Exception Event Processing for Defective Product Identification
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Solution Overview
Problem
Current solutions fail to effectively identify defective products on e-commerce websites, leading to high overhead costs, network bottlenecks, and negative customer experiences due to inefficient processing of refund and exchange requests, as well as the inability to correct defective products, resulting in dissatisfied customers and revenue loss.
Innovation Solution
A system and method that processes exception event requests to identify defective products by receiving and storing requests, accessing a database to track the number of requests for a product, and generating an electronic task when the threshold is exceeded, updating the product status in the inventory database, and transmitting this information to user devices to prevent further purchases of defective items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system processes all refund and exchange requests manually, then customer service quality is maintained, but network resources are overloaded and processing efficiency decreases
Solution Approach 1:
The system performs preliminary analysis of exception events by tracking request patterns and identifying defective products before they cause widespread network overload. By proactively detecting products with high defect rates and automatically removing them from the marketplace, the system prevents the accumulation of excessive refund and exchange requests, thereby maintaining network efficiency without requiring manual processing of all customer requests.
2Reliability
If the system identifies and removes defective products proactively, then customer satisfaction improves, but system complexity increases
Solution Approach 1:
The system implements a feedback mechanism that continuously monitors exception events, tracks request patterns for specific products, and automatically responds by identifying and removing defective products. This closed-loop feedback system maintains product quality assurance through automated detection and removal processes, avoiding the need for complex manual intervention systems while ensuring reliable identification of defective products.
3Measurement precision
If manual review of each exception request is performed, then accurate defect identification is achieved, but processing time increases
Solution Approach 1:
The system enables self-service defect identification by automatically analyzing exception event patterns, tracking product-specific request frequencies, and autonomously determining which products are defective. This automated self-service approach achieves accurate defect identification through pattern recognition and threshold-based detection, eliminating the need for time-consuming manual review of each individual exception request while maintaining high identification accuracy.
Data Source
AI summary
A system for processing exception event request to identify defective product includes one or more processors configured to receive, from a customer device, an exception event request associated with a product included in an order, the received exception event request comprising a product identifier. Access database to identify a number of exception event requests associated with the product identifier stored in the database. Determine when the number of exception event requests associated with the product identifier exceeds a predetermined threshold number of exception event requests for the product identifier. In response to determining that the number of exception event requests associated with the product identifier exceeds the predetermined threshold number of exception event requests, identify the product as a defective product.