Digital twin is a hot topic in manufacturing. This report analyzes data from an end-user survey on perceptions, readiness, and the challenges organizations face in adopting and investing in digital twins. It also highlights use cases.
Digital Twin in Manufacturing End User Perspective – 2022 Analysis
The Analyst Team
Anthony Mukoro
Senior Analyst, Government and Manufacturing
As part of Omdia’s Industrial IoT, software and communications service area, Anthony focuses on industrial software. He provides unique and valuable research and consulting about manufacturing software (manufacturing execution systems, product lifecycle management, digital twin, and IT/OT convergence) through thought leadership.
Prior to joining Omdia in 2021, Anthony worked for five years as a software consultant. He delivered solutions, modules, and device control installations using MES software in the foods industry internationally. Based in London, Anthony holds a Bachelor of Arts (Hons.) in international business management from the London Metropolitan University.
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Digital Twins
A digital twin is a digital representation that provides the elements and dynamics of how a device or ecosystem operates and lives throughout its life cycle. Digital twins are useful for simulating the capabilities of machine tools in a safe and cost-effective way, as well as identifying the root causes of problems occurring in physical tools or infrastructure. If a physical machine tool breaks down or malfunctions, engineers can evaluate the digital traces of the digital twins’ virtual machines for diagnosis and prognosis. The digitization of nearly every industry type is helping to fuel the demand for twinning platforms, as is the desire to monitor, control, and model the future behavior of real-world equipment, systems, and environments. However, like any technology, digital twins must be understood and accepted by several different stakeholders, from the operations workers up to the C-suite. Meanwhile, vendors are highlighting their expertise in analytics and demonstrating domain expertise with specific industry verticals. Some are also spotlighting their experience with incorporating artificial intelligence (AI) and machine learning (ML) technologies, which can provide the ability to model future behavior via digital twins. These technologies are anticipated to drive the functionality of digital twins beyond simply being enhanced analytics tools. Global revenue for digital twins is expected to increase to $9.4 billion in 2025, up from $2.4 billion in 2018. This Tractica report examines 28 use cases for digital twins across industry groups, including manufacturing, aerospace, connected vehicles, smart cities, retail, healthcare, industrial Internet of Things (IoT), and other industries. The report discusses the drivers and barriers related to digital twins, with insight into the growing use of IIoT networks, machine learning technology, and 3D visualization technology. Market forecasts, segmented by world region, use case, and industry, extend through 2025. The study also analyzes the technologies and applications for digital twins and provides profiles of key industry players.
Report / 02 Jun 2019 / Keith Kirkpatrick
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