The master production schedule and uncertainty in the manufacturing sector: a systematic review of the literature
DOI:
https://doi.org/10.26439/ing.ind2022.n43.6108Keywords:
master production schedule, uncertainty, manufacturing sector, PRISMAAbstract
This research addresses two relevant topics for industrial engineering: master production plans (dependent variable) and uncertainty (independent variable). The latter constitutes a critical impact factor in the manufacturing sector. For the investigation, we collected articles related to these variables to establish the state of affairs in the specialized literature. We searched for information in two indexed databases, Scopus and ProQuest, and filtered it using inclusion and exclusion criteria derived from the PRISMA method. We identified sixteen scientific journal articles of interest for the investigation at the end of the process. These studies show essential data about the variables described and additional variables that influence uncertainty and that, in some way, impact the preparation of master production schedules in the manufacturing sector.
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References
Atadeniz, S. N., & Sridharan, S. V. (2020). Effectiveness of nervousness reduction policies when capacity is constrained. International Journal of Production Research, 58(13), 4121-4137. http://dx.doi.org/10.1080/00207543.2019.1643513
Bai, X., & Zhu, B. (2015). Application of production planning and control method in manufacturing enterprise. Management & Engineering, 18, 3-7.
Banco Mundial. (2020, 14 de julio). El aumento de la productividad, el principal motor de reducción de la pobreza, corre peligro debido a las perturbaciones causadas por la COVID-19. https://www.bancomundial.org/es/news/press-release/2020/07/14/productivity-growth-threatened-by-covid-19-disruptions
Chatras, C., Giard, V., & Sali, M. (2015). High variety impacts on master production schedule: a case study from the automotive industry. IFAC-PapersOnLine, 48(3), 1073-1078. https://doi.org/10.1016/j.ifacol.2015.06.226
Chatras, C., Giard, V., & Sali, M. (2016). Mass customisation impact on bill of materials structure and master production schedule development. International Journal of Production Research, 54(18), 5634-5650. http://doi.org/10.1080/00207543.2016.1194539
Dieppe, A. (s.f.). Global productivity: Trends, drivers, and policies. The World Bank Group. Recuperado el 10 de mayo del 2022. https://www.worldbank.org/en/research/publication/global-productivity
Entringer, T. C., & Ferreira, A. D. S. (2020, marzo-abril). A reference model in BPMN for conceptual modelling of master planning schedule. Independent Journal of Management & Production, 11(2), 394-418.
Fondo Monetario Internacional. (2021, abril). La economía mundial se está afianzando, pero con recuperaciones divergentes en medio de aguda incertidumbre. https://www.imf.org/es/Publications/WEO/Issues/2021/03/23/world-economic-outlook-april-2021
Jonsson, P., & Kjellsdotter L. (2015). Improving performance with sophisticated master production scheduling. International Journal of Production Economics, 168, 118-130. http://doi.org/10.1016/j.ijpe.2015.06.012
Khaledi, H., & Reisi-Nafchi, M. (2013). Dynamic production planning model: A dynamic programming approach. The International Journal of Advanced Manufacturing Technology, 67, 1675-1681. http://dx.doi.org/10.1007/s00170-012-4600-7
Martín, A., G., Díaz-Madroñero, M., & Mula, J. (2020). Master production schedule using robust optimization approaches in an automobile second-tier supplier: Central European Journal of Operations Research, 28, 143-166. http://dx.doi.org/10.1007/s10100-019-00607-2
Ministerio de la producción. (s.f.). Desempeño del sector industrial manufacturera - marzo 2022. Recuperado el 10 de mayo del 2021, de https://ogeiee.produce.gob.pe/index.php/en/shortcode/estadistica-oee/estadisticas-manufactura
Olhager, J. (2013) Evolution of operations planning and control: From production to supply chains, International Journal of Production Research, 51(23-24), 6836-6843. http://dx.doi.org/10.1080/00207543.2012.761363
Razavi Hajiagha, S. H., Sadat Hashemi, S., & Sadeghi, M. (2019). Hybrid fuzzy-stochastic approach to multi-product, multi-period, and multi-resource master production scheduling problem: Case of a polyethylene pipe and fitting manufacturer. Scientia Iranica, 26(3), 1809-1823. http://scientiairanica.sharif.edu/article_20329_a8ada7d622b089fe557d37bc2b94d04b.pdf
Reuter, C., & Brambring, F. (2016). Improving data consistency in production control. Procedia CIRP, 41, 51-56. http://doi.org/10.1016/j.procir.2015.12.116
Sun, L. B., Guo, S. S., Tao, S. Q., Li, Y. B., & Du, B. G. (2014). A master production schedule warning approach for cement equipment manufacturing enterprises. Scientia Iranica, 21(3), 1120-1127. http://scientiairanica.sharif.edu/article_3547.html
Supriyanto, I., & Noche, B. (2011). Fuzzy multi-objective linear programming and simulation approach to the development of valid and realistic master production schedule. Logistics Journal: Proceedings, 7. http://doi.org/10.2195/LJ_proc_supriyanto_de_201108_01
Wang, L.-C., & Cheng, C.-Y. (2014). Development of an integrated demand-supply balancing system for supply chain exception handling. International Journal of Information Systems and Change Management, 7(1), 70-91. http://doi.org/10.1504/IJISCM.2014.065059
Wörbelauer, M., Meyr, H., & Almada-Lobo, B. (2019). Simultaneous lotsizing and scheduling considering secondary resources: A general model, literature review and classification. OR Spectrum, 41(1), 1-43. http://doi.org/10.1007/s00291-018-0536-0
Zijm, H., & Schutten, M. (2019). Advanced production planning and scheduling systems. En Zijm, H., Klumpp, M., Regattieri, A., Heragu, S. (Eds.) Operations, Logistics and Supply Chain Management. Lecture Notes in Logistics (pp. 417-439). Springer, Cham. https://doi.org/10.1007/978-3-319-92447-2_19
