The tremendous potential of digital twin technology – with its ability to create digital replicas of physical objects, processes and environments – has applications that span across industries, from replicating hazardous environments to displaying spacecrafts for distant training purposes. Recent evaluation from McKinsey suggests the interest is so profound that the worldwide marketplace for digital twins will grow about 60% per yr over the following five years and reach $73.5 billion by 2027. The interest is clearly there, but has adoption really followed?
The reply – it’s complicated. Digital twin technology and its use cases have evolved immensely, but challenges should be addressed for digital twins to be adopted at scale.
The Evolution of Digital Twins
True adoption of digital twin technology has been slow because, until recently, it lacked the intelligence to transcend simply representing an asset. More beneficial can be the power to accurately simulate, predict, and control its behavior. Digital twins were also bespoke and lacked the power to learn globally from the behavior of comparable assets. Their insights were siloed and never at all times applicable to broader organizational needs, making them a hefty investment with narrow returns.
Even so, some early adopters of digital twins include the manufacturing, retail, healthcare, and automotive industries, which have been in a position to test latest facilities, configurations, and processes in a controlled environment.
With latest AI-driven approaches, we’ll see a rapid shift from “digital twins” to AI-powered “simulation” and “agency” that can dramatically broaden the use cases and drive widespread adoption. Let’s take a look at these categories of use:
- – The early iterations of digital twins were easy digital representations of assets, which weren’t particularly useful beyond select area of interest use cases for improving the design and execution of certain tasks. In essence, that is the “replica” state of digital twin technology.
- – Today, digital twins are evolving from representation to simulation, which advantages a wider set of use cases. Simulation implies that digital twins aren’t only mirroring the asset or environment, but are also accurately simulating future scenarios. On this stage, they’re able to learn from data from other similar processes to garner meaningful insights. Simulation twins use AI algorithms to simulate production outcomes, recommend optimal machine settings, and guide production teams toward improved business objectives in a producing setting.
- – The subsequent evolution after simulation shall be agency, which is able to enable assets, processes, and whole parts of production to plan and act autonomously. On this stage, they may also make complex decisions and work in partnership with people to drive more sustainable production. That is the digital twin agent stage.
Moving between stages requires different levels of supporting technology, and it’s paramount that organizations have the proper tech stack to realize the utmost impact and ROI of digital twins.
Foundational Technology for Digital Twins
The correct foundational technology should be in place before moving from representation to simulation after which, ultimately, agency.
Using manufacturing for instance again, organizations that need to create a digital simulation of a given process or factory environment should have reliable online sensing capabilities. These sensors feed data from the input and output at various critical stages of the journey with a purpose to provide robust insights to tell a simulation. Plenty of this data is quickly available, and we’ve seen process manufacturers with quality online measurements on the outputs (i.e., paper), but there is normally a spot in sensing measurements for the inputs (i.e., wood fibers that go into paper pulp production).
To avoid this, manufacturing teams must clearly define the simulation they try to realize and the assorted inputs, machines, and systems which might be involved, together with different parameters of every stage throughout the method. This likely requires tapping experts across multiple functions to make sure all features of the model are accounted for, which is able to then help ensure the info is powerful enough to power a simulation.
Connectivity and Comparison
Digital twins which might be completely isolated are missing out on learnings from other models in similar scenarios. The models contributing to the digital twin themselves should be fed with data from other similar models and digital twins to reveal what “great” or optimal looks like globally, not only throughout the local process that’s being examined.
Because of this, digital twins require a big cloud component, or else organizations risk losing out on any semblance of the complete promise this technology offers.
The opposite side of the coin is that digital twins must not rely solely on cloud technology since the latency of the cloud can create obstacles for aspects like collecting real-time data and real-time instructions. Consider how pointless it will be to have a simulation intended to forestall machine failures just for the simulation to detect a broken belt well after the piece has stopped functioning properly and all the machine is at a standstill.
To beat these challenges, it could be sensible so as to add a component that’s edge-AI-enabled. This ensures data will be captured as close as possible to the method being simulated.
Possible Pain Points with Deployment and Management
Along with having the proper tech stack and infrastructure to capture the mandatory data for AI-powered simulation twins, trust stays a big roadblock to deployment. Taxi drivers in London may know the town map and all its shortcuts, but GPS typically equips drivers with more accurate routes by factoring in traffic data. Similarly, engineers and manufacturing professionals must experience accurate and protected simulations to completely gain confidence of their capabilities.
Gaining trust takes time, but transparency with the models and with the info feeding the digital twins can speed up this process. Organizations should think strategically in regards to the mindset shift that’s mandatory to get teams to trust the insights from this powerful technology – or risk missing out on ROI.
The Road to Agency
Despite the promise of digital twins, adoption has been relatively slow–until recently. The introduction of AI-powered models can take digital twins from representation to simulation by connecting insights from other models to construct off unique learnings.
As investment and trust increase, digital twins will eventually reach agency status and have the option to make complex decisions on their very own. The true value has yet to be unlocked, but digital twins have the potential to remodel industries from manufacturing to healthcare to retail.
