Session Title:
Use Case: How number of toilet flushes helps to predict failures in bullet trains
We will present a Predikto use case where machine learning software is used to predict failures in bullet trains in Europe. Our software automatically identifies data that correlates to equipment failures. In this use case, a sensor for toilet flushes is part of a comprehensive list of features used to predict component failures in European bullet trains
BIO
Mario Montag (CEO) has 15 years of experience deploying transformational technology solutions to Fortune 500 companies. As CEO of Predikto, he plays a pivotal role in helping transformational customers to deploy actionable and impacting IoT solutions. Mario was an Oracle ERP consulting executive for many years playing the role of a bridge between real world operational challenges and information technology solutions. Mario has worked for global organizations including PwC, IBM, McKinsey&Company, and Hitachi Consulting. Mario founded Predikto with the mission to enable asset intensive organizations to harness the power of their own data to improve operational performance. As a thought leader in Predictive Analytics and IoT, Mario has spoken at a wide variety of industry conferences. Mario has a Bachelor’s degree in Industrial and Systems Engineering from the University of Florida and a MBA from Georgia State University.
