Asset performance is key when designing machines that are interconnected. The more complex assets and systems become, the greater the need to evolve how they are managed and maintained. Using the right digital tools is key to undergoing a successful digital transformation. Digital twin technology is the heart of the digital world and approaching physical assets. Digital twins provide insights into what is happening, or what can happen, with physical assets.

What’s a Digital Twin?

digital twin results when every process, service, or physical product is given a digital form or representation. A physical product can be evaluated and updated based on the analytics gleaned from the digital twin in working environments. The digital twin concept best exemplifies how the physical and virtual worlds meet. A digital twin uses real-world data to create a simulation using a computer program to predict how a product or process will perform. These programs easily integrate with the Internet of Things (IoT), artificial intelligence (AI) and machine learning (ML), and software analytics to enhance output.

Types of Digital Twins

There are several types of digital twin models that organizations can use. Product twins are digital models of separate physical products. Virtual models are created to examine the product under different conditions so the necessary changes can be made on a single product before being deployed to the production line. The more efficient the design, the lower the manufacturing costs and time to market for a new physical product.

Process twins are digital models for manufacturing processes. Organizations can use a virtual model of how a production process will work in different scenarios to evaluate functionality. This makes streamlines the process of creating an efficient production methodology. All aspects of the manufacturing process can be fine-tuned, including the ability to put preventative maintenance systems in place. This results in less downtime and safely and efficiently speeds up the manufacturing process.

Systems twins are digital models made of manufacturing plants or factory systems. This makes it easier to gain valuable insights from the vast amounts of operational data collected from a range of connected devices and products within an existing system. Virtual models are more effective at diagnosing malfunctioning equipment because of their ability to gather information.

Benefits of Digital Twins

Digital twin software is a key piece of the digital transformation puzzle for a range of industries, especially those that implement IoT into operational processes. Digital twin technology aids the cost-friendly production of a fault-free product. Digital twins allow organizations to reduce existing or possible defects during the production stage through product testing and simulation. Correcting errors in a virtual environment is more cost-effective than correcting a physical product. All possible risks in the output can be eliminated before the manufacturing process to ensure the product functions as intended when launched.

Digital twins reduce the amount of time to market for new products. The ability to simulate the lifecycle of the product in a digital environment reduces an organization’s risk and adds a competitive advantage. A validated virtual copy of a product results in quicker development. Digital copies constantly monitor their physical counterparts and collect real-time information via sensors. Real-time analysis helps predict and resolve downtime or breakdowns so that issues can be addressed immediately. Predictive maintenance helps streamline operations and eliminate unplanned downtime.

Automation and data exchange is what fuels Industry 4.0. The concept of digital twins opens the door to endless possibilities when it comes to designing a product, process, or system with the best possible efficiency. Digital twins make complex manufacturing processes simpler by creating a digital footprint of physical objects. These digital creations are interconnected and able to generate real-time insights that help organizations better analyze and predict potential challenges in implementation.

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