What is a Digital Twin? Is it an avatar that represents a person or company in a virtual world? A CAD drawing of a product? A computer algorithm that tests the effect of changing a single design element on multiple outcomes? You'll hear the term a lot these days, but finding a clear definition can be difficult.
This episode McKinsey talks operationsThis first instalment of our short series on digital twins begins our journey into their potential by exploring what they are and what they are not. This conversation between Kimberly Borden and Anna Herlt, partners in McKinsey's Operations practice, is led by Christian Johnson. The transcript below has been lightly edited for clarity.
Christian Johnson: Your future success requires agile, flexible and resilient operations. I'm your host, Christian Johnson. McKinsey talks operationsis a podcast where global executives and McKinsey experts reveal how to cut through the noise and create new operating realities.
The term digital twin is becoming fairly common in the business press, but it's also a buzzword that often lacks a clear definition. And without that clarity, knowing where and how to use digital twins and, more importantly, how to capture the potential value can be difficult, to say the least. To help us gain a deeper understanding of what digital twins are and what they are not, and how they can benefit companies across the value chain, we are thrilled to have two experts in the field join us today. Anna Herlt, a McKinsey partner based in our Munich office, and Kimberly Borden in Chicago, are leaders in working with clients to understand the potential and applications of digital twins. Kimberly and Anna, thank you for joining us today.
So, Anna, let’s start by understanding today’s topic a little bit more. Let’s start with a simple question: What is a digital twin?
Anna Heldt: Christian, that's a really good question. Let me give you a simple answer: A digital twin is actually a digital representation of a physical object, a representation that we see in its environment all the time. The key is to be able to link the digital twin to real data sources from the environment and update the twin in real time.
The key is to link the digital twin to real data sources from the environment and be able to update the twin in real time.
Anna Heldt
Christian Johnson: Kimberly, can you tell us a bit about the types of digital twins available?
Kimberly Bowden: There are common archetypes of digital twins, such as the product twin that represents the product, the production plant twin that represents the entire manufacturing facility, the procurement and supply chain twin, also known as the network twin, and finally the infrastructure twin.
For product twins, this spans different parts of the product lifecycle, from early concept design and engineering through to service. This means you get live, real-time data about the product as it works in service. An everyday example would be Google Maps, which is a digital twin of the Earth's surface. It then links in real-time traffic data to optimize driving routes. This is a very simple version of a digital twin.
Christian Johnson: As we mentioned at the beginning, digital twin is becoming a fairly commonly used term and has become a bit of a buzzword. But can we define what a digital twin is not? What doesn't meet the standards?
Kimberly Bowden: Christian is right. Today, the term is used very loosely. For example, some people refer to a digital twin as a simple simulation or a CAD drawing. This is not really accurate. A true twin often encompasses multiple physics models and processes live real-time data.
Christian Johnson: It seems like there will be multiple models involved, higher levels of precision and accuracy, and links to real-time data will be really important, is that right?
Kimberly Bowden: Yes, that’s right. In fact, a true digital twin works across the entire lifecycle of a product, from design to service.
Christian Johnson: Well, it seems like there is more integration across the value chain.
Kimberly Bowden: exactly.
Christian Johnson: So, now that we know the scope of what we're going to discuss today, can you talk a little bit about the value that companies and organizations can get from digital twins? What can digital twins bring to us?
Kimberly Bowden: The value that digital twins bring is immense. One of the biggest is the ability to reduce time to market, i.e. development time. Digital twins enable rapid iteration and optimization of product designs much faster than physically testing every prototype. Plus, as you can imagine, they often result in a significant improvement in product quality. This can be achieved through the manufacturing process, meaning that after simulating the product as it is being manufactured, you can see where there are flaws in the design so that it can be manufactured better. Plus, the service actually lets you see if your design might not be working properly, so you can redesign it in real time.
Finally, developing a customer twin that allows customers to fully interact and immerse themselves in the product has increased revenue by up to 10 percent. Daimler, for example, has done this very well, allowing customers to test drive the car before actually getting in.
Christian Johnson: That's great. Kimberly just talked about the outcomes you can get from digital twins: faster time to market, improved product quality, increased revenue. How do these outcomes translate to other key business concerns, like environmental sustainability?
