Predictive model based on machine learning for raw material purchasing management in the retail sector.
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Fecha de publicación
2024-06-28
Metadatos
Mostrar el registro completo del ítemEditorial
Association for Computing MachineryJournal
ACM International Conference Proceeding SeriesDOI
10.1145/3677454.3677456Resumen
Making raw material purchase forecasts for companies is very difficult and, if inadequately controlled, can affect the company's decision making and profitability. Currently, there are optimized systems or mathematical models to try to predict the demands and solve this problem. In this study, a raw material purchase prediction model is proposed that uses the Elastic Net algorithm to analyze historical sales and inventory data. The model is used to improve prediction accuracy, allowing SMEs to optimize inventories, reduce costs and improve efficiency. Experimental results indicate that the proposed model obtains better results in the MAE, RMSE and R2 indicators.Tipo
info:eu-repo/semantics/articleDerechos
info:eu-repo/semantics/embargoedAccessIdioma
engae974a485f413a2113503eed53cd6c53
10.1145/3677454.3677456
Scopus Count
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