Decision Support Systems in the Context of Cyber-Physical Systems

Influencing Factors and Challenges for the Adoption in Production Scheduling

  • Pascal Freier University of Goettingen
  • Matthias Schumann University of Goettingen
Keywords: Production scheduling, Cyber-physical systems, Industry 4.0, Challenges, Decision Support System


Cyber-physical systems promise a complete networking of all actors and resources involved in production and thus an improved availability of information. In this context decision support systems enable appropriate processing and presentation of the captured data. In particular, production scheduling could benefit from this, since it is responsible for the short-term planning and control of released orders. Since decision support systems and cyber-physical systems together are not yet widely used in production scheduling, the aim of this research study is to analyse the adoption of these technologies. In order to do so, we conducted a qualitative interview study with experts on production scheduling. Thereby, we identified eleven influencing factors and 22 related challenges, which affect the adoption of decision support systems in production scheduling in the context of cyber-physical systems. We further discuss and assess the identified influencing factors based on the interview study. The results help to explain and improve the adoption of those systems and can serve as a starting point for their development.


Angeles, R. (2013). Using the Technology-Organization-Environment Framework and Zuboff’s Concepts for Understanding Environmental Sustainability and RFID: Two Case Studies. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 7(11), 2878–2887.

Arnott, D., & Pervan, G. (2005). A critical analysis of decision support systems research. Journal of Information Technology, 20(2), 67–87.

Freier, P., & Schumann, M. (2020). Design and Implementation of a Decision Support System for Production Scheduling in the Context of Cyber-Physical Systems. In N. Gronau, M. Heine, K. Poustcchi, & H. Krasnova (Eds.), WI2020 Zentrale Tracks (pp. 757–773). GITO Verlag.

Baker, J. (2012). The Technology–Organization–Environment Framework. In Integrated Series in Information Systems. Information Systems Theory (Vol. 28, pp. 231–245). New York, NY.

Cupek, R., Ziebinski, A., Huczala, L., & Erdogan, H. (2016). Agent-based manufacturing execution systems for short-series production scheduling. Computers in Industry, 82, 245–258.

Dafflon, B., Moalla, N., & Ouzrout, Y. (2018). Cyber-Physical Systems network to support decision making for self-adaptive production system. In 2018 12th International Conference on Software, Knowledge, Information Management & Applications (SKIMA), Phnom Penh, Cambodia.

Doolin, B., & Al Haj Ali, E. (2008). Adoption of Mobile Technology in the Supply Chain: An Exploratory Cross-Case Analysis. International Journal of E-Business Research, 4(4), 1–15.

Eckerson, W. W. (2010). Performance dashboards: Measuring, monitoring, and managing your business (Second edition). Finance professional collection. Hoboken, NJ: Wiley.

Frontoni, E., Loncarski, J., Pierdicca, R., Bernardini, M., & Sasso, M. (2018). Cyber Physical Systems for Industry 4.0: Towards Real Time Virtual Reality in Smart Manufacturing. In L. T. de Paolis & P. Bourdot (Eds.), Lecture Notes in Computer Science. Augmented Reality, Virtual Reality, and Computer Graphics (Vol. 10851, pp. 422–434). Cham: Springer.

Glaser, B. G. (1978). Theoretical sensitivity: Advances in the methodology of grounded theory. Mill Valley, CA: Sociology Press.

Gorry, G. A., & Scott Morton, M. S. (1971). A framework for management information systems. Sloan Management Review, 13(1), 55–70.

Jiang, Z., Jin, Y., E, M., & Li, Q. (2018). Distributed Dynamic Scheduling for Cyber-Physical Production Systems Based on a Multi-Agent System. IEEE Access, 6, 1855–1869.

Karner, M., Glawar, R., Sihn, W., & Matyas, K. (2019). An industry-oriented approach for machine condition-based production scheduling. Procedia CIRP, 81, 938–943.

