Tech Trend Focus 2024: Digital Twin Technology Is Causing Manufacturers To Do a Double Take

A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making. If that definition leaves you thinking “huh?” you are not alone. Let’s unpack the concepts and technology behind digital twins and see why manufacturers are taking notice of their benefits.

Digital twin technology is a means of studying a physical object or business process via a virtual model. In aerospace manufacturing, for example, a digital twin can be used to create a real-time digital representation of a physical asset such as a newly designed jet engine. Using Internet of Things (IoT) sensors that collect real-world data from the physical prototype engine, a digital twin can replicate the engine’s functionality, features and performance. A digital twin is different from a simulation in that an engineer can interact, via a software platform or dashboard, to update the model in real time.

What is AI’s role in digital twins?

AI is a powerful component of digital twin technology that allows for more sophisticated and adaptive representations of real-world systems. Using AI algorithms to process large quantities of sensor data and identify data patterns, digital twin technology provides insights about things like performance optimization, maintenance, emissions outputs and efficiencies. Additionally, predictive modeling that utilizes machine learning allows digital twins to anticipate future behavior and performance based on historical data.

The role of digital twins in manufacturing

Digital twin technology is not only revolutionizing industrial product design, but it can also be used to redesign production scheduling, monitor the condition and performance of machines and equipment in real time, and ensure regulatory compliance. In fact, digital twins can be used to model the entire manufacturing process, from raw materials to finished products.

According to an article by MIT Technology Review (opens in a new window) , “… manufacturers as varied as Raytheon and Swedish distillery Absolut Vodka … are using the technology to design new products and streamline their manufacturing processes, from the supply chain through production and, eventually, to recycling and disposal.” Other industries utilizing digital twins include:

  • Automotive
  • Agriculture
  • Aerospace and aviation
  • Consumer goods
  • Healthcare and medical devices

Benefits of digital twins for manufacturers include:

  • Rapid iterations and optimizations of product designs
  • Increased product quality derived from simulating and analyzing products throughout the manufacturing process and product lifecycle
  • Increased productivity that results from predictive maintenance that allows manufacturers to identify and address potential issues before they occur, thus minimizing downtime and reducing maintenance costs
  • Optimized performance from monitoring, controlling and adjusting systems more effectively
  • Increased safety by identifying and anticipating hazards and risks of manufacturing processes

The skills and knowledge areas related to digital twins that are in demand by employers are:

  • Computer science and software engineerin
    • Proficiency in programming languages such as Python, Java, or C++
    • An understanding of databases and data modeling
  • Data science and analytics
    • Data analysis and visualization tools including Python libraries like NumPy, Pandas and Matplotlib, or tools like Tableau
    • Skills in statistical analysis and machine learning
  • IoT and sensor technologies
    • A fundamental understanding of IoT devices and sensor technologies
    • Skills related to the integration of sensors with networks for the collection of real-time data
  • Control systems
    • Knowledge of modeling and controlling dynamic systems
  • Computer-aided design (CAD)
    • Knowledge of CAD tools, as they are often used in creating digital representations of physical objects
  • Simulation and modeling
    • Knowledge of simulation techniques to model the behavior of physical systems
    • Understanding of mathematical modeling for representing real-world phenomena
  • Cloud computing and edge computing
    • Knowledge of cloud computing for storing and processing large datasets
    • Knowledge of edge computing for real-time processing at the source of data generation


For more information on IT degrees and certifications offered by the Washington State Community and Technical Colleges, please contact:

Brianna Rockenstire
Center of Excellence for Information & Computing Technology | 425.564.4229

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