Non-Conformance Forecasting for Aircraft Maintenance
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Solution Overview
Problem
Current aircraft maintenance scheduling struggles to predict non-conformances, which are degraded conditions that may not lead to immediate failure but require timely replacement, leading to unanticipated part demands and stock-out issues due to their detection during inspections rather than operational failures.
Innovation Solution
A computer-implemented method and system that collates historical work records to calculate probabilities of non-conformances based on work tasks, locations, and timing, determining replacement part requirements and sending requests for specified service locations to anticipate and manage part demands effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If aircraft maintenance is performed on a scheduled basis to ensure airworthiness, then reliability of aircraft operation is improved, but unanticipated part demands occur due to inability to predict non-conformances, leading to stock-out issues
Solution Approach 1:
The system performs preliminary actions by calculating probabilities of non-conformances before maintenance events occur. Historical work records are analyzed to predict which parts are likely to be found non-conforming during upcoming maintenance, allowing replacement parts to be procured in advance before the actual maintenance event, thus preventing stock-outs.
Solution Approach 2:
The system provides beforehand cushioning by maintaining a predicted inventory of replacement parts based on probability calculations. This cushioning inventory is prepared in advance of actual non-conformance discoveries, ensuring that parts are available when needed without requiring excessive overall inventory levels.
2Measurement precision
If historical work records are analyzed to predict non-conformances, then part demand forecasting accuracy is improved, but system complexity increases due to data collation and probability calculation requirements
Solution Approach 1:
The system performs self-service by automatically collating historical work records from existing maintenance databases and performing probability calculations without requiring complex external analytical tools. The system uses its own stored data to generate predictions, reducing the need for additional complex infrastructure.
Solution Approach 2:
The system achieves universality by using a single platform that handles multiple functions: storing historical work records, calculating non-conformance probabilities, generating part demand forecasts, and integrating with existing maintenance scheduling systems. This multi-functionality reduces overall system complexity compared to having separate specialized systems for each function.
Data Source
AI summary
Predicting non-conformance of vehicle parts is provided. The method comprises collating historical work records related to a vehicle model, wherein each work record specifies a work task, non-conformances discovered during the work task, replacement parts required for the work task, when the work task occurred, and where the work task occurred. From the historical work records the system calculates probabilities of non-conformances according to types of work tasks, locations of the work tasks, and timing of the work tasks. Replacement part requirements are determined according to types of non-conformances, and requests are sent for replacement parts for specified service locations according to scheduled work tasks for the vehicle model at the service locations and anticipated probabilities of non-conformances related to the work tasks.