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  • PURE DDMRP

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 2 Dec 2020, available online: https://www.tandfonline.com/doi/abs/10.1080/00207543.2020.1849847

    Accepted author manuscript, 849 KB, PDF document

    Embargo ends: 2/12/21

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Production Planning and Control in Multi-Stage Assembly Systems: An Assessment of Kanban, MRP, OPT (DBR) and DDMRP by Simulation

Research output: Contribution to journalJournal articlepeer-review

E-pub ahead of print
<mark>Journal publication date</mark>2/12/2020
<mark>Journal</mark>International Journal of Production Research
Publication StatusE-pub ahead of print
Early online date2/12/20
<mark>Original language</mark>English

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.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 2 Dec 2020, available online: https://www.tandfonline.com/doi/abs/10.1080/00207543.2020.1849847