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Production planning and control in multi-stage assembly systems: an assessment of Kanban, MRP, OPT (DBR) and DDMRP by simulation

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  • Matthias Thürer
  • Nuno O. Fernandes
  • Mark Stevenson

Abstract

Multi-stage assembly systems where the demand for components depends on the market-driven demand for end products, are commonly encountered in practice. Production Planning and Control (PPC) systems for this production context include Kanban, Materials Requirement Planning (MRP), Optimised Production Technology (OPT), and Demand Driven MRP (DDMRP). All four of these PPC systems are widely applied in practice and literature abounds on each of these systems. Yet, studies comparing these systems are scarce and remain largely inconclusive. In response, this study uses simulation to assess the performance of all four PPC systems under different levels of bottleneck severity and due date tightness. Results show that MRP performs the worst, which can be explained by the enforcement of production start dates. Meanwhile, Kanban and DDMRP perform the best if there is no bottleneck. If there is a bottleneck then DDMRP and OPT perform the best, with DDMRP realising lower inventory levels. If there is a severe bottleneck, then the performance results for DDMRP and OPT converge. This identification of contingency factors not only resolves some of the inconsistencies in the literature but also has important implications for the applicability of these four PPC systems in practice.

Suggested Citation

  • Matthias Thürer & Nuno O. Fernandes & Mark Stevenson, 2022. "Production planning and control in multi-stage assembly systems: an assessment of Kanban, MRP, OPT (DBR) and DDMRP by simulation," International Journal of Production Research, Taylor & Francis Journals, vol. 60(3), pages 1036-1050, February.
  • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:3:p:1036-1050
    DOI: 10.1080/00207543.2020.1849847
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    Cited by:

    1. Carlos Cuartas & Jose Aguilar, 2023. "Hybrid algorithm based on reinforcement learning for smart inventory management," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 123-149, January.
    2. Durdu Hakan Utku, 2023. "The Evaluation and Improvement of the Production Processes of an Automotive Industry Company via Simulation and Optimization," Sustainability, MDPI, vol. 15(3), pages 1-17, January.
    3. Wolfgang Seiringer & Balwin Bokor & Klaus Altendorfer, 2024. "Evaluating Production Planning and Control Systems in Different Environments: A Comparative Simulation Study," Papers 2405.02015, arXiv.org, revised Nov 2024.

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