Arrival Prediction Service Throttling by Model Performance Metrics
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Solution Overview
Problem
Conventional online concierge systems face issues with inaccurate delivery time predictions, leading to overloading and failure to meet delivery guarantees due to erroneous arrival time predictions by the delivery prediction model.
Innovation Solution
The system uses predicted delivery metrics, such as time-to-accept and percentage of late orders, to evaluate the performance of the delivery prediction model and selectively suspend or provide arrival prediction services when these metrics exceed predefined thresholds, ensuring accurate delivery time estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the delivery prediction model is used to provide arrival prediction services, then users receive predicted delivery times and service quality, but the system experiences overloading and fails to meet delivery guarantees when predictions are inaccurate
Solution Approach 1:
The system dynamically adjusts service provision based on real-time model performance metrics. When predicted delivery times fall below thresholds, the system automatically suspends arrival prediction services in affected zones, preventing overloading while maintaining reliability in zones where the model performs adequately.
Solution Approach 2:
The system applies different service levels to different geographic zones based on local model performance. Zones with accurate predictions receive full arrival prediction services, while zones with poor predictions have services suspended, allowing the system to maintain overall reliability without uniformly reducing productivity across all areas.
2Ease of operation
If the system provides arrival prediction services based on the delivery prediction model, then users receive predicted delivery times, but erroneous predictions cause the system to receive more orders than it can successfully deliver
Solution Approach 1:
The system continuously monitors predicted delivery metrics and uses this feedback to determine whether to provide arrival prediction services. When metrics indicate poor model performance, the system suspends services, creating a feedback loop that maintains reliability while preserving user convenience when the model performs well.
Solution Approach 2:
The system proactively suspends arrival prediction services before erroneous predictions can cause overloading. By monitoring predicted delivery metrics in advance and suspending services when thresholds are breached, the system prevents the harmful effect of providing inaccurate predictions that would lead to unmanageable order volumes.
3Reliability
If the system continuously monitors and evaluates delivery metrics to selectively provide services, then delivery reliability is maintained, but system complexity increases
Solution Approach 1:
The system changes the operational parameters of the delivery prediction model by selectively suspending services in zones where predicted delivery metrics fall below thresholds. This parameter-based control mechanism maintains reliability through automated monitoring without requiring complex manual intervention or system redesign.
Data Source
AI summary
An online concierge system provides arrival prediction services for a user placing an order to be retrieved by a shopper. An order may have a predicted arrival time predicted by a model that may err under some conditions. To reduce the likelihood of providing the predicted arrival time (and related services) when the arrival time may be incorrect, the prediction model and related services are throttled (e.g., selectively provided) based on one or more predicted delivery metrics, which may include a time to accept the order by a shopper and a predicted portion of late orders that will be delivered past the respective predicted arrival times. The predicted delivery metrics are compared with thresholds and the result of the comparison used to selectively provide, or not provide, the predicted delivery services.