Leveraging LSTM-Driven Predictive Analytics for Resource Allocation and Cost Efficiency Optimization in Project Management

Abstract

Resource planning and cost optimization are essential elements of effective project management. Conventional models are weak in changing environments because they cannot keep pace with intricate task interdependencies and changing project constraints. To overcome such weaknesses, this research envisions an LSTM-based predictive analytics model that deploys temporal trends and past project information for precise predictions of task duration, resource allocations, and possible delays. The proposed method combines sequential data modeling with Long Short-Term Memory (LSTM) networks, along with data preprocessing and optimization, to enhance project scheduling and cost control decision-making. With TensorFlow implementation, the proposed LSTM-PRO model resulted in a Mean Squared Error (MSE) of 0.0025, Root Mean Squared Error (RMSE) of 0.05, and an R² score of 0.96, which was far better than ARIMA and other baseline models. The model resulted in a cost saving of 20% on project costs and 20% rise in resource utilization from 65% to 85%. The outcome proves the effectiveness and applicability of the model in actual project settings.