Enhancing Enterprise Resource Planning (Erp) Efficiency: A Comparative Analysis of AI And Ml Applications Across Network Implementations

Abstract

This paper presents a comparative analysis of sales forecasting models, focusing on the Novel Convolutional Neural Network (CNN), Random Forest, and Decision Tree approaches. Leveraging Mean Absolute Error (MAE), RMSE metrics, we evaluate the accuracy of each model in predicting sales values within enterprise resource planning (ERP) systems. Our findings demonstrate that the Novel-CNN model consistently outperforms Random Forest and Decision Tree models, exhibiting lower MAE and RMSE values. The superiority of the Novel-CNN approach underscores its potential for enhancing sales forecasting accuracy and facilitating informed decision-making in diverse business environments. This research contributes valuable insights for organizations seeking to optimize their sales strategies and leverage advanced machine learning techniques for improved performance in ERP systems.