E-ISSN:2583-2468

Research Article

Machine Learning

Applied Science and Engineering Journal for Advanced Research

2025 Volume 4 Number 4 July
Publisherwww.singhpublication.com

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

Bavadiya P1*
DOI:10.5281/zenodo.17103533

1* Pathik Bavadiya, Vice President, Production Services (Independent Researcher), BNY, New York, USA.

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.

Keywords: predictive sla management, machine learning, upstream feed reliability, gradient boosting machine, operational efficiency, downtime reduction

Corresponding Author How to Cite this Article To Browse
Pathik Bavadiya, Vice President, Production Services (Independent Researcher), BNY, New York, USA.
Email: 		Bavadiya P, Predictive SLA Management: Leveraging Machine Learning to Improve Upstream Feed Reliability. Appl Sci Eng J Adv Res. 2025;4(4):53-58.
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https://asejar.singhpublication.com/index.php/ojs/article/view/162

Manuscript Received Review Round 1 Review Round 2 Review Round 3 Accepted
2025-06-20 2025-07-07 2025-07-26
Conflict of Interest Funding Ethical Approval Plagiarism X-checker Note
None Nil Yes 4.32

© 2025 by Bavadiya P and Published by Singh Publication. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/ unported [CC BY 4.0].

Appl Sci Eng J Adv Res 2025;4(4)53
Download PDFBack To Article1. Introduction2. Literature
Review3. Research
Methodology4. Data Analysis
and Interpretation5. ConclusionReferences
Bavadiya P. Predictive SLA Management

1. Introduction

In the continually changing industrial world of today, it is essential to maintain high levels of operational reliability in order to guarantee that upstream feed procedures are carried out without any interruptions. Service Level Agreements, also known as SLAs, are extremely important mechanisms for establishing the performance and dependability requirements that are anticipated between service providers and their customers. On the other hand, traditional service level agreement (SLA) management frequently focuses on reactive approaches, which can result in delayed responses to system failures or breaches, resulting in costly downtime and the inefficiency of operational processes. An increasing number of opportunities to transition from reactive to predictive service level agreement management have emerged as a result of the development of powerful data analytics and machine learning approaches. enterprises are able to proactively identify possible service level agreement (SLA) breaches before they occur by utilizing machine learning models to examine historical performance data and real-time operational factors. This enables enterprises to take early interventions to avoid disruptions and boost feed dependability.

Not only can predictive service level agreement management that makes use of machine learning enhance the accuracy of anticipating probable failures, but it also optimizes the allocation of resources, the scheduling of maintenance, and the decision-making processes. Unlike traditional approaches, which frequently struggle to successfully process vast amounts of data and complicated, nonlinear patterns, machine learning algorithms are able to manage both of these types of characteristics. In upstream feed systems, where the reliability of the system is affected by a number of interdependent elements, including environmental conditions, the health of the equipment, and operational loads, this capacity is extremely significant. Through the incorporation of machine learning into service level agreement (SLA) management frameworks, businesses have the ability to dramatically cut down on unplanned downtime, minimize operating costs, and improve overall service quality. In this study, the creation and deployment of predictive machine learning models to improve service level agreement (SLA) management are investigated.

Particular attention is paid to the influence these models have on the reliability of upstream feeds as well as the broader implications for operational efficiency.

1.1. Research Objectives

  • Develop and evaluate machine learning models to predict SLA breaches in upstream feed operations.
  • Compare the predictive performance of Random Forest, SVM, and Gradient Boosting Machine models.
  • Assess the impact of predictive SLA management on feed reliability metrics such as interruptions and downtime.
  • Gather and analyze operations team feedback to evaluate the practical effectiveness of the predictive SLA system.
  • Demonstrate how machine learning-driven predictive analytics can improve operational efficiency and decision-making in upstream feed management.

2. Literature Review

Kalusivalingam et al. (2022) In order to achieve continuous improvement in smart industrial environments, we investigated the possibility of integrating reinforcement learning with predictive analytics. The results of their research revealed that combining these cutting-edge machine learning approaches might increase the efficiency of production processes by enabling adaptive decision-making and modifications to be made in real time. In addition to highlighting considerable improvements in operational efficiency and resource usage, the research also emphasized the potential for AI-driven models to change industrial operations.

Liang et al. (2024) a thorough investigation on the implications of machine learning-enhanced open Radio Access Networks (RAN) for energy usage was carried out. The work that they did involved analyzing the trade-offs that are connected with the deployment of machine learning algorithms in network infrastructure. These trade-offs included performance improvements and energy expenses.


