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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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    2. 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.
    3. 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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    5. 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.
    6. 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.
    7. Aqlan, Faisal & Lam, Sarah S. & Ramakrishnan, Sreekanth, 2014. "An integrated simulation–optimization study for consolidating production lines in a configure-to-order production environment," International Journal of Production Economics, Elsevier, vol. 148(C), pages 51-61.
    8. 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.
    9. Andrea Borenich & Peter Greistorfer & Marc Reimann, 2020. "Model-based production cost estimation to support bid processes: an automotive case study," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(3), pages 841-868, September.
    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. Staeblein, Thomas & Aoki, Katsuki, 2015. "Planning and scheduling in the automotive industry: A comparison of industrial practice at German and Japanese makers," International Journal of Production Economics, Elsevier, vol. 162(C), pages 258-272.
    12. 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.
    13. Chuang, Chia-Hung & Zhao, Yabing, 2019. "Demand stimulation in finished-goods inventory management: Empirical evidence from General Motors dealerships," International Journal of Production Economics, Elsevier, vol. 208(C), pages 208-220.
    14. Thorben Krueger & Achim Koberstein & Norbert Bittner, 2022. "Anticipating technical car sequencing rules in the master production scheduling of mixed-model assembly lines," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 351-407, June.
    15. 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.

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