Business Analytics and Machine Learning for Project Management

The project management process includes the necessity to forecast the effectiveness of each employee’s contribution to the achievement of the targeted result. The transferable utility of each project participant is assessed. The presented study reveals a consolidated approach to the formation of the transferable utility indicator. The proposed model utilizes information from various sources including performance indicators, personnel data and other contextual factors. It is based on the integration of data mining and machine learning techniques and is designed to generate accurate predictive models. The obtained results can be used by top management of companies to make effective managerial decisions in project management and increase the number of financial incentives for those project participants who have shown the highest level of transferable utility.

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Publication Details

Journal
Procedia Computer Science
Published
2025-01-01
DOI
https://doi.org/10.1016/j.procs.2025.09.589
Citations
1
Primary Topic
AI and HR Technologies
Type
article
Field-Weighted Citation Impact
2.76
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article

Business Analytics and Machine Learning for Project Management

Lyudmila V. Glukhova, S.D. Syrotyuk, Anton A. Gudkov, Svetlana A. Gudkova
1 citations
Procedia Computer Science
AI and HR Technologies
2.76
article

Business Analytics and Machine Learning for Project Management

Lyudmila V. Glukhova, S.D. Syrotyuk, Anton A. Gudkov, Svetlana A. Gudkova
article en
1 citations

Abstract

The project management process includes the necessity to forecast the effectiveness of each employee’s contribution to the achievement of the targeted result. The transferable utility of each project participant is assessed. The presented study reveals a consolidated approach to the formation of the transferable utility indicator. The proposed model utilizes information from various sources including performance indicators, personnel data and other contextual factors. It is based on the integration of data mining and machine learning techniques and is designed to generate accurate predictive models. The obtained results can be used by top management of companies to make effective managerial decisions in project management and increase the number of financial incentives for those project participants who have shown the highest level of transferable utility.

Procedia Computer ScienceVol. 270
Togliatti State University (RU), Volga Region State University of Service (RU)
Openalex Percentile: Top 7%
AI and HR Technologies
2.76
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