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Behavioral Analysis of Human-Machine Interaction in the Context of Demand Planning Decisions

  • Tim LauerEmail author
  • Rebecca Welsch
  • S. Ramlah Abbas
  • Michael Henke
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 965)

Abstract

The trend of digitalization has led to disruptive changes in production and supply chain planning, where autonomous machines and artificial intelligence gain competitive advantages. Besides, the satisfaction of customers’ wishes has reached top priority for demand-driven companies. Consequently, companies implement digital applications, for instance neural networks for accurate demand forecasting and optimized decision-making tools, to cope with nervous operational planning activities. Since planning tasks require human-machine interaction to increase performance and efficiency of planning decisions, this analysis focuses on forms of interaction to determine the right level of collaboration. The paper outlines various levels of interaction and analyses the impact of human reactions in the context of an industrial demand planning algorithm use case at Infineon Technologies AG conducting a behavioral experiment. The results show that a variance in the levels of human-machine interaction has influence on human acceptance of algorithms, but further experiments need to be conducted to outline an overall framework.

Keywords

Behavioral analysis Human-machine collaboration Demand planning Digitalization Supply chain planning 

References

  1. 1.
    Chapman, S., Ettkin, L., Helms, M.: Supply chain forecasting – collaborative forecasting supports supply chain management. Bus. Process Manage. J. 6, 392–407 (2000)CrossRefGoogle Scholar
  2. 2.
    Goodwin, P., Önkal, D., Thomson, M.: Do forecasts expressed as prediction intervals improve production planning decisions? Eur. J. Oper. Res. 205, 195–201 (2010)CrossRefGoogle Scholar
  3. 3.
    Holden, K., Peel, D., Thompson, J.: Economic Forecasting. Cambridge University Press, Cambridge (1991)CrossRefGoogle Scholar
  4. 4.
    O’Connor, M., Webby, R.: Judgmental and statistical time series forecasting - a review of the literature. Int. J. Forecast. 12, 91–118 (1996)CrossRefGoogle Scholar
  5. 5.
    Anderson, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Makridakis, S., Newton, J., Parzen, E., Winkler, R.: The accuracy of extrapolation (time series) methods - results of a forecasting competition. J. Forecast. 1, 111–153 (1982)CrossRefGoogle Scholar
  6. 6.
    Clemen, R.: Combining forecasts: a review and annotated bibliography. Int. J. Forecast. 5, 559–583 (1989)CrossRefGoogle Scholar
  7. 7.
    Armstrong, J.S., Collopy, F.: Integration of statistical methods and judgment for time series forecasting - principles from empirical research. In: Wright, G., Good-win, P. (eds.) Forecasting with Judgment, pp. 269–293. Wiley, New York (1998)Google Scholar
  8. 8.
    Armstrong, S., Collopy, F.: Rule-based forecasting: development and validation of an expert systems approach to combining time series extrapolations. Manage. Sci. 38, 1394–1414 (1992)CrossRefGoogle Scholar
  9. 9.
    Allen, G., Fildes, R.: Econometric forecasting. In: Armstrong, J.S. (ed.) Principles of Forecasting - A Handbook for Researchers and Practitioners. Springer, Norwell (2001)Google Scholar
  10. 10.
    Edmundson, R., Lawrence, M., O’Connor, M.: The accuracy of combining judgmental and statistical forecasts. Manage. Sci. 32, 1521–1532 (1986)CrossRefGoogle Scholar
  11. 11.
    Bunn, D., Wright, G.: Interaction of judgmental and statistical forecasting methods - issues & analysis. Manage. Sci. 37, 501–518 (1991)CrossRefGoogle Scholar
  12. 12.
    Leitner, J., Leopold-Wildburger, U.: Experiments on forecasting behavior with several sources of information – a review of the literature. Eur. J. Oper. Res. 213, 459–469 (2011)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Goodwin, P., Lawrence, M., O’Connor, M., Önkal, D.: Judgmental forecasting: a review of progress over the last 25 years. Int. J. Forecast. 22, 493–518 (2006)CrossRefGoogle Scholar
  14. 14.
    Fildes, R., Goodwin, P., Lawrence, M.: The design features of forecasting support systems and their effectiveness. Decis. Support Syst. 42, 351–361 (2006)CrossRefGoogle Scholar
  15. 15.
    Edmundson, R., Lawrence, M., O’Connor, M.: The accuracy of combining judgemental and statistical forecasts. Manage. Sci. 32, 1521–1532 (1986)CrossRefGoogle Scholar
  16. 16.
    Sheridan, T., Verplank, W.: Human and Computer Control of Undersea Teleoperators. MIT Man-Machine Systems Laboratory, Cambridge (1978)CrossRefGoogle Scholar
