Late Delivery Prediction Model Without Batching Feedback Loops
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Solution Overview
Problem
Conventional online systems fail to differentiate between standard ETA and priority ETA deliveries in predicting late deliveries, leading to suboptimal performance and maintenance costs due to a self-perpetuating feedback loop in linear machine-learning models.
Innovation Solution
A trained machine-learning model that predicts late delivery rates without relying on batching features and zonal structures, using independent marketplace supply and demand signals to optimize delivery service options in real time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a linear machine-learning model with zonal structure is used to predict late deliveries, then the model can handle different zonal coverages, but the maintenance cost increases due to hundreds of linear functions
Solution Approach 1:
The patent applies universality by training a single machine-learning model that can predict late delivery rates across different zonal coverages without requiring separate zonal models. The model processes features including zonal coverage information as input, allowing one model to serve multiple zones, thereby reducing maintenance complexity while maintaining adaptability.
2Measurement precision
If batching features are used in the machine-learning model, then the model can predict late deliveries, but a feedback loop and self-perpetuating cycle are created
Solution Approach 1:
The patent extracts and removes batching features from the machine-learning model's input features. By excluding features that directly result from batching decisions, the model avoids creating feedback loops where predictions influence batching which then influences future predictions. This extraction maintains prediction precision while eliminating the self-perpetuating cycle.
3Productivity
If predicted lates are used to control batching, then delivery service options can be optimized, but the system creates a feedback loop that reduces prediction accuracy
Solution Approach 1:
The patent extracts batching-related features from the model inputs to prevent feedback loops. The model predicts late delivery rates using features that do not include batching decisions, allowing the system to optimize delivery services without creating circular dependencies that reduce prediction accuracy.
Solution Approach 2:
The patent introduces an intermediary approach where the machine-learning model predicts late delivery rates independently of batching decisions. These predictions then serve as inputs for batching control, creating a unidirectional flow rather than a feedback loop. This intermediary prediction layer maintains accuracy while enabling optimization.
4Reliability
If the model differentiates between sETA and pETA deliveries, then performance can be optimized, but the model complexity increases
Solution Approach 1:
The patent applies local quality by training the machine-learning model to predict late delivery rates specifically for priority ETA (pETA) deliveries, which have different characteristics than standard ETA (sETA) deliveries. The model uses features specific to pETA orders such as priority handling indicators, allowing differentiated performance optimization without requiring a completely separate model for each service type.
Data Source
AI summary
A trained model is used to predict, in real time, a late delivery rate for orders placed at an online system. Upon receiving data related to the placed orders and signals related to supply and demand states of the online system, the online system applies a delivery prediction model trained to output, based on the received data and signals, late delivery scores, each late delivery score indicative of a respective rate of late deliveries for a respective service option for delivery of the orders. The online system compares each late delivery score with a respective threshold score, and responsive to each late delivery score being greater than the respective threshold score, the online system triggers an action in relation to the respective service option for delivery of the orders.