“Simulation? We tried using AI and it didn’t work for us.”

We often hear this response from organisations who have failed to solve a problem using artificial intelligence. Often considered a magic bullet (especially on LinkedIn right now!), AI automates tasks that traditionally require human intelligence – and its benefits are lauded far and wide.

While predictive simulation also involves data and computational modelling, it replicates real-life processes and systems in a digital environment. This makes it distinct from AI – in both the objectives it helps you achieve and the ways in which it’s best used.

There’s no disputing that AI adds value across numerous applications. However, it’s not a universal solution. Simulation enables you to better understand dynamic business processes and data – and how they impact each other. As a result, it’s able to solve problems that AI can’t.

In this blog, we look at the use of both AI and simulation – exploring cases where each approach is best applied discretely, as well as those where a combination delivers optimal results.

AI is best for identifying patterns in static data

Encompassing numerous techniques, including machine learning and natural language processing, AI centres around creating a computer that replicates the way the mind works. This allows it to recognise patterns and make predictions based on static data.

So where is it most useful? Here are 2 common use cases:

• Natural language processing – This is a branch of AI focused on helping computers understand human language as it’s spoken or written in real-time. It involves interpreting meaning and predicting intent using text or voice data. Among other uses, it’s become an essential element of customer service.
• AI-powered image recognition – of faces, food, objects and more, delivers multiple benefits across industries from healthcare to education, fintech and airport security. Agile and accurate, it enables greater efficiencies, delivers valuable insights and powers evidence-based decision-making.

However, at its core, AI is using data to make a prediction that you would otherwise do manually (if you could do it at speed and scale). It’s replicating the process of a person analysing the data and finding the trend – but it doesn’t simulate the evolution of a system over time. For example, while it can predict the likelihood of equipment breaking down to inform predictive maintenance, it can’t tell you what the impact of downtime would be on other processes.

Simulation helps you understand processes, data and how they impact each other

Utilised by growing number of sectors – manufacturing, logistics, healthcare and more –simulation models real-world systems and processes, allowing you to predict how they’ll evolve over time. AI doesn’t have this capability.

As a result, predictive simulation generates powerful data – helping you understand business processes, data and how they affect each other while giving you the tools to future-proof your operations.

So, when should you opt for predictive simulation over AI?

It’s the best approach if your goal relates to the performance of a system that evolves over time – the output from a factory, for example, or the best processes for expanding facilities. And if you’re searching for strategies to boost productivity and optimise profitability, simulation enables data-driven decision-making.

Bear in mind that simulation models are stochastic models where uncertainty is modelled through statistical distributions. Because they’re based on a probability of something happening – or not happening – they’re able to consider a multitude of unintuitive or unpredictable possibilities that could occur over time and allow you to look at the full spectrum of possible outcomes. In addition, they’re able to account for dynamic interaction between the different elements of the simulated system, something that AI can’t do.

Used in synergy, AI and simulation can deliver data-driven answers to complex questions

Though they can work in tandem, you might be surprised to learn that 99% of Twinn predictive simulation projects don’t involve AI. However, utilising AI can help you improve your simulation results, particularly when it comes to optimising input data – putting it into clusters, tidying it up, creating trends or making predictions, for instance.

The following examples demonstrate ways in which AI can be used to optimise input data to improve simulation outcomes across a variety of sectors and situations:

• Use AI to make a prediction based on your sales data – then input the information into your simulation to validate your prediction over time
• Based on sensor data at your factory, AI can predict that a breakdown will happen – use simulation to understand the impact on production across a range of scenarios over time, helping you identify the best option for tackling the challenge
• You have 24 types of parts in different quantities, which can only go into your factory ovens in certain combinations – using mathematical optimisation and AI, you can create the optimal sequence for the parts, which are then inputted into your oven simulation
• Use AI to identify patterns and make predictions in traffic flows based on static data, then use simulation to validate the effectiveness of the pattern recommendations in a virtual environment before implementing them in the real world – putting the behaviour of the system into a simulation model that evolves over time enables you to visualise transportation predictions
• Optimising input data isn’t the only way in which AI and simulation can complement each other.

Other areas include:

• Analysing results of simulations – AI can help you extract insights from complex outputs that might not be immediately apparent
• Automating certain elements of simulation modelling – model selection and parameter tuning, for example
• Improving simulation accuracy – AI can enable the simulation model to learn from data and adjust its behaviour accordingly

And it’s not purely a one-sided relationship in which AI boosts simulation outcomes. You can also use data generated by multiple runs of a simulation model to train your AI model.

In the digital twin field, predictive simulation and AI work in harmony particularly effectively, for example in our ground-breaking work in partnership with Washington River Protection Solutions (WRPS).

Using sensor data, AI made predictions in real-time while an app enabling cloud-based simulation used the data to run different scenarios to predict the impact on production. This has reduced the time it takes to answer a standard 80-scenario question from 20-25 days to just 2-4 days – boosting efficiency and reducing human error.

Leverage simulation and AI to future-proof your business

Blending predictive simulation expertise with AI and machine learning, Twinn enables you to make complex decisions with confidence – helping you understand businesses processes, data and how they impact each other. Get in touch to request a demo or for a chat about how we can help.