How manufacturing companies can use digital twins to remain competitive
Companies that make physical products sometimes struggle to stay relevant as digital natives and find creative ways to capture the highest margin fringes of age-old businesses. One specific challenge these companies face is that digital native businesses have developed advanced data processing capabilities to create better customer experiences and identify new opportunities. This is much harder for established physical goods industries, which rely on legacy systems and manufacturing equipment.
Digital twins could help bridge this gap between legacy systems and modern customer experiences, Michael Carroll, VP at Georgia-Pacific, predicted at the Digital Twin Summit. Carroll leads corporate transformation strategy development at the paper and forest products giant.
He argues that physical products industries don’t have suitable mechanisms for dealing with the exponential growth in data. Most business leaders he talks to know that data is growing, but they take a linear rather than exponential perspective. This limits the ability to capture value from new data streams like the IoT, ecommerce services, manufacturing equipment and customer interactions.
The permission bottleneck
Business leaders also face the challenge of implementing a permission-based approach to help integrate information technology (IT) and operational technology (OT) used for managing physical machines. To do so, business and engineering teams must ask the IT department for access to digital representations of the assets they manage. Then, the IT department needs to ask for permission to get more data from the physical assets.
“We end up in a permission asking cycle in a world that is growing exponentially,” Carroll said.
He observed that in the mid-1970s, the bulk of the S&P 500 was made of companies whose tangible assets made up 85% of their value. But today, the balance between tangible assets like goods created in factories and intangible assets like brands and experiences is reversed.
The leading companies are systems-based rather than functions and process-based companies. They have created connected ecosystems that generate, aggregate and analyze customer, market and supplier information. As a result, they understand what their customer wants before competitors do.
The exponential model
Established businesses need to take a similar approach that extends these traditional tools to support digital twins of real-world goods, manufacturing processes and marketplaces. To do this at scale, the IT organization needs to plan a more self-service and democratized approach to provision, update and leverage digital twins.
“This means that in order to create value at the rate that data grows, which is exponential, you might have to reconstruct yourself so that you don’t have to ask permission to go create value,” Carroll said.
This allows business executives and operations teams to stand up new devices, create new applications or change configurations on their own.
“Now they are responsible for the digital representation of the thing they are in charge of,” he said.
This new approach could also allow enterprises to create digital twins powered by artificial intelligence (AI) to understand and respond to customer values and decisions. “We do not know a lot of the answers, except to say that we’re pretty sure that tomorrow is about creating value in the exponential age and creating value at scale, with data growing exponentially,” Carroll said. “Digital twins will be a huge part of that, and it will be powered by AI.”