Evaluación de algoritmos de predicción de la demanda en la industria alimentaria: comparación de métodos de aprendizaje automático y de series temporales
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Date
2025-10-16
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Ecuador: Latacunga: Universidad Técnica de Cotopaxi (UTC)
Abstract
Production planning in the food industry faces several chal lenges due to the absence of current technology and appropriate forecast ing techniques. This paper proposes to improve forecast by comparing machine learning algorithms and time series method. Three forecasting methods were evaluated: i) Crystal Ball, using the SARIMA model; ii) Weka, using neural networks; and iii) Machine learning models, created in Python, with algorithms such as Random Forest (RF). Error met rics such as MAE, RMSE, MAPE and R2 were evaluated, in addition to a survey based on the System Usability Scale (SUS) to assess ease of use. The case study was developed in a company that uses traditional tools such as Microsoft Excel. The results revealed that Weka was the most accurate in product 001002 with a MAPE of 1%, RMSE of 8.66 and MAEof 6.58. In terms of usability, Crystal Ball achieved the highest score (70 points), outperforming the other two proposals, indicating that the software selection is based on a balance between accuracy and ease of use. The incorporation of sophisticated models enhances demand an ticipation, improving inventory management and operational efficiency.
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Keywords
MACHINE LEARNING, OPTIMIZATION, FORECASTING, SUS
Citation
Gualotuña, M., Toapanta, K.P. y Naranjo, J.E. (2026). Evaluación de algoritmos de predicción de la demanda en la industria alimentaria: comparación de métodos de aprendizaje automático y series temporales. En: Arai, K. (ed.) Actas de la Conferencia de Tecnologías del Futuro (FTC) 2025, Volumen 2. FTC 2025. Lecture Notes in Networks and Systems, vol. 1676. Springer, Cham. https://doi.org/10.1007/978-3-032-07989-3_33