【Industry 4.0 Series】What is the digital twin?

Industry and academia define a digital twin in several different ways. However, a digital twin can be defined, fundamentally, as an evolving digital profile of the historical and current behavior of a physical object or process that helps optimize business performance. The digital twin is based on massive, cumulative, real-time, real-world data measurements across an array of dimensions. These measurements can create an evolving profile of the object or process in the digital world that may provide important insights on system performance.

Indeed, the real power of a digital twin—and why it could matter so much—is that it can provide a near-real-time comprehensive linkage between the physical and digital worlds. It is likely because of this interactivity between the real and digital worlds of product or process that digital twins may promise richer models that yield more realistic and holistic measurements of unpredictability.

Figure 1. Manufacturing process digital twin model

Digital twins are designed to model complicated assets or processes. Figure 1 represents a model of a manufacturing process in the physical world and its companion twin in the digital world. This model specifically finds expression through five enabling components—sensors and actuators from the physical world, integration, data, and analytics—as well as the continuously updated digital twin application. These constituent elements of figure 1 are explained at a high level below:

• Sensors — Sensors distributed throughout the manufacturing process create signals that enable the twin to capture operational and environmental data pertaining to the physical process in the real world.

  
  

• Data — Real-world operational and environmental data from the sensors are aggregated and combined with data from the enterprise, such as the bill of materials (BOM), enterprise systems, and design specifications. Data may also contain other items such as engineering drawings, connections to external data feeds, and customer complaint logs.

  
  

• Integration — Sensors communicate the data to the digital world through integration technology (which includes edge, communication interfaces, and security) between the physical world and the digital world, and vice versa.

  
  

• Analytics — Analytics techniques are used to analyze the data through algorithmic simulations and visualization routines that are used by the digital twin to produce insights.

  
  

• Digital twin — The “digital” side of figure 1 is the digital twin itself—an application that combines the components above into a near-real time digital model of the physical world and process. The objective of a digital twin is to identify intolerable deviations from optimal conditions along any of the various dimensions.

  
  

• Actuators — Should an action be warranted in the real world, the digital twin produces the action by way of actuators, subject to human intervention, which trigger the physical process.

Clearly, the world of a physical process (or object) and its digital twin analogue are vastly more complex than a single model or framework can depict. And, of course, the model of figure 1 is just one digital twin configuration that focuses on the manufacturing portion of the product life cycle.

Digital twins are designed to model complicated assets or processes. As insightful as digital twins of specific deployed assets may be, the digital twin of the manufacturing process appears to offer an especially powerful and compelling application.

Complicated assets or processes can interact in many ways with their environments for which it is difficult to predict outcomes over an entire product life cycle. Indeed, digital twins may be created in a wide variety of contexts to serve different objectives. For example, digital twins are sometimes used to simulate specific complex deployed assets such as jet engines and large mining trucks in order to monitor and evaluate wear and tear and specific kinds of stress as the asset is used in the field. A digital twin of a wind farm may uncover insights into operational inefficiencies. Other examples of deployed asset-specific digital twins abound.

Much of the discussion thus far has focused on a digital twin model of the manufacturing process portion of the product life cycle. The manufacturing process represents but one digital twin configuration. Indeed, another viable vision for a broad digital twin use case is a product-based application over the product’s entire life cycle: from idea development to use. One such example involves an industrial manufacturer, facing numerous quality issues in the field, resulting in costly maintenance and high warranty liability. The manufacturer was attempting to determine the source of the issues that were adversely affecting customer confidence and brand image. These issues created additional stress on the manufacturer’s supply network and higher cost as it attempted to address the identified problem.

In the effort to address these issues, the engineering and supply network organizations pursued a digital twin approach that sought to solve quality problems and improve sustainable aftersales services related to the maintenance warranty. First, they decided to combine the “as-designed” BOM with all the analogous information produced from manufacturing, termed the “as-manufactured” BOM. To help differentiate between them, the as-designed BOM consisted of development and tested elements, while the as-manufactured BOM consisted of elements aggregated by equipment that was used to produce the product, including procured parts details and assembly details. These results allowed engineers to run analytics and provide insights in production variation that impacted quality. As a result, the team was able to deliver unexpected insights to improve the assembly process, reducing rework by 15–20 percent.

With information at hand from this partial digital twin effort, the aftersales department is planning to soon expand this use case to leverage the digital twin process to more efficiently use information from products in the field—the “as-maintained” BOM—to better understand how process variation impacts performance changes and improvements that should be addressed. The complete information obtained from the as-designed BOM, as-manufactured BOM, and as-maintained BOM creates a “cradle-to-grave” digital journey that allows for a new era of business opportunity, including asset availability management, spare parts inventory optimization, predictive maintenance, and services.

In the past, the creation of digital twins was costly and of limited benefit. With the emergence of increasingly favorable storage and computing costs, the number of use cases and possibilities to enable a digital twin has greatly expanded, in turn driving business value.

When considering the business value that the digital twin offers, companies should focus on issues related to strategic performance and marketplace dynamics, including improved and longer-lasting product performance, faster design cycles, potential for new revenue streams, and better warranty cost management. These strategic issues, among others, can translate into specific applications that might afford broad business value that a digital twin may realize. Table 1 lists a summary of such values by category.

Table 1. Digital twin business values

Besides the areas of business values mentioned above, a digital twin may help address many other key performance and efficiency metrics for a manufacturing company. Overall, the digital twin may offer many applications to drive value and start to fundamentally change how a company does business. Such value may be measured in tangible results that may be tracked back to key metrics for a business.