CorporateJanuary 21, 2020

Preventing fatal work incidents through predictive AI

The Enablon 4.0 team pitched a product suite that changes how employers predict operational risks, in real time. We met Fatiha Achour, Lead Product Manager, to learn about the new technology.

Research shows that around 374 million work-related incidents happen every year, of which over 380,000 are fatal. The Wolters Kluwer Enablon solution helps mitigate these incidents. The team behind Enablon 4.0, a next-generation environmental, health, and safety platform, sought to make sense of this data to understand, predict, and prevent incidents. They found that for every fatality, 300,000 unsafe conditions and behaviors are reported by workers in industries across the board. Leveraging this reported data can help save lives.

In 2018, Enablon 4.0 won in the game-changer category of Wolters Kluwer’s annual global innovation tournament, the Global Innovation Awards.

Using sensor-enabled equipment, cloud computing applications, mobile capabilities and big data analyses, the Enablon 4.0 team was able to create a fully integrated and automated system to detect danger in real time. The team’s solution not only offers a 360-degree view of all operations, the AI engine they created continuously learns risk and control patterns from the industries it operates in. This allows customers to make data-driven decisions and prevent operational risks from happening.

Interview with Fatiha Achour, Lead Product Manager at Enablon.

How did your idea for this innovation arise? What was the problem you were trying to solve?

The main challenge for our customers is to provide safe working conditions for their workers. And despite the resources spent on improving safety and resilience, employers across the board still struggle because of the unpredictable nature of these incidents. What we discovered is not all incidents are as unpredictable as is commonly believed because all incidents follow a common path with similar causes and that’s why data is key here. With all the historical information we have, we knew that we could provide predictions with a high level of accuracy, to avoid potentially fatal incidents.

Learn how one GIA winner has innovated customer experience measurement programs.

What role do AI and big data play in this?

We use AI to predict where and when the next incident is likely to happen. We also use AI to select the most relevant machine learning model and features to apply to a specific site. Our engine can compare thousands of combinations until it finds one that fits best. This is AI applied to AI itself… leveraging big data means we can explore the context of every incident and increase the opportunities and accuracy to predict incidents.

We are the first in the market to deliver this predictive capability. And that’s why we were in the game-changer category. We believe this will change the landscape of safety and operational risk management.

What are some highlights of your experience with operationalizing Enablon 4.0, so far and what do you hope to achieve in the future?

Enablon 4.0 has been in the works to be operationalized for a year now. We believe we have contributed to mitigating many potentially fatal incidents, so far. Last year when we won best in category at the GIA, we had a proof of concept of something with a user interface.

Some of the highlights of our journey so far have involved working on-site with our customers to test the solution. For instance, while working with one construction company, both our team and the customer were surprised to learn how significant a role weather plays as a variable in incidents. So sometimes a small element can have a huge impact on injuries.

Our aspiration is to continue expanding the possibilities that these 4.0 capabilities offer, increase the accuracy of our model by introducing more data sets, and continue working with customers as we roll out our 4.0 offering. And, ultimately, we want to contribute to zero on-site incidents and injuries which is a goal for our customers.

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