Human Interaction Under Risk in Cyber-Physical Production Systems
The emergence of cyber-physical production systems poses new challenges for designing the interface between production systems and the human-in-the-loop. In this study, we investigate how human operators interact with risks in a supply chain scenario. We varied the financial magnitude and the expected value of the decisions, the combination of two types of risk (risk in delivery amount and risk in timeliness), as well as three different task displays as within-subject factors. As explanatory user factors we studied the influence of Need for Achievement and the Attitude towards Risk-taking on the dependent variables task speed and accuracy. Results of the user study with 33 participants show that each of the investigated factors either influences decision speed, decision accuracy, or both. Consequently, the human-in-the-loop profits from adequate decision support systems that help to increase decision efficiency and effectiveness and reduce uncertainty and workload. The article concludes with a research agenda to support the human-in-the-loop in production systems.
KeywordsDecision support systems Cyber-physical production systems Human factors Decision under risk Socio-technical system Risk
The work is funded by the German Research Foundation (DFG) as part of the German Excellence Initiative and the Cluster of Excellence “Integrative Production Technology for High Wage Countries” (EXC 128). We thank all participants for their willingness to support our study and Lara Mhetawi, Katharina Merkel, Fabian Comanns, and Wiktoria Wilkowska for research support.
- 1.Evans PC, Annunziata M (2012) Industrial internet: pushing the boundaries of minds and machines. Technical report, General ElectricGoogle Scholar
- 2.Bruner J (2013) Industrial internet—the machines are talking. Reilly Media Inc., NewtonGoogle Scholar
- 4.Schlick C, Stich V, Schmitt R, Schuh G, Ziefle M, Brecher C, Blum M, Mertens A, Faber M, Kuz S, Petruck H, Fuhrmann M, Luckert M, Brambring F, Reuter C, Hering N, Groten M, Korall S, Pause D, Brauner P, Herfs W, Odenbusch M, Wein S, Stiller S, Berthold M (2017) Cognition-enhanced, self-optimizing production networks. In: Brecher C, Özdemir D (eds) Integrative production technology—theory and applications. Springer, Berlin, pp 645–743CrossRefGoogle Scholar
- 7.Knight FH (1921) Risk, uncertainty and profit. Houghton Mifflin Co., BostonGoogle Scholar
- 11.Brauner P, Ziefle M (2016) Game-based learning in manufacturing and business. In: 6th international conference on competitive manufacturing (COMA 2016)Google Scholar
- 12.Beierlein C, Kovaleva A, Kemper CJ, Rammstedt B (2014) Eine Single-Item-Skala zur Erfassung von Risikobereitschaft: Die Kurzsskala Risikobereitschaft-1 (R-1) [A Single Item scale for measuring risk-taking attitude]Google Scholar
- 13.Schuler H, Prochaska M (2001) Leistungsmotivationsinventar [Need for achievement inventory]. Hogrefe, GöttingenGoogle Scholar
- 14.Baxter G, Rooksby J, Wang Y, Khajeh-Hosseini A (2012) The ironies of automation—still going strong at 30? In: Proceedings of ECCE 2012 conference, 29th–31st August, Edinburgh, pp 65–71Google Scholar
- 15.Brauner P, Valdez AC, Philipsen R, Ziefle M (2016) On studying human factors in complex cyber-physical systems. In: Workshop human factors in information visualization and decision support systems held as part of the mensch und computer 2016, Gesellschaft für InformatikGoogle Scholar