Predictive maintenance software solutions from IBM access multiple data sources in real time to predict asset failure or quality issues so company can avoid costly downtime and reduce maintenance costs. Driven by predictive analytics, these solutions detect even minor anomalies and failure patterns to determine the assets and operational processes that are at the greatest risk of problems or failure. This early identification of potential concerns helps you deploy limited resources more cost effectively, maximize equipment uptime and enhance quality and supply chain processes, ultimately improving customer satisfaction.
About the speakers:
Dominik Imrich graduated in Informatics at the University of Pavol Jozef Safarik in Kosice in 2016. Currently working in IBM as Data Scientist. During the studies, he participated in the simulation phase of the JEM-EUSO experiment - the detection of Ultra-High Energy Cosmic Rays for the ISS (International Space Station). - implementing pattern mining (customized kernels) over simulated data. At IBM, he was involved in predictive analytics for a large industrial company, identifying important factors causing an outage of the production line. Currently he is working for an external Swiss client in insurance sector (search engine, indexing, Watson Explorer Foundational Components, ElasticSearch).
Stefan Pero is part of IBM’s Cognitive & Advanced Analytics practice and has strong Data Science and problem solving skills. Currently working as Cognitive Computing consultant and Data Scientist, specialized on Watson Explorer and Analytical solutions, model building, predictive analysis and automation, nowadays supporting Swiss clients on implementation of cognitive search engine based on Watson platform. He has a RNDr. (Doctor of natural sciences) in Informatics from the University of P. J. Safarik in Kosice and in his Ph.D. he focused on recommender systems in retail later in education domain linked with intelligent tutoring systems and problem solving.