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AI simulation: the power of the computational twin
Artificial intelligence (AI) is disrupting the way businesses operate, but many organisations are still focused on implementing single purpose machine learning models that show the cause and effect of small operational changes. But the next frontier in AI transformation will be using AI to predict the likely outcomes and repercussions of different decisions across an entire system. But how, exactly?
One major innovation paving the way for this capability is the computational twin. This new technology is reshaping how organisations deploy AI to make smarter decisions.
But what is a computational twin? What are the benefits? And how do they differ from existing AI models?
Computational twins: the basics
A computational twin (CT) is a sophisticated digital simulation that can replicate a workflow, department, or even an entire global supply chain in a safe virtual environment.
A CT connects data sources, operational processes, and machine learning models together to create an interactive, virtual replica. This simulation can be used to test the impact of different interventions or workflow changes in a safe, controlled environment before rolling them out in the real world.
Computational twins can be applied to a wide range of scenarios, from financial modelling and supply chain optimisation to climate simulations and banking compliance risk assessments.
In the simplest terms, a computational twin can tell you what is happening now in your business, why it’s happening, and what will happen in the future based on the decisions you make.
Computational twins vs digital twins
Computational twins and digital twins both use data and machine learning to understand cause and effect, make predictions, and optimise processes to improve outcomes.
Formula 1 teams famously use digital twins as virtual replicas of cars and their components. These digital twins are used to optimise performance, reduce development time, and enhance overall efficiency. The key to converting this concept from a single car to a world-wide organisation is a computational twin.
A computational twin offers significantly more scope and capabilities than a digital twin. Acting as a powerful AI engine, it connects multiple data sources, processes, AI models, and business KPIs to create a powerful simulation of a complex system or environment. While a digital twin is a virtual copy of a physical entity or machine, a computational twin can represent an entire complex system by gathering data and insights from diverse sources to illustrate how decisions are made within them.
Take, for example, a CT for a supply chain. It goes beyond modelling a single plan to simulate how different factors like demand, logistics, and inventory levels will interact with each other. By inputting multiple data sources into the CT – such as inventory, warehousing, distribution, and procurement – you can analyse and optimise your entire supply chain without physically moving any goods. This distinction is crucial in understanding the broader implications and applications of computational twins.
Elements that feed into the structure of a computational twin
What are computational twins used for?
As interactive models, CTs allow business decision-makers to test different variables and understand the business impact and likely outcome of their decisions. A computational twin ensures that decisions are optimised based on whatever performance metrics are important to the business.
Some common use cases for CTs:
- A manufacturing business can use a CT to know how much stock it needs to satisfy seasonal customer demand while avoiding excess inventory when new products launch.
- Telecom companies can use a CT to build high-fidelity simulations of the customer lifecycle and identify what communication and promotion decisions will lead to higher Average Revenue Per User (ARPU) and lower customer support costs.
- The NHS uses computational twins to predict the levels of incoming patients, allowing hospitals to manage ward and bed capacity in a way that balances the demands of both urgent and non-urgent care.
How CTs can transform how business decisions are made
The insights gained from CTs can provide invaluable information for business leaders. They allow decision-makers to identify potential issues ahead of time, to incrementally optimise processes at every step, and to explore ‘what-if’ scenarios based on the data they have collected.
Businesses today generate 50 times more data than they did ten years ago. But without the right tools to make better decisions, much of it can go to waste.
Computational twins provide a pathway for extracting tangible, measurable value from that data by giving decision-makers the insight they need to get the big (and small) calls right every time.
What could a computational twin do to optimise your business processes and maximise your bottom line? See how Frontier uses computation twins to enable AI-powered decision making.
