Attribute-Based Delivery Time Prediction for Location Complexity
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Solution Overview
Problem
Pickers face inefficiencies and inaccuracies in delivery times due to incomplete or incorrect information about delivery locations, leading to increased time spent delivering orders and negative experiences for both pickers and customers.
Innovation Solution
An online concierge system uses a machine learning model to predict the difference between arrival and delivery times at a location based on attributes provided by customers, allowing for more accurate delivery time estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customers provide complete delivery location details, then delivery time estimation accuracy is improved, but information collection complexity increases
Solution Approach 1:
The system collects delivery location attributes (gated community status, floor number, elevator availability, stairs count) in advance during order placement. This preliminary collection of information allows the machine learning model to make accurate delivery time predictions without requiring additional real-time interactions, thus improving estimation accuracy while avoiding complexity during the actual delivery process.
2Productivity
If pickers are given detailed delivery location information, then delivery efficiency is improved, but picker workload increases
Solution Approach 1:
The patent replaces manual picker assessment of delivery complexity with an automated machine learning model that calculates delivery time based on location attributes. The system automatically processes information about gated communities, elevators, stairs, and other factors to generate accurate time estimates, eliminating the need for pickers to manually evaluate each delivery location's complexity and reducing their cognitive and physical workload.
3Speed
If delivery time estimation uses simple methods, then calculation speed is improved, but prediction accuracy deteriorates
Solution Approach 1:
The system transforms delivery time estimation from a simple fixed-time calculation to a multi-parameter machine learning prediction that considers gated community status, floor number, elevator availability, stairs count, and other location-specific attributes. This parameter expansion enables accurate predictions while maintaining computational efficiency through the use of trained models that can process multiple inputs simultaneously.
Data Source
AI summary
An online concierge system receives, from a client device associated with a user of the online concierge system, order data associated with an order placed with the online concierge system, in which the order data describes a delivery location for the order. The online concierge system receives information describing a set of attributes associated with the delivery location and accesses a machine learning model trained to predict a difference between an arrival time and a delivery time for the delivery location. The online concierge system applies the model to the set of attributes associated with the delivery location to predict the difference between the arrival time and the delivery time for the delivery location and determines an estimated delivery time for the order based at least in part on the predicted difference. The online concierge system sends the estimated delivery time for the order for display to the client device.