Industry 4.0 And The Digital Twin Manufacturing Meets Its Match
As manufacturing processes become increasingly digital, the digital twin is now within reach. By providing companies with a complete digital footprint of products, the digital twin enables companies to detect physical issues sooner, predict outcomes more accurately, and build better products.
THERE can be no turning back. Manufacturing processes are becoming increasingly digital. As this trend unfolds, many companies often struggle to determine what they should be doing to drive and deliver real value both operationally and strategically. Indeed, digital solutions may promise significant value for an organization—value that could never have been realized prior to the advent of connected, smart technologies. Of particular fascination of late seems to be the notion of a digital twin: a near-real-time digital image of a physical object or process that helps optimize business performance.
Until recently, the digital twin—and the massive amounts of data it processes—often remained elusive to enterprises due to limitations in digital technology capabilities as well as prohibitive computing, storage, and bandwidth costs. Such obstacles, however, have diminished dramatically in recent years.1 Significantly lower costs and improved power and capabilities have led to exponential changes that can enable leaders to combine information technology (IT) and operations technology (OT) to enable the creation and use of a digital twin.2
So why is the digital twin so important, and why should organizations consider it? The digital twin can allow companies to have a complete digital footprint of their products from design and development through the end of the product life cycle. This, in turn, may enable them to understand not only the product as designed but also the system that built the product and how the product is used in the field. With the creation of the digital twin, companies may realize significant value in the areas of speed to market with a new product, improved operations, reduced defects, and emerging new business models to drive revenue.
The digital twin may enable companies to solve physical issues faster by detecting them sooner, predict outcomes to a much higher degree of accuracy, design and build better products, and, ultimately, better serve their customers. With this type of smart architecture design, companies may realize value and benefits iteratively and faster than ever before.
It can be a daunting task to create a digital twin if a company would like to try this all at once. The key could be to start in one area, deliver value there, and continue to develop. But before anything else, enterprises should first understand the definition of and approach to the development of the digital twin in order to avoid being overwhelmed. In the pages that follow, we discuss the digital twin—its definition, the way it can be created, how it could drive value, its typical applications in the real world, and how a company can prepare for the digital twin planning process.
Digital twin: What it is, and why it matters
Industry and academia define a digital twin in several different ways. However, perhaps neither group places the required emphasis on the process aspects of a digital twin. For example, according to some, a digital twin is an integrated model of an as-built product that is intended to reflect all manufacturing defects and be continually updated to include the wear and tear sustained while in use.3 Other widely circulated definitions describe the digital twin as a sensor-enabled digital model of a physical object that simulates the object in a live setting.4
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, leading to actions in the physical world such as a change in product design or manufacturing process.
A digital twin differs from traditional computer-aided design (CAD), nor does it serve as merely another sensor-enabled Internet of Things (IoT) solution.5 It could be much more than either. CAD is completely encapsulated in a computer-simulated environment that has demonstrated moderate success in modeling complex environments;6 and more simple IoT systems measure things such as position and diagnostics for an entire component, but not interactions between components and the full life cycle processes.7
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. And thanks to cheaper and more powerful computing capabilities, these interactive measurements can be analyzed with modern-day massive processing architectures and advanced algorithms for real-time predictive feedback and offline analysis. These can enable fundamental design and process changes that would almost certainly be unattainable through current methods.
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.
A MANUFACTURING PROCESS EXAMPLE
Digital twins are designed to model complicated assets or processes that interact in many ways with their environments for which it is difficult to predict outcomes over an entire product life cycle.8 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. Such digital twins may yield important insights that could affect future asset design. A digital twin of a wind farm may uncover insights into operational inefficiencies. Other examples of deployed asset-specific digital twins abound.9
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. Figure 1 represents a model of a manufacturing process in the physical world and its companion twin in the digital world. The digital twin serves as a virtual replica of what is actually happening on the factory floor in near-real time. Thousands of sensors distributed throughout the physical manufacturing process collectively capture data along a wide array of dimensions: from behavioral characteristics of the productive machinery and works in progress (thickness, color qualities, hardness, torque, speeds, and so on) to environmental conditions within the factory itself.
These data are continuously communicated to and aggregated by the digital twin application.
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.
The digital twin application continuously analyzes incoming data streams. Over a period of time, the analyses may uncover unacceptable trends in the actual performance of the manufacturing process in a particular dimension when compared with an ideal range of tolerable performance. Such comparative insight could trigger investigation and a potential change to some aspect of the manufacturing process in the physical world.
This is the journey of interactivity between the physical and digital worlds, which figure 1 endeavors to convey.
Such a journey underscores the profound potential of the digital twin: thousands of sensors taking continuous, nontrivial measurements that are streamed to a digital platform, which, in turn, performs near-real-time analysis to optimize a business process in a transparent manner.