Predictive Analytics Applications for Resource Allocation in Large-Scale Engineering Projects

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@article{1710779,
      author = {Oluwagbemisola Faith Akinlade, Opeyemi Morenike Filani, Priscilla Samuel Nwachukwu},
      title = {Predictive Analytics Applications for Resource Allocation in Large-Scale Engineering Projects},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {6},
      pages = {310-324},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710779.pdf},
      abstract = {Efficient resource allocation is a critical determinant of success in large-scale engineering projects, where delays, budget overruns, and operational inefficiencies frequently stem from mismanagement of labor, equipment, and materials. Predictive analytics offers a transformative approach to addressing these challenges by leveraging historical data, real-time project information, and advanced statistical or machine learning models to forecast future resource demands with greater accuracy. This paper examines the applications of predictive analytics in optimizing resource allocation for large-scale engineering projects, emphasizing its potential to enhance planning precision, risk mitigation, and overall project performance. Predictive models enable project managers to anticipate fluctuations in material requirements, labor productivity, and equipment utilization, thereby minimizing resource shortages and reducing idle capacity. By identifying patterns and correlations across large datasets, predictive analytics facilitates proactive decision-making, such as adjusting workforce deployment in response to expected demand surges or scheduling equipment maintenance to prevent costly downtime. Additionally, resource allocation models can integrate external variables, including weather conditions, market volatility, and geopolitical risks, to improve robustness in uncertain project environments. The adoption of predictive analytics also strengthens collaboration and transparency across stakeholders by providing evidence-based insights that inform procurement planning, budgeting, and scheduling. Furthermore, it enhances sustainability outcomes by reducing resource waste, optimizing energy consumption, and aligning project practices with environmental goals. Despite these benefits, challenges such as data availability, model calibration, and organizational resistance to analytics-driven practices remain significant barriers to widespread adoption. This underscores predictive analytics as a strategic enabler for resource optimization in engineering megaprojects, offering the potential to reduce delays, control costs, and improve efficiency. Future research should focus on hybrid models that combine predictive capabilities with real-time monitoring systems, ensuring adaptive and resilient resource allocation in dynamic project environments.},
      keywords = {Predictive Analytics, Resource Allocation, Large-Scale Engineering Projects, Project Management, Data-Driven Decision Making, Workload Optimization, Scheduling Efficiency, Risk Assessment, Capacity Planning, Project Performance Forecasting, Cost Optimization, Time Management, Resource Utilization, Construction Engineering, Operational Efficiency},
      month = {December},
  }