HARNESSING EARNED VALUE MANAGEMENT AND MACHINE LEARNING FOR DATA-DRIVEN DECISION-MAKING IN RURAL BUILDING PROJECTS
DOI:
https://doi.org/10.5281/zenodo.15697830Keywords:
Earned Value Management, Machine Learning, Rural Construction, Project Forecasting, Linear Regression, Polynomial Regression, Schedule Performance Index, Cost Performance IndexAbstract
Rural construction projects often face volatile costs and shifting schedules due to limited resources and site conditions. This study combines Earned Value Management (EVM) with simple Machine Learning (ML) to offer clear, forward-looking insights into a 24-month, one-storey school built in a remote community. Using the Bill of Quantity sample (total budget ₹ 1,996,650), we calculated quarterly EVM metrics—PV, EV, AC, SV, CV, SPI, and CPI—from BoQ line-item budgets and actual costs. Historical values from Quarters 1–5 were then used to train two regression models—Linear and second-degree Polynomial—to forecast EV and AC for Quarters 6–8. Those forecasts yielded predicted SV, CV, SPI, and CPI one quarter in advance, flagging cost overruns and schedule slippages early. The Polynomial model captured nonlinear mid-project shifts (e.g., scope changes) with under 10% error, while the Linear model provided a straightforward baseline. By marrying EVM’s objective measures with ML’s predictive power, project managers and stakeholders gain a practical, data-driven tool that spots issues early, helping rural builds stay on time and budget.
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