AUTOMATION OF PROJECT KPI MANAGEMENT THROUGH THE USE OF AI AND PREDICTIVE ANALYTICS
DOI:
https://doi.org/10.32782/tnv-tech.2024.5.7Keywords:
project management, key performance indicators, predictive analytics, artificial intelligence, Agile, data management, DevOps, real-time monitoring, automation, decisionmakingAbstract
The article focuses on automating the management of key performance indicators (KPIs) through the use of artificial intelligence (AI) and predictive analytics. The advantages of AI-driven KPI monitoring include improved forecast accuracy through big data analysis, proactive risk detection and management, and automation of routine project management tasks. The objective of the article is to explore methods and approaches for leveraging AI and predictive analytics to automate KPI management in project environments. The study analyzes existing tools, such as Power BI, Azure Machine Learning, and Google Cloud AI, assessing their effectiveness in predicting metrics like Lead Time, Cycle Time, and Budget Variance. The research employs methods of time series analysis, regression models, and neural networks, alongside practical application scenarios to optimize managerial decision-making. Scientific novelty lies in the development of a systematic approach for integrating predictive analytics into project management processes. The article categorizes metrics for Agile, Scrum, DevOps, and scaled frameworks (SAFe, LeSS) and demonstrates the advantages of AI-based KPI tracking. Practical significance includes the potential application of proposed approaches by project managers to implement AI solutions that enhance team productivity, optimize resources, and mitigate risks. Recommendations for integrating AI into project management systems enable proactive decision-making. Conclusions. The study provides an analysis of predictive analytics methods, describing models and tools for automating KPI monitoring. A systematic approach is proposed for integrating AI solutions into project management processes, enhancing management efficiency and ensuring process transparency. Future perspectives for using AI in KPI monitoring within scaled frameworks are also discussed.
References
J. Sravanthi, R. Sobti, A. Semwal, M. Shravan, A. A. Al-Hilali and M. Bader Alazzam, "AI-Assisted Resource Allocation in Project Management," 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, 2023, pp. 70-74, doi: https://doi.org/10.1109/ICACITE57410.2023.10182760
M. Odeh, "The Role of Artificial Intelligence in Project Management," in IEEE Engineering Management Review, vol. 51, no. 4, pp. 20-22, Fourthquarter,Dec. 2023, doi: https://doi.org/10.1109/EMR.2023.3309756
Ayadi, O., El-Hassani, I., Barka, N., Masrour, T. (2023). Real-Time KPI Forecasting with 1D Convolutional Time Series for Enhanced Manufacturing Efficiency. In: Masrour, T., El Hassani, I., Barka, N. (eds) Artificial Intelligence and Industrial Applications. A2IA 2023. Lecture Notes in Networks and Systems, vol 771. Springer, Cham. https://doi.org/10.1007/978-3-031-43524-9_3
EL Mazgualdi, C., Masrour, T., El Hassani, I. et al. Machine learning for KPIs prediction: a case study of the overall equipment effectiveness within the automotive industry. Soft Comput 25, 2891–2909 (2021). https://doi.org/10.1007/s00500-020-05348-y
Zheng, L., Baron, C., Esteban, P. et al. Using Leading Indicators to Improve Project Performance Measurement. J. Syst. Sci. Syst. Eng. 28, 529–554 (2019). https://doi.org/10.1007/s11518-019-5414-z
Dahmani, S., Ben-Ammar, O., Jebali, A. (2021). Resilient Project Scheduling Using Artificial Intelligence: A Conceptual Framework. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 630. Springer, Cham. https://doi.org/10.1007/978-3-030-85874-2_33
Li, H. et al. (2024). Harnessing AI for Project Risk Management: A Paradigm Shift. In: Yazdi, M. (eds) Progressive Decision-Making Tools and Applications in Project and Operation Management. Studies in Systems, Decision and Control, vol 518. Springer, Cham. https://doi.org/10.1007/978-3-031-51719-8_16
Dr. Md. Mahfuzul Islam Shamim. (2024). Artificial Intelligence in Project Management: Enhancing Efficiency and Decision-Making. International Journal of Management Information Systems and Data Science, 1(1), 1–6. https://doi.org/10.62304/ijmisds.v1i1.107
Auth, Gunnar & Jokisch, Oliver & Dürk, Christian. (2019). Revisiting automated project management in the digital age – a survey of AI approaches. Online Journal of Applied Knowledge Management. 7. https://doi.org/10.36965/OJAKM.2019.7(1)27-39
Kassem, B., Costa, F., Staudacher, A.P. (2021). Lean Monitoring: Boosting KPIs Processing Through Lean. In: Powell, D.J., Alfnes, E., Holmemo, M.D.Q., Reke, E. (eds) Learning in the Digital Era. ELEC 2021. IFIP Advances in Information and Communication Technology, vol 610. Springer, Cham. https://doi.org/10.1007/978-3-030-92934-3_32
N. Mohamed and J. Al-Jaroodi, "Real-time big data analytics: Applications and challenges," 2014 International Conference on High-Performance Computing & Simulation (HPCS), Bologna, Italy, 2014, pp. 305-310, doi: https://doi.org/10.1109/HPCSim.2014.6903700
Indelicato G. Book Review: Project Management Metrics, KPIs, and Dashboards: A Guide to Measuring and Monitoring Project Performance. Project Management Journal. 2012. Vol. 43, no. 2. P. 102. URL: https://doi.org/10.1002/pmj.21263 (дата звернення: 01.11.2024).
Brahimi, S., Aljulaud, A., Alsaiah, A., AlGuraibi, N., Alrubei, M., Aljamaan, H. (2019). Performance Dashboards for Project Management. In: Alfaries, A., Mengash, H., Yasar, A., Shakshuki, E. (eds) Advances in Data Science, Cyber Security and IT Applications. ICC 2019. Communications in Computer and Information Science, vol 1098. Springer, Cham. https://doi.org/10.1007/978-3-030-36368-0_19
D. B. Abdullah and R. A. -G. Mohammed, "Real-Time Big Data Analytics Perspective on Applications, Frameworks, and Challenges," 2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM), Mosul, Iraq, 2021, pp. 1-6, doi: https://doi.org/10.1109/ICCITM53167.2021.9677849
W. Chen, Z. Milosevic, F. A. Rabhi and A. Berry, "Real-Time Analytics: Concepts, Architectures, and ML/AI Considerations," in IEEE Access, vol. 11, pp. 71634-71657, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3295694
W. Villegas-Ch, J. García-Ortiz and S. Sánchez-Viteri. Toward Intelligent Monitoring in IoT: AI Applications for Real-Time Analysis and Prediction, in IEEE Access, vol. 12, pp. 40368-40386, 2024, doi: https://doi.org/10.1109/ACCESS.2024.3376707
