DoorDash builds ML forecasting and optimization system to balance Dasher supply and delivery demand
DoorDash needed an automated system to allocate Dasher incentives ahead of anticipated supply-demand imbalances across thousands of regional markets and time units, but lacked a reliable, maintainable way to forecast and optimize these allocations at the appropriate granularity.
The mobilization system reduced delivery times, cancellations, and extreme lateness for consumers; drove down merchant order cancellations; enabled more reliable budget adherence with less spending variability; and increased the team's experimentation velocity on the incentive system.
Overly complex ML pipelines with long data dependency chains were identified as a reliability risk: initially outperforming naive forecasting but degrading to 'Terrible' performance after three or more months, with high oncall burden. Blind correlation learning also risked learning spurious relationships, such as mistakenly concluding that high incentives lead to fewer Dashers on the road.
https://careersatdoordash.com/blog/managing-supply-and-demand-balance-through-machine-learning/