Improving Demand Forecasting by Implementing Machine Learning in Poultry Production Company

• Autores: Garcia Arismendiz, Joaquin Antonio, Huertas Zúñiga, Sandra Larissa, Lizárraga Portugal, Carlos Augusto, Quiroz Flores, Juan Carlos, García López, Yván Jesús
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/17993
• Enlace del recurso: https://hdl.handle.net/20.500.12724/17993; https://doi.org/10.14445/22315381/IJETT-V71I2P205
• Nivel de acceso: acceso abierto
• Materia: Sales forecasting; Poultry industry; Machine learning; Time-series analysis; Supply chain management; Data mining; Food industry and trade; Perishable goods; Inventory control; Supply and demand; https://purl.org/pe-repo/ocde/ford#2.11.04

The use of manual methods to forecast demand in perishable food companies is generally subject to the variability of internal and external factors in the company, causing excess inventories and significant monetary losses, so it is relevant to carry out this research with the objective of to demonstrate that by implementing Machine Learning it is possible to improve the accuracy of the demand forecast. A case study in a company in the poultry sector in Peru, forecasting the last quarter of 2022, based on a real sales database and applying the time series method, comparing the results of the Machine Learning model, and obtaining as a result in a model with high Forecast Accuracy (FA) of 97.56% and a high Forecast Bias (FB) of 2.44%. The research is an important contribution to knowledge, demonstrating that Machine Learning is an ideal tool to project the demand for perishable food products, ideal for its application in various fields, such as loss reduction control, preventive maintenance of machines and control of supplies such as water and energy, among others.