Abstract
On-time delivery is one of the most critical performance characteristics of manufacturing companies. To remain competitive, companies must constantly strive to optimize their logistical performance. Poor on-time delivery has complex causes that are difficult to identify due to the many logistical interdependencies. Increasing market volatility, complex products and production processes, and individual customer requirements further complicate the situation. Digitalization has led to more and more data being available, which requires additional capabilities in data analysis. In order to obtain a fundamental overview of planning quality in production, this paper presents two simple descriptive models. These models can visualize the progression of different KPIs for measuring the planning quality along different production steps. In addition, they allow conclusions to be drawn about the extent to which specific product characteristics have an influence on the planning quality. A case study evaluates the models using a real data set from a maintenance service provider. As production is a complex process that cannot be perfectly planned, these models help to fundamentally understand planning errors and provide a basis for further exploration.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
For confidentiality reasons, the values of the plan deviations have been removed. Still, the relations shown correspond to the real situation.
References
Handfield, R., Straube, F., Pfohl, H.-C., Wieland, C.: Trends and Strategies in Logistics and Supply Chain Management: Embracing Global Logistics Complexity to Drive Market Advantage. DVV Media Group, Hamburg, 81 p. (2013)
Schuh, G., Stich, V., Brosze, T., Potente, T., Jasinski, T., Cuber, S.: Delivery reliability in machinery and equipment industry. Int. J. Appl. Logist. 3(1), 20–38 (2012)
Schmidt, M., Bertsch, S., Nyhuis, P.: Schedule compliance operating curves and their application in designing the supply chain of a metal producer. Prod. Plan. Control 25(2), 123–133 (2014)
Wiendahl, H.-H., von Cieminski, G., Wiendahl, H.-P.: Stumbling blocks of PPC: towards the holistic configuration of PPC systems. Prod. Plan. Control 16(7), 634–651 (2005)
Hu, S.J., Zhu, X., Wang, H., Koren, Y.: Product variety and manufacturing complexity in assembly systems and supply chains. CIRP Ann. 57(1), 45–48 (2008)
Mack, O., Khare, A., Krämer, A., Burgartz, T.: Managing in a VUCA World, 259 p. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-16889-0
Duffie, N., Bendul, J., Knollmann, M.: An analytical approach to improving due-date and lead-time dynamics in production systems. J. Manuf. Syst. 45, 273–285 (2017)
Bueno, A., Godinho Filho, M., Frank, A.G.: Smart production planning and control in the Industry 4.0 context: a systematic literature review. Comput. Ind. Eng. 149, 106774 (2020)
Cowling, P., Johansson, M.: Using real time information for effective dynamic scheduling. Eur. J. Oper. Res. 139(2), 230–244 (2002)
Vieira, G.E., Herrmann, J.W., Lin, E.: Rescheduling manufacturing systems: a framework of strategies, policies, and methods. J. Sched. 6(1), 39–62 (2003)
Lucht, T., Mütze, A., Kämpfer, T., Nyhuis, P.: Model-based approach for assessing planning quality in production logistics. IEEE Access, 1 (2021)
Büttner, K., Antons, O., Arlinghaus, J.C.: Applied machine learning for production planning and control: overview and potentials. IFAC-PapersOnLine 55(10), 2629–2634 (2022)
Kuo, Y.-H., Kusiak, A.: From data to big data in production research: the past and future trends. Int. J. Prod. Res. 57(15–16), 4828–4853 (2019)
Soepenberg, G.D., Land, M., Gaalman, G.: The order progress diagram: a supportive tool for diagnosing delivery reliability performance in make-to-order companies. Int. J. Prod. Econ. 112(1), 495–503 (2008)
Schilp, J.: PPS-Report 2021 – Studienergebnisse (2021)
Heinrich, J., Weiskopf, D.: State of the Art of Parallel Coordinates, 22 p. (2012)
Kempf, K., Uzsoy, R., Smith, S., Gary, K.: Evaluation and comparison of production schedules. Comput. Ind. 42(2–3), 203–220 (2000)
Conway, R.W., Maxwell, W.L., Miller, L.W.: Theory of Scheduling, p. 294. Dover, Mineola (2003)
Hiller, T., Deipenwisch, L., Nyhuis, P.: Systemising data-driven methods for predicting throughput time within production planning & control. In: 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Kuala Lumpur, Malaysia, 07 December 2022–10 December 2022, pp. 716–721. IEEE (2022)
Bertsch, S., Felix, C., Nyhuis, P.: Modeling the sequence dependent lateness of a single workstation. AMR 1140, 443–448 (2016)
Wiendahl, H.-P., Wiendahl, H.-H.: Betriebsorganisation für Ingenieure, 9., vollständig überarbeitete Auflage ed. Hanser, München (2019)
Inselberg, A.: Parallel Coordinates. Springer New York (2009). https://doi.org/10.1007/978-0-387-68628-8
Lucht, T., Kämpfer, T., Nyhuis, P.: Characterization of supply chains in the regeneration of complex capital goods. In: Dimitrov, D., Hagedorn-Hansen, D., von Leipzig, K. (eds.) International Conference on Competitive Manufacturing (COMA 19), 31 January–2 February 2019, Stellenbosch, South Africa. Department of Industrial Engineering, Stellenbosch University, Stellenbosch, pp. 444–449 (2019)
Hoffmann, L.-S., Kuprat, T., Kellenbrink, C., Schmidt, M., Nyhuis, P.: Priority rule-based planning approaches for regeneration processes. Procedia CIRP 59, 89–94 (2017)
Eickemeyer, S.C., Mische, V., Goßmann, D.: Regenerationsplanung mit belastbarem Prognosewert. Zeitschrift für wirtschaftlichen Fabrikbetrieb 110(11), 706–709 (2015)
Reményi, C., Staudacher, S.: Systematic simulation based approach for the identification and implementation of a scheduling rule in the aircraft engine maintenance. Int. J. Prod. Econ. 147(1), 94–107 (2014)
Härtel, L., Nyhuis, P.: Data-Based Identification of Throughput Time Potentials in Production Departments, Hannover (2020)
Acknowledgment
This project is funded by the German Federal Ministry of Education and Research, as part of the Aviation Research and Technology Program of the Lower Saxony Ministry of Economics, Labor, Transport and Digitalization (funding code ZW 1 - 80157862).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 IFIP International Federation for Information Processing
About this paper
Cite this paper
Hiller, T., Osterkamp, L., Vinke, L., Holtsch, P., Mütze, A., Nyhuis, P. (2023). Simple Analysis of Planning Quality in Production Logistics. In: Alfnes, E., Romsdal, A., Strandhagen, J.O., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures. APMS 2023. IFIP Advances in Information and Communication Technology, vol 691. Springer, Cham. https://doi.org/10.1007/978-3-031-43670-3_50
Download citation
DOI: https://doi.org/10.1007/978-3-031-43670-3_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43669-7
Online ISBN: 978-3-031-43670-3
eBook Packages: Computer ScienceComputer Science (R0)
