Digital twins vs. simulations: the quick cheat sheet

Digital twins vs. simulations: the quick cheat sheet

The concept of digital twins and their relationship to simulations is exciting but often hard to grasp. With this short article, we will relate relevant industry experience and detailed “double clicks” to help you make better decisions and understand how and why these technologies will become increasingly relevant for most industries.

What is a digital twin?

Wikipedia defines a digital twin as a “virtual representation that serves as the real-time digital counterpart of a physical object or process.”

In theory, a digital twin will gather input from connected sensors, machinery, and people to store and display them in a cloud-hosted application. However, besides being able to look back in time what happened when and perform some post-mortem analysis, a digital twin limited to a backward-looking view won’t have much business interest.

Therefore, often digital twins will also integrate simulations of what is represents. Those simulations can be at the device, process, or even plant level. It will allow users to leverage this combination of real-time data and system-level behavior modeling through the simulator for multiple use cases.

Uses of digital twin

For instance, a digital twin can be used to: 

  • Replay a system behavior based on historical data 
  • Do advanced “what-if” analysis before deciding which path to choose 
  • Train new operators on virtual processes before letting them work on the actual real-life process 
  • Simulate a process to train an AI Agent using Deep Reinforcement Learning (DRL), for example by integrating it into the Microsoft Project Bonsai platform 
  • And more depending on the industry that digital twins are used in 

“Digital twins represent a great business improvement opportunity for customers across industries. However, what they are, how they work, how they can positively impact operations, and what technologies are involved with digital twins are often questions customers struggle to answer” – Manish Amin, IoT Advisor, Microsoft

Digital twin vs. simulator 

A simulator’s scope is often limited to a particular piece of equipment or process, although not always. Once programmed or trained, the simulator will run separately from the real-life process.

Conversely, a digital twin will often encompass a broader process comprised of multiple pieces of equipment and it will remain connected to the live system to represent it faithfully.

Therefore, a simplified way to think about the difference between digital twin and simulations is to consider that a digital twin is a simulation whose states (inputs, outputs) are updated to accurately reflect their real-life value. A simulator could end up drifting from real-life or even provide wrong data, a digital twin won’t if it remains connected.

simulator and digital twins for a factory extruder

Conversely, simulators operate separately from a real-life process and can even be developed without an existing process to test hypothesis.

Enabling technologies for digital twins and simulations

To build and run a digital twin, several technology blocks are required.

  1. A simulation engine
  2. Real-time process data collection technologies
  3. Cloud and data services to collect, store and analyze the process and simulation data

How do you build a simulation?

Simulation or digital twin development options

There are multiple ways to build a simulator and the three most used are:

  • Physics-based simulators, such as the plastic extruder one that Neal developed in Python for the Microsoft Project Bonsai in box demo 
  • Software package-based simulators using products such as AnyLogic or Simulink 
  • AI or data-based simulators that train AI models, most often deep neural networks-based ones 

For the latter approach, data-logging only digital twins can be used to create the dataset necessary to train this AI simulator. The historical process data (both inputs, states, and outputs) that the digital twin recorded can provide the breadth and quantity of labelled data required for those type of AI simulators supervised learning. This is one of the areas where a partner with extensive data science experience can significantly help with the speed and quality of the simulation development.

Additional enabling technologies 

Required instrumentation and infrastructure for simulations

To collect real-time process data, smart sensors using technologies such as Azure IoT are going to be required. Adding intelligence at the edge to existing sensors or deploying new smart sensors such as vision AI ones, we will be able to instrument all the relevant process inputs and outputs.

This real-time data and the simulator(s) will be hosted on an appropriate cloud platform to enable the above-mentioned use cases. Solutions such as Azure Digital Twin will enable easy integration of those elements and access to device, process, line (or building), or plant (or campus) digital twins.

“IoT is inextricably linked with digital twins. To create a comprehensive digital twin of a manufacturing environment, one must connect every major process on a manufacturing floor to IoT for process digitization, modeling, and simulation” – Mohammad Ahmed, Principal IoT Solutions Specialist, Microsoft 

Although this article somewhat oversimplifies both what digital twins are and what is required to build and run them, it provides a base for more in-depth research if a more comprehensive understanding of the subject is required. To help with this additional research, we listed a few links below to get you started.  

Also, as Neal Analytics possesses the end-to-end technological capabilities required to instrument, build, train, deploy and maintain digital twins, feel free to contact us  if you are interested in learning more about this topic. 

Additional resources on digital twins: