Increase in Digital Twins May Give Rise to Marketplaces

Lisa Johnston
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digital twin
The adoption of digital twins face many of the same challenges as all new technologies, such as the availability of data and the ability to organize it.

Predictions for the new year abound, and digital twins are expected to tally one in the winners’ column for 2022.

That’s according to new research from ABI Research that lays out a series of tech hits and misses for the year ahead. Among the hits include an emergence of digital twin marketplaces to support the need for the suite of technologies required to deploy them. 

Digital twins, which create digital or virtual replications of products and processes, are used in a variety of environments across the consumer goods industry to increase speed to market and reduce costs. The ability to use data and analytics to model and understand complex business environments is particularly valuable right now, and digital twins can help identify disruptions in advance in both manufacturing and the supply chain.

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While they’re not new, they’re having a bit of a moment in the sun thanks to both the pandemic’s uncertainty and the amount of data that’s now available, David Simchi-Levi, leader of the Massachusetts Institute of Technology’s Data Science Lab, confirms to CGT, noting that their use across industries, including consumer goods, has significantly increased.

They are also being leveraged when it comes to making inventory and capacity decisions to increase flexibility and agility in both the business and its supply chain. In the supply chain, “this is really about moving away from just using KPIs,” he says, and instead making decisions to solve a problem that may occur six weeks from now.

Johnson & Johnson, for example, leverages digital twins in product testing to compare results from different facilities, which it says enables it to innovate at a lower cost and reduce the number of clinical trials required. Adidas recently announced it will invest in digital twins to speed up its product design process and increase collaboration with both consumers and its designers.

Meanwhile, Mars uses the technology to reduce waste and improve margins in its manufacturing plants, and Diageo has prepared virtual twin trials for its Johnnie Walker brand with the hopes of developing a new coating that reduces microcracks in the bottles’ glass — potentially reducing the CO2 impact and carbon emissions in both the bottles’ manufacturing and transport.  

Digital twins have entered mainstream territory over the last few years thanks to IoT dashboards and near-real-time reporting, ABI Research says in its 70 Technology Trends that Will — and Will Not — Shape 2022 whitepaper, and spending on industrial digital twins is pegged to grow from $4.6 billion in 2022 to $33.9 billion in 2030 at a 28% CAGR.

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But given that digital twins are a combination of technologies, including cloud computing, CAD modeling, machine learning, plant floor hardware and many others, the firm predicts that continued maturity will require support and innovation from third parties.

As a result, ABI says, digital twin marketplaces that enable independent solution providers and third parties to build relevant tools for the ecosystem are emerging and will be necessary for continued adoption, as are the use and implementation of artificial intelligence at scale.

Also increasing as a result of changing requirements, it says, are a need for standards organizations and model libraries.

The adoption of digital twins face many of the same challenges as all new technologies, agrees Simchi-Levi, who has helped develop digital twins for several companies, including Ford — such as the availability of data and the ability to organize it.

“Most companies have the data, but they have not organized it in a way that we can use effectively,” he says, estimating that 80% of a typical digital twin development project is spent on data collection, management, and ensuring accuracy.

Furthermore, progress can be hindered by a lack of data and analytics expertise in a business, as well as organizational challenges in leveraging the information properly across the enterprise.

[See also: How PMI Is Using Digital Twins in the Supply Chain]

“These are not unique to digital twins, but the digital twin is an example that highlights why these are important issues,” he says. “A digital twin does not exist if you don't have the data. A digital twin does not exist if you don't have the data science. These just highlight the importance of these capabilities.”

And while there is a lot of traction for digital twins right now in the market, it will take time for companies to move into action.

“There is a lot of focus on digital twins. We see the power for companies that have implemented this, but it takes time for the company to move from, ‘I don't have anything. How do I start to implement a digital twin in my business?’”

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