English
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@Select Types{,
Journal = "Band-1",
Title= "Design and Implementation of a Decision Support System for Production Scheduling in the Context of Cyber- Physical Systems",
Author= "Pascal Freier, Matthias Schumann",
Doi= "https://doi.org/10.30844/wi_2020_g5-freier",
Abstract= "The use of cyber-physical systems in production promises great potential for production scheduling since a larger information base is available for the scheduling of production orders. However, the mere acquisition of realtime data does not inherently lead to improvements. On the contrary, a targeted preparation of the data is required in order to prevent an information overload. Decision support systems that support decision makers in production scheduling can perform this task. However, the design of such systems in combination with cyber-physical systems has hardly been investigated so far. In this paper, we therefore design and implement a corresponding decision support system in a design science approach. For this, we identify meta-requirements based on a literature analysis and an interview study. Finally, we evaluate the created meta-artifact in a laboratory setting in order to obtain generalizable knowledge about building such a decision support system.",
Keywords= "decision support system, production scheduling, cyber-physical systems, industry 4.0, design science research
",
}
Pascal Freier, Matthias Schumann: Design and Implementation of a Decision Support System for Production Scheduling in the Context of Cyber- Physical Systems. Online: https://doi.org/10.30844/wi_2020_g5-freier (Abgerufen 27.11.24)
Open Access
Abstract
Abstract
The use of cyber-physical systems in production promises great potential for production scheduling since a larger information base is available for the scheduling of production orders. However, the mere acquisition of realtime data does not inherently lead to improvements. On the contrary, a targeted preparation of the data is required in order to prevent an information overload. Decision support systems that support decision makers in production scheduling can perform this task. However, the design of such systems in combination with cyber-physical systems has hardly been investigated so far. In this paper, we therefore design and implement a corresponding decision support system in a design science approach. For this, we identify meta-requirements based on a literature analysis and an interview study. Finally, we evaluate the created meta-artifact in a laboratory setting in order to obtain generalizable knowledge about building such a decision support system.
Keywords
Schlüsselwörter
decision support system, production scheduling, cyber-physical systems, industry 4.0, design science research
References
Referenzen
1. Schuh, G., Potente, T., Thomas, C., Hempel, T.: Short-term Cyber-physical Production Management. Procedia CIRP 25, 154–160 (2014)
2. Cupek, R., Ziebinski, A., Huczala, L., Erdogan, H.: Agent-based manufacturing execution systems for short-series production scheduling. Computers in Industry 82, 245–258 (2016)
3. Gregor, S., Hevner, A.R.: Positioning and Presenting Design Science Research for Maximum Impact. MIS Q 37, 337–355 (2013)
4. Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S.: A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems 24, 45–77 (2007)
5. Schneeweiß, C.: Einführung in die Produktionswirtschaft. Springer, Berlin, Heidelberg (1999)
6. Sabuncuoglu, I., Goren, S.: Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research. International Journal of Computer Integrated Manufacturing 22, 138–157 (2009)
7. Shim, J.P., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R., Carlsson, C.: Past, present, and future of decision support technology. Decision Support Systems 33, 111–126 (2002)
8. Arnott, D., Pervan, G.: A critical analysis of decision support systems research. Journal of Information Technology 20, 67–87 (2005)
9. Sprague, R.H., Carlson, E.D.: Building effective decision support systems. Englewood Cliffs, N.J. (1982)
10. Eckerson, W.W.: Performance dashboards. Measuring, monitoring, and managing your business. Wiley, Hoboken, NJ (2010)
11. Lee, E.A.: Cyber Physical Systems: Design Challenges. In: 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC), pp. 363–369. IEEE (2008)
12. Siepmann, D., Graef, N.: Industrie 4.0 – Grundlagen und Gesamtzusammenhang. In: Roth, A. (ed.) Einführung und Umsetzung von Industrie 4.0, pp. 17–82. Springer, Berlin, Heidelberg (2016)
13. Lee, J.: Smart Factory Systems. Informatik Spektrum 38, 230–235 (2015)
14. Krumeich, J., Jacobi, S., Werth, D., Loos, P.: Big Data Analytics for Predictive Manufacturing Control – A Case Study from Process Industry. In: IEEE International Congress on Big Data, pp. 530–537. IEEE (2014)
15. Mieth, C., Meyer, A., Henke, M.: Framework for the usage of data from real-time indoor localization systems to derive inputs for manufacturing simulation. Procedia CIRP 81, 868–873 (2019)
16. Karner, M., Glawar, R., Sihn, W., Matyas, K.: An industry-oriented approach for machine condition-based production scheduling. Procedia CIRP 81, 938–943 (2019)
