Predict the Future Using the Power of Simulation

What if we had the power to predict outcomes, to see the result of our decisions before taking any step? Simulations can make this possible. Unlike traditional simulations, which are often one-time or periodic analyses relying on specific data or scenarios, a digital twin is a dynamic model that continuously updates and develops alongside its physical counterpart throughout its lifecycle. The ongoing evolution of digital twins allows industries to enhance fault prediction in their system workflows, empowering them to make well-informed decisions. This session emphasizes the significance of predictive maintenance and underscores the necessity of employing digital twins to simulate a range of scenarios to address potential challenges such as time constraints, high costs, or feasibility issues. We will use a real-world example to delve into the fundamentals of predictive maintenance, demonstrate the creation of a digital twin for a physical system, and showcase its utilization in predictive maintenance practices.