Integration of Machine Learning Models for Predictive Maintenance in SAP Financial Operations

Authors: Surya Sai Ram Parimi

DOI: https://doi.org/https://doi.org/10.5281/zenodo.12805533

Short DOI: https://doi.org/gt442j

Country: India

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Abstract: Predictive maintenance, powered by machine learning models, offers a proactive approach to enhancing the reliability and efficiency of SAP financial operations. This survey explores the integration of predictive maintenance models within SAP systems, highlighting their potential to prevent financial system downtime, detect and prevent fraud, optimize financial asset management, ensure data integrity, and automate compliance monitoring. By examining data collection and preprocessing methods, model selection criteria, and deployment strategies, this paper provides a comprehensive overview of best practices and challenges in implementing predictive maintenance. Successful case studies from various industries demonstrate significant benefits, including reduced downtimes, cost savings, and improved compliance. Despite the challenges of data quality, model accuracy, real-time processing, and regulatory compliance, the continuous evolution of machine learning technologies promises new opportunities for innovation in financial operations. This survey underscores the strategic importance of integrating predictive maintenance into SAP financial systems, offering valuable insights for researchers and practitioners aiming to enhance financial management through advanced analytics.

Keywords: Predictive Maintenance, Machine Learning, SAP Financial Operations, Real-Time Processing

Paper Id: 230796

Published On: 2024-01-02

Published In: Volume 12, Issue 1, January-February 2024

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