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Bavadiya P. Predictive SLA Management

The research offered significant insights into the optimization of energy efficiency without sacrificing system dependability, thereby contributing to the achievement of sustainable development goals in the field of telecommunications research.

Næss (2023) An investigation on the use of machine learning for demand forecasting in the food manufacturing industry was carried out utilizing data collected from point-of-sale systems. The results of his research shown that a number of different predictive models are useful in precisely forecasting demand, which contributed to the reduction of waste and the improvement of supply chain management. In addition to highlighting the significance of making decisions based on data, the thesis illustrated how machine learning has the potential to improve operational planning and responsiveness in the face of dynamic market conditions.

Nwulu et al. (2024) inside the oil and gas business, an investigation was conducted into the utilization of predictive modeling to improve the reliability of equipment. According to the findings of their research, a general framework was provided that made use of previous operational data and machine learning algorithms in order to foresee breakdowns of equipment and schedule preventative maintenance. When it comes to improving asset management and operational continuity in complex industrial contexts, the results indicated considerable benefits in decreasing unplanned downtime and optimizing maintenance resources. This highlights the value of predictive analytics in that regard.

Puligheddu (2022) management architectures for edge services within 5G networks that were powered by machine learning were thoroughly investigated. Increasing service latency, reliability, and scalability were the primary goals of the project, which focused on the deployment of intelligent algorithms at the network edge. The research demonstrated how machine learning might be used to dynamically manage network resources and maximize performance, so allowing edge computing infrastructures that are more efficient and adaptable. Through the incorporation of artificial intelligence for real-time network management, this effort made a contribution to the advancement of next-generation telecommunications.

3. Research Methodology

Through the use of a mixed-methods approach, this study analyzed historical service level agreement (SLA) data using machine learning models and gathered input from operations team members through surveys. The GBM, which is the best-performing model, was utilized in order to enhance the feed reliability. The results demonstrated a decrease in interruptions and downtime, which was reinforced by good comments from the team, so validating the effectiveness of the system.

3.1 Research Design

For the purpose of determining whether or not machine learning-based predictive service level agreement (SLA) management is useful in enhancing upstream feed dependability, this study utilized a mixed-methods research design that combined qualitative and quantitative analytical approaches. The quantitative component consisted of the development, training, and testing of multiple machine learning models by making use of historical service level agreements (SLA) and operational data. On the other hand, the qualitative component consisted of gathering feedback from the operations team through structured surveys and interviews in order to evaluate the practical impact of the system that was implemented.

3.2 Data Collection

For the purpose of this investigation, quantitative data were collected from the operational database of the organization. These data included historical service level agreement (SLA) performance records, feed parameters, and environmental conditions. Additionally, instances of SLA breaches and normal operations were labeled in order to facilitate supervised machine learning. Furthermore, for the purpose of comparison, data on feed disruptions, downtime, and maintenance logs were collected both before and after the implementation of predictive service level agreement management. In order to evaluate the perspectives of the operations team regarding the influence of predictive alerts on maintenance planning, emergency interventions, and overall feed reliability, qualitative data was collected through the use of a structured questionnaire that utilized a Likert scale with five points. In addition, interviews were conducted in order to investigate the system's advantages and disadvantages in significantly greater detail.


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3.3 Model Development and Evaluation

The prediction of service level agreement (SLA) violations was accomplished through the utilization of three machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM). In order to enhance the robustness of the models, the dataset was partitioned into training and testing sets, and cross-validation was utilized during the training process. For the purpose of evaluating and contrasting the predictive capabilities of the models, performance metrics such as accuracy, precision, recall, and F1-score were computed. For the purpose of operational deployment, the model that exhibited the highest performance (GBM) model was chosen.

3.4 Data Analysis

Quantitative analysis was performed to compare key upstream feed reliability metrics, such as the number of feed interruptions, average interruption duration, and total downtime, before and after the implementation of predictive service level agreement management. Statistical methods were utilized to calculate percentage improvements and demonstrate the practical benefits of the approach. In the meantime, qualitative feedback from the operations team was examined by calculating mean scores and standard deviations for each survey item. This allowed for the measurement of consensus and variability in perceptions, which ultimately assisted in confirming the system's acceptance and effectiveness from the users' point of view.

4. Data Analysis and Interpretation

This article covers the performance characteristics of three machine learning models that are used to anticipate service level agreement (SLA) breaches. These models are Random Forest, Support Vector Machine, and Gradient Boosting Machine. A number of criteria, such as accuracy, precision, recall, and F1-score, are utilized to evaluate the usefulness of the models in accurately identifying breaches. This comparison of the strengths and shortcomings of the models is made much simpler by the visual representation of these measurements that can be found in Figure 1.

