Analytics and prediction for high precision Industry

Industry 4.0, better known as the fourth industrial revolution, represents a new stage on the industrial value chain's control and organization. It is characterized by cyber-physical systems which bridge the physical and digital world, producing real-time data. With the large amount of data produced by cyber-physical systems, industries can perform data analytics and machine learning in order to gain actionable insight to make smart decisions. This thesis is about perform data analytics and prediction for high precision industry using data that come from cyber-physical systems. It begins with a brief presentation of the collected data, then it presents how data were organized and structured. At the end of this phase, a first analysis of the data is done, trying to find patterns and correlations. Finally, the development of several predictive algorithms is presented in detail, followed by the development of an application and a framework written in Python, which allows to provide the predicted data to a web dashboard.