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One of the largest aerospace companies with a portfolio of commercial, cargo and military spacecraft, and comprehensive aftermarket support services for aircraft fleet.

The manufacturer provides customized aircraft maintenance and upgrade services as part of integrated product support for lessees and global clients. Prompt maintenance increases the service life of aircraft by decades. The aircraft manufacturer sought to optimize scheduled maintenance while providing round-the-clock support.

Key Challenges

  • Scheduled inspections accounted for 16% of total maintenance cost
  • Engineers spent 18-24 months for basic maintenance data analysis and inspection scheduling

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The Solution

Sophisticated module analyzes failure of aircraft parts based on data from log books, shop records, and maintenance and repair reports

The manufacturer adopted a pre-determined inspection schedule for each aircraft – for instance, every 3,000 days for semi-critical parts and weekly visual inspection for mission-critical components. Infosys replaced the time-bound maintenance scheduling system with a predictive analytics-based application.

We used the Maximum Likelihood Estimation method and Monte Carlo simulations to optimize the interval for each task. We built a Java interface to understand analytical output and inspection scenarios.

Predictive models and statistical tools recommend the frequency of inspection

  • Aircraft-specific repositories collate historical data of parts, such as the date of replacement, repair and survival period
  • Saves more than 400 person hours for each airplane every year, irrespective of the model
  • Reduces annual maintenance costs for an aircraft by ~ US$ 100,000
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Benefits

Infosys combined predictive modeling and advanced analytics to increase intervals for scheduled inspection

from 25 to 37 months for parts that did not impact safety

from 25 to 37 months for parts that did not impact safety

from 100 to 125 days for general inspection

from 100 to 125 days for general inspection