Imagine that today is the day that the world is inventing the railway, and you are part of the steering committee. With access to all of the technology that this modern age has to offer, how would you choose to manage the systems, and which methods and solutions would you use?
There are some easy answers to these questions. But do we approach our current systems like this? Of course we don’t. We are stuck with existing technology, organizations and businesses. We will never be able to use the latest technology nor the most innovative solutions. There is an (un)natural process with many obstacles to overcome before a new technology can be introduced. Many obstacles can be traced back to man’s inability to adapt to new technology, adding the complexity of doing so in a safety environment, which the railway is. There are multitudes of guides and standards to address before you are allowed to use new technology in the rail context, many based on history which you need to respect.
But, for a moment, let us not consider those obstacles. Instead, let us ask, how would we like our rail system to be built and maintained today? And what, from this, can we adapt to in our current reality?
This blogpost is more about methods and solutions for managing and developing our systems than the actual devices or products they would use. Why? Because technology will change over time, and designing a system that is independent of the actual technology is key to making our rail solutions work for a long time.
Why do we need a railway system in the first place?
In other words, is this the transportation system we will have for all time? Possibly not. What will the value of transporting people and goods in “connected boxes” on rails be in the future? (Again, let us not get stuck with technological solutions.)
What we are trying to achieve in our railway systems is the safe transportation of people and goods on rails with specific conditions, such as low resistance between metal rails and metal wheels. Low resistance actually forms the basis of the business case for the very existence of railways; low energy consumption gives a better climate, higher speed, etc.
How hard can it be to safely move boxes on a predefined network of tracks? This is the solution we need to invent, and it’s not the hardest case for any engineer today. The ease of the challenge, or the underestimation and disrespect of the complexity in signaling, is actually one of the biggest hurdles to overcome when managing the introduction of a signaling system in a project. With or without knowledge, unknown challenges and uncertainties will arise that need to be managed. (Many of such hurdles arise from history, which are discussed in another blogpost that you can read here.)
Now, back to the solution.
Of course, if given the chance, engineers would use modeling and AI to support the innovation of a railway system. They would structure the innovation utilizing best practices from a system engineering perspective and use tool support. They would use several models to understand the innovation. Labs would be built to manage and evaluate the technical solutions. Real tracks, vehicles or products would be used only after all other possible means of analyzing and guaranteeing their function have been used. And, if we also include the maintainability and upgradability aspect, all operations would be logged and recycled back to all parts of the innovation.
This process, the system life cycle, would be automated with the generation of new versions that could be evaluated and controlled before being taken into revenue service. We would be in full control of all modules, communications, behaviors and interactions,both in operation and in digital form. We would have capabilities to evaluate and introduce new technology over time.
So, how far away are we from such a system? And where do we start?
Many of the steps that need to be taken to make this system a reality would involve accepting the challenge and acknowledging the complexity in today’s system. First, we need to be in control of our current system and its life cycle. One way to get there is to digitize our system data, create digital models and automate our processes.
Digitization is initially an uphill battle with upfront costs and payback later on. We can sometimes argue for direct effects from digitalization, but the greater return of investment will come in the future. Still, it is necessary in order to understand current relations between subsystems and to remove obstacles before introducing new system technology. At the same time, it could be done stepwise, system by system, with the acceptance that we will gradually upgrade our knowledge and will not realize the full effects until later—with the introduction of a new subsystem, technology or easier maintenance.
Digitization is initially a cost that pays off over time, if the digital system is introduced with the aim of developing over time.
Process automation is the key to being able to maintain the digital representation of our evolving system
When we are in control, we can actually start to simplify and become more efficient. Today’s systems are, in many ways, overly complicated by regulations (because we do not dare to remove anything from a safety system). Regulations are usually the last thing to change. Having extensive experience with a given technology and methods is one way to guarantee safety but, at the same time, it poses a barrier to innovation. Regulations should be focused on the end product in its own context, with qualitative requirements and quantitative measurements that are formalized–not as it is today, where the focus is on the development process which, contentiously, will evolve.
As a researcher, at KTH the department of Engineering Design, complexity and uncertainties once taught me, “complexity is not managed by adding requirements nor processes. It is managed by awareness and competences that continuously work to reduce the complexity.”
Being in control and continuously working with a system on all levels will create a sustainable system that is up to date. Knowledge will be kept by the system model, and system managers should direct resources towards means of improvement, not just towards keeping the system alive.
These principles mirror the ideas and background behind the development of digital twin technology, which you can read more about here.
About the author
Mats Boman has been working in the railway industry since 1999. His career started at Prover and, after switching gears to drive a consulting business within rail control system management and then serve as the CEO of the rail engineering company STHK, he recently returned to Prover as the Vice President of Business Development. Mats has a master’s degree in computer science from Uppsala University.