Finding design errors early gives teams more time to solve them together, before the first physical prototype is built. Digital twins and the digital thread support this work and help teams put product data to use across the whole lifecycle. With that foundation in place, AI can support design too. Gofore’s white paper explores how to turn these capabilities into practical, lasting value.
For machinery and equipment manufacturers, keeping development manageable is getting harder. Machines are more complex, development cycles are shorter, and European manufacturers face growing competition from emerging players in Asia. The challenge is to find ways of working that help people develop and test machines with confidence.
“Emerging Asian machinery manufacturers are building their entire product development around virtual environments, simulation and rapid validation cycles. Traditional ways of working simply can’t keep up. Digital twins are one of the most important ways to stay competitive,” says Atte Aunola, Digital Product Lifecycle Program Lead at Gofore.
Aunola is one of the authors of the white paper, which looks at how digital twins support the digital product lifecycle.
Digital twins start at the drawing board
A digital twin is a virtual counterpart of a machine, device or component. It gives teams a way to test how a device works and check whether it meets its intended purpose. You do not need to wait until the physical device is ready: a twin can start supporting the work during design.
The digital thread serves a different purpose. It connects product lifecycle data from different systems so people can use it in their work. Alongside supporting digital twins, it provides a foundation for new digital services and revenue streams. The starting point is not the technology itself, but the work it needs to support.
“The twin starts taking shape at the drawing board, alongside the machine itself. You don’t have to wait for the physical device to start seeing the benefits,” Aunola says.
A twin does not have to be a complete replica. Model the parts that matter for the task at hand. When teams find problems virtually, they need fewer physical prototypes. Designers and developers can examine the results together and fix errors without delay. The purpose is to make their work easier, not to add complexity.
Build confidence through testing and simulation
Smart industrial machines operate around the world, often in conditions very different from those in which they were built. Testing and simulation help teams explore those conditions before problems arise in use. With so many machine variants, testing every one physically is not possible. Simulation gives teams another way to investigate faults and understand risks.
“When variant testing is done thoroughly through simulation, you won’t find yourself fixing overlooked problems in a machine, USB stick in hand, somewhere in a South American jungle,” Aunola says. “Done right, it can cut variant testing time by as much as 90 per cent compared with traditional ways of working.”
These benefits take shared effort. A pilot may help one team, but if it stays within that team, its lessons and potential benefits never reach others. Sharing what works, and what does not, is part of building value across the organisation.
Gofore’s white paper explores how to move forward. Aunola sums up the lessons in five key findings.
Five key findings
1. Treat digital twins as a leadership question, not just a technology choice. Start with the people and the business need. Agree on the problem you want to solve and what a useful result would look like, then choose the technology. This keeps the work connected to everyday needs and longer-term goals.
2. Give development a clear focus. You could put simulation at the centre and connect requirements management, data analytics and AI to it. Choose an approach that teams can work with, rather than trying to build everything at once.
3. Leave room to learn. Your starting approach does not have to be permanent or stand alone. As your organisation gains experience, bring in other approaches where they serve a clear purpose.
4. Make validation a shared practice. Use scenarios and measurable criteria to check results before making costly decisions that are hard to reverse. Discuss the evidence and the risks together. Responsible decisions depend on understanding both what the results show and what still needs checking.
5. Use AI to support people’s work, not to set the direction. A well-managed digital product lifecycle gives AI a stronger foundation. Keep its use tied to clear goals, and consider its effects on people and their work.
Start small and move forward together
If readers take just one thing away from the white paper, Aunola says it should be this: start with a clear goal.
“Pilots shouldn’t be left to run in isolation. When a digital twin serves a broader purpose, the benefits become visible and there’s a clear case for expanding its use.”
Start with a manageable part of the work, such as the hydraulics development process from requirements to testing. Bring the people involved together to agree on the goal and review the results. Once the approach works, share what you have learned and extend it one team at a time.
Changing how an organisation works takes time. A clear starting point helps turn a longer-term ambition into practical choices. Aunola invites CEOs and their leadership teams to consider one question: “Do we want to stay competitive?” The next step is to agree on what needs to change and where to begin.
According to Aunola, Gofore’s white paper draws on extensive experience to help you make informed decisions. It offers criteria for comparing approaches and a roadmap for taking a pilot into use across your organisation. Keep the wider purpose in view: use technology responsibly and ethically to support people, build trust and work towards safer, more sustainable industry and society.
For a practical starting point, read Gofore’s white paper Creating value with digital twins.
