Statistical and predictive process monitoring Monitoring complex processes in the age of big data

In this thesis, we investigate the possibilities of the increase in the size and frequency of data for both statistical and predictive process monitoring. This includes adjusting statistical process monitoring techniques based on large samples using the Central Limit Theorem and updating parameter estimates to increase the flexibility for high-frequency data. Furthermore, combining the increase in data with advances in modeling techniques paves the way for predictive monitoring. Signaling as early as possible can be imperative in taking preventive measures in sectors such as healthcare, education, manufacturing, maintenance, and more. It can be vital to ensure the quality of products and services.