Why Digital Transformation Journeys need Digital Twins
Digital twins in manufacturing are not a new concept, however, it’s often misunderstood. Most manufacturers are infusing their digital transformation journey with cloud, edge and other concepts so why not digital twins?
In this two-part blog series, we’ll dive into the definition of digital twins and dissect the challenges and advantages. Let’s get started.
What is a Digital Twin?
Just as scientists replicate and test physical experiments, the digital world now has a similar process to build a simulation and test it before launching into the world. In layperson terms, a digital twin is a visual representation of an industrial asset. It could be any asset – a PLC, an industrial machine, an assembly line, or a complete site.
Enterprise data buffs can use a digital twin to simplify the creation and maintenance of purpose-driven data models. For example, the same assembly line could have three different data models – one for energy monitoring, another for predictive maintenance, and yet another for production optimization. Each of these models use both static and dynamic data from the assembly line, ideally follows a certain hierarchy in the data, has contextual information in the form of meta data that are relevant to the specific application, and produces as an output different calculation, KPIs, or alerts and events. The output from this model can also serve as an input to a larger data model. In our example, it could be an input to the digital twin of the entire site.
Each digital twin model can have multiple instances. Ideally, if the manufacturer uses a centralized management platform for multiple sites, they can clone an instance at the enterprise level to easily deploy it on similar assets across all those sites.
Now that you’ve got the basics, let’s break down the benefits and challenges for digital twins.
Bigger Than Just a Buzzword
Often used by NASA to simulate experiences that are beyond the reach of earth, digital twins have many benefits for manufacturers too.
Digital twins provide a big-picture view of what’s happening on the factory floor from start to finish – replicating sensors and systems and modeling various scenarios.
Manufacturers can use digital twins to:
- Identify problems before they become cost prohibitive, timely and a drain on resources,
- Recognize potential opportunities for better product design, and the ability to test options in product variety with minimum time and cost.
- Plan for future improvements or opportunities by testing scenarios and leveraging analytics.
To put these three concepts to the test, let’s dive into a use case for digital twins.
A Digital Twin Use Case: Automobile Manufacturing
One industry blazing the trail with digital twins in manufacturing is the automotive industry, using various features of digital twins to improve their vehicle design, manufacturing, performance monitoring, maintenance, autonomous driving development, and energy systems. Let’s take a look at a few of the use cases.
Vehicle Design and Manufacturing
Virtual Prototyping and Production Process Optimization: This progress is most evident in the vehicle design phase, with manufacturers creating detailed virtual models of cars before they are physically built. Engineers simulate and analyze various aspects of the vehicle and production process, to simulate, test and identify issues before real-world implementation.
Vehicle Performance and Maintenance
Real-time Data Monitoring: Sensors support modeling battery life, motor function, and driving dynamics that feeds into a digital twin model of each individual vehicle.
Predictive Maintenance: Data collected is used to predict maintenance needs by comparing real-time data with the expected performance from the digital twin.
Autonomous Driving and AI
Simulation for Autopilot Development: Testing and refining systems through simulation and digital twin technology for a wide range of conditions is not only safer, but also provides more robust testing than can be done on real roads.
Machine Learning and AI Training: The digital twin concept is integral to training autonomous vehicles’ machine learning algorithms as well. The vast amounts of data collected are used to continuously train and improve the AI systems responsible for autonomous driving capabilities.
“The OT and IT stack is getting more dynamic and complicated,” said Vatsal Shah, Co-Founder and CEO of Litmus. “Realizing the potential of digital twins at scale in the industry now requires an integrated approach that extends beyond the factory floor, across the enterprise, with various cloud, consortium-driven or legacy asset frameworks.”
The Potential Pitfalls
As you can see, the digital twin concept has made huge strides in the automotive industry, however, the technology offers substantial advancements across the Industry 4.0 movement. Despite all the positives, there are a few potential pitfalls that you should be aware of.
Data Privacy and Security
The extensive data collection involved in digital twin technology often raises data privacy and security concerns. As these systems gather and store vast amounts of detailed information, ensuring the protection of sensitive data becomes a critical challenge. This requires robust security measures to prevent unauthorized access and data breaches, which have the potential for far-reaching consequences.
Complexity and Cost
There is also a lot to be said about the complexity and costs associated with setting up and maintaining these systems, particularly for small or medium-sized enterprises. The need for specialized sensors and distinct data sets for each product, for example, requires an investment of time and money, particularly for businesses with a wide range of products. The practicality of digital twins diminishes further with simpler products, such as basic consumer goods, compared to complex machinery. Many argue that their use is more justified for large-scale items or in industries with high-value assets.
Data Collection Accuracy
The precision of data collection in digital twin technology is essential too. Any inaccuracies in data can significantly impact the reliability of predictions and decisions made using these models. This emphasizes the need for meticulous data gathering and validation to ensure the effectiveness of digital twins.
Digital twin systems also have limitations in replicating the full complexity of the real world. While they provide valuable insights, there’s a threshold as to how intricately they can mirror real-life scenarios, reflecting a gap between simulation capabilities and real-world intricacies. That gap is constantly narrowing but is it narrow enough?
So Now What?
There’s no debate that the digital twin movement in manufacturing is here to stay. The positive benefits outweigh the potential pitfalls for many given the significant time, resource and financial savings. In the second part of our two-part digital twin blog, we’ll share how you can harness the power of edge computing and Litmus edge to put digital twins in action.