Predictive Maintenance
The company needed to generate more accurate fixed-price aircraft maintenance, repair and overhaul (MRO) sales quotes by predicting likely defects, turnaround time, and required tools, assets, labor, and budget, because quotes that were too high could lose business while quotes that were too low could create major cost overruns.
Reported impact
The quotation application helps the company create more competitive and accurate proposals, improve the probability of winning contracts, and start new contracts with a better planning basis through more accurate predictions of aircraft defects and turnaround times.
DXC and Luxoft worked with the German aircraft maintenance provider to prove whether AI could improve quotation accuracy for overhaul work. The implementation started as an MVP with KPIs to test whether prediction quality was high enough to support a business case. In the MVP, data from historic MRO events was combined with information from previous proposals and other internal sources and fed into an AI model that predicts the most probable real-world defects an aircraft will show during overhaul, along with the likely resource implications. The source also cites prior aircraft performance, maintenance events, and operating environment as relevant contextual inputs for better accuracy. After validation, DXC turned the MVP into a stable production solution by embedding the defect predictions into a specifically developed cloud-based sales quote application, which employees use to prepare proposals and plan new contracts, and which is serviced through the company's Microsoft Azure-based Aviator platform.
Historic MRO event data, previous proposals, and other internal sources are combined and fed into an AI model that predicts probable overhaul defects and resource requirements. Those predictions are embedded into a cloud-based sales quote application, which employees access through the Microsoft Azure-based Aviator platform to prepare proposals and support planning for new contracts.
“Aircraft maintenance services is a highly competitive field, and we needed a sales quote tool that would provide more accurate estimates.”
“Having more accurate predictions of aircraft defects and turnaround times is very valuable to our business.”
“In the MVP, data from historic MRO events was combined with information from previous proposals and other sources, then mingled into an AI model to come up with a prediction of the most probable real-world defects.”
“The defect predictions were embedded into a specifically developed cloud-based sales quote application, which is now being used by the company’s employees in the preparation of proposals and being serviced through its Microsoft Azure- based Aviator platform.”
“The goal was to see if AI models could be used to predict potential defects for the purposes of preparing sales quotes, in much the same way that AI is used for predictive maintenance.”
“This has allowed the company to present more competitive proposals to its customers and improve the probabilities of winning contracts.”
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