Interconnected ML Models for Production Throughput Variability
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Solution Overview
Problem
Original equipment manufacturers (OEMs) face high variability in production due to complex manufacturing systems, leading to issues such as failed customer contracts, lost revenue, and quality rejections, which result in negative consequences like fines and inventory liquidation.
Innovation Solution
The implementation of an automated production intelligence system using interconnected machine-learning models that predict production throughput and identify causal factors for variability, providing alerts and recommended actions to optimize manufacturing processes and minimize costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If complex manufacturing systems are deployed to produce technically complex products, then product capability and functionality are improved, but production variability increases
Solution Approach 1:
The patent segments the complex manufacturing system into multiple discrete value streams, each monitored and controlled independently through dedicated machine learning models. This segmentation allows for targeted optimization of individual streams while managing overall system complexity, thereby reducing production variability without sacrificing product capability.
Solution Approach 2:
The patent implements real-time feedback mechanisms through machine learning models that continuously monitor value stream performance and provide actionable insights. This feedback loop enables dynamic adjustment of manufacturing processes to maintain consistent output quality and reduce variability, while preserving the adaptability needed for complex product production.
2Device complexity
If traditional production monitoring is used, then system simplicity is maintained, but production intelligence and predictive capability are insufficient
Solution Approach 1:
The patent introduces machine learning models as intermediary layers between traditional monitoring systems and production decision-making. These models process raw data, extract meaningful patterns, and provide actionable intelligence without requiring complete system redesign, thus maintaining relative simplicity while significantly enhancing predictive capability and reducing information loss.
Solution Approach 2:
The patent replaces traditional mechanical and manual production monitoring with intelligent software-based machine learning models. This substitution transforms raw operational data into predictive intelligence, enabling proactive production management while keeping the underlying physical manufacturing systems relatively simple and maintainable.
3Ease of operation
If manual production management is used, then implementation ease is maintained, but operational costs and response time to variability are excessive
Solution Approach 1:
The patent enables the production system to self-monitor and self-diagnose issues through machine learning models that automatically detect variability patterns and provide root cause analysis. This self-service capability reduces the need for manual intervention while significantly improving operational efficiency and reducing response times to production issues.
Solution Approach 2:
The patent implements preliminary action through predictive analytics that identify potential production issues before they occur. By detecting patterns and predicting failures in advance, the system enables proactive interventions that prevent disruptions, improve operational efficiency, and maintain ease of operation through automated early warning systems.
Data Source
AI summary
Disclosed herein are systems and methods for automating production intelligence across value streams using interconnected machine-learning models. An embodiment of a system includes an upstream machine-learning model corresponding to each of one or more upstream entity in a production value stream of a product; a final-assembly machine-learning model corresponding to a final-assembly process in the production value stream of the product; a causal-analysis machine-learning model for the production value stream of the product; an action-and-alert process for the production value stream of the product; and an implementation interface for the production value stream of the product. The upstream machine-learning models and the final-assembly machine-learning model are interconnected to provide product-throughput prediction for the product. The causal-analysis machine-learning model infers causal factor for the product-throughput prediction, and alerts and/or recommended actions are issued to the implementation interface.