Component Batch Matching Using Predictive Models for Variability Control
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Solution Overview
Problem
In manufacturing processes, especially in the pharmaceutical industry, component variability leads to increased variability in final product characteristics, resulting in higher reject rates and user complaints, and tightening component tolerances to meet these standards can be prohibitively expensive.
Innovation Solution
A predictive machine learning model is used to optimize the pairing of component batches by predicting properties and results of combination devices based on input characteristics, with an optimizer determining the best component combinations to meet desired performance measures, thereby reducing variability and improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If component tolerances are tightened to meet final product tolerance targets, then manufacturing precision of the final product is improved, but manufacturing cost increases prohibitively
Solution Approach 1:
The system performs preliminary matching of component batches before final assembly by predicting compatibility outcomes using machine learning models. This advance planning allows selection of component combinations that will naturally result in compliant final products, eliminating the need for overly tight tolerances on individual components and reducing manufacturing costs.
Solution Approach 2:
The system changes the approach from controlling individual component parameters tightly to controlling the combination parameters. By using predictive models to evaluate and select optimal component batch combinations, the system achieves final product compliance through intelligent pairing rather than through stringent individual component specifications.
2Device complexity
If conventional process control measures are used to manage component variability, then manufacturing simplicity is maintained, but product performance consistency deteriorates
Solution Approach 1:
The system replaces traditional mechanical/statistical process control methods with an information-based approach using machine learning predictive models. By substituting computational prediction and optimization algorithms for conventional control measures, the system achieves superior product consistency while maintaining operational simplicity through software-based batch matching recommendations.
3Productivity
If random component pairing is used in manufacturing, then manufacturing speed is maintained, but product variability increases leading to higher reject rates
Solution Approach 1:
The system performs preliminary batch matching calculations before production using predictive machine learning models. By pre-determining optimal component batch pairings, the system enables manufacturers to maintain high production speeds while ensuring low product variability, as the matching decisions are made in advance rather than requiring real-time adjustments during manufacturing.
Data Source
AI summary
A method of reducing combination device variability includes identifying potential combinations of at least first and second components, where each combination can form one or more units of the combination device. The method also includes, for each potential combination, predicting a property or result of the units of the combination device when formed from at least the first and second components of the combination, at least by applying values of one or more characteristics of the first component of the combination, and values of one or more characteristics of the second component of the combination, as inputs to a predictive model. The method also includes selecting, based on the predicted properties or results for the potential combinations, a subset of combinations from among the potential combinations, and providing an indication of the selected subset of combinations.