Manual crew forecasting relies on static productivity factors, ignoring weather's massive impact on labor efficiency. This workflow automates the ingestion of hyper-local historical weather data, site-specific conditions, and granular past project performance to model productivity losses. The result is a dynamic labor forecast that quantifies schedule risk and cost exposure, turning a historical guesswork process into a data-driven, defensible line item that protects project margins from the start.




