Abstract
Data Science plays a crucial role in driving new approaches to process optimization. With the increasing complexity of internal logistics systems, data-oriented methods have become essential in addressing the challenges that arise. However, standardized process analytics frameworks are lacking due to the heterogeneity of the underlying processes and the resulting data. This article aims to address this complexity by presenting a categorization of internal logistics data, consolidating the current state of the art. The categorization takes into account both real-world and scientifically proposed data architectures, providing a comprehensive overview. It includes a classification of comparative data fields based on their importance, the associated internal logistics processes, and potential usage scenarios. This classification is designed to cater to different use cases, such as diagnostics or prescriptive analytics. By presenting this categorization, the article enables practitioners to effectively leverage generated process data in a more goal-oriented manner. It empowers them to conduct suitable analyses tailored to their specific needs and objectives, based on the provided data architectures. In summary, this article offers valuable insights into internal logistics data categorization, providing a framework for practitioners to make informed decisions and optimize processes using data-driven approaches.
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References
Vogel-Heuser, B., Konersmann, M., Aicher, T., Fischer, J., Ocker, F., Goedicke, M.: Supporting evolution of automated Material Flow Systems as part of CPPS by using coupled meta models. In: IEEE Industrial Cyber-Physical Systems (ed.) Proceedings 2018 IEEE Industrial Cyber-Physical Systems (ICPS): ITMO University, Saint Petersburg, Saint Petersburg, Russia, 15–18 May 2018, pp. 316–323. IEEE, Piscataway, NJ (2018)
Knoll, D., Prüglmeier, M., Reinhart, G.: Materialflussanalyse mit ERP-Transportaufträgen: Automatisierte Ableitung und Visualisierung von Materialflüssen in der Produktionslogistik. Werkstattstechnik online 107(3), 129–133 (2017)
Gehlhoff, F., Fay, A.: On agent-based decentralized and integrated scheduling for small-scale manufacturing. Automatisierungstechnik 68(1), 15–31 (2020)
van der Aalst, W.: Process Mining: Data Science in Action. Springer-Verlag, Berlin, Heidelberg (2016)
Becker, T., Intoyoad, W.: Context aware process mining in logistics. Procedia CIRP 63, 557–562 (2017)
Wuennenberg, M., Muehlbauer, K., Fottner, J., Meissner, S.: Towards predictive analytics in internal logistics – an approach for the data-driven determination of key performance indicators. CIRP J. Manuf. Sci. Technol. 44, 116–125 (2023)
Muehlbauer, K., Wuennenberg, M., Meissner, S., Fottner, J.: Data driven logistics-oriented value stream mapping 4.0: a guideline for practitioners. IFAC-PapersOnLine 55(16), 364–369 (2022). https://doi.org/10.1016/j.ifacol.2022.09.051
Burow, K., Franke, M., Deng, Q., Hribernik, K., Thoben, K.-D.: Sustainable data management for manufacturing. In: IEEE Conference on Engineering, Technology and Innovation (ed.) 2019 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC). IEEE (2019)
Wünnenberg, M., Hujo, D., Schypula, R., Fottner, J., Goedicke, M., Vogel-Heuser, B.: Modellkonsistenz in der Entwicklung von Materialflusssystemen: Eine Studie über Entwicklungswerkzeuge und Einflüsse auf den Produktentstehungsprozess. ZWF 116(11), 820–825 (2021)
Vernickel, K., et al.: Machine-learning-based approach for parameterizing material flow simulation models. Procedia CIRP 93, 407–412 (2020)
Milde, M., Reinhart, G.: Automated model development and parametrization of material flow simulations. In: Mustafee, N. (ed.) 2019 Winter Simulation Conference (WSC), pp. 2166–2177. IEEE, Piscataway, NJ (2019)
Knoll, D., Reinhart, G., Prüglmeier, M.: Enabling value stream mapping for internal logistics using multidimensional process mining. Expert Syst. Appl. 124, 130–142 (2019)
Knoll, D., Prüglmeier, M., Reinhart, G.: Predicting future inbound logistics processes using machine learning. Procedia CIRP 52, 145–150 (2016)
Wuennenberg, M., Vollmuth, P., Xu, J., Fottner, J., Vogel-Heuser, B.: Transformability in material flow systems: towards an improved product development process. In: Matt, D.T., Vidoni, R., Rauch, E., Dallasega, P. (eds.) Managing and Implementing the Digital Transformation: Proceedings of the 1st International Symposium on Industrial Engineering and Automation ISIEA 2022, pp. 3–14. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-14317-5_1
ten Hompel, M., Schmidt, T., Dregger, J.: Materialflusssysteme. Springer, Berlin Heidelberg (2018)
Wiendahl, H.-P., Reichardt, J., Nyhuis, P.: Handbuch Fabrikplanung: Konzept, Gestaltung und Umsetzung wandlungsfähiger Produktionsstätten. 2nd edn. Hanser, München (2014)
Verband Deutscher Maschinen- und Anlagenbau: Datenschnittstellen in Materialflußsteuerungen., VDMA 15276 (1994)
Wuennenberg, M., Wegerich, B., Fottner, J.: Optimization of Internal Logistics using a combined BPMN and simulation approach. In: Hameed, I.A., Hasan, A., Alaliyat, Abdel-Afou, S. (eds.) Proceedings of the 36th ECMS International Conference on Modelling and Simulation ECMS 2022, pp. 13–19. Pirrot, Saarbrücken (2022)
Verband der Automobilindustrie: Schnittstelle zur Kommunikation zwischen Fahrerlosen Transportfahrzeugen (FTF) und einer Leitsteuerung., VDMA 5050 (2022)
Klare, H., Kramer, M.E., Langhammer, M., Werle, D., Burger, E., Reussner, R.: Enabling consistency in view-based system development — The Vitruvius approach. J. Syst. Softw. 171(110815), 1–35 (2021)
Kargul, A.: Entwicklung eines Baumaschinenmanagements zur integrativen und adaptiven Steuerung des Maschinenbestandes über den Lebenszyklus. Dissertation, Lehrstuhl für Fördertechnik Materialfluss LogistikTechnische Universität München, Garching b. München (2020)
Schuh, G., et al.: Data mining definitions and applications for the management of production complexity. Procedia CIRP 81, 874–879 (2019)
Rebala, G., Ravi, A., Churiwala, S.: An Introduction to Machine Learning. Springer International Publishing, Cham (2019)
Rother, M., Shook, J.: Learning to See: Value-Stream Mapping to Create Value and Eliminate Muda, 1st edn. Lean Enterprise Inst, Boston (2018)
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Wuennenberg, M., Haid, C., Fottner, J. (2023). A Classification of Data Structures for Process Analysis in Internal Logistics. In: Terzi, S., Madani, K., Gusikhin, O., Panetto, H. (eds) Innovative Intelligent Industrial Production and Logistics. IN4PL 2023. Communications in Computer and Information Science, vol 1886. Springer, Cham. https://doi.org/10.1007/978-3-031-49339-3_4
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