17. Jiang, Z., Jin, Y., E, M., Li, Q.: Distributed Dynamic Scheduling for Cyber-Physical Production Systems Based on a Multi-Agent System. IEEE Access 6, 1855–1869 (2018)
18. Dafflon, B., Moalla, N., Ouzrout, Y.: Cyber-Physical Systems network to support decision making for self-adaptive production system. In: 12th IEEE International Conference on Software, Knowledge, Information Management & Applications (SKIMA), pp. 1–8. IEEE (2018)
19. Krumeich, J., Werth, D., Loos, P., Schimmelpfennig, J., Jacobi, S.: Advanced planning and control of manufacturing processes in steel industry through big data analytics: Case study and architecture proposal. In: IEEE International Conference on Big Data (Big Data), pp. 16–24. IEEE (2014)
20. Schuh, G., Potente, T., Fuchs, S., Thomas, C., Schmitz, S., Hausberg, C., Hauptvogel, A., Brambring, F.: Self-Optimizing Decision-Making in Production Control. In: Robust Manufacturing Control, 54, pp. 443–454. Berlin, Heidelberg (2013)
21. Schuh, G., Potente, T., Thomas, C., Hauptvogel, A.: Steigerung der Kollaborationsproduktivität durch cyber-physische Systeme. In: Industrie 4.0 in Produktion, Automatisierung und Logistik, pp. 277–295. Wiesbaden (2014)
22. Schuh, G., Fuß, C. (eds.): ProSense: Ergebnisbericht des BMBF-Verbundprojektes. Hochauflösende Produktionssteuerung auf Basis kybernetischer Unterstützungssysteme und intelligenter Sensorik. Apprimus, Aachen (2015)
23. Schreiber, M., Vernickel, K., Richter, C., Reinhart, G.: Integrated production and maintenance planning in cyber-physical production systems. Procedia CIRP 79, 534–539 (2019)
24. Webster, J., Watson, R.T.: Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS Q 26, xiii–xxiii (2002)
25. Fettke, P.: State-of-the-Art des State-of-the-Art. WIRTSCHAFTSINFORMATIK 48, 257– 266 (2006)
26. Myers, M.D.: Qualitative research in business & management. London (2013)
27. Mayring, P.: Qualitative content analysis: theoretical foundation, basic procedures and software solution. Klagenfurt (2014)
28. Döring, N., Bortz, J.: Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften. Springer Berlin Heidelberg, Berlin, Heidelberg (2016)
29. Wilde, T., Hess, T.: Forschungsmethoden der Wirtschaftsinformatik. WIRTSCHAFTSINFORMATIK 49, 280–287 (2007)
30. Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science 35, 982–1003 (1989)
31. Abele, E., Anderl, R., Metternich, J., Wank, A., Anokhin, O., Arndt, A., Meudt, T., Sauer, M.: Effiziente Fabrik 4.0: Einzug von Industrie 4.0 in bestehende Produktionssysteme. Zeitschrift für wirtschaftlichen Fabrikbetrieb 110, 150–153 (2015)
32. Kletti, J.: Das MES der Zukunft. Productivity Management 18, 17–20 (2013)
33. Gronau, N.: Der Einfluss von Cyber-Physical Systems auf die Gestaltung von Produktionssystemen. Industrie Management 31, 16–20 (2015)
34. Kropp, S., Schuh, G.: Event-Driven Production Control based on Sensor Events. In: European Conference on Smart Objects, Systems and Technologies (Smart SysTech), pp. 1–9 (2014)
35. Lanza, G., Stricker, N., Moser, R.: Concept of an intelligent production control for global manufacturing in dynamic environments based on rescheduling. In: IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 315–319 (2013)
36. Niggemann, O., Biswas, G., Kinnebrew, J.S., Khorasgani, H., Volgmann, S., Bunte, A.: Datenanalyse in der intelligenten Fabrik. In: Vogel-Heuser, B., Bauernhansl, T., Hompel, M. ten (eds.) Handbuch Industrie 4.0 Bd.2, 75, pp. 471–490. Springer, Berlin, Heidelberg (2017)
37. Bergmann, S., Fiedler, A., Straßburger, S.: Generierung und Integration von Simulationsmodellen unter Verwendung des Core Manufacturing Simulation Data (CMSD) Information Model. In: Integrationsaspekte der Simulation. Technik, Organisation und Personal, pp. 461–468 (2010)
38. Berger, C., Berlak, J., Reinhart, G.: Service-based Production planning and Control of Cyber-Physical Production Systems. In: 29th Bled eConference. Digital Economy, pp. 491–502 (2016)
39. Stark, R., Kim, M., Damerau, T., Neumeyer, S., Vorsatz, T.: Notwendige Voraussetzungen für die Realisierung von Industrie 4.0: Ein Beitrag aus der Sicht der industriellen Informationstechnik. Zeitschrift für wirtschaftlichen Fabrikbetrieb 2010, 134–141 (2015)
40. Kück, M., Ehm, J., Freitag, M., Frazzon, E.: Adaptives simulationsbasiertes Optimierungsverfahren – Konzept zur Planung und Steuerung dynamischer Produktionssysteme. Industrie 4.0 Management 32, 26–31 (2016)
41. Mertens, P.: Industrie 4.0 = CIM 2.0? Industrie Management 30, 27–30 (2014)
42. Lanza, G., Stricker, N., Peters, S.: Ad-hoc Rescheduling and Innovative Business Models for Shock- robust Production Systems. Procedia CIRP 7, 121–126 (2013)
43. Luo, H., Fang, J., Huang, G.Q.: Real-time scheduling for hybrid flowshop in ubiquitous manufacturing environment. Computers & Industrial Engineering 84, 12–23 (2015)
44. Wrobel, M.: Multidimensionale, heterogene, visualisierbare Datenräume: Anforderungen, Entwurf und Implementierung einer adaptiven und interaktiven Schnittstelle für transdisziplinäre wissenschaftliche Daten im Kontext der Erdsystemanalyse (2004)