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AI for Root Cause Analysis

12 June 2020 • Industrial application of Artificial Intelligence

AI for Root Cause Analysis

Problem Statement

At a large aircraft manufacturing company, during Wing Assembly stages, small gaps were seen between the interfacing components. These gaps had to be filled by a manual ‘overhead operation’ called Shimming. This operation was a significant disruption to enable the desired ramp rate. Hence the objective of this activity was to understand the root cause leading to gapping and extract non-intuitive insights to reduce shimming process.

Solution Main Activities

  • Collected key product characteristics data for all the interfacing components (i.e. Wing Covers, Ribs etc.), Jig calibration data and Wing deflection
  • Combined previously isolated ‘historical data set’ with the corresponding ‘events in real life’ i.e. gaping during assembly
  • Developed supervised machine learning algorithm to identify the root cause characteristics leading to gaping

Key Achievements

  • Predictive data analytics that enabled the aircraft manufacturer to reduce and eliminate cost of non-quality i.e. reduce amount of gap-filling per wing assembly and enable production ramp up
  • Prediction algorithm predicting gap filling cases with 95 % accuracy to enable accurate workforce deployment planning
  • Spread awareness about benefits of data analytics techniques applicable to shop floor environment
  • Promote a culture for data curation and data harvesting within the business