Delivery Time Estimation Using Location Attribute Prediction
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Solution Overview
Problem
Customers often fail to provide detailed delivery location information, leading to inefficiencies and inaccuracies in delivery times, as pickers may spend excessive time navigating unfamiliar locations, resulting in delayed deliveries and negative experiences for both parties.
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, enabling more accurate delivery time estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If customers provide detailed delivery location information, then delivery time accuracy is improved, but customer input burden increases
Solution Approach 1:
The system performs preliminary actions by automatically gathering delivery location attributes (gated community status, building type, floor information, elevator availability) from external databases and historical data before the delivery occurs. This pre-collection of information eliminates the need for customers to manually input detailed location information, while still enabling accurate delivery time predictions through the machine learning model.
2Productivity
If pickers are provided with detailed location attributes, then delivery efficiency is improved, but system complexity increases
Solution Approach 1:
The system implements self-service by automatically extracting and processing delivery location attributes from available data sources without requiring manual intervention from pickers or customers. The machine learning model autonomously predicts delivery time adjustments based on location characteristics, eliminating the need for complex manual assessment procedures while maintaining high delivery efficiency.
3Measurement precision
If machine learning models are used to predict delivery time differences, then delivery time accuracy is improved, but computational requirements increase
Solution Approach 1:
The machine learning model focuses on predicting only the specific aspect of delivery time that varies by location (the difference between arrival time and delivery time) rather than predicting the entire delivery timeline. This localized prediction approach reduces computational complexity and energy requirements while maintaining high accuracy for the critical delivery time estimation.
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.