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Reference Number ENA_10037420
Title Predictive Safety Interventions
Status Started
Energy Categories FOSSIL FUELS: OIL, GAS and COAL(Oil and Gas, Refining, transport and storage of oil and gas) 90%;
OTHER CROSS-CUTTING TECHNOLOGIES or RESEARCH(Environmental, social and economic impacts) 10%;
Research Types Applied Research and Development 100%
Science and Technology Fields ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 100%
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Project Contact
No email address given
SGN - Southern England
Award Type Network Innovation Allowance
Funding Source Ofgem
Start Date 01 August 2022
End Date 01 February 2023
Duration 6 months
Total Grant Value £498,618
Industrial Sectors Energy
Region South East
Programme
 
Investigators Principal Investigator Project Contact , SGN - Southern England (99.999%)
  Other Investigator Project Contact , SGN - Scotland (0.001%)
Web Site https://smarter.energynetworks.org/projects/ENA_10037420
Objectives
Abstract Worksite safety in the utilities sector has plateaued for 8 years.Utility companies are facing the challenge of reducing costs, while improving standards of service to customers and employee safety. At least 10,000 working days were lost to injury in the sector in 2021. The network cannot afford the continued disruption.Currently, the process for reducing lost-time injuries involves a large manual data-capture effort and experimental process changes. By the nature of this process, a worksite is already unsafe before anything is done to prevent it.Instead of waiting for a site to become unsafe, FYLD and SGN want to analyse which conditions contribute the most to worksite safety, then multiply them throughout the network.In 2021 SGN field teams recorded more than 31,000 video risk assessments using FYLDs AI-assisted technology, leading to a 20% decrease in safety events and £2.9m realised in related benefits. Of the safety events recorded, ~20% were failures to make the site safely accessible for teams and members of the public.FYLDs vision is to assist every fieldworker to take corrective actions and put unsafe conditions right in real time, before they develop into something more serious.During the Alpha phase, FYLD will build a machine-learning model to assess how effectively site controls have been deployed and determine which strategies lead to the safest outcomes. This model will be used to power an augmented reality proof-of-concept that will demonstrate how interventions can be made in real time -- with significant benefits to workers and members of the public.In the SIF discovery phase, FYLD set out to determine whether the risk assessment data could be used to forecast the potential risk of a safety incident. We found a statistically significant inverse correlation between the number of risk assessments recorded and safety incidents logged. We tested 15 predictive machine-learning models and two showed potential - in both cases, recall surpassed the 50% threshold on multiple occasions.However, we discovered that risk assessment data, alone, doesnt give the full picture. Fieldworkers at different sites can record nearly identical risk assessments, but only some of those sites will result in a safety event. This pattern presents even where the same control measures are, theoretically, applied to the same degree.Thats where the opportunity lies.The earliest point that an intervention can be made to improve site safety is the moment after a risk assessment is completed.Making worksites safer will improve the efficiency and resilience of the network, reducing time lost to injury and the disruption caused when incidents occur. SGN saved £240,000 in fines, in 2021, by simply recording evidence from the worksite.SGN have been working with FYLD since the companys inception. In 2021, SGN realised £2.9m in benefits through using FYLD and both companies have recently entered a three-year innovation partnership targeting a further £16m in savings. At the last count, just over 2800 people at SGN are already using FYLD -- this has resulted in more than 143,000 point of work site assessments.FYLD are best placed to assist SGN and bring this solution to market:A high-performing team with experience launching and maintaining AI/ML productsA demonstrable history of realising significant cost savings for utilities companies by deploying innovative solutionsExisting technology and datasets that can be built uponIn-house experience and expertisein change-management required for digital transformation, specifically within safety and productivity of utilities companies, at scale
Publications (none)
Final Report (none)
Added to Database 14/10/22