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Mitie

T: +4401179708800  /  E: info@mitie.com

Partnering with Red Bull Racing to optimise performance

What was the client looking for?

Red Bull Racing is a company that is serious about its people, and its cars.

The organisation is synonymous with success and high performance, and is dedicated to achieving gains, both large and small, that could give them the edge during a race. Continuous improvement is a constant focus for the Red Bull Racing team, which led to their decision to implement Mitie’s intelligent buildings technology at their site in Milton Keynes.

Mitie’s intelligent building solution, uses advanced building telemetry to improve occupants’ comfort, reduce energy use and maximise the effectiveness of the building’s assets.

At the heart of the solution sit live data and a building’s users. By collecting and analysing live environmental data using sensor technology, the solution works on the principle that monitored variables such as air quality, light levels and humidity can affect human productivity. The key to getting this right is to first understand an individual company’s own productivity measures and linking them to the built environment they occupy.

Establishing the baseline

For Mitie, the first leg of the journey was to establish a comfort policy at Red Bull Racing around five key areas, including temperature, air quality, lighting, humidity and noise. Over 270 people took part in a building user survey which allowed Mitie to identify the key areas of focus and set the baseline.

Over 400 sensors were then positioned and installed around this central building to collect data on air quality, light levels, temperature, humidity, noise and occupancy levels – variables which can affect both physical manufacturing processes and human performance. Experienced engineers then analyse and interpret this data at Mitie’s state-of-the-art remote operating centre. They can remotely make changes through the building management system (BMS), or provide analytical diagnostics to the onsite FM team to help them make adjustments for optimisation.

Mitie has been collecting and analysing live environmental data from Red Bull Racing’s office and factory buildings since January 2016 with impressive results. The data demonstrated that the premises could be optimised through a series of fundamental building adjustments, such as increased office airflow, isolation of loud workshop processes and control over temperature. This insight has given Red Bull Racing a new-found understanding of their workplace, and the ability to control the conditions of their working environment in a way they could not before.

Using the data

Comfort policies around environmental conditions have also been created for teams in different areas to ensure that everyone is able to work at their peak performance and at their preferred temperature and lighting levels. The rich array of data means that numerous variables can be monitored and tailored according to conditions on a particular day. In addition to this, trends can be identified and measures put in place in order to anticipate changing  conditions and colleague habits. Mitie’s ultimate aim is to take the vast amount of data from the buildings and turn it into meaningful and actionable information. By collecting, analysing and interpreting data across Red Bull Racing’s premises, the team have a detailed understanding of the workplace and the optimum conditions that improve employee productivity, wellbeing and drive a high performing team.

“Competing in races is not just about performance on the track; it’s about leveraging the whole team in preparation for each race, and making sure they have a healthy and comfortable environment in which they can produce their best work,” says Al Peasland, Head of Technical Partnerships at Red Bull Racing. “Across all areas of our facility, our relationship with Mitie is helping us to measure and analyse our working environments and ensure that our team members have the optimum working conditions.” Races are won long before we reach the track” he adds.