A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

[EN] This document presents a comparison of demand forecasting methods, with the aim of improving demand forecasting and with it, the production planning system of Ecuadorian textile industry. These industries present problems in providing a reliable estimate of future demand due to recent changes in the Ecuadorian context. The impact on demand for textile products has been observed in variables such as sales prices and manufacturing costs, manufacturing gross domestic product and the unemployment rate. Being indicators that determine to a great extent, the quality and accuracy of the forecast, generating also, uncertainty scenarios. For this reason, the aim of this work is focused on the demand forecasting for textile products by comparing a set of classic methods such as ARIMA, STL Decomposition, Holt-Winters and machine learning, Artificial Neural Networks, Bayesian Networks, Random Forest, Support Vector Machine, taking into consideration all the above mentioned, as an essential input for the production planning and sales of the textile industries. And as a support, when developing strategies for demand management and medium-term decision making of this sector under study. Finally, the effectiveness of the methods is demonstrated by comparing them with different indicators that evaluate the forecast error, with the Multi-layer Neural Networks having the best results with the least error and the best performance.The authors are greatly grateful by the support given by the SDAS Research Group (https://sdas-group.com/).Lorente-Leyva, LL.; Alemany Díaz, MDM.; Peluffo-Ordóñez, DH.; Herrera-Granda, ID. (2021). A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry. Lecture Notes in Computer Science. 131-142. https://doi.org/10.1007/978-3-030-64580-9_11S131142Silva, P.C.L., Sadaei, H.J., Ballini, R., Guimaraes, F.G.: Probabilistic forecasting with fuzzy time series. IEEE Trans. Fuzzy Syst. (2019). https://doi.org/10.1109/TFUZZ.2019.2922152Lorente-Leyva, L.L., et al.: Optimization of the master production scheduling in a textile industry using genetic algorithm. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds.) HAIS 2019. LNCS (LNAI), vol. 11734, pp. 674–685. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29859-3_57Seifert, M., Siemsen, E., Hadida, A.L., Eisingerich, A.B.: Effective judgmental forecasting in the context of fashion products. J. Oper. Manag. 36, 33–45 (2015). https://doi.org/10.1016/j.jom.2015.02.001Tratar, L.F., Strmčnik, E.: Forecasting methods in engineering. IOP Conf. Ser. Mater. Sci. Eng. 657, 012027 (2019). https://doi.org/10.1088/1757-899X/657/1/012027Prak, D., Teunter, R.: A general method for addressing forecasting uncertainty in inventory models. Int. J. Forecast. 35, 224–238 (2019). https://doi.org/10.1016/j.ijforecast.2017.11.004Gaba, A., Tsetlin, I., Winkler, R.L.: Combining interval forecasts. Decis. Anal. 14, 1–20 (2017). https://doi.org/10.1287/deca.2016.0340Zhang, B., Duan, D., Ma, Y.: Multi-product expedited ordering with demand forecast updates. Int. J. Prod. Econ. 206, 196–208 (2018). https://doi.org/10.1016/j.ijpe.2018.09.034Januschowski, T., et al.: Criteria for classifying forecasting methods. Int. J. Forecast. 36, 167–177 (2020). https://doi.org/10.1016/j.ijforecast.2019.05.008Box, G.E., Jenkins, G.M., Reinsel, C., Ljung, M.: Time Series Analysis: Forecasting and Control, 5th edn. Wiley, Hoboken (2015)Murray, P.W., Agard, B., Barajas, M.A.: Forecast of individual customer’s demand from a large and noisy dataset. Comput. Ind. Eng. 118, 33–43 (2018). https://doi.org/10.1016/j.cie.2018.02.007Bruzda, J.: Quantile smoothing in supply chain and logistics forecasting. Int. J. Prod. Econ. 208, 122–139 (2019). https://doi.org/10.1016/j.ijpe.2018.11.015Bajari, P., Nekipelov, D., Ryan, S.P., Yang, M.: Machine learning methods for demand estimation. Am. Econ. Rev. 105, 481–485 (2015). https://doi.org/10.1257/aer.p20151021Villegas, M.A., Pedregal, D.J., Trapero, J.R.: A support vector machine for model selection in demand forecasting applications. Comput. Ind. Eng. 121, 1–7 (2018). https://doi.org/10.1016/j.cie.2018.04.042Herrera-Granda, I.D., et al.: Artificial neural networks for bottled water demand forecasting: a small business case study. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2019. LNCS, vol. 11507, pp. 362–373. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20518-8_31Dudek, G.: Multilayer perceptron for short-term load forecasting: from global to local approach. Neural Comput. Appl. 32(8), 3695–3707 (2019). https://doi.org/10.1007/s00521-019-04130-ySalinas, D., Flunkert, V., Gasthaus, J., Januschowski, T.: DeepAR: probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast. (2019). https://doi.org/10.1016/j.ijforecast.2019.07.001Weng, Y., Wang, X., Hua, J., Wang, H., Kang, M., Wang, F.Y.: Forecasting horticultural products price using ARIMA model and neural network based on a large-scale data set collected by web crawler. IEEE Trans. Comput. Soc. Syst. 6, 547–553 (2019). https://doi.org/10.1109/TCSS.2019.2914499Zhang, X., Zheng, Y., Wang, S.: A demand forecasting method based on stochastic frontier analysis and model average: an application in air travel demand forecasting. J. Syst. Sci. Complexity 32(2), 615–633 (2019). https://doi.org/10.1007/s11424-018-7093-0Lorente-Leyva, L.L., et al.: Artificial neural networks for urban water demand forecasting: a case study. J. Phys: Conf. Ser. 1284(1), 012004 (2019). https://doi.org/10.1088/1742-6596/1284/1/012004Scott, S.L., Varian, H.R.: Predicting the present with Bayesian structural time series. Int. J. Math. Model. Numer. Optim. 5, 4–23 (2014). https://doi.org/10.1504/IJMMNO.2014.059942Gallego, V., Suárez-García, P., Angulo, P., Gómez-Ullate, D.: Assessing the effect of advertising expenditures upon sales: a Bayesian structural time series model. Appl. Stoch. Model. Bus. Ind. 35, 479–491 (2019). https://doi.org/10.1002/asmb.2460Han, S., Ko, Y., Kim, J., Hong, T.: Housing market trend forecasts through statistical comparisons based on big data analytic methods. J. Manag. Eng. 34 (2018). https://doi.org/10.1061/(ASCE)ME.1943-5479.0000583Lee, J.: A neural network method for nonlinear time series analysis. J. Time Ser. Econom. 11, 1–18 (2019). https://doi.org/10.1515/jtse-2016-0011Trull, O., García-Díaz, J.C., Troncoso, A.: Initialization methods for multiple seasonal holt-winters forecasting models. Mathematics 8, 1–16 (2020). https://doi.org/10.3390/math8020268Biau, G., Scornet, E.: A random forest guided tour. Test 25(2), 197–227 (2016). https://doi.org/10.1007/s11749-016-0481-