  17. 17.
    Parasuraman, R., Sheridan, T., Wickens, C.: A model for types and levels of human interaction with automation. IEEE Trans. Syst. Man Cybern. - Part A: Syst. Hum. 30, 286–297 (2000)CrossRefGoogle Scholar
  18. 18.
    Johannsen, G.: Human-Machine Interaction. Control Systems, Robotics, and Automation. Encyclopedia of Life Support Systems (EOLSS). EOLSS Publishers, Oxford (2007)Google Scholar
  19. 19.
    Zheng, N., Liu, Z., Ren, P., Ma, Y., Chen, S., Yu, S., Xue, J., Chen, Ba., Wang, F.: Hybrid-augmented intelligence: collaboration and cognition. Front. Inf. Tech. Electron. Eng. 18, 153–179 (2017)CrossRefGoogle Scholar
  20. 20.
    Johnson, M., Bradshaw, J.M., Feltovich, P.J.: Tomorrow’s human-machine design tools: from levels of automation to interdependencies. J. Cogn. Eng. Decis. Making 12, 77–82 (2017)CrossRefGoogle Scholar
  21. 21.
    Bonaccio, S., Dalal, R.: What types of advice do decision-makers prefer? Organ. Behav. Hum. Decis. Process. 112, 11–23 (2010)CrossRefGoogle Scholar
  22. 22.
    Fischer, I., Harvey, N.: Taking advice - accepting help, improving judgment, and sharing responsibility. Organ. Behav. Hum. Decis. Process. 70, 117–133 (1997)CrossRefGoogle Scholar
  23. 23.
    Kahneman, D., Tversky, A.: Judgment under uncertainty - heuristics and biases. Science 185, 1124–1131 (1974)CrossRefGoogle Scholar
  24. 24.
    Kleinberger, E., Yaniv, H.: Advice taking in decision making - egocentric discounting and reputation formation. Organ. Behav. Hum. Decis. Process. 83, 260–281 (2000)CrossRefGoogle Scholar
  25. 25.
    Fischer, I., Harvey, N.: Taking advice - accepting help, improving judgment, and sharing responsibility. Organ. Behav. Hum. Decis. Process. 70, 117–133 (1997)CrossRefGoogle Scholar
  26. 26.
    Önkal, D., Goodwin, P., Thomson, M., Gönül, S., Pollock, A.: The relative influence of advice from human experts and statistical methods on forecast adjustments. J. Behav. Decis. Making 22(4), 390–409 (2009)CrossRefGoogle Scholar
  27. 27.
    Dietvorst, B., Massey, C., Simmons, J.: Algorithm aversion - people erroneously avoid algorithms after seeing them err. J. Exp. Psychol. Gen. 144, 114–126 (2015)CrossRefGoogle Scholar
  28. 28.
    Bonaccio, S., Dalal, R.: Advice taking and decision-making - an integrative literature review, and implications for the organizational sciences. Organ. Behav. Hum. Decis. Process. 101, 127–151 (2006)CrossRefGoogle Scholar
  29. 29.
    Dijkstra, J.: User agreement with incorrect expert system advice. Behav. Inform. Technol. 18, 399–411 (1999)CrossRefGoogle Scholar
  30. 30.
    Logg, J.: Theory of machine - when do people reply on algorithms? Hav. Bus. Sch. 17-086, 1–92 (2017)Google Scholar
  31. 31.
    Madhavan, P., Wiegmann, D.: Effects of information source, pedigree, and reliability on operator interaction with decision support systems. Hum. Factors 49, 773–785 (2007)CrossRefGoogle Scholar
  32. 32.
    Kleinberger, E., Yaniv, H.: Advice taking in decision making - egocentric discounting and reputation formation. Organ. Behav. Hum. Decis. Process. 83, 260–281 (2000)CrossRefGoogle Scholar
  33. 33.
    Dietvorst, B., Massey, C., Simmons, J.: Overcoming algorithm aversion - people will use imperfect algorithms if they can (even slightly) modify them. Manage. Sci. 64, 1–17 (2016)CrossRefGoogle Scholar
  34. 34.
    Griggs, K., O’Conner, M., Remus, W.: Does feedback improve the accuracy of recurrent judgmental forecasts? Organ. Behav. Hum. Decis. Process. 66, 22–30 (1996)CrossRefGoogle Scholar
  35. 35.
    Fildes, R., Goodwin, P.: Judgmental forecasts of time series affected by special events - does providing a statistical forecast improve accuracy? J. Behav. Decis. Making 12, 37–53 (1983)Google Scholar
  36. 36.
    Schiller, C., Yachi, G.: Introduction to Demand Planning. Supply Chain Academy, Infineon Technologies AG, Munich (2017)Google Scholar
  37. 37.
    Schiller, C., Yachi, G.: Production Program, Demand Planning, Target Stock Entry in SPLUI. Supply Chain Academy, Infineon Technologies AG, Munich (2014)Google Scholar
  38. 38.
    Andersen, A., Carbone, R., Corriveau, Y., Corson, P.: Comparing for different time series methods the value of technical expertise individualized analysis, and judgmental adjustment. Manage. Sci. 29, 559–566 (1983)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Tim Lauer
    • 1
    • 2
    Email author
  • Rebecca Welsch
    • 1
  • S. Ramlah Abbas
    • 1
  • Michael Henke
    • 2
  1. 1.Infineon TechnologiesNeubibergGermany
  2. 2.Technical University DortmundDortmundGermany

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