Automated Break Team Assignment for Machine Exit from Test Environments
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Solution Overview
Problem
Current methodologies lack an accurate and efficient way to determine when machines are ready to exit testing environments, leading to inefficiencies, increased production costs, and financial penalties due to the variability in testing processes and the limited availability of skilled human operators.
Innovation Solution
An automated computing tool that uses machine learning-trained computer models to prioritize machines nearing completion and dynamically compile break teams based on operator availability, skill sets, and machine-specific needs, optimizing the allocation of human resources for efficient exit operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual or semi-automatic processes are used to administer tests and collect data, then human expertise can be applied to complex testing scenarios, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent combines automated testing systems with human operator expertise by integrating computer models that simulate operator actions with actual human operators who review and validate results. This merging allows the system to handle complex testing scenarios efficiently while maintaining the reliability of human judgment.
Solution Approach 2:
The testing system performs self-monitoring and self-evaluation through automated computer models that track testing progress, detect completion criteria, and generate reports without requiring continuous human intervention. This self-service capability reduces time loss while maintaining testing accuracy.
2Adaptability or versatility
If human operators manually determine when machines are ready to exit testing environments, then decision-making flexibility is maintained, but productivity decreases due to limited availability and variability in human response
Solution Approach 1:
The system continuously monitors testing progress and provides real-time feedback to both the automated computer models and human operators. This feedback mechanism enables the system to dynamically adjust exit criteria and notify operators when machines are ready for exit, improving productivity while maintaining decision flexibility through operator input.
Solution Approach 2:
The computer models perform preliminary analysis of testing data to predict when machines will meet exit criteria before human operators need to make decisions. This preliminary action reduces the response time required for exit operations while operators can still provide flexibility when needed.
3Reliability
If skilled human operators are deployed to manage exit operations, then operational expertise is ensured, but costs increase due to limited availability and the need for specialized training
Solution Approach 1:
The patent introduces computer models as intermediaries between the testing systems and human operators. These models automatically handle routine exit operations and data analysis, while human operators focus only on complex cases requiring their expertise. This intermediary approach ensures operational expertise is maintained where needed while significantly increasing operator availability.
Solution Approach 2:
The exit operation process is segmented into automated components handled by computer models and manual components requiring human operators. This segmentation allows routine tasks to be performed automatically, reducing the quantity of skilled operators needed while maintaining expertise for complex decisions.
4Reliability
If machines remain in testing environments until manually exited, then testing completeness is ensured, but production costs increase due to extended occupancy time
Solution Approach 1:
The computer models continuously monitor testing progress and automatically identify when completion criteria are met, eliminating idle time between testing and exit operations. This continuous action ensures testing completeness is maintained while reducing the time machines occupy testing environments, thereby lowering production costs.
Solution Approach 2:
The patent replaces manual monitoring and decision-making mechanisms with automated computer models that track testing progress and determine exit readiness. This substitution eliminates human reaction time delays and ensures immediate action when testing is complete, reducing machine occupancy time and associated costs.
Data Source
AI summary
Mechanisms are provided for assigning at least one operator to a break team to break a machine from a test environment. The mechanisms, in response to installing a machine under test (MUT) in a test environment, generate a MUT data structure in a MUT database that stores machine detail data. The mechanisms monitor the testing process to detect the testing process nearing completion and, in response to nearing completion, the mechanisms: (1) execute a first computer model on the MUT data structure to prioritize breaking of the MUT from the test environment; (2) execute a second computer model on operator data in an operator database to identify eligibility operators for the break team; and (3) generate an output specifying the break team in association with the MUT and a priority for breaking the MUT from the test environment based on the execution of the first and second computer models.