Your experiences with digital twins in industry?
Hi everyone,
I’m currently looking more into industrial automation and thinking about shifting my portfolio a bit in that direction. Things like virtual commissioning and digital twins seem pretty interesting to me, so I was wondering if anyone here is invested in this space or has some experience with it.
The idea of being able to test machines and production lines digitally before they actually go into operation sounds pretty promising to me.
Do you guys think virtual commissioning is already becoming a standard thing in mid-sized manufacturing companies, or is it still more of an emerging technology?
If there are any engineers or software developers here, I’d also be interested in hearing how difficult these solutions are to implement in practice. Which industries are using them besides automotive and packaging? And how do you decide whether virtual commissioning is actually worth it for a project?
Also, feel free to tell me if you think I’m overestimating the potential here. 😄 I’m mainly curious to hear what people with actual experience think.
Based on the most helpful WSO content, the rise of automation and digital technologies like virtual commissioning and digital twins is a significant trend across industries. While the context doesn't specifically address digital twins, it highlights the broader impact of automation and technology on industries like manufacturing, healthcare, and transportation.
Here are some insights that might align with your query:
Adoption in Industries: Automation and advanced technologies are increasingly being adopted in industries like automotive, healthcare, and logistics. For example, robotics and AI are replacing manual labor and basic decision-making tasks, which suggests that digital twins and virtual commissioning could follow a similar trajectory.
Emerging vs. Standard Practice: Technologies like virtual commissioning are likely still emerging in mid-sized manufacturing companies. Larger firms, especially in sectors like automotive, are often early adopters of such innovations due to their resources and need for efficiency.
Implementation Challenges: Implementing advanced solutions like digital twins can be complex, requiring significant domain expertise and investment. Industries that rely on precise modeling and simulation, such as aerospace or high-tech manufacturing, might find these solutions more practical and valuable.
Evaluating ROI: The decision to use virtual commissioning often depends on the scale and complexity of the project. For high-stakes projects where errors can be costly, the upfront investment in digital twins can be justified by the potential savings in time, resources, and risk mitigation.
Future Potential: The context suggests that automation and digital technologies are reshaping industries, but their adoption depends on factors like cost, ease of implementation, and the specific needs of the industry. While the potential is vast, the pace of adoption might vary.
If you're considering shifting your portfolio towards industrial automation, focusing on sectors with high automation potential (like automotive, healthcare, and logistics) could be a strategic move. However, keep in mind that the adoption curve might still be in its early stages for mid-sized companies.
Sources: How To Quit Your Job (And Still Win), How To Quit Your Job (And Still Win), https://www.wallstreetoasis.com/forum/investing/leisure-and-hospitality-take-the-lead-the-daily-peel-4102023?customgpt=1, Most Promising Industries, Is tech all that or nothing more than a buzz word?
I made my digital twin have an even bigger hog. He floats around the metaverse just absolutely railing e-girls without legs.
venn and float financial?
I specialise in PE, so mechanical engineering isn’t really my area of expertise. However, I have come across Machineering time and again through partners and a few friends.
The name it's known by in a few years isn't all that important to me. For me, it comes down to the fairly banal question of whether it will actually pay off in the end. When a plant like that is down for half a day, for example, and that immediately costs real money, things look different. Apparently, changes don’t just stop after initial commissioning either.
Yeah, exactly. I’m also wondering at what point a digital twin actually starts to pay off, or whether you can even define that using fixed parameters.
Personally, I see a lot of potential in digital twins, but I’m also realizing that I still need a bit more information to really understand where the sweet spot is.
From a PE perspective, I wouldn’t get too hung up on the size of the project. If a fault isn’t spotted until late on and the system suffers unexpected downtime, it quickly becomes expensive. With modifications, the costs can probably add up in much the same way.
What happens to the model afterwards is another point for me. If it practically ends up in a drawer after commissioning, the effort involved would be harder to justify. If, on the other hand, changes are constantly being made to the system anyway, it’s a different story.
Whether the company is large or small is something I’d put towards the bottom of the list.
Your perspective on downtime is super interesting from an investor's point of view. At the end of the day, it all comes down to ROI, and if a twin prevents costly days of downtime, that definitely makes a strong case for it.
However, if the model just ends up gathering dust after commissioning, it becomes a one-off project business with a heavy service component, which is tough to scale commercially. For investors, things only get really exciting when it generates recurring revenue (Software-as-a-Service, predictive services, etc.).
I’d love to know how the market is actually handling this in practice right now:
Are machine builders starting to sell the digital twin as an ongoing service model to operators so it pays for itself over its entire lifecycle?
In my opinion, that's where it starts to get interesting for investors. I don't see every machine builder going to build a SaaS model out of that right away, though. Among partners and friends, I hear that there's still a lot to do after commissioning. Later on, the model is still used for testing or making changes, and sometimes it comes back onto the table during training sessions. With iPhysics, things are likewise moving in this direction. For me, that’s precisely where things get more interesting.
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