Uncertainties abound in today’s world: the COVID-19 pandemic, environmental change, and personalization in products and services. The engineering world is no different; uncertainty plays a large role in simulation, autonomous vehicle design and development, digital twins/thread, and digital transformations. Companies and organizations must continue to reevaluate and adapt their decision-making processes to the ever-changing environment. Now more than ever, executives, managers, engineers, and data scientists must ask themselves: How do I allow for variability or uncertainty in my decision-making?
The answer is the use of machine learning (ML) and artificial intelligence (AI), powerful predictive tools that can be used to efficiently enable techniques for capturing and accounting for variability and uncertainty in decisions across the product lifecycle and engineering and business processes. But, applying these techniques requires a change in thinking for decision-makers. Instead of depending on the deterministic point estimate that could drastically miss the mark, decision-makers must rely upon data-driven decision-making that incorporates a range of possible outcomes and results in actionable insights. This 60-minute Webinar discusses how ML and AI can help decision-makers in these uncertain times.
An audience Q&A follows the technical presentation.