At Razorleaf, we have been talking about Digital Twins for awhile, trying to help educate on what it really is and how it fits into helping companies with their product lifecycles. We found a couple of reports from McKinsey & Company, who provided their take on Digital Twins. It is similar to what we see. Here is a summary:
What is it?
A digital twin is a digital representation of a physical object in context—and the last two words are the key. While some might consider a single CAD file to be a digital twin, in the podcast transcript Digital twins: What could they do for your business?, two McKinsey partners and digital twin experts explain, “What’s important is that you link the digital twin with real data sources from the environment and are able to update the digital twin in real time.” A digital twin is usually comprised of multiple physical models, and more importantly, is likely to be continuously receiving and processing real-time data. Another key to the definition: a true digital twin would be active and relevant throughout the product life cycle.
A number of different types of digital twins can exist. A product twin is the most common, but other types include a production plant twin (for a manufacturing facility), a procurement and supply chain twin, an infrastructure twin, and even a customer twin (allowing customers to virtually interact with a product).
In a recent webinar by Razorleaf on Digital Twins, Jonathan Scott outlined the different types of digital twins:
Why do I need it?
Digital twins can deliver tremendous return on investment. One of the most obvious and most prevalent ways they provide value is by reducing a product’s time to market. Simulations, rapid iterations, and design optimizations can all be performed on a digital twin in a fraction of the time of a physical prototype—which means faster development time, but also big improvements in product quality, whether in design, manufacturing, or even in service.
They can also be valuable for the more intangible benefits. A product twin, used in the development process, can improve sustainability by either reducing the material used in a design or reducing quality issues, which can ultimately reduce environmental waste.
Based on the experience of companies that have already implemented digital twins, McKinsey estimates that “digital-twin technologies can drive a revenue increase of up to 10 percent, accelerate time to market by as much as 50 percent, and improve product quality by up to 25 percent” (McKinsey & Company, April 28, 2022, Digital twins: The art of the possible in product development and beyond). While the potential benefits and value are compelling, it’s also true that the digital-twin journey will require some investment.
Define your goals
Start by understanding digital twin best practices, and then define a suitable use case, which their podcast experts define as “complex or dynamic environments that benefit from real-time optimization.” And finally, prototype select use cases to test the waters of digital twinning.
Understand your strengths and weaknesses
This means assessing your organization’s digital maturity, meaning your supporting data infrastructures (e.g., PDM or PLM) and access to quality data, whether from testing, manufacturing or in service. It also means evaluating what skills you have in-house, including robust data engineering and data science resources to support the data infrastructure, physical modeling capabilities, and advanced simulation and analytics.
Plan your implementation, but be flexible
Invest time in creating a cross-functional, agile team that can develop a minimum viable product (MVP) digital twin for priority use cases, while also building digital capabilities. Also, follow agile methodology and perform a retrospective on the MVP process to understand how you need to change or improve your processes. And once those processes are functioning well and delivering good results, scale up for more ROI.
The rest is up to you
With guidance for the What, Why, and How of digital twins, it’s up to you and your organization to answer the Who, Where, and When. Like any aspect of the digital transformation, creating true digital twins isn’t an easy process—you’ll need to ensure you have the skills, the resources (financial and human) to invest, and the multiple high-quality data sources. But the potential for ROI, increased customer satisfaction, and greater impact in the marketplace is hard to ignore.