Restaurant Tech · Regression ML · Real-Time Prediction · Order Lifecycle Optimization
No understanding of time estimation across the four order stages: Make, Ready, On Road, and Complete.
Without a predictive model, stage-time estimation relied on averages — ignoring queue size, events, and location.
Customers and operations teams lacked real-time estimated completion times driven by data.
Order patterns varied significantly by weekday, hour, holidays, and events — requiring adaptive modeling.
ML regression model (Random Forest / deep learning) trained on order time, item make time, location, distance, and event data.
Weekday, location, distance, events, and item-making time selected as strong predictors through correlation analysis.
Hourly order count analysis, weekday distribution, queue size impact, and average stage time estimation.
Deployed and integrated the model with the app — enabling real-time visualization of each order's estimated progress.
Make → Ready → On Road → Complete — all modeled.
Random Forest regression for stage-time prediction.
Order estimation deployed on a live production system.
Weekday, location, events, queue size all factored.