Predicting the Impact of Scope Changes on Project Cost and Schedule Using Machine Learning Techniques

• URL: http://arxiv.org/abs/2412.02041v1
• Date: Mon, 02 Dec 2024 23:54:00 GMT
• Title: Predicting the Impact of Scope Changes on Project Cost and Schedule Using Machine Learning Techniques
• Authors: Soheila Sadeghi,
• Abstract summary: This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques.<n>The research utilizes a comprehensive dataset containing detailed information on project tasks.<n>It is identified that productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are powerful predictors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Model robustness and generalization are assessed using cross-validation techniques. To evaluate the performance of models, we use Mean Squared Error (MSE) and R2. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The study identifies the most influential project attributes in determining the magnitude of cost and schedule deviations caused by scope modifications. It is identified that productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are powerful predictors.

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