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Applicability of Demand-Driven MRP in a complex manufacturing environment

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  • Angela Patricia Velasco Acosta
  • Christian Mascle
  • Pierre Baptiste

Abstract

Push and pull methods have been adopted for specific volume production and uncertainty scenarios in order to plan and control production. The further development of hybrid or integrated methods allows benefit to be drawn from opposing approaches. The literature concerning Demand-Driven Material Requirements Planning (DDMRP) proves its superiority under conditions of internal and external uncertainty for high-volume production compared to the most implemented push method (manufacturing requirements planning MRPII). Companies that have adopted this method, manufacture on average 10–15 parts per product, with 2 or 3 levels of bills of materials. In this paper, we evaluate the applicability of DDMRP in a complex manufacturing environment (e.g. products of four levels of bill of materials) in terms of customer satisfaction and stock levels. Buffered and non-buffered items clustered in seven types of decoupling structures contributed to this complexity. We developed a DDMRP model for planning and execution purposes, which was simulated in ARENA's discrete events software. We analysed the model's on-hand stock and delayed orders. DDMRP works effectively under the manufacturing conditions considered. It is found to prevent inventory stockouts and overstocks, reduce lead time by 41% and reduce stock levels by 18%. The success of this method; however, depends on the strategic positioning of the buffers.

Suggested Citation

  • Angela Patricia Velasco Acosta & Christian Mascle & Pierre Baptiste, 2020. "Applicability of Demand-Driven MRP in a complex manufacturing environment," International Journal of Production Research, Taylor & Francis Journals, vol. 58(14), pages 4233-4245, July.
  • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:14:p:4233-4245
    DOI: 10.1080/00207543.2019.1650978
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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.

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