PrediQ implementation at the Paint Shop of a large automotive manufacturer

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Business Challenge:

  • 90% of car bodies that pass painting booth have one of 13 paint defects
  • Some defects need to be heavily reworked – to the extent of re-painting the body.
  • Temperature and Relative Humidity settings in the booth are sub-optimal


  • Grant power to the user : A platform that uses ML powered models to predict outcomes, simulate scenarios, take informed actions.
  • Prevention is better than cure : Identify optimal operating control ranges so that the plant operator can prevent a potentially defective body from entering the booth.

Highlights of our work


Business ‘wowed’ by
counter-intuitive findings

The exploratory data analysis revealed findings that surprised veterans who worked 20+ years at the plant


Rigorous machine learning

Our rigor leaves no stone unturned. We built 22 ML models, and each model was tested using 10+ algorithms


Friendly interface
customized to user

We design the user interfaces by putting ourselves in the feet of the user

We help predict, simulate and optimize – thus granting power to
the user

predictive quality platform implementation 2
predictive quality platform implementation
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