Revealing savings through descriptive analytics: data driven analytics of an automotive paint shop

Challenge

Our client, one of the major car manufacturers in the world, finds that the energy efficiency projects are not as good, ROI wise, that in the past, and wonders how to reduce energy consumption environmental footprint.

Solution

We carried out a POC on the paint shop, which is one of the main energy consumers ( €2.5 M /year). Although our client had a limited number of energy sub meters available, we took advantage of the hundreds of production related data available, to train our machine learning algorithms to create gas and electricity consumption models.

Benefits

As a result, we identified a 10% energy savings potential, including 5% reachable with no investment. Our approach was confirmed by the plant engineering team and the POC was declared successful with a 0.3 y ROI.