Predictive SLA Management: Leveraging Machine Learning to Improve Upstream Feed Reliability

Abstract

In order to improve the reliability of upstream feeds, this study investigates the use of machine learning approaches for the management of anticipatory Service Level Agreements (SLAs). Random Forest, Support Vector Machine, and Gradient Boosting Machine (GBM) were the three models that were constructed and assessed with the help of historical service level agreement (SLA) and operational data. The GBM model displayed exceptional performance, with an accuracy of 94.1%, which enabled it to accurately predict service level agreement (SLA) breaches and carry out proactive interventions. After using predictive service level agreement management, there was a considerable decrease in the number of feed disruptions (26.7%), the average duration of interruptions (34.2%), and the total amount of downtime (51.8%). In addition, the operations team provided qualitative input that emphasized improvements in maintenance planning, a reduction in the number of emergency interventions, and an increase in the level of satisfaction with feed reliability. These findings provide further evidence that incorporating machine learning-driven predictive analytics into service level agreement management (SLA) management improves operational efficiency, decreases downtime, and strengthens decision-making capability in upstream feed operations.