Real-Time Analytics for Optimizing Resource Utilization in Project Management

  • Authors

    • Ruban Riguel Roger University of Chile, Chile. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2019PI7H3Q

    Published 03-05-2019

  • Real-time analytics, Resource utilization, Project management, Resource optimization, Data-driven decision making, Predictive analytics, Machine learning, AI in project management, Resource tracking, Project management tools, IoT in project management, Optimization algorithms, Project forecasting

    Issue

    Vol. 2 No. 1 (2019)

    Section

    Articles

    How to Cite

    [1]
    R. Riguel Roger, “Real-Time Analytics for Optimizing Resource Utilization in Project Management”, IJADSMC, vol. 2, no. 1, pp. 01–13, Mar. 2019, doi: 10.67228/30713498/IJADSMC-2019PI7H3Q.

  • Abstract

    In today’s fast-paced business environment, effective resource management is a critical factor for the success of projects. Project managers face significant challenges in ensuring that resources such as time, manpower, and capital are utilized optimally. Real-time analytics, driven by emerging technologies like artificial intelligence, machine learning, and the Internet of Things, presents a powerful solution for enhancing resource utilization in project management. This paper explores the concept of real-time analytics in project management, examining its ability to optimize resource allocation, enhance decision-making, and improve project outcomes. By providing real-time insights into resource usage, potential bottlenecks, and future needs, these analytics enable project managers to make informed, data-driven decisions. Furthermore, the paper discusses the various techniques, tools, and strategies for incorporating real-time analytics into project management practices, along with the challenges and barriers to their adoption. The paper concludes by exploring future trends and opportunities for the continued integration of real-time analytics in project management processes.

  • References

    [1] Ghasemzadeh, F., & Archer, N. P. (1999). Project selection and scheduling with resource constraints: integer programming models. European Journal of Operational Research — presents early optimization models with explicit resource constraints relevant to project resource utilization.

    [2] Hilman, M. H., Rodriguez, M. A., & Buyya, R. (2018). Task runtime prediction in scientific workflows using an online incremental learning approach. arXiv preprint — online learning for resource behaviour prediction under near real time conditions.

    [3] Henriksen, A. & Traynor, G. (1999). Project evaluation and selection in a portfolio, Journal of Project Portfolio Management — early work on evaluating resource constraints in multi project environments.

    [4] Garrehy, P. (1999). Project managers need real-time data—and a couple ERP modules of their own. PM Network, 13(2), 33–35.

    [5] Levine, H. A. (2002). Practical Project Management: Tips, Tactics, and Tools. John Wiley & Sons.

    [6] Carmichael, D. G. (2006). Project Planning and Control. Routledge.

    [7] Project Management Institute. (2017). A Guide to the Project Management Body of Knowledge (PMBOK® Guide). PMI.

    [8] Woodward, J. F. (1997). Construction Project Management: Getting It Right First Time. Thomas Telford.

    [9] Wauters, M., & Vanhoucke, M. (2016). A comparative study of artificial intelligence methods for project duration forecasting. Expert Systems with Applications, 46, 249–261.

    [10] Vanhoucke, M., Coelho, J., & Batselier, J. (2016). An overview of project data for integrated project management and control. Journal of Modern Project Management, 3, 6–21.

    [11] Wauters, M., & Vanhoucke, M. (2017). A nearest neighbour extension to project duration forecasting. European Journal of Operational Research, 259(3), 1097–1111.

    [12] Akanni, O. O., Adisa, J. A., & Pule, K. (2013). Project selection: Artificial neural network approach. Science Journal of Business and Management, 1(3), 37–42.

    [13] Kotsiantis, S. B. (2013). Decision trees: A recent overview. Artificial Intelligence Review, 39, 261–283.

    [14] Liu, Y., Wang, Y., & Zhang, J. (2012). A new machine learning algorithm: Random Forest. Lecture Notes in Computer Science, 7473.

    [15] Agostinelli, S., et al. (2002). Real-time analytics for decision-making in project environments. (Referenced in project analytics literature).

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