Written by Don Haupt

3 Tips to Be Successful With Digital Twin

The digital twin is hardly a new concept. It has been a top technology trend from Gartner for several years running. Every industry conference tends to have a theme around digital twins and transformation these days. Based on my years in the industry and my self-proclaimed role of PLM Elder, the efforts needed to make digital twins successful are very challenging.

Talking through every challenge relating to digital twin would take a great many blogs, if only because every company’s approach and priority around digital twin is different. But for me, I keep hearing about three challenges in particular:

  • Companies aren’t adequately prepared to intake and analyze the massive amounts of data that digital twins provide.
  • Decades of innate silos between different departments keep companies from the true value in a digital twin.
  • Companies don’t always account for how much is still needed on the human application side of a digital twin.

1) Prepare For An Overwhelming Amount of Data

I’ve found that most companies don’t fully brace themselves for the amount of data that a digital twin generates. I was recently reading a report from IDC that highlights this point very clearly. This year, there will be an estimated 20 billion IoT sensors on physical products. In the next five years, those devices will create over 90 zettabytes of worldwide data. To help put that in perspective, storing that much data onto BluRays would create a stack that gets you to the moon nearly 12 times.

That information is just from IoT and physical sensors. That doesn’t even include product data from areas such as initial product designs, manufacturing BOMs, service reports, and dealer networks. The list can go on and on. It’s no wonder that companies aren’t fully prepared to deal with that level of information.

So what can you do? This may sound too simplistic and trite, but you need to have a well thought out and developed plan! Too many companies don’t fully prepare or have a robust plan for tackling all the information they have at their disposal. First, have a plan for the data you’re really going to need and use. Measuring, calculating, and storing information is great, but if you’re not going to use it, why capture it? Determine what you’re going to need and use with a strategic purpose.

Second, have a plan for how you’re going to analyze the data. Along with digital twin technology, invest in tools that will help manage the influx of information. Look at tools like AI, edge technologies, and machine learning solutions. Consider also that cloud can help create a system of engagement based off all your systems of record. With the right solutions in place, companies can get clear indicators of actionable insights.

2) Start Breaking Down Silos

Digital transformation promises to bring product information from different steps of the lifecycle together. As such, it’s safe to assume that digital transformation can also help bring historically distant departments together. But the phrase “old habits die hard” has always been felt strongly, especially in the manufacturing space. Decades of separated teams and disconnected processes are naturally going to be hard to overcome.

Think about this. Even today, the most common way to find and access engineering information is to physically ask people. Most engineers manage product information across multiple systems, including CAD and PLM. A select few companies actually integrate multiple digital twins for a holistic product into one single source.

I believe that most of these barriers can be traced to cultural change and access to the right technology. I see that a lot of companies try to overcome these problems by trying to get different departments access to different systems of record, which comes with its own host of other problems. My advice: integrate a system of engagement that lets teams gain information without sifting through different record systems.

3) Set The Right Expectations

A lot of companies believe that technologies like machine learning, predictive analytics, and automation are enough to digitally transform a company. Then, they are surprised by how much is still needed on the human application side.

Half of all work done in manufacturing companies include things like interacting with stakeholders, solving problems with product designs, and applying expertise to create innovations. Most of this work can’t be automated. Engineers in particular look at the amount of work they still have to do in spite of all the new digital tools they use, then say, “This digital twin thing isn’t working, look at all the manual effort I still have to apply.”

What I tell companies is: the goal of the digital twin is to give you the intelligence to focus your efforts on strategic and valuable outputs. Digital transformation and digital twins make certain aspects of your job a lot easier, but they don’t replace the work you need to do – they just shift it. If a company can understand this, then they’re much more likely to see benefit from digital twins.

Gain Business Intelligence

Throughout this blog, I’ve been alluding to the true benefit of a digital twin: it gives you business intelligence to make better decisions in the future. It doesn’t eliminate or minimize the work you’re doing now, but it fundamentally changes what you’re going to do next. That’s one of the reasons I’ve been working with Vertex Software in my retirement. I’ve seen firsthand that the wrong expectations are making companies leery about the digital twin.

I’d like to leave you with a few final pieces of advice. Take a look at Vertex Software in the context of the challenges and opportunities I’ve discussed so far:

  • Managing your data. Vertex gives companies an engagement platform to gain access to the data they need so they get the full ROI out of their current tools.
  • Breaking down silos. Vertex brings teams, suppliers, and partners together in a secure platform for 3D collaboration.
  • Meeting your expectations. Vertex makes it easy and intuitive to interact with stakeholders, which helps engineers prioritize their time on other mission-critical activities.
About Don Haupt

Don worked with Caterpillar Inc. from 1979-2017 in a variety of roles, including Lead Business Process Strategist and Technical Steward for PLM and engineering systems. Don worked in multiple product development disciplines and product lines. He has significant experience in product design, simulation and validation, engineering processes, and PLM strategy and process, and environment and systems definition. Don’s experience as the strategic technical director for PLM World also contributes to his expertise with PLM. Today, Don is enjoying retirement and serving as a PLM consultant, providing customized technical guidance and counsel for engineering management and engineering design processes. He leverages his deep expertise in Product Lifecycle Management, Model Based Engineering, Advanced Product Quality Planning and many other product design and development processes.