Maplebear Inc.

Eric Hermann

Haochen Luo

Jagannath Putrevu

Rishab Saraf

Teodor Lefter

Abhinav Darbari

Maksym Brodin

Sam Crocker

An online concierge system applies a predictive model to predict demand of items, and facilitates preemptive picking of items in advance of receiving orders to enable efficient procurement and delivery. The online concierge system may apply a time-series model and/or machine learning model that predicts demand based on historical data. Depending on the predicted demand, items may be preemptively moved from a storage location to a staging area that enables the items to be more rapidly processed and delivered to customers when orders come in.

Preemptive picking of items by an online concierge system based on predictive machine learning model

An online concierge shopping system fulfills orders using workers who pick items at a warehouse to complete an order and workers to deliver the orders to a customer's location. To optimize the staffing of workers for each task, the system uses a trained model to predict the number of workers needed to achieve an optimal outcome based on an input set of contextual information. The system also schedules specific workers to various shifts using the predicted number of workers needed and then searching a feasibility space for an optimal solution. The trained model may be updated based on performance observations.

Resource planning for an online concierge system based on predictive modeling

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San Francisco, CA, United States of America

Eric Hermann