza, J., Kumara, S.: Agent-based systems for manufacturing. CIRP Ann. 55(2), 697–720 (2006)CrossRefGoogle Scholar
  3. 3.
    Simondon, G., Malaspina, C., Rogove, J.: On the Mode of Existence of Technical Objects. Univocal Publishing, Minneapolis (2017)Google Scholar
  4. 4.
    Müller, R., Vette, M., Hörauf, L., Speicher, C., Jatti, K.: Concept and implementation of an agent-based control architecture for a cyber-physical assembly system. In: Proceedings of the 3rd International Conference on Control, Mechatronics and Automation (ICCMA 2015) (2016)Google Scholar
  5. 5.
    Li, K., Wieringa, P.A.: Understanding perceived complexity in human supervisory control. Cogn. Technol. Work 2(2), 75–88 (2000)CrossRefGoogle Scholar
  6. 6.
    Park, J.: The Complexity of Proceduralized Tasks. Springer, LondonGoogle Scholar
  7. 7.
    Pekrun, R., Vogl, E., Muis, K.R., Sinatra, G.M.: Measuring emotions during epistemic activities: the epistemically-related emotion scales. Cogn. Emotion 31(6), 1268–1276 (2016)CrossRefGoogle Scholar
  8. 8.
    Schutz, A.: Reflections on the Problem of Relevance. Yale University Press, New Haven (1970)Google Scholar
  9. 9.
    Ihde, D.: Technology and the Lifeworld: From Garden to Earth. Indiana University Press, Bloomington (1996)Google Scholar
  10. 10.
    Verbeek, P.-P., Crease, R.P.: What Things Do: Philosophical Reflections on Technology, Agency, and Design. Pennsylvania State University Press, University Park (2005)Google Scholar
  11. 11.
    Huber, G.P., Daft, R.L.: The information environments of organizations. In: Jablin, F.M., Putnam, L.L., Roberts, K.H., Porter, L.W. (eds.) Handbook of Organizational Communication: An Interdisciplinary Perspective, pp. 130–164. Sage Publications, Thousand Oaks (1987)Google Scholar
  12. 12.
    Bedford, T., Cooke, R.: What is uncertainty? In: Probabilistic Risk Analysis: Foundations and Methods, pp. 17–38. Cambridge University Press, Cambridge (2001)Google Scholar
  13. 13.
    Xing, J., Manning, C.: Complexity and Automation Displays of Air Traffic Control: Literature Review and Analysis. Federal Aviation Administration, Civil Aeromedical Institute, Oklahoma City, OK (2005)Google Scholar
  14. 14.
    Federal Ministry of Labour and Social Affairs: White Paper Work 4.0: Re-Imagining Work. Federal Ministry of Labour and Social Affairs, Directorate-General for Basic Issues of the Social State, the Working World and the Social Market Economy, Berlin, Germany (2017)Google Scholar
  15. 15.
    Floridi, L.: The Philosophy of Information. Oxford University Press, Oxford (2011)Google Scholar
  16. 16.
    Floridi, L.: Is semantic information meaningful data? Phil. Phenomenol. Res. 70(2), 351–370 (2005)CrossRefGoogle Scholar
  17. 17.
    Floridi, L.: The method of levels of abstraction. Minds Mach. 18(3), 303–329 (2008)CrossRefGoogle Scholar
  18. 18.
    Floridi, L.: The logic of design as a conceptual logic of information. Minds Mach. 27(3), 495–519 (2017)CrossRefGoogle Scholar
  19. 19.
    Devlin, K.J.: Logic and Information. Cambridge University Press, Cambridge (1997)Google Scholar
  20. 20.
    Hinton, A.: Understanding Context: Environment, Language, and Information Architecture. O’Reilly, Sebastopol (1991)Google Scholar
  21. 21.
    Edmonds, B.: What is complexity? — the philosophy of complexity per se with application to some examples in evolution. In: Heylighen, F., Aerts, D. (eds.) The Evolution of Complexity, pp. 1–18. Kluwer, Dordrecht (1999)Google Scholar
  22. 22.
    Deshmukh, A.V., Talavage, J.J., Barash, M.M.: Complexity in manufacturing systems, Part 1: analysis of static complexity. IIE Trans. 30(7), 645–655 (1998)Google Scholar
  23. 23.
    Elmaraghy, W., Urbanic, R.: Modelling of manufacturing systems complexity. CIRP Ann. 52(1), 363–366 (2003)CrossRefGoogle Scholar
  24. 24.
    Saracevic, T.: The Notion of Relevance in Information Science: Everybody Knows What Relevance is, but, What is it Really?. Morgan & Claypool, San Rafael (2017)Google Scholar
  25. 25.
    Büttner, S., Wunderlich, P., Niggemann, O., Röcker, C.: Managing complexity: towards intelligent error-handling assistance trough interactive alarm flood reduction. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction, pp. 69–82. Springer, Heidelberg (2017)CrossRefGoogle Scholar
  26. 26.
    Robert, S., Büttner, S., Röcker, C., Holzinger, A.: Reasoning under uncertainty: towards collaborative interactive machine learning. In: Holzinger, A. (ed.) Machine Learning for Health Informatics: State-of-the-Art and Future Challenges, pp. 357–376. Springer, Heidelberg, Germany (2016)CrossRefGoogle Scholar
  27. 27.
    Fellmann, M., Robert, S., Büttner, S., Mucha, H., Röcker, C.: Towards a framework for assistance systems to support work processes in smart factories. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) Machine Learning and Knowledge Extraction, pp. 59–68. Springer, Heidelberg (2017)Google Scholar
  28. 28.
    Röcker, C.: Socially dependent interaction in smart spaces: how the social situation influences the interaction style in computer-enhanced environments. In: Proceedings of the International IEEE Conference on Mechanical and Electrical Technology (ICMET 2010), pp. 314–318 (2010)Google Scholar
  29. 29.
    Büttner, S., Sand, O., Röcker, C.: Exploring design opportunities for intelligent worker assistance: a new approach using projection-based AR and a novel hand-tracking algorithm. In: Braun, A., Wichert, R., Maña, A. (eds.) Ambient Intelligence, pp. 33–45. Springer, Heidelberg (2017)CrossRefGoogle Scholar
  30. 30.
    Paelke, V., Röcker, C.: User Interfaces for Cyber-Physical Systems: Challenges and Possible Approaches. In: Marcus, A. (ed.) Design, user experience, and usability: design discourse, pp. 75–85. Springer International Publishing, Switzerland (2015)CrossRefGoogle Scholar
  31. 31.
    Bainbridge, L.: Ironies of automation. Automatica 19(6), 775–779 (1983). Scholar
  32. 32.
    Suchman, L.A.: Plans and Situated Actions: The Problem of Human-Machine Communication. Cambridge University Press, Cambridge (1999)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Hitesh Dhiman
    • 1
    Email author
  • Daniela Plewe
    • 1
    • 2
  • Carsten Röcker
    • 2
  1. 1.Ostwestfalen-Lippe University of Applied SciencesLemgoGermany
  2. 2.Fraunhofer IOSB-INALemgoGermany

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