Abstract
The selection of a suitable order processing strategy from an economic and logistic point of view plays a fundamental role in the achievement of efficient and waste-free production processes. Many factors influence the order processing strategy and the choice of the order processing strategy affects many variables. The problem for companies that has not yet been solved is the holistic selection of the best possible order processing strategy for each product or product group and, if necessary, subordinate components.
The authors present an approach to analyze the effects of the choice of the order processing strategy on the economic and logistic objectives. The description and modeling of the interdependencies between the order processing strategies and the influenced objectives refer to existing logistic models. A case study to evaluate the impact of different order processing strategies on costs shows the practicality of the proposed approach. The exemplary application of the presented approach showed a potential of an average reduction of 30% of the variable costs resulting from the change of the order processing strategy. The savings varied between 1% and 62% depending on the order quantity and frequency for the individual products.
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References
Branke, J., Nguyen, S., Pickardt, C.W., Zhang, M.: Automated design of production scheduling heuristics: a review. IEEE Trans. Evol. Comput. 20(1), 110–124 (2015)
Aarabi, M., Hasanian, S.: Capacity planning and control: a review. Int. J. Sci. Eng. Res. 5(8), 975–984 (2014)
Jans, R., Degraeve, Z.: Modeling industrial lot sizing problems: a review. Int. J. Prod. Res. 46(6), 1619–1643 (2008)
Cheng, Y., Chen, K., Sun, H., Zhang, Y., Tao, F.: Data and knowledge mining with big data towards smart production. J. Ind. Inf. Integr. 9, 1–13 (2018)
Altintas, N., Trick, M.: A data mining approach to forecast behavior. Ann. Oper. Res. 216(1), 3–22 (2014)
Maaß, D., Spruit, M., de Waal, P.: Improving short-term demand forecasting for short-lifecycle consumer products with data mining techniques. Decis. Anal. 1(1), 1–17 (2014)
Hoekstra, S., Romme, J., Argelo, S.M.: Integral Logistic Structures Developing Customer-Oriented Goods Flow. Industrial Press, New York (1992)
Stevenson, M., Hendry, L.C., Kingsman, B.G.: A review of production planning and control: the applicability of key concepts to the make-to-order industry. Int. J. Prod. Res. 43(5), 869–898 (2005)
Rajagopalan, S.: Make to order or make to stock: model and application. Manage. Sci. 48(2), 241–256 (2002)
Mundt, C., Winter, M., Heuer, T., Hübner, M., Seitz, M., Schmidhuber, M., Maibaum, J., Bank, L., Roth, S., Scherwitz, P., Theumer, P.: PPS-Report 2019. TEWISS, Garbsen (2019)
Rafiei, H., Rabbani, M.: Order partitioning and order penetration point location in hybrid make-to-stock/make-to-order production contexts. Comput. Ind. Eng. 61(3), 550–560 (2011)
Gudehus, T., Kotzab, H.: Comprehensive Logistics, 2nd edn. Springer, Berlin, Heidelberg (2012)
Westkämper, E., Decker, M.: Einführung in die Organisation der Produktion. Springer, Berlin, Heidelberg (2006)
Syska, A.: Produktionsmanagement. Das A - Z wichtiger Methoden und Konzepte für die Produktion von heute (engl. title: Production management: important methods and concepts for today’s production). Gabler, Wiesbaden (2006)
Schmidt, M., Münzberg, B., Nyhuis, P.: Determining lot sizes in production areas – exact calculations versus research based estimation. Procedia CIRP 28, 143–148 (2015)
Rauch, E., Dallasega, P., Matt, D.T.: Complexity reduction in engineer-to-order industry through real-time capable production planning and control. Prod. Eng. 12(3–4), 341–352 (2018)
Hadj Youssef, K., van Delft, C., Dallery, Y.: Priority optimization and make-to-stock/make-to-order decision in multiproduct manufacturing systems. Int. Trans. Oper. Res. 25(4), 1199–1219 (2018)
Nyhuis, P.: Lagerkennlinien - ein Modellansatz zur Unterstützung des Beschaffungs- und Bestandscontrollings. In: Baumgarten, H. (eds.): RKW-Handbuch Logistik. 2nd edn., 1–30. Erich Schmidt, Berlin (1996)
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 Manag. Oper. 25(2), 123–133 (2014)
Münzberg, B.: Multikriterielle Losgrößenbildung (engl. title: Multicriterial lot sizing). Berichte aus dem IFA. PZH, Garbsen (2013)
REFA-Verband für Arbeitsstudien und Betriebsorganisation: Methodenlehre der Planung und Steuerung. Carl Hanser, München (1985)
Schuh, G., Westkämper, E.: Liefertreue im Maschinen- und Anlagenbau: Stand - Potenziale - Trends. Forschungsinstitut für Rationalisierung an der RWTH Aachen, Stuttgart, Fraunhofer-Institut für Produktionstechnik und Automatisierung (2006)
Acknowledgements
The research project was carried out in the framework of the industrial collective research programme (IGF no. 20906 N). It was supported by the Federal Ministry for Economic Affairs and Energy (BMWi) through the AiF (German Federation of Industrial Research Associations eV) and the BVL (Bundesvereinigung Logistik eV) based on a decision taken by the German Bundestag.
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Maier, J.T., Heuer, T., Nyhuis, P., Schmidt, M. (2020). Supporting the Decision of the Order Processing Strategy by Using Logistic Models: A Case Study. In: Lalic, B., Majstorovic, V., Marjanovic, U., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. The Path to Digital Transformation and Innovation of Production Management Systems. APMS 2020. IFIP Advances in Information and Communication Technology, vol 591. Springer, Cham. https://doi.org/10.1007/978-3-030-57993-7_39
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DOI: https://doi.org/10.1007/978-3-030-57993-7_39
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