Table 1: Comparative Performance of Machine Learning Models

ModelAccuracy (%)Precision (%)Recall (%)F1-Score (%)
Random Forest (RF)92.590.288.789.4
Support Vector Machine (SVM)89.787.585.386.4
Gradient Boosting Machine (GBM)94.191.890.591.1

asejar_162_01.PNG
Figure 1: Graphical Representation of Comparative Performance of Machine Learning Models

When compared to the other two models, the Gradient Boosting Machine (GBM) consistently performs better across all criteria. It achieves the highest accuracy (94.1%) and F1-score (91.1%). Based on this, it can be concluded that GBM is the most reliable method for accurately forecasting SLA violations while simultaneously limiting the number of false positives and false negatives. Random Forest likewise has good performance, but it is somewhat behind GBM in terms of performance, whereas SVM performs significantly worse in comparison. Based on these findings, it appears that GBM is the most suitable option for enhancing upstream feed dependability through predictive service level agreement management because of its capacity to organize and process complicated data patterns.

Prior to and following the deployment of predictive service level agreement management, the following major upstream feed reliability metrics are compared in Table 2. The number of feed interruptions, the average duration of each interruption, and the overall amount of downtime measured in hours are all included in the metrics. A visual representation of these enhancements is provided in Figure 2, which highlights the good impact that the predictive method has had on feed dependability.


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Table 2: Upstream Feed Reliability Metrics Before and After Predictive SLA Management

MetricBefore ImplementationAfter ImplementationImprovement (%)
Number of Feed Interruptions15011026.7
Average Interruption Duration (hours)3.82.534.2
Total Downtime (hours)57027551.8

asejar_162_02.PNG
Figure 2: Graphical Representation of Upstream Feed Reliability Metrics Before and After Predictive SLA Management

The data demonstrates unequivocally that anticipatory service level agreement management has greatly improved the reliability of upstream feed. A decrease of 26.7% was observed in the frequency of feed interruptions, while a decrease of 34.2% was observed in the average duration of disruptions. Most notably, the total amount of downtime was cut by more than fifty-eight percent (51.8%), which indicates that feed operations have become more steady and constant. Based on these enhancements, it appears that proactive prediction and intervention made possible by machine learning can assist in preventing frequent and extended disruptions, which ultimately results in upstream feed processes that are more efficient and provide more reliability.

The input from the operations team regarding the efficiency of predictive service level agreement management is presented in Table 3. The feedback was gathered by calculating mean scores and standard deviations on a Likert scale with five points. Improvements in maintenance planning, a reduction in the number of emergency interventions, and general satisfaction with feed reliability are all included in the statements. Figure 3 provides a graphical representation of this favorable feedback.

Table 3: Operations Team Feedback on Predictive SLA Management

StatementMean ScoreStandard Deviation
Predictive alerts improved maintenance planning4.60.5
Reduction in emergency interventions4.40.7
Overall satisfaction with feed reliability4.70.4

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Figure 3: Graphical Representation of Operations Team Feedback on Predictive SLA Management

The high mean scores, which range from 4.4 to 4.7, indicate that the operations team is in complete agreement that predictive warnings have significantly enhanced maintenance planning and reduced the number of emergency interventions. The overall satisfaction with feed dependability is also noticeably high, which is an indication that the predictive SLA system has had a good impact on day-to-day operations. Considering that the standard deviations are quite low, it is likely that all members of the team have had favorable experiences on a constant basis. This highlights the importance of incorporating machine learning-based predictions into operational procedures.

5. Conclusion

Using machine learning for predictive service level agreement management is shown to greatly improve upstream feed reliability, as the study indicates. This is accomplished by enabling proactive identification of possible service level agreement breaches. The Gradient Boosting Machine demonstrated greater prediction accuracy among the models that were studied, which resulted in significant reductions in feed interruptions, average downtime, and total downtime. favorable qualitative feedback from the operations team, which highlighted improved maintenance planning, fewer emergency interventions, and higher overall satisfaction with feed dependability, further validates these quantitative gains.


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These enhancements have received favorable feedback from the operations team. These findings, taken as a whole, provide evidence that incorporating predictive analytics into service level agreement (SLA) management not only improves operational efficiency but also enhances decision-making procedures, which eventually leads to upstream feed operations that are more dependable and cost-effective.

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