Krumeich, J., Jacobi, S., Werth, D., & Loos, P. (2014). Big Data Analytics for Predictive Manufacturing Control - A Case Study from Process Industry. In 2014 IEEE International Congress on Big Data (BigData Congress), Anchorage, AK, USA.

Krumeich, J., Werth, D., Loos, P., Schimmelpfennig, J., & Jacobi, S. (2014). Advanced planning and control of manufacturing processes in steel industry through big data analytics: Case study and architecture proposal. In 2014 IEEE International Conference on Big Data (Big Data), Washington, DC, USA.

Lee, E. A. (2008). Cyber Physical Systems: Design Challenges. In 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing, Orlando, FL, USA.

Lee, J. (2015). Smart Factory Systems. Informatik-Spektrum, 38(3), 230–235.

Mayring, P. (2014). Qualitative content analysis: Theoretical foundation, basic procedures and software solution. Klagenfurt: Beltz.

Myers, M. D. (2013). Qualitative research in business & management (2. ed.). London: Sage.

Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems, 24(3), 45–77.

Pinedo, M. L. (2009). Planning and Scheduling in Manufacturing and Services. New York, NY: Springer New York.

Sabuncuoglu, I., & Goren, S. (2009). Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research. International Journal of Computer Integrated Manufacturing, 22(2), 138–157.

Schneeweiß, C. (1999). Einführung in die Produktionswirtschaft (Siebte, neubearbeitete Auflage). Springer-Lehrbuch. Berlin, Heidelberg: Springer. Retrieved from

Schreiber, M., Vernickel, K., Richter, C., & Reinhart, G. (2019). Integrated production and maintenance planning in cyber-physical production systems. Procedia CIRP, 79, 534–539.

Schuh, G., & Fuß, C. (Eds.) (2015). ProSense: Ergebnisbericht des BMBF-Verbundprojektes; hochauflösende Produktionssteuerung auf Basis kybernetischer Unterstützungssysteme und intelligenter Sensorik (1. Aufl.). Aachen: Apprimus.

Schuh, G., Potente, T., Fuchs, S., Thomas, C., Schmitz, S., Hausberg, C., Brambring, F. (2013). Self-Optimizing Decision-Making in Production Control. In Lecture Notes in Production Engineering. Robust Manufacturing Control (Vol. 54, pp. 443–454). Berlin, Heidelberg: Springer.

Schuh, G., Potente, T., Thomas, C., & Hauptvogel, A. (2014). Steigerung der Kollaborationsproduktivität durch cyber-physische Systeme. In Industrie 4.0 in Produktion, Automatisierung und Logistik (pp. 277–295). Wiesbaden: Springer.

Schuh, G., Potente, T., Thomas, C., & Hempel, T. (2014). Short-term Cyber-physical Production Management. Procedia CIRP, 25, 154–160.

Shim, J. P., Warkentin, M., Courtney, J. F., Power, D. J., Sharda, R., & Carlsson, C. (2002). Past, present, and future of decision support technology. Decision Support Systems, 33(2), 111–126.

Sprague, R. H., & Carlson, E. D. (1982). Building effective decision support systems. Englewood Cliffs, NJ: Prentice-Hall.

Suh, E. E., Kagan, S., & Strumpf, N. (2009). Cultural competence in qualitative interview methods with Asian immigrants. Journal of Transcultural Nursing: Official Journal of the Transcultural Nursing Society, 20(2), 194–201.

Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Issues in organization and management series. Lexington, Mass.: Lexington Books.

Vieira, G. E., Herrmann, J. W., & Lin, E. (2003). Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods. Journal of Scheduling, 6(1), 39–62.

How to Cite
Freier, P., & Schumann, M. (2021). Decision Support Systems in the Context of Cyber-Physical Systems: Influencing Factors and Challenges for the Adoption in Production Scheduling. Australasian Journal of Information Systems, 25.
Selected Papers from the Australasian Conference on Information Systems (ACIS)