Problem Statement

Record demand for commercial Aircrafts (prior to the pandemic and hopefully the demand will resume shortly) had led to huge backlog of orders (greater than 7 years in case of single aisle aircrafts). This had resulted into pressure to ramp up the production rate and reduce the delivery lead times.

However, Simultaneously, Aerospace manufacturers were also faced with a challenge to reduce the energy consumption at their facilities. This was a result of both voluntary commitment towards industrial sustainability as well as mandate from the Governments.

Combination of the above two factors had led to the need for investigating novel methods for identifying opportunities that could lead to reduction in energy consumption whilst meeting the business objective of increasing production rate.

We developed an Artificial Intelligence based solution to identify such novel opportunities to reduce carbon foot print in high volume manufacturing environement.

Some of the salient features of our solution are as below.

Solution Main Activities

  • Manufacturing process parameters data captured for both drilling operations as the ‘predictors’
  • Corresponding energy consumption data captured as ‘labels’
  • Performed data pre-processing and visualisation, feature engineering and subsequent down selection
  • Development, evaluation and down selection of various supervised machine learning models

Key Achievements

  • Developed a supervised machine learning model predicting energy consumption corresponding to various drilling operations
  • Generated insights on manufacturing process parameter tuning to reduce energy consumption
  • Spread awareness about benefits of data analytics techniques applicable to shop floor environment
  • Promoted a culture for data curation and data harvesting within the business