Primary Health Care from the perception of women living in a rural area

AbstractOBJECTIVEUnderstanding the perception of women living in a rural area about the actions and services of Primary Health Care (PHC) in a municipality of southern Brazil, which is the only one regarded as predominantly rural.METHODA descriptive study of qualitative approach, carried out with women who lived in the countryside and required health services in the 15 days prior to collection.RESULTSThe results registered low fidelity to PHC attributes, focusing its functional axis on sickness, transforming the unit into small points of emergency care and a bureaucratic place where patients are referred to other types of services. The quality of service offered is compromised to offering quick, fragmented and unequal treatment in the rural context.CONCLUSIONThe findings of this study highlight the need for greater efforts in order to adequate the new care model in the development of appropriate actions as designated by PHC in the rural context studied

Aplicação da geotecnologia no estudo de cadastro técnico rural e no mapeamento de áreas de preservação permanente e reservas legais

Este trabalho teve como objetivo principal mapear as classes de cobertura e o uso da terra, bem como as áreas de preservação permanente (APPs) e de reservas legais de imóveis rurais. A área de estudo compreendeu parte dos municípios de Canaã, Araponga e Ervália, Estado de Minas Gerais. Foi utilizada uma imagem ortorretificada de alta resolução do sensor Ikonos II com 1 m de resolução espacial. A partir da interpretação visual da imagem, foram criadas sete classes temáticas, a saber: cobertura florestal, pasto sujo, pasto limpo, cafezal, edificações, área agrícola e reflorestamento. As APPs foram obtidas a partir de um modelo digital de elevação hidrologicamente consistente. Os resultados mostraram a predominância das classes de cafezal com 24,5% e de cobertura florestal com 28,8%, perfazendo mais de 50% da área de estudo. As áreas delimitadas como de preservação permanente totalizaram 55,1%

Community policing does not build citizen trust in police or reduce crime in the Global South.

Is it possible to reduce crime without exacerbating adversarial relationships between police and citizens? Community policing is a celebrated reform with that aim, which is now adopted on six continents. However, the evidence base is limited, studying reform components in isolation in a limited set of countries, and remaining largely silent on citizen-police trust. We designed six field experiments with Global South police agencies to study locally designed models of community policing using coordinated measures of crime and the attitudes and behaviors of citizens and police. In a preregistered meta-analysis, we found that these interventions led to mixed implementation, largely failed to improve citizen-police relations, and did not reduce crime. Societies may need to implement structural changes first for incremental police reforms such as community policing to succeed