Procedure for demand forecasting in a cuban micro and SME
DOI:
https://doi.org/10.53591/easi.v1i2.1783Keywords:
operations management, SMEs, demand forecastingAbstract
Demand forecasting is a fundamental element in the planning of micro, small and medium-sized enterprises (MSMEs). In this sense, the objective of this work is to develop the monthly demand forecast of the production in a footwear manufacturing company, using the methodological tool that best suits the conditions of the entity under study. The proposed procedure allows the selection of the forecast model that best suits the specificities of the demand, as well as develops a group of equations that facilitate the development of the forecast considering the predominant trend and seasonality patterns in the historical data series. The practical application of the proposal highlights the advantage of adopting techniques, methods and tools of Operations Management in Mypymes. With this application, in a footwear manufacturing company, it is possible to identify the most appropriate forecasting method according to the characteristic parameters of the studied demand (trend and seasonality), as well as to forecast the monthly sales of shoes for the year 2022 with an accurate estimated level of precision (according to the error measure considered).
References
Badulescu, Y., Hameri, A.-P. & Cheikhrouhou, N. (2021). Evaluating demand forecasting models using multi-criteria decision-making approach. Journal of Advances in Management Research, 18(5), 661-683. https://doi.org/10.1108/JAMR-05-2020-0080
Bisht, D. C. S. & Ram, M. (2022). Recent Advances in Time Series Forecasting. Editorial CRC Press.
Bozarth, C., & Handfield, R. (2019). Introduction to operations and supply chain management. (5a ed.). Editorial Pearson.
Cachon, G. & Terwiesch, C. (2020). Matching Supply with Demand. An Introduction to Operations Management. (4a ed.). Editorial McGraw Hill Education.
Collier, D. A. & Evans, J. R. (2021). Operations and Supply Chain Management. (2a ed.). Editorial CENGAGE.
Ensafia, Y., Amin, S. H., Zhang, G. & Shah, B. (2022). Time-series forecasting of seasonal items sales using machine learning. A comparative analysis. International Journal of Information Management Data Insights, 2(1), 100058. https://doi.org/10.1016/j.jjimei.2022.100058
Goyes Noboa, J. E., Alvarado Pacheco, E. S., Gavilánez Cárdenas, C. V. & Arguello Pazmiño, A. M. (2020). Evolución de las Mipymes según el ciclo de vida. Revista Universidad y Sociedad, 12(S1), 47-52. https://rus.ucf.edu.cu/index.php/rus/article/view/1750
Heizer, J., Render, B. & Munson, C. (2020). Operations Management. Sustainability and Supply Chain Management. (13a ed.). Editorial Pearson.
Helmold, M. (2020). Lean Management and Kaizen. Fundamentals from Cases and Examples in Operations and Supply Chain Management. Editorial Springer.
Jacobs, F. R. & Chase, R. B. (2020). Operations and Supply Chain Management. The Core. (5a ed.). Editorial McGraw Hill Education.
Jewalikar, A. D. & Shelke, A. (2017). Lean Integrated Management Systems in MSME Reasons, Advantages and Barriers on Implementation. Materials Today: Proceedings, 4(2), 1037–1044. https://doi.org/10.1016/j.matpr.2017.01.117
Jiang, W., Wu, X., Gong, Y., Yu, W. & Zhong, X. (2020). HolteWinters smoothing enhanced by fruit fly optimization algorithm to forecast monthly electricity consumption. Energy, 193, 1-8. https://doi.org/10.1016/j.energy.2019.116779
Johnston, R., Shulver, M., Slack, N. & Clark, G. (2021). Service operations management. (5a ed.). Editorial Pearson.
Krajewski, L. J. & Malhotra, M. K. (2022). Operations Management. Processes and Supply Chains. (13a ed.) Editorial Pearson.
Ministerio de Justicia (2021). Decreto-Ley 46 Sobre las Micro, Pequeñas y Medianas Empresas. Gaceta Oficial de la república de Cuba, No. 94 Ordinaria, 19 de agosto.
Mulchandani, K., Jasrotia, S. S. & Mulchandani, K. (2022). Determining supply chain effectiveness for Indian MSMEs: A structural equation modelling approach. Asia Pacific Management Review. https://doi.org/10.1016/j.apmrv.2022.04.001
Omar, M. O. & Kawamukai, H. (2021). Prediction of NDVI using the Holt-Winters model in high and low vegetation regions: A case study of East Africa. Scientific African, 14, e01020. https://doi.org/10.1016/j.sciaf.2021.e01020
Schroeder, R. & Goldstein, S. M. (2021). Operations managements in the supply chain. Decisions and cases. (8a ed.). Editorial McGraw Hill Education.
Stevenson, W. J. (2021). Operations management. (14a ed.). Editorial McGraw Hill Education.
Vásquez, J., Aguirre, S., Puertas, E., Bruno, G., Priarone, P. C. & Settineri, L. (2021). A sustainability maturity model for micro, small and medium-sized enterprises (MSMEs) based on a data analytics evaluation approach. Journal of Cleaner Production, 311, 127692. https://doi.org/10.1016/j.jclepro.2021.127692
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