Predictive Item Availability Notification System
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Solution Overview
Problem
Current systems face inefficiencies in managing orders with potentially missing or unavailable items, leading to increased network and computing resource usage as buyers and merchants often need to manually resolve issues related to item delivery or availability.
Innovation Solution
A system that analyzes historical data to predict the risk of items being missing or unavailable, sending notifications to couriers and merchants to ensure item inclusion or substitution, and prompts buyers for instructions on how to proceed if an item is unavailable, thereby automating the resolution process and reducing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual processing is used to resolve item delivery issues, then buyers and merchants can directly communicate and resolve problems, but network and computing resources are excessively consumed
Solution Approach 1:
The system performs preliminary analysis of historical data to predict missing or unavailable items before delivery occurs. Notifications are sent in advance to buyers and merchants, enabling them to prepare appropriate actions (refunds, replacements, substitutions) before the delivery issue arises, thereby reducing the need for reactive manual processing and associated resource consumption.
Solution Approach 2:
The system enables automated self-service resolution by sending notifications to buyers about potentially missing items and providing options for automatic resolution (refunds, replacements). The system also notifies merchants of predicted unavailable items, allowing them to proactively manage inventory and order fulfillment without requiring continuous manual intervention for each delivery issue.
2Measurement precision
If historical data analysis is performed to predict missing items, then the accuracy of predicting missing or unavailable items improves, but computing resources are consumed
Solution Approach 1:
The system applies partial analysis by focusing computational resources on analyzing historical data patterns related to specific merchants, items, and delivery routes that are most likely to exhibit missing or unavailable item issues. Rather than analyzing all possible data uniformly, the system identifies and prioritizes high-risk cases for detailed analysis, achieving good prediction accuracy with reduced overall computing resource consumption.
Data Source
AI summary
In some examples, a computing device sends, to an application on a user device, item information to cause the application to present a user interface including information about items available from a merchant. In response to receiving a user input via the user interface to select an item from the merchant, the computing device determines that a likelihood that the item will be unavailable exceeds a threshold. The computing device sends a communication that causes the application to present a plurality of selectable options to indicate actions to perform when the item is unavailable. The computing device receives a request to place the order for the item, and an indication of the action to perform. In response to receiving a communication from the merchant device indicating that the item is unavailable, the computing device sends an instruction to the merchant device indicating the action to perform.