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19
Apr
2017

The PSA Group has chosen the Blu.e by ENGIE digital

The PSA Group has chosen the Blu.e by ENGIE digital platform to manage its overall energy and environmental policy. In 2017, the Blu.e solution will be applied on a pilot basis at the Group’s Poissy manufacturing plant, in the Paris region.

The PSA Group, a leading automobile manufacturer, is adopting the Blu.e Big Data solution to optimize the energy consumption of its industrial sites. It is aiming to economize 5% on its energy bill.


For the PSA Group’s plants, energy is a significant item of expenditure the cost of which is inevitably bound to rise. The two levers to apply are acting on energy prices (€/MWh) and reducing energy consumption. Without the need for investment, Blu.e enables industrial sites to optimize the operation of their machinery and thus reduce their energy footprint.

Blu.e by ENGIE provides industrial customers with digital solutions for the optimal management of their assets. It employs a collaborative method which gives “super powers” to the operational teams and to managers, who regularly employ the best software technologies, methods and professional expertise, with tailored support until they are able to apply it autonomously.

Blu.e enables the PSA Group to use the Blu.e pilot® platform, which covers ever link of the value chain, from the recording and logging of data to processing and reformatting in industrial interfaces.

The digital platform makes it possible to exploit all data generated by the various plants and so progress towards the “plant of the future”.

“We wanted a tool that already exists but was nevertheless efficient and evolving, proven and adapted to the world of industry, which is why we chose Blu.e pilot®,” explains Patrice Peslier, head of industrial performance at the PSA Group. “It is an ambitious project, and the stakes are very high for the Group. Our target is to save at least 5% of our energy bill.”

PSA’s Poissy plant implements the automated Big Data tool

Based on the methods of continuous improvement, the Poissy plant will benefit from measuring and autonomous monitoring of its energy performance indicators. A Big Data study of influencing factors and the functioning of machinery will make it possible to optimize the energy management of the site, particularly through identifying and applying the best operating practices.

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