Digital Twin and Simulation: Replicating Industrial Systems to Enable Improvement

Summary

Digital models, digital shadows and digital twins help transform and optimize industrial and business operations.

Digital twins are currently being used in several industries and organizations to design and operate complex products and processes. This technology adoption is massively accelerating process development and optimizing operations, and its success comes from enabling enhanced decision making. In terms of business outcomes, the main results companies pursue through digital twins are increased efficiency, reduced costs, better product design and boosted innovation.

Before we get to the applications, let’s look at some definitions. The International Organization for Standardization (ISO) has developed a digital twin framework for manufacturing where the concept of digital twins is defined as: “A fit-for-purpose digital representation of an observable manufacturing element with a means to enable convergence between the element and its digital representation at an appropriate rate of synchronisation.”

This means that a digital twin is a virtual replica of a physical component, product, system, or process within a manufacturing setting. Its function is to reflect the state or performance of the physical object in real time—not to enable further capabilities, such us simulation, orchestration, or prediction—and ultimately support operational decision making.

The digital twin concept comprises three main elements: the physical product, the digital product, and data connecting the two. Depending on the level of data integration, digital twins can be categorized into three subcategories (Figure 1):

• A digital model is a digital representation of a physical object without any automated data exchange between the physical object and the digital object. This means that a change in the physical object is not reflected automatically into the digital object and vice versa.
• A digital shadow is a digital representation where an automated one-way data exchange exists, which means that a change in the physical object is reflected automatically into the digital object, but not vice versa.
• A digital twin is a digital representation where data is fully integrated and flows automatically in both directions between the physical object and the digital object.

Why digital twins are important now

During the past decade, digital twin capabilities have been evolving rapidly because of a series of technology enablers and drivers:

• Access to larger volumes of data and machine learning make it possible to create more detailed simulations with enhanced depth and usefulness of insights.
• Better asset monitoring technologies and new sources of data enable continuous real-time simulations.
• Enhanced industry standards for communications between sensors and operational hardware and diverse platforms have improved interoperability.
• Better data visualization technology (e.g., 3D, VR, AR, AI-enabled visualizations) can handle greater volumes of data.
• Instrumentation is becoming smaller, more accurate, more powerful and cheaper.
• Compute power, network and storage are becoming more powerful and cheaper.

It is worth mentioning that a few other key enabling technologies are becoming cost effective and are being adopted: AI, ML, IoT, high-performance computing, cloud computing, and more are what allow digital twins to be so powerful today as opposed to five years ago. Technology companies are significantly investing in improving these digital enablers. Some of these investments are supporting the development of specific digital twin use cases.

Technology vendors have been shifting their attention to developing strong digital twin offerings. In the past few years, several—including IBM, Oracle, SAP, Microsoft, Amazon AWS, Rockwell, Siemens, and GE—have developed and launched digital twin offerings. Some also have made acquisitions to strengthen and build advanced digital twin capabilities.

Digital twin trends and smart factories

Digital twins are being used alongside other Industry 4.0 and smart factory applications in a variety of industries. A growing number of organizations in asset-heavy industries, such as aerospace, automotive, industrial products, and oil and gas, are implementing digital twins to transform their operations. Nonheavy and nonmanufacturing industries in sectors such as consumer goods, retail operations, facility management, health care, and smart cities also are piloting and starting to adopt digital twins.

Other companies are increasing the scale of digital twin deployment, because it provides real business value: It helps companies transform and future proof their businesses to deal with uncertainties and stringent competition. According to Deloitte, digital twin technology can deliver specific business value in the following areas: 

• improve quality of product and processes and help predict and detect quality defects quicker
• improve customer service by enabling a better understanding of equipment and determining warranty costs and claim issues more accurately
• reduce operating costs by improving product design, equipment performance, and by streamlining operations and reducing process variability
• create record retention of serialized parts and raw materials to support tracking and quality investigation
• reduce time to market and cost to produce a new product by reducing lead times of components and optimizing supply chain performance
• create new revenue growth opportunities by helping to identify new products and improving efficiency and cost to service.

To help understand where digital twins can be used within a smart factory, a framework created by IoT Analytics breaks down the cases and capabilities, using three dominant dimensions: the hierarchical level of a digital twin (six levels), the lifecycle phase in which the digital twin is applied (six levels), and the use or capability of the digital twin (seven levels). An additional fourth dimension can be added that specifies the data type used by the digital twin; this can be real-time, historical, or test data (Figure 2). Therefore, according to this framework, there are at least 250 combinations or types of digital twins (6×6×7=252).