How the Digital Twin Drives Smart Manufacturing

Discover how the digital twin is significantly impacting how companies implement smart manufacturing, in order to simulate, predict, optimize, and maintain products, assets, and production systems.

Dick Slansky, senior analyst, ARC Advisory Group
Dick Slansky, senior analyst, ARC Advisory Group

Multiple technologies have emerged in recent years that are instrumental in driving the advancement of smart manufacturing and the Industrial Internet of Things (IIoT). While all these technologies are changing the face of manufacturing today, ARC Advisory Group research suggests that the IIoT, connected smart assets, and especially the digital twin are having the most immediate and significant impact on how companies implement smart manufacturing.

The basic concept of the digital twin is not new. It involves merging virtual engineering models with the physical product or equipment in an environment that allows for change and optimization of the as-designed and as-built product. However, due to the advancement and evolution of enabling technologies, we’re seeing renewed focus on the implementation of the digital twin and associated benefits that can be gained. Using digital twins that represent the product and production systems, manufacturers can reduce the time and cost associated with assembling, installing, and validating factory production systems. Additionally, implementing digital twins for asset management typically provides quantifiable benefits for maintaining equipment in the field. 

In manufacturing, the digital twin is a virtual representation of the as-designed, as-built, and as-maintained physical product; augmented by real-time process data and analytics based on accurate configurations of the physical product, production systems, or equipment. This is, in essence, the operational context of the digital twin needed to support performance optimization. While virtual models are conceptual in nature, the real-time and operational data is a digital representation of real physical events. CAD models represent the virtual fit, form, and function of the digital twin’s physical counterpart. However, real-time operational and asset data are required to execute analytics applications that define the state and behavior of the performance-based digital twin and allow optimization and process improvement.

How the digital twin is being implemented today
One of the initial areas of focus for implementation of the digital twin has been asset lifecycle management (ALM). Maintaining assets in the field has traditionally been a time-consuming and costly task, but critical to equipment and system uptime. Today, maintenance technicians can leverage technologies like augmented reality (AR) that allows them to access virtual engineering models and overlay these models over the physical equipment on which they are performing maintenance using specialized AR goggles or glasses. This enables them to use the most accurate and up-to-date engineering, helping ensure that the correct maintenance and performance specifications are performed efficiently. These same maintenance methods, based on merging of virtual and physical environments, can be applied to factory production systems, machines, and work cells. 

In addition, products, production systems, machines, and work cells can be simulated virtually to test and validate physical systems prior to assembly and installation. Moreover, the virtual commissioning of production automation—an established technology and process—is merging with the more expansive scope of the digital twin. Virtual commissioning is typically a one-time validation of an automated production system. In contrast, the digital twin represents an ongoing analytical and optimizing process that takes place in real time.

It is becoming clear that digital twins will be used throughout the product and process lifecycle to simulate, predict, optimize, and maintain products, assets, and production systems in lieu of developing physical prototypes and test equipment. Today, a significant percentage of companies and organizations implementing IIoT already use, or are planning to use, some form of a digital twin as an important component of a predictive analytics strategy. 

The need for contextual configuration
A CAD model, virtual simulation, or engineering bill of material alone do not define a digital twin. A digital twin is only created when the product has been manufactured and its serialized parts and components are recorded as data representing the physical asset. This becomes the operational data required for a digital twin to function. 

When implementing, companies need to include context within the digital twin configuration. For predictive analytics or IIoT to be effective, the context (physical configuration) of the asset and system are required to know exactly what is needed to collect the relevant operational and performance data. Companies implementing any digital twin project should begin by capturing and managing the actual physical configuration of the asset. 

The operational element of the digital twin aligns closely with concepts and technologies associated with IIoT. While virtual CAD models define the fit, form, and function of the product; the real-time and operational data is the digital output of the physical assets in operation. The information isn’t created through a process of abstraction, but instead captured through sensors, IIoT endpoints, and intelligent edge devices. This connects the digital twin to physical reality. 

Combined with the various forms of contextual data, this knowledge provides a foundation for insightful and timely decision-making, leading to process improvement and optimization.

Companies in this article
More in IIoT