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@article{189362,
        author = {Aalok Kumar Dubey and Dr. Ajay Jain},
        title = {OPTIMIZING BUSINESS OPERATIONS: LEVERAGING AI AND ML IN DIVERSE ERP ENVIRONMENTS AND NETWORK INFRASTRUCTURES},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {7},
        pages = {8073-8080},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189362},
        abstract = {This paper investigates the efficacy of sales forecasting within enterprise resource planning (ERP) systems using advanced machine learning models, with a focus on the proposed novel Convolutional Neural Network (CNN) algorithm. Two key performance metrics, namely Mean Absolute Percentage Error (MAPE) and Forecast Bias, are analyzed to assess the accuracy and reliability of the forecasting models. Through a comparative study involving the Novel CNN, Random Forest, and Decision Tree approaches, our research examines their respective capabilities in predicting sales values. The findings reveal that the Novel CNN model demonstrates superior performance, exhibiting lower MAPE values and forecast bias compared to the other models. Notably, the Novel CNN model showcases the lowest median bias and the narrowest spread of data points, highlighting its potential for precise and consistent sales forecasting. By leveraging the proposed Novel CNN algorithm, organizations can enhance decision-making processes, optimize resource allocation, and gain a competitive edge in today's dynamic business landscape. This study underscores the significance of innovative machine learning techniques in driving operational efficiency and fostering growth within ERP environments.},
        keywords = {MAPE, Forecast Bias, Novel-CNN, ERP, ML},
        month = {January},
        }