All images: Max Bögl
Trends in integrated rail data management
A recent webinar hosted by the International Railway Summit explored the challenges and opportunities for rail data management that were born out of the big systemic shifts of 2020, including Covid-19 and its consequences. Ilaria Grasso Macola reports from the event.
Held by the International Railway Summit as the second of a two-part debate, a webinar titled ‘Integrated rail data management: new trends in unpredictable times’ gathered stakeholders from all around the industry.
They discussed how systemic shifts, such as the Covid-19 pandemic, have created new problems to the railway industry but have also created the space for drastic innovations and digitalisation. Here are three lessons we learned at the event.
SkedGo CEO John Nuutinen. Credit: Skedgo
MOTIONTAG managing director Fabien Sauthier. Credit: MOTIONTAG
The industry needs a flexible data management system
According to PwC Italy partner Paolo Guglielminetti, the Covid-19 pandemic and other systemic shifts are increasing the challenges the transport sector - especially railway - faces. The industry can no longer rely on precedent to predict passenger behaviour, freight demands and maintenance requirements.
“The lack of predictability is becoming the most common situation, not an exceptional one,” he explained. “Therefore, we have to think ahead and create a flexible system in order to be prepared to manage demand and supply conditions.”
The best way to move forward, explained Guglielminetti, is to observe the changes with data and make it available to the industry.
Credit: SkedGo | MOTIONTAG
Advanced analytics can help manage maintenance and faults
UK railway company Network Rail believes that advanced analytics are fundamental if operators want to simplify the management of faults along routes. The company has developed an intelligent infrastructure tool called Insight, which converts the data into intelligence to predict deterioration, avoiding delays for passengers.
“Insight is not there to make decisions for engineers but is there to give them the ability to take all of the data and to convert it into intelligence that they can use their engineering knowledge to apply to, to make the right decisions,” explained Network Rail predictive maintenance director Martin Mason.
This insight will be helpful when preventing failure on train tracks. As explained by Mason, Network Rail’s approach to track management has always been reactive: the company uses the data gathered to fix tracks based on their current condition.
“What this means is that we’re very quick to go and think things, which means the effectiveness of the intervention may not give you the best life extension to the problem,” he added.
With Insight, the approach will become proactive, preventing problems before they even occur. Operatives will also be able to view the state of the tracks in terms of degradation and make future predictions.
“It’s the first time in our industry in the UK that we’ve managed to produce these degradation models, as well as provide an operative with an inbox which allows them to prioritise models and manage faults as they are predicted to occur in a structured way,” he continued. “it’s more efficient than doing it with the paper-based method used at the moment.”
A successful digitalisation strategy requires a holistic approach
When the Covid-19 pandemic hit, Spanish company Smart Motors faced several challenges, including labour risk prevention, confusing government measures, and the huge drop in revenues.
A spin-off of Metro Barcelona with railway clients all over the world, Smart Motors used its technology to help its clients deal with issues such as keeping social distancing measures, while running more trains with fewer people on board.
“Those issues were things that our customers didn’t expect to have but started to in 2020, so we had to face them using an overall holistic strategy,” said Smart Motors CEO Marc Gispert.
Another problem Smart Motors faced was that there were issues with the number of data sources and the lack of data governance.
“The need in the market was to integrate, simplify and create value out of that data,” Gispert added. “Because the final objective was to gain stability and whenever something unexpected happens, we could give a response in a short time.”
The digital transformation needs to be transversal and in every department; everyone needs to know what the data is, where it is coming from and how they can use it.
To solve the issue, Smart Motors came up with Savana, a rational and scalable platform that merges all data from information systems and puts them to work.
“Basically, we collect all the data and dump it into a data lake and use different intelligence to streamline the data, providing the information directly to our customers through integrated business planning or platform screens, allowing them to use it for condition-based maintenance or managing the workforce and operations,” Gispert explained.
The digital transformation is not only about having the tools, said Gispert, but also involves the whole company.
“We need to gain capillarity, which means the digital transformation needs to be transversal and in every department; everyone needs to know what the data is, where it is coming from and how they can use it or ask for new developments and change,” he added.
Technology systems like Savanna are not static projects but are ways to improve activities and create value day by day.
“Our first goal is for Savana to be the only place for any initiative, any data source can be integrated in Savana and can also be the typical ally on the day-to-day operations,” he concluded.
Main image: The future looks bright for night trains. Credit: Shutterstock