The industry is changing faster than ever due to advanced technologies such as AI and ML. The goal of most advanced AI models is to increase productivity and efficiency through automation while lowering operating costs. The digitalization of manufacturing processes and practices is also known as Industry 4.0.
AI offers all kinds of new features and approaches to manufacturing. Running simulations and using real-world data to create digital twins of the entire production process are redefining business practices. But which one is better? This article will help you answer that question.
Simulations Explained
The process of business digitalization comes with all kinds of benefits. One of them is the ability to run simulations using digital models to imitate real-life operations and performance. For example, companies can run simulations to analyze product performance and test new ideas. Simulations turned out to be very useful for engineers and technicians who want to test new products and systems without creating a prototype.
Most industries use design software applications to simulate real-life conditions. However, to get the most accurate results, engineers use special simulation software that can include many more variables. For example, architects and bridge designers use simulation software to test their models for stress. The software can replicate pressure effects on multiple materials, allowing designers to improve their blueprints before the construction starts.
Apart from design testing simulations, engineers use software to simulate all kinds of things. Discrete event simulations, deterministic simulations, and stochastic simulations are some of the methods for testing designs in various environments.
With that said, simulations are only possible if the company digitalized some of its processes. Engineers need a digital representation of real assets to run simulations. Once they have that, they can use different variables to test product features in a digital environment. Think of 2D and 3D design.
Digital Twin Technology Explained
Digital twin technology is similar to simulations, but it's far more accurate. Unlike simulations where engineers have to set all parameters manually, digital twin technology uses real data to copy processes in a digital environment. It integrates all relevant data into a digital system, effectively mirroring the entirety of the product, services, or process.
It's able to "simulate" real-world conditions down to the last detail, including real-life data transferring. The digital twin technology uses IoT sensors, edge hardware, and other embedded devices to recreate processes in a digital environment. In other words, DT technology will turn all physical assets into digital assets that work and behave like the real thing.
That allows engineers to run real-time simulations however they want. The process proved to be useful for processes such as planning, training, management, testing new ideas, etc. Engineers have to change the digital environment to simulate how a process would behave if the same thing happened in real life. Every digital twin includes 2D or 3D assets using real-world data.
The Differences Between Simulations and Digital Twins
Even though these two technologies offer similar benefits, there are many differences between them. They are both designed to replicate processes and products in a digital environment. However, DT offers more flexibility and features.
While simulations can only replicate existing processes, a digital twin can run multiple simulations on the same system. Moreover, all simulations use real-time data and a constant exchange of information between real-world sensors and the digital environment. That makes it the ideal technology for making accurate performance predictions and is much easier to monitor and manage. Let's see that in more detail:
Static Simulations VS. Active Digital Twin
Simulations depend on accurate parameters and design elements. Once the digital model is created, the parameters won't change unless the designer inputs new parameters. The static model can only provide information about that specific design. New designs require engineers to build another simulation from scratch.
Digital twin technology, on the other hand, starts off the same way as a simulation. However, since it uses real-time data, it will change the simulation automatically. It will keep looking for ways to improve the product through active simulation. For example, it can simulate the entire product lifecycle over time, allowing designers to get information a simple simulation can't provide.
Possibility VS. Actual Outcomes
Since simulations accurately replicate real-world products in a digital environment, they can only simulate what could happen with the product after extended use. Designers don't have the option to change the setting and use different setups, so they can't test the product in various environments.
Digital twin wins the race here as well. It's not only able to replicate the product but the entire environment and different use cases. Simulations depend on the designer's ability to enter accurate parameters to change the outcome.
The data used by DT is generated in the real world, allowing designers to test product performance using accurate information. That way, they can simulate various use cases and find better design solutions based on real-world applications. The same approach can be used to test complex manufacturing processes using real data in a changing environment. The bottom line is that simulations are solely theoretical, while DT is actual and accurate.
Use Cases
The scope of use is also very different between the two technologies. CAD-based simulations can test products in different scenarios as long as the parameters are accurate. That makes it excellent for product design testing.
A digital twin is far more flexible, allowing designers to test the product through all stages in many different settings. As long as you have the data, the digital twin will replicate the entire process. It simply gives you a broader scope to test various uses, allowing you to improve product design and make informed business decisions. Digital twin simulations are more versatile and offer a deeper understanding of processes when compared to simulations.
Advantages of Using Digital Twin Technology
There is no doubt that DT is far more versatile than simulations. It comes with some huge advantages, including the ability to simulate performance throughout the entire product lifecycle. The results don't rely on the designer's ability to input parameters, leaving him more time to improve performance issues.
Moreover, a digital twin can work with many different areas, including simulating entire business operations. It can also use data from multiple systems to improve model accuracy and compare performances. The digital twin technology is much more advanced than simple CAD-based simulations.
The downside to this technology is that it's much more expensive than simulations. It also takes more time to set up and integrate with existing data analytics software. It's only worthwhile if it's applied to primary business practices.
Conclusion
Choosing between simulations and digital twin isn't easy. It's clear that digital twins provide more insights and can help improve all business processes. Simulations have limited capabilities compared to DT, but they are more affordable and easier to set up.
However, digital twins are used for practices other than running simulations. They provide easy monitoring of complex systems with hundreds or thousands of assets. Furthermore, DT can help improve operations with predictive maintenance, reduced downtimes, and increased employee safety. It's the core of the digital transformation, and it will only get more powerful in the future.
