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Volkswagen Group Logistics Applies Operations Research to Optimize Supplier Development

Author

Listed:
  • Sönke Wieczorrek

    (Volkswagen AG, 38440 Wolfsburg, Germany; Institute of Automotive Management and Industrial Production, Technische Universität Braunschweig, 38106 Braunschweig, Germany)

  • Christian Thies

    (Resilient and Sustainable Operations and Supply Chain Management Group, Hamburg University of Technology, 21073 Hamburg, Germany)

  • Christian Weckenborg

    (Institute of Automotive Management and Industrial Production, Technische Universität Braunschweig, 38106 Braunschweig, Germany)

  • Martin Grunewald

    (Volkswagen AG, 38440 Wolfsburg, Germany)

  • Thomas S. Spengler

    (Institute of Automotive Management and Industrial Production, Technische Universität Braunschweig, 38106 Braunschweig, Germany)

Abstract

Volkswagen Group Logistics (VWGL) is responsible for the logistics and supply processes of the automotive brands of the Volkswagen Group. In this context, supplier development is vital for efficient and reliable material flows between the process partners. In recent years, VWGL implemented a collaborative approach for supplier development in logistics wherein it is crucial to identify disrupting suppliers and apply improvement measures to increase their logistics performance. Against this background, VWGL initiated a project to examine how supplier development measures can be implemented efficiently to improve the overall logistics performance of VWGL’s supply base. This paper presents the developed operations research approach, which integrates Monte Carlo simulation and a knapsack model on the specific problem of supplier development. The approach consists of three stages: (1) data preparation, (2) measure evaluation, and (3) measure allocation. The approach is validated based on 18 existing less-than-truckload networks of VWGL. We find that, on average, considerable cost savings of 31% can be achieved throughout the networks compared with VWGL’s previous procedure. A new workflow facilitates our approach to lift its potential in practical application sustainably.

Suggested Citation

  • Sönke Wieczorrek & Christian Thies & Christian Weckenborg & Martin Grunewald & Thomas S. Spengler, 2024. "Volkswagen Group Logistics Applies Operations Research to Optimize Supplier Development," Interfaces, INFORMS, vol. 54(2), pages 147-161, March.
    • Handle: RePEc:inm:orinte:v:54:y:2024:i:2:p:147-161
    DOI: 10.1287/inte.2022.0026
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    References listed on IDEAS

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    1. Glock, C. H. & Grosse, E. H. & Ries, J. M., 2017. "Decision support models for supplier development: systematic literature review and research agenda," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 88898, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Zhou, Rui & Bhuiyan, Tanveer Hossain & Medal, Hugh R. & Sherwin, Michael D. & Yang, Dong, 2022. "A stochastic programming model with endogenous uncertainty for selecting supplier development programs to proactively mitigate supplier risk," Omega, Elsevier, vol. 107(C).
    3. Ho, William & Xu, Xiaowei & Dey, Prasanta K., 2010. "Multi-criteria decision making approaches for supplier evaluation and selection: A literature review," European Journal of Operational Research, Elsevier, vol. 202(1), pages 16-24, April.
    4. S. Lin & B. W. Kernighan, 1973. "An Effective Heuristic Algorithm for the Traveling-Salesman Problem," Operations Research, INFORMS, vol. 21(2), pages 498-516, April.
    5. Christian Weckenborg & Karsten Kieckhäfer & Thomas S. Spengler & Patricia Bernstein, 2020. "The Volkswagen Pre-Production Center Applies Operations Research to Optimize Capacity Scheduling," Interfaces, INFORMS, vol. 50(2), pages 119-136, March.
    6. Sherwin, Michael D. & Medal, Hugh & Lapp, Steven A., 2016. "Proactive cost-effective identification and mitigation of supply delay risks in a low volume high value supply chain using fault-tree analysis," International Journal of Production Economics, Elsevier, vol. 175(C), pages 153-163.
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