Order Fulfillment Supply State Prediction Using TTA Distributions
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Solution Overview
Problem
Conventional online concierge systems lack the ability to predict and react in real time to variations in supply states, leading to oscillations between undersupplied and oversupplied conditions, which affects order fulfillment efficiency.
Innovation Solution
Utilizing a trained machine-learning model to predict supply states by measuring and comparing distributions of time-to-acceptance (TTA) values for sample orders, enabling adjustments to order fulfillment parameters such as routing and matching to balance supply and demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional online concierge systems operate without prediction capabilities, then the system structure remains simple, but the system cannot react in real time to supply state variations, causing oscillations between undersupplied and oversupplied states
Solution Approach 1:
The system performs preliminary actions by training a machine learning model offline to predict supply states. The model is trained on historical data and then deployed to make real-time predictions without adding complex runtime processing. This allows the system to react in real time while keeping the operational structure relatively simple.
Solution Approach 2:
A machine learning model is introduced as an intermediary component between the order fulfillment mechanism and the supply state monitoring. This model acts as a predictor that translates historical patterns into future supply state predictions, enabling real-time adaptation without directly complicating the core fulfillment logic.
2Productivity
If the system uses a trained machine-learning model to predict supply states, then the system can dynamically adjust to supply state imbalances, but the model training and distribution process becomes more complex
Solution Approach 1:
The system segments the complexity into two distinct phases: an offline training phase where the machine learning model is developed using historical data, and an online prediction phase where the trained model is deployed for real-time supply state forecasting. This segmentation allows the complex training process to occur separately from the order fulfillment operations, maintaining high productivity during runtime.
Solution Approach 2:
The machine learning model is trained in advance on historical order fulfillment data to learn patterns in supply state variations. This preliminary training action enables the model to make accurate real-time predictions during order fulfillment operations without requiring complex runtime processing or data collection.
3Reliability
If the system continuously monitors and predicts supply states, then the system can maintain optimal fulfillment performance, but the computational resources and processing time increase
Solution Approach 1:
The machine learning model performs the computationally intensive pattern recognition and prediction work in advance during the training phase. During real-time operation, the model quickly predicts supply states based on current inputs, and the system compares predicted versus actual values to detect imbalances. This preliminary action approach maintains high prediction accuracy while minimizing real-time processing time.
Solution Approach 2:
The system implements a feedback mechanism where predicted supply states are continuously compared with actual observed states. This feedback loop allows the system to detect supply state imbalances quickly and trigger appropriate responses, maintaining high reliability without requiring excessive computational resources for each individual prediction.
Data Source
AI summary
Embodiments are related to using a trained computer model to predict a supply state of an online system for state-aware management of order fulfillments. The online system measures first values of a metric for a set of sample orders. The online system accesses the computer model trained to predict a value of the metric for an order placed with the online system. The online system applies the computer model to predict second values of the metric for the set of sample orders, based on one or more features of each sample order. The online system compares a distribution of the first values to a distribution of the second values and determines the supply state of the online system based on the comparison. Responsive to the determination of the supply state, the online system triggers a remedial action for the online system that adjusts the supply state of the online system.