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Reference Number ENA_10027191
Title Predictive Safety Interventions
Status Completed
Energy Categories FOSSIL FUELS: OIL, GAS and COAL(Oil and Gas, Refining, transport and storage of oil and gas) 100%;
Research Types Applied Research and Development 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 25%;
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 75%;
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 March 2022
End Date 01 May 2022
Duration 2 months
Total Grant Value £58,729
Industrial Sectors Energy
Region South East
Programme
 
Investigators Principal Investigator Project Contact , SGN - Southern England (100.000%)
Web Site https://smarter.energynetworks.org/projects/ENA_10027191
Objectives
Abstract Our project addresses Challenge 2: Data & Digitalisation, along with Challenge 1: Whole System Integration.SGN is moving towards data-led operational management to drive improved safety and productivity. Our data strategy is based on the concept of building digital twins of operations as they actually occur in the field. We have already deployed FYLD to 68,000 jobs over the past 18 months, which has vastly improved our data set. Worker buy-in is critical to achieving high-quality data sets. Therefore, our goal is to deliver more wins that benefit our workers, SGN as an organisation and our customers.SGN has shown its capability to adapt to new, data-led ways of working in its initial deployment of FYLD. The entire business has deployed FYLD to proactively manage fatigue whilst concurrently driving down network operation costs. In addition, 750 operatives in its repair, replacements and connections business unit now use FYLD as their primary work management platform. The next logical step in our data transformation and AI journey is to harness FYLD data to predict job sites with a high risk of safety incidents or injuries and drive more productive field force operations. FYLD has developed significant expertise in terms of AI and building deployable machine learning models. Additionally, we have a world-renowned AI expert Distinguished Professor & Executive Director of Data Science at the University of Technology, Sydney Dr Fang Chen as a core advisor to the business. Dr Chen has a proven track record of deploying predictive analytics models to the utilities industry and has collaborated extensively on the commercialisation of such systems with the CEO of FYLD, Shelley Copsey, during their years working together at Australias national research agency CSIRO. SGN and FYLD innovation partnership is two years old, with a further three-year term being agreed. We are well placed to deliver this project together, given our past success in safety and productivity outcomes for SGNs field force operations.FYLD has gained substantial traction in the utilities industry supply chain, including with Morrisons, Ferrovial, Lanes Group and Galliford Try, as well as being part of the HS2 supply chain after successfully securing a place on its coveted Accelerator Program. Potential users of our innovation include the SGN supply chain and beyond, given the safety and productivity outcomes FYLD delivers today, combined with the step-change in worker safety outcomes we are targeting with this project.
Publications (none)
Final Report (none)
Added to Database 14/10/22