Data-driven predictive maintenance and analytics in SAP environments enhanced by machine learning
1 American National Insurance Company, One Moody Plaza, Galveston, TX, USA.
2 Tractor Supply Company, 5401 Virginia Way, Brentwood, TN, USA.
2 Tractor Supply Company, 5401 Virginia Way, Brentwood, TN, USA.
Review Article
World Journal of Advanced Research and Reviews, 2023, 17(02), 926–932
Publication history:
Received on 02 January 2022; revised on 21 February 2023; accepted on 24 February 2023
Abstract:
This article explores how data-driven predictive maintenance, enhanced by machine learning, is transforming SAP environments. It highlights the benefits of integrating predictive maintenance with SAP, made possible by robust data collection, real-time monitoring, and predictive models. Incorporating these models within the SAP Analytics Cloud (SAC) significantly boosts their efficiency and scalability. The advantages include reduced downtime, cost savings, improved asset lifespan, enhanced operational efficiency, and data-driven decision-making. This approach not only anticipates equipment failures but also optimizes maintenance schedules and resource allocation. The article also acknowledges challenges such as data quality, integration complexity, skill requirements, and scalability. Ultimately, the fusion of machine learning and predictive analytics within SAC is set to redefine enterprise resource planning and asset management, providing valuable insights and proactive solutions across various business processes.
Keywords:
Predictive maintenance; Predictive Analytics; SAP; Machine Learning; ERP; Artificial Intelligence; SAP Analytic Cloud (SAC); SAP Cloud
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0