Journal of Advances in Developmental Research

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Data-Driven Decision Support: A Framework for Dynamic Resource Allocation and Capacity Planning in Multi-Project Engineering Environments

Author(s) Somraju Gangishetti
Country United States
Abstract Engineering organizations increasingly operate in multi-project environments, where multiple projects compete for limited resources such as engineers, infrastructure, and budgets. Traditional project management approaches rely on static planning models that fail to adapt to rapidly changing project conditions. As organizations scale their project portfolios, dynamic resource allocation and proactive capacity planning become critical for maintaining operational efficiency and meeting project delivery timelines.
This research proposes a data-driven decision sup-port framework designed to enable dynamic resource allocation and capacity planning across multi-project engineering environments. The framework integrates predictive analytics, optimization algorithms, real-time data pipelines, and decision dashboards to support data-driven management of engineering resources.
The proposed architecture includes data ingestion pipelines, predictive modeling engines, optimization modules, and visualization layers to enable real-time decision-making. Simulation results demonstrate improvements in resource utilization (up to 32%), reduction in project delays (22%), and improved scheduling efficiency compared with traditional resource planning approaches.
The research demonstrates how data-driven decision systems can significantly improve project portfolio performance and engineering productivity in complex multi-project environments.
Keywords Decision Support Systems, Resource Allocation, Capacity Planning, Multi-Project Management, Predictive Analytics, Engineering Management
Field Engineering
Published In Volume 15, Issue 2, July-December 2024
Published On 2024-08-09
DOI https://doi.org/10.71097/IJAIDR.v15.i2.1980
Short DOI https://doi.org/hb59h5
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