Our Reliability Analytics Engine has been proven in aerospace applications. The algorithms, data, and software can be used for many other applications. The main prerequisite is the ability to collect the asset management data such as repairs and replacement information combined with the asset serial number. In the sustainment of aircraft, the reliability event data has been collected for over a decade. The Industrial IoT enables collection of the reliability event data in many other MRO applications.
A case study – MRO for Aerospace market analysis
- More passengers, more planes: 3.4% annual growth of aircraft over 2015-2025 (Source: ICF’s MRO Market Update & Industry Trends)
- Complex supply chain: at Boeing, 4 million aircraft parts are sourced from 20,000+ suppliers (Source: jifaveze68.over-blog.com/plane-construction)
- Paper is king: 90% of airline maintenance documentations are still on paper records (Source: Aviation Week)
- Tech adoption by airlines: Digitization of aircraft maintenance records; Leverage on software and predictive maintenance capabilities (Source: KPMG’s Aviation Industry Leaders 2018 Report)
- Growing MRO market: $96B global MRO market by 2025 (Source: ICF’s MRO Market Update & Industry Trends)
Challenges: Ogden Air Logistics center (OO-ALC) is one of the three ALCs responsible for sustainment of USAF aircraft - a $16 billion/year operation
- Air Force databases collect over a decade of historical MRO data.
- Just a couple of analysts were able to interpret the data for a given part, after hard work with Excel.
- Needed intelligent solution with analysis results well understood by reliability engineers.
Solution: Industrial AI application that analyzes data from sustainment operation and aircraft usage data
- Custom ML algorithms to train explainable engineering models on historical data
- Predictive trending of fleet reliability with full statistical rigor
- Root case analysis of bad reliability by identifying Bad Actors, parts and host aircraft
- Sub-population analysis of reliability to quantify the effect of investment
Result: Improvement of fleet reliability and actionable information of sustainment operation
- Fleet reliability monitoring supports executive decisions on resource allocation
- Estimated $200M savings resulting from using Industrial AI Analytics just for one part