Capacity plan, prioritize, and monitor production orders
Enforce process adherence and handle production incidents
Optimize production order execution
The formal idea of Digital Twin technology is closing in on nearly 20 years of existence, originating in 2002 by Michael Grieves, the idea of Digital Twins in manufacturing has remained largely opaque and daunting for firms smaller than enterprise. But just because the capabilities of Digital Twins can get to be extremely complex, doesn’t mean embracing them has to be.
Digital Twins are virtual models of critical physical entities which rely on good data and structure to be useful.
One of the biggest misconceptions about Digital Twins, is what really constitutes as one? To be sure, as technology has continued to advance, the capabilities of Digital Twins have continued to grow as well. In its simplest form a Digital Twin is a digital representation of a physical, real world entity. Do you use CAD files in your engineering processes? Congratulations! You’re already employing a simple form of Digital Twin technology. How about a cloud-based MES to show real-time events of your shop floor? That again, can be considered a form of Digital Twin technology.
Adopting Digital Twin technology does not require manufacturers to go full-speed to a digital transformation--automation, IIoT and all. This is because, at the end of the day, Digital Twins are not simply off-the-shelf technology, but rather entities a firm builds in order to understand a certain physical asset’s state. That is, they are entities that can be built over time. Digital Twins are virtual models of critical physical entities which rely on good data and structure to be useful.
For manufacturers already dedicated towards embracing digitization, Digital Twins represent more than simply real-time views of the shop floor, or virtual representations of their products. By creating end-level Digital Twins, manufacturers can view information and changes which have occurred in the past, interactions that are occurring in the present, and plan for scenarios in the future. Imagine a new assembly line needs to be built within the factory. With advanced organizational Digital Twins, a manufacturer could simulate in a virtual environment what this rollout and installation may look like to ensure any issues and roadblocks are identified in advance.
For manufacturers looking to maintain a competitive edge, adopting Digital Twin technology is more a matter of when, and how, rather than why. However, on the other end of the spectrum, many smaller manufacturers may be thinking, why should I bother investing in building a Digital Twin? Unfortunately, the answer is of course, it depends! It depends on the size, industry, and business objectives of each individual manufacturer.
Remember, Digital Twins are not defined in a single way. With that in mind, it is more useful to think about what type of Digital Twin is most beneficial for your business today versus in the future. We can break down the Digital Twin types into three key categories:
Asset, or discrete unit Digital Twins are critical in supporting faster development times for manufacturers and engineering organizations. For instance, they were used by the US Air Force in order to build and test virtual versions of aircrafts before prototype construction began. This drastically reduces time-to-market and development costs for organizations building highly complex and valuable products. Not only can businesses build Digital Twins of discrete assets for use in pre-production, but they can likewise create more advanced representations of their products for post-production use. For example, GE has built digital twin components for aviation products to track the health and status of assets such as their GE60 engines even mid-flight. Alternatively, discrete unit Digital Twins can be used to track the real-time states of equipment and machinery on the shop floor. Largely supported by the proliferation of IIoT, digital representations of critical machinery on the shop floor can help support predictive maintenance. Building an asset-based Digital Twin can give businesses the advantage of having faster, more effective production cycle times, reduced maintenance costs on production facilities, and greater insight into how assets are used by customers and how well they perform post-production.
Building an asset-based Digital Twin can give businesses the advantage of having faster, more effective production cycle times, reduced maintenance costs on production facilities, and greater insight into how assets are used by customers and how well they perform post-production.
Process-based, or relational Digital Twins help to answer the question of, “How well is X process performing?” The process in question can be internally focused, such as the actual performance of the manufacturing process. In this regard, manufacturing execution systems acting as Digital Twins can help eliminate the need for age-old processes such as Gemba Walks. Bottlenecks, waste, and process flows can be seen in real-time through Digital Twins and instructions or changes can be pushed out to the floor without ever requiring an executive or manager from leaving their desk. Process-based Digital Twins can also be externally focused however. According to EY, supply chain Digital Twins are one of the most impactful process changes organizations can look to adopt in the near future, especially after seeing the disastrous impact the bullwhip effect has had on supply chains throughout the COVID-19 pandemic. Process-based Digital Twins give organizations an edge in streamlining production and supply chains, allowing them to react faster and predict more accurately than their competitors changes required by the business.
Process-based Digital Twins give organizations an edge in streamlining production and supply chains, allowing them to react faster and predict more accurately than their competitors changes required by the business.
Lastly, composite, or organizational Digital Twins are the highest level of digital representation an organization can have. As in the name, it is often a composite of many separate Digital Twins, from asset representations of each piece of machinery on the floor, to the processes involved in their supply chain and manufacturing execution. An ideal organizational Digital Twin acts as a living, digital replica of the organization as whole. While far and away the most complex form of Digital Twin, organizational models empower firms to have full insight and control of their resources to better execute strategy.
While far and away the most complex form of Digital Twin, organizational models empower firms to have full insight and control of their resources to better execute strategy.
With all this in mind, how is a business to get started embracing Digital Twin technology? If it hasn’t been obvious yet, Digital Twins require massive amounts of data, which ultimately rely upon fully digitized processes. Without a digital environment to support and collect the data required throughout a manufacturing or supply chain process, a Digital Twin has no fuel to grow.
This first step of digitizing a manufacturing process is a critical one to get right. It’s crucial that businesses seek to future proof their digital transformation, which means more than just taking paper processes and throwing them into spreadsheets. Installing and building software ecosystems which offer open API’s ensures that a business will be able to take advantage of the data being generated in any department of a business. Legacy ERP and MES systems likely won’t fit the bill either unfortunately, as the data they generate are often siloed and not easily accessible by outside systems. The strength and capabilities of a Digital Twin will ultimately rely on the data and systems it has access to.
With that said, organizations which are still determining how to best establish a strategy of digitization often experience growing pains surrounding change management. Without proper buy-in throughout the organization the internal change required to see the fruits of labor of Digital Twins will likely be too large to overcome.
Once these basic building blocks of digitization have been embraced and implemented by an organization, the next step is in tying these data sources together is through analytics, AI, and machine learning to develop the end-state Digital Twin desired. The final step in actually building these Digital Twins is a topic in and of itself, but is getting easier and easier everyday. Choices range from cloud and industrial vendors who support the creation of Digital Twins, to in-house or outsourced deployments on vendor agnostic frameworks. To be sure, Digital Twin technology has come a long way since 2002, and it’s become more possible than ever for SMB and mid-market manufacturers to start embracing the technology behind them.
Head of Solutions Consulting