Problem Statement

At a large aircraft manufacturing company, during robotic drilling and Wing assembly operation, key product characteristics were measured only at predefined intervals to reduce cycle time. However, any anomaly detected by this approach was 'reactive to the event' and the production had to be halted till solution to fix anomaly was identified and implemented. Hence a more comprehensive, automated and proactive quality inspection process was required to predict and prevent production line disturbance.


We employed a holistic approach of tracking and processing every aspect of manufacturing process (IoT data). Supervised machine learning algorithm was developed to deduce insights by correlating historical manufacturing key process characteristics (IoT process data) with corresponding key product characteristics (inspection data). The algorithm was then able to detect anomalies and make proactive predictions about the product characteristics.

Key Achievements

  • Unlocked the value stored in the shop floor IoT data
  • Promoted a culture of data curation and data harvesting within the business
  • Reduced/eliminating inspection step
  • Improved yield and production efficiency
  • Enabled two-way flow of information, by first generating insights from manufacturing data and then in the feedback loop send information back to the robots and adapt their settings to dynamically adjust manufacturing operations based on real-time conditions