A look at simulation-powered digital twins

Earlier this month, our CTO, Frances Sneddon , sat down with The OR Society to discuss how simulation, a key technique in operations research, is evolving with the introduction of simulation-powered digital twins. Exploring the impact of simulation-powered digital twins, as they revolutionize decision-making and process optimization. Here are some of her insights.

How can technologies such as simulation-powered digital twins support continuous improvement, and do they offer a more dynamic approach to process optimization?

Simulation-powered digital twins help organisations spot continuous improvement opportunities more frequently. This includes smaller, incremental changes that might otherwise go unnoticed. It’s like having a live dynamic playbook that identifies peaks and troughs in operations. The digital twin gives you the most effective processes to implement to cope with fluctuating demand.?

A key advantage of this technology is it supports forward-looking predictions and retrospective analysis, helping organisations understand past problems and prevent future ones. The more time process owners have to optimize operations, the greater the time frame for corrective actions.?

It also encourages a reflective mindset, allowing teams to make proactive adjustments rather than rely solely on reactive problem-solving. Traditional continuous improvement methods will always be important, but integrating simulation-powered digital twins offers a more responsive, data-driven way to optimize operations in real time.?

How do you believe simulation-powered digital twins will influence the field of operations research moving forward?

Businesses no longer have the luxury of just relying on? long-term analysis to determine the best process. Why? Because in fast-paced sectors such as manufacturing, healthcare and logistics, the slightest disruption can impact daily or weekly workflows. Process owners have to make decisions in the here and now.?

From an operations research perspective, it’s about adapting to the real time environment. How do we do what we do with ever-changing data sets and make that practical and feasible so decisions are made quickly? The more frequently we can do this, the more granular decisions we’ll be able to influence. The aim is to go from making weekly decisions to daily ones, solving every problem not just the big ones.?

What role will artificial intelligence and machine learning play in the future of simulation software and what impact will they have on traditional operational research methods?

At Simul8 we have always been innovators and driving technical-firsts in simulation, so we’ve had machine learning incorporated in our tools for a while now.? We’re also exploring the new forms of generative AI, and they have the potential to really open up what we do to a much larger audience.?

?Similar to how ChatGPT can support writing an article, it could become an analyst helping to develop simulations and analyse the results. This will accelerate build times and make operations research methodologies accessible to people from all sectors, process owners and decision makers, not just analysts.?

It won’t replace us as specialists, however, because we are the ones to train the algorithms and understand the space. We need to monitor it to ensure it’s not making bad decisions, particularly in areas such as patient care.?

Our mission has always been to take simulation from being something reserved only for the few and make it accessible to anyone that needs to make process decisions. AI will be a big enabler for this.?

If you'd like to read more, you can find the full article with more insights from Frances in the September issue of Inside OR.