Digital Twins: No Longer a Pipe Dream for Manufacturing?

(“In Case You Missed It Monday” is my chance to showcase something that I wrote and published in another venue, but is still relevant. This week’s post originally appeared on TechInvest)

After several years, digital twins may finally live up to its hype of revolutionising manufacturing forever. Recent trials have proven its viability: companies like Unilever have deployed eight digital twins to drive global operations, while an Australian mining company has tapped into digital twins to predict mining yields and future plant performance. The promise is clear: more efficient, safe, and productive data-driven operations.

Sure, it’s not as sexy as what you’ll see in Iron Man—at least, not yet—but digital twins provide manufacturing IT teams with greater levels of visibility, predictability, and control over systems and equipment. That’s reason enough to pursue the tech, but decision-makers need a little more convincing. How can IT leaders build the case for digital twins and encourage adoption, before the competition does the same?

Clearing the Air Around Digital Twins
For some time, the buzz around digital twins has largely been obscured by popular technologies like artificial intelligence or blockchain. Therefore it’s essential for IT to align management with the real possibilities of digital twins and dispel any incorrect notions of the technology. This doesn’t just ensure everyone’s on the same page, it’s also important for the business to understand the digital twin solution that makes the most sense. IT can kickstart things by detailing the three common types of digital twins being commercially used today:

  1. The Simulation Twin
    This is a virtual reconstruction of a physical object which cannot be manipulated. To enable this twin, IT must advocate for investments into greater data collection and compute capabilities, due to the huge data, modelling, and processing requirements simulations of operations will require.
  2. The Operational Twin
    Created using data drawn from sensors and databases, the operational twin allows manufacturers to model different conditions and scenarios and see how they would affect physical systems or equipment. Implementing this twin will require IT teams to acquire proficient data analysis and database management software.
  3. The Status Twin
    An unalterable digital twin image used mostly for around-the-clock monitoring. Robust monitoring solutions in data monitoring and application performance will form the bedrock for the creation of status twins to track equipment and applications in real-time.

Manufacturing’s Seamless Leap to Digital Twins
Perhaps one reason today’s manufacturers hesitate to adopt digital twins is the perceived high level of investment needed to bring the technology to life. This cannot be further from the truth. Modern manufacturing operations already have most of the basic components to build a digital twin running on their factory floors.

Data fuels the potential of digital twins. In other words, IT teams must—if they haven’t already—make it a goal to improve data collection, monitoring, and analysis capabilities. First, they must determine the extent of data that must be collected. Depending on the type of digital twins they want to build, they should consider data from manufacturing cycles, telemetry data like temperature, speed and pressure from sensors and equipment, even data from logistics and warehouses operations.

Most established manufacturers already have a good web of sensors in place. All IT needs to do is ensure they have the proper data solutions in place to drive digital twin implementation. To construct an operational digital twin—and use it for modelling and simulations—IT teams must deploy the right database performance monitoring and management solutions. Such solutions are built to seamlessly link together the various SQL databases employed by today’s manufacturers, leading to greater diversity of data that can be used in constructing a digital twin. Data monitoring solutions will also play a vital part in tracking the execution and data results from simulations run using operational twins, as well as processing those results into actionable insights for management.

Preparing for What’s Ahead
With both the right data tools and solutions, IT will have everything they need to construct and exploit the full capabilities of digital twins to monitoring or modelling possibilities in a manufacturer’s operations. And by running the proper simulations, they could even obtain the data necessary to convince management to invest into emerging technologies, like AI and automation, for better productivity, efficiency, and safety.

With digital twins, manufacturers have access to a sandbox tool that allows them to test and evaluate concepts in ways a physical, interconnected, and costly manufacturing environment can never provide. It’s the stepping stone to Industry 4.0 technologies like automation, machine learning, and blockchain. All manufacturers—and IT teams—need to do is to set aside their fears, and take the first step into the virtual world.

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