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Improving performance with sophisticated master production scheduling

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  • Jonsson, Patrik
  • Kjellsdotter Ivert, Linea

Abstract

Literature addressing master production scheduling (MPS) typically focuses on the development of sophisticated MPS methods with the expectation that these methods will result in feasible plans and improved performance. However, empirical evidence showing that sophisticated methods are better than simpler ones remains scarce, and companies have reported difficulties with using sophisticated planning methods. In this study, we therefore investigate how sophisticated MPS methods impact three perception-based performance variables—namely, plan feasibility, inventory turnover rate, and delivery service—while accounting for the complexities of planning environments and MPS maturity. We define six MPS methods, ranging from those that ignore capacity to those exhibiting capacity-constrained planning using optimisation. An analysis of survey data from a sample of Swedish manufacturing companies reveals a significant negative effect of less sophisticated methods compared to highly sophisticated ones in terms of plan feasibility, as well as a significant negative effect of the simplest method in considering available capacity compared to highly sophisticated methods in terms of delivery service. The maturity of the MPS process most significantly impacts all performance measures, whereas planning environment complexity shows only a weak negative impact. Findings also indicate that both MPS process maturity and sophisticated MPS methods mediate the negative performance prompted by complex planning environments. Results thus suggest that sophisticated MPS may generally affect performance both directly and indirectly. Using sophisticated MPS methods reduces the negative effects of complex planning environments and results in more feasible plans irrespective of environment complexity and process maturity.

Suggested Citation

  • Jonsson, Patrik & Kjellsdotter Ivert, Linea, 2015. "Improving performance with sophisticated master production scheduling," International Journal of Production Economics, Elsevier, vol. 168(C), pages 118-130.
  • Handle: RePEc:eee:proeco:v:168:y:2015:i:c:p:118-130
    DOI: 10.1016/j.ijpe.2015.06.012
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    References listed on IDEAS

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    4. Erick Miranda-Meza & Iván Derpich & Juan M. Sepúlveda, 2024. "An Icon-Based Methodology for the Design of a Prototype of a Multi-Process, Multi-Product, Aggregated Production Planning Software," Mathematics, MDPI, vol. 12(2), pages 1-25, January.
    5. Stüve, David & van der Meer, Robert & Lütke Entrup, Matthias & Agha, Mouhamad Shaker Ali, 2020. "Supply chain planning in the food industry," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Lo, volume 29, pages 317-353, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    6. Liu, Weihua & Liang, Zhicheng & Ye, Zi & Liu, Liang, 2016. "The optimal decision of customer order decoupling point for order insertion scheduling in logistics service supply chain," International Journal of Production Economics, Elsevier, vol. 175(C), pages 50-60.
    7. Estefania Tobon-Valencia & Samir Lamouri & Robert Pellerin & Alexandre Moeuf, 2022. "Modeling of the Master Production Schedule for the Digital Transition of Manufacturing SMEs in the Context of Industry 4.0," Sustainability, MDPI, vol. 14(19), pages 1-28, October.

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