Anna Heldt: That's a very good question. Sustainability is one of the topics we've been discussing a lot lately. Indeed, digital twins, especially product digital twins – twins that you use in your product development process – can be very helpful. They can help you reduce the materials you use in your design, thus reducing your overall material consumption, improve product traceability to reduce quality issues, and ultimately reduce waste to the environment. This is an area where a home appliance manufacturer has really made significant improvements, reducing their waste by about 20 percent.
Christian Johnson: There is clearly a lot of value to be gained, both for companies and their customers. But as with many things, the challenge is where to start. What are the necessary conditions for implementing a digital twin?
Anna Heldt: I think one of the key elements that needs to be in place is digital maturity: you need the supporting data infrastructure such as PLM, PDM, etc., and access to high-quality data from test and real-world data environments in manufacturing or service.
Kimberly Bowden: You also need the right use case. Generally, complex or dynamic environments that can benefit from real-time optimization are good candidates for initial use cases. That said, some companies will tailor simple products to get real-time customer feedback, such as toothbrushes or everyday items that people use.
Christian Johnson: That's interesting. One of the things we've talked about in previous podcasts, and something that's been really talked about in the press, is the skills shortage, especially the digital skills shortage. Let's say you're trying to do this as a company. What are the skill sets that you need to implement a digital twin?
Kimberly Bowden: One of the key things you need to have in place is a strong data infrastructure. So you'll need data engineering and data science resources to support that data infrastructure and to assemble the twin. You'll also need physical modeling capabilities to model the products and facilities you're trying to twin. And finally, in complex situations, you'll need advanced simulation and analytics skills to accelerate your compute power. As you can imagine, trying to twin fighter jets, for example, is going to require a lot more compute power than twinning toothbrushes. Overall, the higher your digital maturity, the easier it will be to implement.
Christian Johnson: So, with that in mind, what are the barriers to adopting digital twins, other than the skills (or rather the lack thereof) required for successful implementation that we just discussed?
Anna Heldt: When we talk to companies, one of the biggest barriers is skills, but another barrier is the significant upfront investment required before you can really derive full value, and not having access to the high-quality data that you actually need to twin.
Kimberly Bowden: Additionally, it often requires linking to multiple data sources, which takes time and requires the highly skilled resources mentioned earlier.
Anna Heldt: And I think a lot of companies need to identify where to start, what is the best use case to start with, and we usually try to pick a use case that is not too complex and build the whole digital twin framework from there.
Christian Johnson: That's great. Thank you. So as we wrap up our conversation today, do you have any practical advice for our listeners? What advice would you give to people who want to get started with digital twins?
Anna Heldt: I think what's really important when you start is understanding where you're starting from, doing something like a digital maturity assessment to understand your current strengths and weaknesses and determining if you really want to start where you are or what is the infrastructure that you need to build, and then like I said before, start with a little MVP and learn from there and grow from there.
Kimberly Bowden: Additionally, it's also important to have a well-defined business case that's very clearly tied to value. Make that use case very specific when you start. Additionally, it's really important to think about digital twins in the context of your broader digital strategy – where does digital twin fit in and how can you best leverage this capability for competitive advantage in the marketplace. For example, if you can deliver better product quality faster than your competitors through a twin, that's a true competitive advantage and makes you a great candidate for leveraging digital twin capabilities.
It is crucial to consider digital twins in the context of your broader digital strategy – where they fit and how you can best leverage this capability for competitive advantage in the marketplace.
Kimberly Bowden
Christian Johnson: Thank you again Kimberly and Anna, that was really valuable advice on taking a holistic view and making it part of a broader strategy.
Kimberly Bowden: Thank you, Christian.
Anna Heldt: Thank you, Christian.
Christian Johnson: We've talked a lot today about the definition and basic requirements for a digital twin implementation, but it's clear that there's a lot more to say about digital twins and their uses. We'll go into more detail in future episodes. There's a lot more to talk about. Kim and Anna, thank you for your time today. You've listened to McKinsey Talks Operations with Christian Johnson. If you liked what you heard, please subscribe to the show on Spotify, Apple Podcasts, or wherever you prefer.
