1. Proceedings
  2. I3M
  3. 2021
  4. EMSS
  5. 10.46354/i3m.2021.emss.025

An approach for target-oriented process analysis for the implementation of Digital Process Optimization Twins in the field of intralogistics

  • a Pascal Zuhr,
  • b Konstantin Mühlbauer,
  • c Stephanie Bäuml,
  • d Sebastian Meißner 
  • a,b,c,d  Technology Center for Production and Logistics Systems, University of Applied Sciences Landshut, Am Lurzenhof 1, Landshut, 84036, Germany
Cite as
Zuhr P., Mühlbauer K., Bäuml S., Meißner S. (2021). An approach for target-oriented process analysis for the implementation of Digital Process Optimization Twins in the field of intralogistics. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 183-191. DOI: https://doi.org/10.46354/i3m.2021.emss.025

Abstract

The importance of Digital Twins has increased significantly in recent years. Despite a large number of papers on Digital Twin concepts, Digital Twins are hardly implemented in manufacturing companies, especially in the area of intralogistics. Companies lack know-how, implementation concepts and methods. Thus, the potential of digital twins to improve performance in intralogistics remains largely unused, although, logistics processes have a significant impact on manufacturing performance. For lean, customer-oriented logistics, process thinking and measurement must prevail. Hence, processes should be planned, controlled, and optimized by means of Key Performance Indicators (KPI). Consequently, KPI are the pivotal point for so-called Digital Process Optimization Twins (DPOT). The focus of this paper is to develop an approach to support the planning and implementation of DPOT in the area of intralogistics. For this purpose, a process analysis method as well as an evaluation model for DPOT are presented. The approach analyzes and evaluates the processes for the implementation of DPOTs to improve the intralogistics KPI. Its advantages include the structured assessment of the processes through the DPOTs’ autonomy level and the existing/ respective implementation technologies. This enables an improved process understanding with the aim to uncover weaknesses and to identify optimization potentials for DPOT.

References

  1. Barth L. and Ehrat M. (2020). Value Creation with Digital Twins – a Conceptual Framework. University of Applied Sciences Zürich.
  2. Bäuml S., Hilpoltsteiner D., Seel C. and Meißner S. (2019). Information modeling of rule-based logistic planning processes. Bernardino S.; Salgado A. and Joaquim F. (eds). In Proceedings of the 11th International joint Conference on Knowledge Engineering and Knowledge Management. IC3K. Volume 3, 167-175. Vienna, Austria.
  3. Klenk E., Klevers M. and Günthner W.-A. (2013). Logistikorientierte Wertstromanalyse, Günthner W.-A; Durchholz J.; Klenk E.; Boppert J. (eds) Schlanke Logistikprozesse – Handbuch für Planer (pp. 135 et seqq.). Berlin. Heidelberg: Springer Vieweg.
  4. Eigner M. Detzner A., Schmidt P.H. et al. (2019). Definition des Digital Twin im Produktlebenszyklus. Zeitschrift für wirtschaftlichen Fabrikbetrieb 6:345-350. Carl Hanser Verlag.
  5. Fottner J., Clauer D., Hormes F., et al. (2021). Autonomous Systems in Intralogistics – State of the Art and Future Research Challenges. Logistics Research.
  6. GEL/65/3 (2020). OPC unified architecture. Overview and concepts. IEC TR 62541-1:2020 IDT. British Standards Institution.
  7. Günthner, W.-A., Chisu R. and Kuzmany F. (2006). Trends und Perspektiven in der Intralogistik, VDI- Berichte 1928, 279-289. München, Germany.
  8. Jasperneite J., Neumann A. and Pethig F. (2015). OPC UA versus MTConnect. Control & Drives 2015, 16-21.
  9. Klein M., Maschler B., Zeller A. et al. (2019). Architektur und Technologiekomponenten eines digitalen Zwillings. VDI-Kongress Automation, 2351:89-102. Baden-Baden, Germany.
  10. Klostermeier R., Haag S. and Benlian A. (2018). Digitale Zwillinge – Eine explorative Fallstudie zur Untersuchung von Geschäftsmodellen, HMD 55:297-311, Springer. https://doi.org/10.1365/s40702-018-0406-x.
  11. Knössl T. (2013). Logistikorientierte Wertstromanalyse, Günthner W.A.; Boppert J. (eds) Lean Logistics. Springer.
  12. Langlotz P., Glatt M., Sousa F. et al. (2020). Modelle als Grundlage für den Digitalen Zwilling. Zeitschrift für wirtschaftlichen Fabrikbetrieb 115 5:340-343. Carl Hanser Verlag.
  13. Lee J., Bagheri B. and Kao H. (2015). A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters 3:18-23. Society of Manufacturing Engineers (SME). http://dx.doi.org/10.1016/j.mfglet.2014.12.001.
  14. Lutz M., Münch M., Turgut A., et al. (2020). Der Digitale Zwilling entlang des Produktlebenszyklus. Zeitschrift für wirtschaftlichen Fabrikbetrieb 6:422-424.
  15. Meudt T., Rößler M. P., Böllhoff J. and Metternich J. (2016). Wertstromanalyse 4.0. Zeitschrift für wirtschaftlichen Fabrikbetrieb 6: 319-323. Carl Hanser Verlag.Miebach J. and Müller P. (2006). Intralogistik als wichtigstes Glied von umfassenden Lieferketten. In D., Arnold Intralogistik (pp. 20-31). Berlin. Heidelberg: Springer.
  16. Mucksch H. and Behme W. (2000). Das Data Warehouse Konzept (4th ed., pp. 4-6). Wiesbaden: Springer.
  17. Pistorius J. (2020). Industrie 4.0 – Schlüsseltechnologien für die Produktion (pp. 41-47). Berlin. Heidelberg: Springer.
  18. Riedelsheimer T., Lünnemann P., Wehking S., et. al. (2020). Digital Twin Readiness Assessment, 49.
  19. Roeren, S. (2016). DCI – Dingolfing Complexity Index, Working Group Automated Defect Detection in subsequent processes of casting methods. China Die Casting. Dingolfing. Shanghai.
  20. Rother M. and Shook J. (2011). Sehen lernen – mit Wertstromdesign die Wertschöpfung erhöhen und Verschwendung beseitigen. Lean Management Institut. Mühlheim an der Ruhr.
  21. Schuh G., Walendzik P., Luckert M., et.al. (2016). Keine Industrie 4.0 ohne den Digitalen Schatten. Zeitschrift für wirtschaftlichen Fabrikbetrieb 11:745-748. Carl Hanser Verlag.
  22. Schulte C. (2016). Logistik - Wege zur Optimierung der Supply Chain (pp. 195-197). München: Verlag Franz Vahlen.
  23. Scully P. (2020). IoT Analytics - How the world’s 250 Digital Twins compare? Same, same but different. Retrieved from https://iot-analytics.com/how-the-worlds-250-digital-twins-compare/.
  24. Stark R. and Damerau T. (2019). Digital Twin. The International Academy for Production Engineering, Chatti S., Tolio T. (eds) CIRP Encyclopedia of Production Engineering, 4. Berlin. Heidelberg: Springer.
  25. Weber U. and Grosser H. (2019). Detecon Studie-Digital Twin, 22.Mucksch H. and Behme W. (2000). Das Data Warehouse Konzept (4th ed., pp. 4-6). Wiesbaden: Springer.