E.ON - Using people analytics to improve health and safety at work

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E.ON is the second largest supplier of gas and electric in the UK. The business has over 9,000 employees in its workforce split over a variety of different departments and sites which makes it particularly challenging to take into account the needs of all of its people.

We worked with Rob Mason, MI Senior Analyst, to understand the experiences of their vast workforce – particularly those roles that involve driving considerable distances.

Qlearsite’s analysis it gave a predictive element of what is likely to happen in the future. This will be incredibly useful in the future to help build strategies to mitigate against those things potentially happening.

Rob Mason

MI Senior Analyst at E.ON

The challenge: improving safety for their large, dispersed workforce

  • 9000 employees in multiple locations, departments, and roles
  • A large car fleet covering several million miles yearly
  • The need to improve driving safety, and reduce accidents

E.ON employees drive several million miles a year through a large commercial and company car fleet and so driving risk is very high on their agenda and really important to them.

In this particular project, they were keen to understand the factors influencing the driving behaviour of their employees. This was in an effort to improve driver safety of their fleet by reducing the number of driving related Road Traffic Accidents.

The solution: our people analytics technology

  • A bespoke project using our advanced people analytics software
  • Using different hypotheses to look at factors influencing driver risk

Planning effective actions requires brilliant information. To gather this data, E.ON decided to run a bespoke people analytics project with Qlearsite to figure out what were the right things to do to ensure a safer fleet of drivers.

E.ON came up with over 100 different hypotheses around the different possible factors that influence driver risk. Using our analytics platform, E.ON were able to work with their existing systems to compute all data sources within their business to help test the different hypotheses and pinpoint the key drivers of risk in their workforce.

Our analytics tech was then able to compute the data and look for complex correlations in order to predict with great accuracy the impact of certain variables on driver safety.

The results: previous theories disproved, and a better understanding of risk

  • Previous theories around how to understand risk were disproved
  • High-risk groups could be more accurately identified

By combining their systems with behavioural and demographic data, we were able to help E.ON fundamentally re-evaluate how they understand risk within their health and safety department.

The commonly held assumptions that they had previously used to rate the risk of the different types of drivers in their workforce were disproved. That meant they were able to identify far more accurate high risk groups by analysing the data in a different way, and improve health and safety for their workforce.

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