Defective Still Deflective – How Correctness of Decision Support Systems Influences User’s Performance in Production Environments
The increasing dynamic and complexity of todays global supply chains and the growing amount and complexity of information challenge decision makers in manufacturing companies. Decision Support Systems (DSS) can be a viable solution to address these challenges and increase the overall decision efficiency and effectivity. However, a thought-through design and implementation of these systems is crucial for their efficacy.
This article presents the current state-of-the-art of Decisions Support Systems and highlights their benefits and pitfalls. Also, we present an empirical study in which we compared different levels of decision support and decision automation in a simulated supply chain game environment.
We identify and quantify how human factors influence the decision quality and decision performance in this supply chain scenario. We show that an adequately designed system raises the overall performance. However, insufficiently designed systems have the reverse effect and lead operators to miss severe situations, which can have fatal consequences for manufacturing companies.
KeywordsDecision support systems Automation Enterprise resource planning Supply chain management Business simulation game Human factors Industrial Internet Industry 4.0 Integrative Production Technology Usability
We thank all participants of this strenuous experiment and Frederic Speicher and Anaïs Habermann for their support. The German Research Foundation (DFG) founded this project within the Cluster of Excellence “Integrative Production Technology for High-Wage Countries” (EXC 128).
- 2.Shim, J.P., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R., Carlsson, C.: Past, present, and future of decision support technology (2002)Google Scholar
- 3.Gorry, G.A., Morton, M.S.S.: A framework for management information systems. Sloan Manage. Rev. 13, 50–70 (1971)Google Scholar
- 4.Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modelling. Wiley, New York (1996)Google Scholar
- 5.Codd, E., Codd, S., Salley, C.: Providing OLAP to user-analysts: an IT mandate (1993)Google Scholar
- 6.Bra, A., Lungu, I.: Improving decision support systems with data mining techniques. In: Advances in Data Mining Knowledge Discovery and Applications. InTech (2012)Google Scholar
- 8.Tomaszewski, W.: Computer-based medical decision support system based on guidelines, clinical pathways and decision nodes. Acta Bioeng. Biomech. 14(1), 107–116 (2012)Google Scholar
- 16.Mittelstädt, V., Brauner, P., Blum, M., Ziefle, M.: On the visual design of ERP systems – the role of information complexity, presentation and human factors. In: 6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015, pp. 270–277 (2015)Google Scholar
- 19.Ekstrom, R.B., French, J.W., Harman, H.H., Dermen, D.: Kit of Factor-Referenced Cognitive Tests. Educational Testing Service, Princeton (1976)Google Scholar
- 20.Fisk, A.D., Rogers, W.A.: Handbook of Human Factors and the Older Adult. Academic Press, Cambridge (1997)Google Scholar
- 21.Forrester, J.W.: Industrial Dynamics. MIT Press, Cambridge (1961)Google Scholar
- 22.Philipsen, R., Brauner, P., Stiller, S., Ziefle, M., Schmitt, R.: Understanding and supporting decision makers in quality management of production networks. In: Advances in the Ergonomics in Manufacturing. Managing the Enterprise of the Future 2014: Proceedings of the 5th International Conference on Applied Human Factors and Ergonomics, AHFE 2014, pp. 94–105. CRC Press, Boca Raton (2014)Google Scholar
- 23.Philipp, B., Ralf, P., Martina, Z.: Projecting efficacy and use of business simulation games in the production domain using technology acceptance models. In: Proceedings of the Applied Human Factors and Ergonomics Conference, AHFE 2016 (2016, in press)Google Scholar