Spare Parts Bundle Prediction Through Maintenance Event Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Companies maintaining vehicle fleets face challenges in predicting the bundles of spare parts needed for maintenance tasks, leading to inefficiencies and higher costs due to individual part sales rather than bundled offerings.
Innovation Solution
A computer-implemented method and system that determine maintenance events for replacement parts, generate clusters of replacement parts based on these events, and predict bundles of replacement parts, allowing for more efficient and cost-effective part sales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If spare parts are sold individually to airlines, then airlines can purchase only the parts they need for specific maintenance tasks, but OEMs are disadvantaged in price and airlines face inefficiencies in managing multiple individual part purchases
Solution Approach 1:
The patent combines multiple individual spare parts into bundled offerings based on maintenance event requirements. The system analyzes historical maintenance data to identify parts that are frequently purchased together and presents them as pre-configured bundles, allowing airlines to purchase multiple parts in a single transaction while maintaining the ability to buy only what is needed.
2Quantity of substance
If airlines purchase only the parts needed for specific maintenance tasks, then unnecessary cost and storage of unused parts is avoided, but the complexity of managing multiple individual part purchases increases
Solution Approach 1:
The system performs preliminary analysis of maintenance requirements and pre-configures bundles of parts based on historical data and predicted maintenance events. This allows airlines to receive ready-made bundles that match their actual needs before purchase, eliminating the complexity of manually selecting and managing individual parts while maintaining precise inventory control.
3Productivity
If spare parts are offered as bundles, then operational efficiency and cost-effectiveness improve, but the challenge lies in accurately predicting which parts should be bundled together
Solution Approach 1:
The system implements feedback loops that continuously analyze actual maintenance data, purchase patterns, and maintenance event outcomes. This feedback is used to refine and update the prediction models, improving the accuracy of bundle compositions over time. The system learns from real-world usage to better predict which parts should be bundled together for specific maintenance events.
Solution Approach 2:
The system performs preliminary prediction of maintenance events and required parts using historical data analysis and machine learning algorithms. By predicting maintenance needs in advance, the system can pre-configure appropriate bundles before the actual maintenance event occurs, ensuring accurate part selection while improving operational efficiency.
Data Source
AI summary
Techniques for intelligently predicting bundles of replacement parts. These techniques include determining a plurality of maintenance events for a plurality of replacement parts. The determining includes identifying one or more replacement parts for a maintenance event, based on one or more replacement part events occurring within a time period related to the maintenance event. The techniques further include generating one or more clusters of replacement parts based on the plurality of maintenance events, and predicting one or more bundles of replacement parts, based on the clusters, wherein each bundle comprises a plurality of replacement parts.