[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120326-en":3,"doc-seo-120326-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120326,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Forecasting Performance - Leveraging Machine Learning on Earned Value Data for Proactive Control","Earned Value Management (EVM) has long been used to evaluate project performance by comparing planned progress with actual results, yet conventional EVM methods often fall short in forecasting future outcomes and managing risks before they intensify. This study investigates how machine learning can strengthen EVM-based prediction and proactive control. Regression analysis, decision trees, and neural networks are applied to EVM metrics to forecast cost and schedule trends, identify potential risks early, and improve decision-making. The approach aims to deliver more immediate, data-driven insights for project oversight and regulation.","| E-ISSN:2583-2468 Publisher |  |  |\n| --- | --- | --- |\n|  | \u003Cbr>\u003Cbr>\u003Cbr>2025 Volume 4 Number 2 March | \u003Cbr>[www.singhpublication.com](www.singhpublication.com) |\n| \u003Cbr>\u003Cbr>\u003Cbr>1*\u003Cbr>\u003Cbr>DOI:10.5281/zenodo.15278924\u003Cbr>1* Rohit Shinde, Project Controls Lead Analyst (Independent Researcher), Black & Veatch Corporation, Houston, Texas, United States of\u003Cbr>America.\u003Cbr>\u003Cbr>This study investigates the utilisation of machine learning methodologies in the context of earned value management (EVM) data, aiming for the anticipatory prediction and regulation of project outcomes. This research seeks to utilise machine learning frameworks, including regression analysis, decision trees, and neural networks, to forecast upcoming project outcomes, pinpoint possible risks, and improve the decision-making process. The study illustrates how the incorporation of sophisticated algorithms alongside conventional EVM data can yield enhanced, immediate insights into cost and schedule effectiveness. The document further explores the ramifications of this methodology for project leaders, providing a comprehensive structure for enhancing project oversight and regulation via insights derived from data.\u003Cbr>Keywords: machine learning, earned value management, project performance forecasting, proactive control, regression analysis, decision trees, neural networks, project monitoring, risk management, data-driven decision-making\u003Cbr>Corresponding Author How to Cite this Article To Browse\u003Cbr>Rohit Shinde, Project Controls Lead Analyst (Independent Researcher), Black & Veatch Corporation, Houston, Texas, United States of America.\u003Cbr>\u003Cbr>Shinde R, Forecasting Performance: Leveraging Machine Learning on Earned Value Data for Proactive Control. Appl. Sci. Eng. J. Adv. Res. . 2025 ;4(2):30- 38.\u003Cbr>Available From [https://asejar.singhpublication.com/index.php/ojs/ar](https://asejar.singhpublication.com/index.php/ojs/ar)[ticle/view/139](ticle/view/139)\u003Cbr>\u003Cbr>Manuscript Received Review Round 1 Review Round 2 Review Round 3 Accepted\u003Cbr>2025-02-03 2025-02-28 2025-03-25\u003Cbr>Conflict of Interest Funding Ethical Approval Plagiarism X-checker Note\u003Cbr>None Nil Yes 3.63 \u003Cbr>© 2025 by Shinde R 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](https://creativecommons.org/licenses/by/4.0/ unported) [CC BY 4.0].\u003Cbr> |  |  |\n| \u003Cbr>30 | Appl. Sci. Eng. J. Adv. Res. 202542 |  |\n\n\n| Shinde R. Forecasting Performance: Leveraging Machine Learning |  |  |\n| --- | --- | --- |\n| \u003Cbr>In the realm of project management, it is crucial to uphold oversight regarding expenses and timelines to ensure the triumph of the project. Earned Value Management (EVM) has historically served as a crucial instrument for evaluating project performance, enabling project managers to juxtapose the intended progress against the actual outcomes. Nonetheless, conventional EVM techniques frequently prove inadequate in forecasting future outcomes and managing risks prior to their intensification. This is the realm in which machine learning (ML) presents considerable benefits. (1)\u003Cbr>Machine learning, a subset of artificial intelligence, is capable of processing large amounts of data to uncover patterns and relationships that may not be immediately obvious. By integrating ML with EVM data, project managers can gain more accurate, proactive insights into project health, allowing them to predict potential cost overruns or schedule delays before they happen. This approach enhances decision-making, improves project forecasting, and provides a more nuanced view of project performance than traditional methods.(2)\u003Cbr>This study seeks to explore the capabilities of machine learning algorithms in enhancing the prediction and proactive management of project performance utilising EVM metrics. Through the examination of diverse machine learning frameworks, such as regression analysis, de","cbCailjxMLb7c9B0","https://ap.wps.com/l/cbCailjxMLb7c9B0","pdf",600125,1,9,"English","en",105,"# Introduction\n## Project Performance Management\n## Earned Value Management (EVM)\n## Machine Learning for Proactive Control","[{\"question\":\"What limitation of conventional EVM motivates this research?\",\"answer\":\"Conventional EVM techniques often do not adequately forecast future outcomes or manage risks before they become more serious.\"},{\"question\":\"Which machine learning methods are used to enhance EVM-based forecasting?\",\"answer\":\"The study examines regression analysis, decision tree algorithms, and neural network architectures applied to EVM metrics.\"},{\"question\":\"How does combining machine learning with EVM improve project control?\",\"answer\":\"It enables more accurate, proactive insights into project health by predicting potential cost overruns or schedule delays earlier, supporting improved decision-making.\"}]","Forecasting Performance - 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