An interview with Poul Wend Hansen, Head of Sustainability at ScotRail, on using data reporting requirements to drive innovation by using digital twin technology

When you joined ScotRail three years ago, what was the biggest shift you needed to make in how sustainability was approached?

When I arrived, we were transitioning from a franchise model to public ownership under the Scottish Government. This wasn't just a funding change – it changed our responsibilities and expectations.

Under public ownership and with Scotland's carbon net zero 2045 target written into law, we needed to become something entirely different. We couldn't just say "we're going to do this" and then figure out a plan later. Everything we put forward has be based in fact and be achievable because of the responsibilities and expectations of public ownership.

Previously, sustainability teams were often seen as the people who gathered data, produced reports, and made sure we ticked the regulatory boxes. With public ownership Sustainability activities had the opportunity to change from being compliance  driven to one that drives and guides organisational innovation.

How did this change your team's day-to-day operations?

We moved from being a reporting department to a team who others seek to lead and develop innovation. Yes, we still handle compliance – we're responsible for large contracts of operational costs covering utilities, waste, electricity, gas, water, and oil across over 370 locations. But the exciting part is that we're now tasked with finding innovative solutions to drive real efficiency gains.

Can you explain how digital twin technology fits into this transformation?

This is where platforms like Predict and Optimise from Optimise AI have been game-changers for us. When I arrived, we were looking at very expensive, high-level designs for digital twins – but just for heating systems on a handful of assets. We're talking beyond six figures for something that wouldn't give us a demonstrable return on investment.

The catch-22 with obtaining funding is that in order to gain approval for large investment, you must prove its value with a robust business case, which in some cases, require a significant spend on design work. What Optimise AI offered was a smart way to spend a relatively small amount of money –rather than hundreds of thousands – to get to a proof of concept that we could actually hang our hat on. Instead of taking 2-3 years just to get to an answer, we could target and demonstrate value quickly for a fraction of the cost.

How does this technology work in practice at ScotRail?

The beauty of the Predict platform is that it's based on our actual direct feeds of utility consumption data – electricity and gas from our stations and depots. So whatever business case we develop for a location, or across all our locations, we can say it's based on our real consumption data. We can track the predicted savings against actual performance.

We started with 15 stations, expanded to 25, and now we're using the platform across multiple locations. The tool allows us to do scenario planning – do we spend money on LED lighting, or heating systems, or both? Should we prioritise Glasgow or go up to Thurso first? We can model different approaches and see which gives us the best return on investment.

What kind of results are you seeing?

We're looking at potential savings of 20-50% on our £9 million utilities cost. Even conservatively, that's £3 million in annual savings. Over the past three years, we've invested about £200k in innovation testing, and we can use Optimise AI tools to that this relatively small investment could deliver a big return with the right targeted investments.

But it's not just about the numbers. The tool has become a communication device. When I demonstrated it to a key stakeholder, they immediately said, "What I really like about this is that it's actually about cost efficiency." That's exactly the point – we're solving business problems while achieving our environmental goals.

How has this changed how others in the organisation view your team?

The biggest shift is that we're no longer seen as the people who just tell you what you can't do or what regulations you need to follow. We're now viewed as problem-solvers who can help different departments achieve their objectives more efficiently.

The tool takes away the emotive reasons for decision-making and drops everything into financial terms. We can show that if we implement everything we've identified, we'd get to a pretty low cost for running our stations and depots.

What's next for your innovation approach?

We're looking at expanding the tools use to our other utility costs- water and waste management. The goal is to create a comprehensive view of our operational assets' operational impact. Eventually, we hope to integrate actual BIM models so we can check whether proposed designs will be the most efficient option over their operational life.

The fundamental issue is the bigger an asset is, the more complicated it becomes, and inefficiencies get harder to resolve.

Also, when seeking budget to resolve inefficiencies or to invest in the elimination of fossil fuels and renewable energy not everyone is an architect or an engineer.

If we could take a digital design and immediately check whether it might cost more upfront but save money over its operational life, we'd be filling a crucial gap in how we make investment decisions.

That's the future I see – sustainability teams as drivers of innovation, using technology to make better decisions faster, more cost effectively and ultimately helping organisations become more efficient while meeting their environmental obligations.