Resumen

MORALES CASTRO, Arturo; RAMIREZ REYES, Eliseo  y  RODRIGUEZ ALBOR, Gustavo. SALES FORECAST OF COMPANIES IN THE FOOD SECTOR: A NEURAL NETWORKS APPLIANCE. Semest. Econ. [online]. 2019, vol.22, n.52, pp.161-177. ISSN 0120-6346.  https://doi.org/10.22395/seec.v22n52a7.

The goal of this research is to forecast the sales of the following companies: Industrias Bachoco, Grupo Bafar, Grupo Bimbo, Gruma, Grupo Herdez, Grupo Lala y Grupo Industrial Maseca in the period 2006-2015 through a linear model (linear regression) and a non-linear model (artificial neural networks, decision tables, decision tress and gaussian process) for measuring the performance of each of these models and selecting for each company the model which adjusts more precisely to the historical data. As a result, in the 2006-2015 period the multiple linear regression models show a better performance in determining the sales of Bachoco, Bafar, Herdez, Lala and Maseca with more than 90% of the data recovered within this period.

JEL CODE: E31, C45, C55.

CONTENTS: Introduction; 1. Integration of the forecast within the management activities; 2. Forecasts with artificial neural networks; 3. Methodology; 4. Results; 5. Conclusions; Bibliography.

Palabras clave : Sales forecast; economic-financial variables; data mining; linear regression; food companies.

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