Predictive Effectiveness of Machine Learning and Traditional Models in Production and Sales: A Systematic Literature Review (#422)

Authors

Farroñan-Soplapuco, Carol Nicoll

Yalico-Fernandez, Manuel Adrian

Abstract

In recent years, the application of Machine Learning (ML) and Deep Learning (DL) techniques in sales forecasting has gained significant relevance as a strategic tool to optimize business processes and decision-making. This Systematic Literature Review (SLR) aims to identify the most widely used models and assess their effectiveness in sales estimation across various commercial settings. Following the PRISMA methodology, five academic articles published between 2022 and 2025 were analyzed. The results indicate that the most commonly employed models are Random Forest, XGBoost, LSTM, and CNN, all of which outperform traditional methods such as ARIMA and linear regression. It is noteworthy that DL techniques and hybrid models achieve R² values above 90% and mean absolute percentage errors (MAPE) below 10%, confirming their effectiveness in multivariable and dynamic contexts.