Predicting Item Availability Fluctuations for Online Ordering
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Solution Overview
Problem
Users of online systems, such as online concierge systems, often experience dissatisfaction when items with fluctuating availability are not available at the time of collection, leading to cancelled orders, changes, or acquiring items elsewhere, which is time-consuming and inconvenient.
Innovation Solution
An online system predicts the availability of items at retailer locations by using a machine-learning model trained to identify predictable availability items based on their fluctuating availability throughout the day, and updates the ordering interface with this information to help users select optimal timeframes for their orders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If items are ordered without availability prediction, then ordering process is simple and fast, but item availability at collection time is unreliable
Solution Approach 1:
The system performs availability prediction in advance before the user places the order. Historical availability data is analyzed and stored, and when an order is placed, the system retrieves pre-computed availability predictions for the requested timeframe, avoiding real-time computation complexity while ensuring reliable availability information is available.
Solution Approach 2:
The patent introduces an intermediary availability prediction system that sits between the user ordering process and the actual item collection. This intermediary layer analyzes historical data and provides predicted availability information to guide ordering decisions, resolving the contradiction by adding a specialized component rather than complicating the entire system.
2Reliability
If users are provided with availability information, then user satisfaction improves, but data processing requirements increase
Solution Approach 1:
Availability predictions are computed in advance using historical data and stored for quick retrieval. When users place orders, the system retrieves pre-computed predictions rather than performing new analysis, significantly reducing real-time computational resource requirements while still providing reliable availability information to improve order fulfillment.
Solution Approach 2:
The system computes availability predictions for all items and timeframes in advance, even though not all predictions will be used. This excessive computation approach allows the system to store comprehensive availability information and quickly retrieve only what is needed, trading off some unnecessary computation for much faster response times and reduced real-time processing demands.
3Measurement precision
If real-time availability tracking is implemented, then item availability accuracy improves, but system complexity and processing time increase
Solution Approach 1:
The system pre-computes availability predictions based on historical data before they are needed. By analyzing patterns in historical availability information in advance and storing the results, the system provides accurate availability predictions without requiring real-time processing when orders are placed, thus maintaining measurement precision while avoiding time loss during order processing.
Data Source
AI summary
An online system displays an ordering interface, and responsive to receiving a request from a client device to place an order including one or more items to be collected from a retailer location, the system retrieves data associated with each item. The system accesses and applies a machine-learning model to predict a likelihood of each item being a predictable availability item having at least a threshold measure of fluctuating availability throughout the day at the retailer location based on data associated with a corresponding item. The system identifies a set of predictable availability items based on the predicted likelihood(s) and predicts an availability of each identified predictable availability item at the retailer location during a future timeframe. The system then updates the ordering interface to describe the predicted availability of each predictable availability item at the retailer location during the future timeframe.