Digital Twin

What is a Digital Twin?

A digital twin is a virtual representation (i.e., digital version) of a physical asset or process used to analyze and optimize product designs, manufacturing processes, factory efficiency, and other operations.

By using digital twins to simulate real-world scenarios, manufacturers can compare alternatives rapidly and make decisions confidently to address their goals, such as reducing product design cost, accelerating time to market, comparing cost and sustainability trade-offs, and evaluating reshoring/friend-shoring alternatives.

Digital twins underpin the Internet of Things (IoT), Industry 4.0, and related digital transformation initiatives that combine data from multiple sources to create new capabilities and gain new levels of insight and intelligence. Manufacturers can simulate, analyze, and predict outcomes using this virtual replica, leading to more informed decision-making and optimization of processes.

How do Digital Twins Work?

The digital twin blueprint uses a model-based definition (MBD) approach to centralize product design and specification data in either a 3D CAD model or a product development lifecycle (PLM) system.

Centralizing data from multiple sources is critical because product engineering is too complex for a single model (digital twin), according to the July 2022 Journal of Manufacturing Systems. A product design team may use one digital twin for finite element analysis [FEA] and a second to optimize the manufacturing process for cost and performance.

A digital thread connects the digital models/twins across manufacturing operations.

What Types of Digital Twins are There for Manufacturers?

Digital twin technology broadly falls into the following three categories:

1. Digital Product Twins refer to 3D CAD models (virtual replicas) of physical products that include components and assemblies, along with overall product size and shape. Product development teams use simulation to see how design changes affect product quality, sustainability, cost, and manufacturability. Digital product twins, also known as digital asset twins, can also refer to sensors built into a product to monitor its performance and behavior.
2. Digital Process Twins determine the most effective product manufacturing method by simulating manufacturing processes and routings (e.g., injection molding and additive manufacturing). Companies simulate manufacturing processes using the digital twin to identify the most appropriate production process/workflow for cost, sustainability, and manufacturability.
3. Digital Factory Twins are virtual models of physical factories that include precise factory-specific production capabilities and detailed costs including labor, electricity, materials, and overhead rates across the globe. Companies use this smart manufacturing capability to help evaluate their CO2e across their supply chain ecosystem (Scope 3) by creating a virtual representation of supplier factories and their own manufacturing locations.

What are the Benefits of Digital Twins?

• Improve Decision-Making:  Apply data and analysis provided by digital twins for more informed and strategic decision-making across all levels of the organization.
• Recalibrate Product Development for New Levels of Success:  Use simulation to investigate “what if” scenarios rapidly and fast-track time to market while uncovering opportunities to reduce cost, carbon impact, and more.
• Optimize Manufacturing Processes:  Identify the most effective manufacturing process for each product design. Insights can lead to faster and more cost-efficient production and opportunities to reduce your carbon footprint.
• Reach New Levels of Manufacturing Efficiency:  Use digital factories to evaluate “build vs. buy” decisions, compare suppliers, and optimize selected factories down to the machine level for production.
• Gain a Competitive Advantage Using Real-world Performance Data:  Apply product field data from IoT sensors to make product enhancements, improve customer satisfaction with predictive maintenance, and identify “hidden” product use cases based on real-time performance data.