Batch Recall Assessment Using Quality Threshold Risk Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing product recall processes rely heavily on manual human resources, leading to time-consuming, costly, and error-prone decisions, which can result in unnecessary delays, regulatory penalties, and potential harm to consumers due to inefficient identification of defective product batches.
Innovation Solution
An automated system for batch recall assessment that quantifies quality concerns for faulty products, clusters them by batch, compares count values against pre-determined thresholds, and generates risk notifications for timely corrective actions, reducing human error and improving decision-making efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual human resources are used for product recall decisions, then expertise and judgment can be applied, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent replaces manual human resources with an automated computer-based system that processes quality data, compares it against historical recall data, and generates recall recommendations. This substitution eliminates human errors while maintaining decision accuracy through systematic data analysis and threshold-based automated decision-making.
Solution Approach 2:
The system enables self-service by automatically performing data collection, analysis, and recall recommendation generation without requiring manual intervention. The computer system independently processes quality data, compares it with historical data, and produces actionable recall recommendations, freeing human resources from time-consuming manual analysis.
2Reliability
If comprehensive quality investigations are conducted, then recall decisions are more accurate, but the cost and time increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing quality data from manufacturing processes, customer complaints, and field observations. This preliminary data preparation enables rapid recall assessments when needed, as the data is already organized and accessible, eliminating the need for time-consuming investigations at the moment of decision-making.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously collecting quality data from multiple sources and using historical recall data to refine future assessments. The automated comparison of current quality data against historical thresholds provides continuous feedback that improves recall decision accuracy without increasing investigation time.
3Productivity
If automated systems are implemented, then processing speed increases, but system complexity increases
Solution Approach 1:
The patent implements a universal system that handles multiple functions including data collection from various sources, data storage, automated analysis, historical comparison, and recall recommendation generation. This multi-functional approach consolidates what would otherwise require multiple separate systems into a single integrated platform, managing complexity while maintaining high productivity.
Solution Approach 2:
The system segments the recall assessment process into distinct modular components: data collection module, data storage module, analysis module, comparison module, and recommendation module. This segmentation allows each component to be independently developed, tested, and maintained, reducing overall system complexity while enabling parallel processing for high productivity.
4Adaptability or versatility
If manual recall processes are used, then flexibility in decision-making is maintained, but human error increases
Solution Approach 1:
The system maintains flexibility by allowing adjustable parameters such as quality thresholds, data weightings, and recall criteria. These parameters can be modified based on changing business requirements or regulatory standards, preserving adaptability while the automated execution eliminates human errors in applying these parameters consistently across all decisions.
Data Source
AI summary
Approaches for automated batch recall assessment are described. The approach includes identifying product batches having a plurality of faulty products manufactured by the organization. For each of the identified product batches, a plurality of quality concerns raised for faulty products manufactured as part of the product batch are quantified. Accordingly, for each product batch, the quantified values of each of the plurality of quality concerns is compared with a corresponding pre-determined threshold count value to enable determination of a quality risk level associated with the product batch. Based on the quality risk level, a batch recall assessment is performed to determine whether to recall product batches having the plurality of faulty products. Thus, the described approaches provide an automated technique for early detection of problematic batches, facilitating quick decision-making on potential batch recalls and improving overall quality management in manufacturing processes.