Order Quantity Anomaly Detection via Historical Frequency Distribution
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Solution Overview
Problem
E-commerce websites face challenges in detecting potential errors in customer-specified product order quantities, particularly due to typographical errors during the ordering process, which can lead to incorrect orders.
Innovation Solution
A system and method that analyze historical ordering data to determine frequency distributions for product quantities, comparing customer-specified quantities against these distributions to identify anomalies and notify customers of potential errors before finalizing their orders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system analyzes historical ordering data to detect quantity errors, then order accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary analysis of historical ordering data to establish frequency distributions before the actual order placement. By pre-comparing customer-specified quantities against these pre-established distributions, the system can detect potential errors before the order is finalized, improving accuracy without requiring complex real-time analysis during order placement.
Solution Approach 2:
The system implements feedback by comparing current order quantities against historical frequency distributions and notifying customers of potential errors. This feedback loop allows the system to use historical data to validate current orders, improving reliability while managing complexity through automated comparison rather than manual verification.
2Reliability
If the system notifies customers of suspected quantity errors, then order accuracy is improved, but processing time increases
Solution Approach 1:
The system performs the comparison of order quantities against historical distributions before the order is placed, allowing for early detection and notification. This preliminary action minimizes the time added to the ordering process by conducting the verification upfront rather than during or after order placement.
Solution Approach 2:
The system automatically performs the comparison and generates notifications without requiring manual intervention. The automated process reduces processing time by eliminating human involvement in the verification step, allowing the system to self-validate order quantities against historical data and communicate with customers automatically.
Data Source
AI summary
Prior ordering history of a product is examined to determine an historical ordering quantity distribution for a product. A customer specified, to-be-purchased quantity of a product is then compared against the determined historical ordering-quantity frequency distribution for the product. When the customer specified, to-be-purchased quantity of the product falls below a certain probability threshold determined using the historical ordering-quantity frequency distribution, or is otherwise suspicious, the customer is notified of a suspected error in the provided product order quantity and/or asked to verify if the provided product quantity is correct.