Initially slower to catch up with the data-driven transformation, the heavy-asset industry has certainly accelerated the adoption of predictive analytics systems that provide multiple maintenance advantages such as fewer outages and breakdowns, extended lifecycle of the assets and equipment, better plant safety, and enhance the overall quality of operations and supply chain processes.
We just wrapped up Conference Day 2 and the last day of the Data Innovation Summit 2020 – the biggest online Data & AI spectacle of the year. We’ve had an incomparable experience together with all the delegates, speakers and partners taking part in a fantastic collection of keynote presentations, deep-dive analysis, interactive discussion groups, panel debates, case studies and success stories of the most innovative companies in the world.
Machine learning has a proven track record of advancing our lives, whether it is automating tasks and processes, gaining valuable insights out of massive quantities of data and enabling us to take the most effective data-driven actions. But machine learning and IoT also play a crucial role in saving lives from train accidents.
Digital twins are virtual representations of real-life manufacturing assets. But they are not only models of the physical objects; they serve as a bridge and co-creation between data scientists and engineers. However, what digital twins fail to capture is the risk of assets and the environment to prevent failure.
End of content
No more pages to load