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Modeling and simulation of order-driven planning policies in build-to-order automobile production

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  • Volling, Thomas
  • Spengler, Thomas S.

Abstract

In adopting build-to-order order fulfillment systems, automotive companies strive to better synchronize their production output with market demand. This essentially gives rise to a new paradigm in production planning. Since all business is linked to customer orders, the operational performance is substantially determined by order-driven planning. Therefore, a clear understanding of the associated planning tasks, order promising and master production scheduling, as well as their dynamic interaction is essential. Based on the analysis of the decision situation of order-driven planning in build-to-order settings, we provide a framework comprising separate interlinked quantitative models for order promising and master production scheduling. The focus of the contribution is on the modeling and evaluation of both models in a dynamic setting. The approach is evaluated by means of a simulative analysis using empirical data from the automotive industry. Conclusions regarding the potentials of such systems with respect to customer service, the leveling of resource utilization, and holding are presented.

Suggested Citation

  • Volling, Thomas & Spengler, Thomas S., 2011. "Modeling and simulation of order-driven planning policies in build-to-order automobile production," International Journal of Production Economics, Elsevier, vol. 131(1), pages 183-193, May.
    • Handle: RePEc:eee:proeco:v:131:y:2011:i:1:p:183-193
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    4. Ullah Saif & Zailin Guan & Li Zhang & Fei Zhang & Baoxi Wang & Jahanzaib Mirza, 2019. "Multi-objective artificial bee colony algorithm for order oriented simultaneous sequencing and balancing of multi-mixed model assembly line," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1195-1220, March.
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    7. Baoxi Wang & Zailin Guan & Saif Ullah & Xianhao Xu & Zongdong He, 2017. "Simultaneous order scheduling and mixed-model sequencing in assemble-to-order production environment: a multi-objective hybrid artificial bee colony algorithm," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 419-436, February.
    8. Saurabh Chandra & Debabrata Ghosh & Samir K. Srivastava, 2016. "Outbound logistics management practices in the automotive industry: an emerging economy perspective," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 43(2), pages 145-165, June.
    9. Laurent Lim, Lâm & Alpan, Gülgün & Penz, Bernard, 2014. "Reconciling sales and operations management with distant suppliers in the automotive industry: A simulation approach," International Journal of Production Economics, Elsevier, vol. 151(C), pages 20-36.
    10. Volling, Thomas & Matzke, Andreas & Grunewald, Martin & Spengler, Thomas S., 2013. "Planning of capacities and orders in build-to-order automobile production: A review," European Journal of Operational Research, Elsevier, vol. 224(2), pages 240-260.
    11. Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), 2019. "Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains," Proceedings of the Hamburg International Conference of Logistics (HICL), Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management, volume 27, number 27.
    12. Amirhosein Gholami & Nasim Nezamoddini & Mohammad T. Khasawneh, 2023. "Customized orders management in connected make-to-order supply chains," Operations Management Research, Springer, vol. 16(3), pages 1428-1443, September.
    13. Diedrich, Katharina & Klingebiel, Katja, 2019. "Smart risk analytics design for proactive early warning," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Artificial Intelligence and Digital Transformation in Supply Chain Management: Innovative Approaches for Supply Chains. Proceedings of the Hamburg Int, volume 27, pages 559-585, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
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    15. Ben Ali, M. & D’Amours, S. & Gaudreault, J. & Carle, M-A., 2018. "Configuration and evaluation of an integrated demand management process using a space-filling design and Kriging metamodeling," Operations Research Perspectives, Elsevier, vol. 5(C), pages 45-58.

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