Intelligent Project Control ۴.۰: Integrating Explainable AI with Stochastic EDM

The research establishes an original framework which combines Explainable Machine Learning (XML) with Earned Duration Management (EDM) metrics to achieve project control and precise project duration prediction. The low success rate of projects with substantial funding and the opaque nature of machine learning models compared to traditional EDM static formulas create a clear need for a new approach. The case study presents a three-layered system which combines Monte Carlo simulation for uncertainty modeling with Gradient Boosting prediction and SHAP explainability. The proposed method creates training data through ۵۰,۰۰۰ simulations to enable both forward-looking proactive risk management and backward-looking root cause identification of deviations. The integration of EDM with simulation and XAI produces more accurate and interpretable predictions which increases project managers' trust in the system. The research establishes a method to convert project control from its current reactive state into a proactive system.

کلیدواژه ها:

Stochastic project control ، Explainable Artificial Intelligence (AI) ، Earned Duration Management (EDM) ، Machine Learning ، SHAP (Shapley Additive Explanations) ، Shapley values