TY - GEN
T1 - Predictive model based on machine learning for raw material purchasing management in the retail sector.
AU - Antunez, Julio C.
AU - Salazar, Johnny D.
AU - Castañeda, Pedro S.
N1 - Publisher Copyright:
© 2024 ACM.
PY - 2024/6/28
Y1 - 2024/6/28
N2 - 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.
AB - 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.
KW - Inventory management
KW - Model interpretation
KW - SMEs
UR - https://www.scopus.com/pages/publications/85202845935
U2 - 10.1145/3677454.3677456
DO - 10.1145/3677454.3677456
M3 - Contribución a la conferencia
AN - SCOPUS:85202845935
T3 - ACM International Conference Proceeding Series
SP - 6
EP - 11
BT - ARAEML 2024 - 2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning
PB - Association for Computing Machinery
T2 - 2024 International Conference on Advanced Robotics, Automation Engineering and Machine Learning, ARAEML 2024
Y2 - 28 June 2024 through 30 June 2024
ER -