New advances in aircraft MRO services: data mining enhancement

Aircraft Maintenance, Repair and Overhaul (MRO) agencies rely largely on row-data based quotation systems to select the best suppliers for the customers (airlines). The data quantity and quality becomes a key issue to determining the success of an MRO job, since we need to ensure we achieve cost and quality benchmarks. This paper introduces a data mining approach to create an MRO quotation system that enhances the data quantity and data quality, and enables significantly more precise MRO job quotations. Regular Expression was utilized to analyse descriptive textual feedback (i.e. engineer’s reports) in order to extract more referable highly normalised data for job quotation. A text mining based key influencer analysis function enables the user to proactively select sub-parts, defects and possible solutions to make queries more accurate. Implementation results show that system data would improve cost quotation in 40% of MRO jobs, would reduce service cost without causing a drop in service quality.

Improving aircraft maintenance, repair, and overhaul: A novel text mining approach

Aircraft Maintenance, Repair and Overhaul (MRO) feedback commonly includes an engineer’s complex text-based inspection report. Capturing and normalizing the content of these textual descriptions is vital to cost and quality benchmarking, and provides information to facilitate continuous improvement of MRO process and analytics. As data analysis and mining tools requires highly normalized data, raw textual data is inadequate. This paper offers a textual-mining solution to efficiently analyse bulk textual feedback data. Despite replacement of the same parts and/or sub-parts, the actual service cost for the same repair is often distinctly different from similar previously jobs. Regular expression algorithms were incorporated with an aircraft MRO glossary dictionary in order to help provide additional information concerning the reason for cost variation. Professional terms and conventions were included within the dictionary to avoid ambiguity and improve the outcome of the result. Testing results show that most descriptive inspection reports can be appropriately interpreted, allowing extraction of highly normalized data. This additional normalized data strongly supports data analysis and data mining, whilst also increasing the accuracy of future quotation costing. This solution has been effectively used by a large aircraft MRO agency with positive results.