The need for tools to help guide decision making is growing within the manufacturing industry. The analysis performed by these tools will help operators and engineers to understand the behaviour of the manufacturing stations better and thereby take data-driven decisions to improve them. The tools use techniques borrowed from fields such as Data Analytics, BigData, Predictive Modelling, and Machine Learning. However, to be able to use these tools efficiently, data from the factory floor is required as input. This data needs to be extracted from two sources, the PLCs, and the robots. In practice, methods to extract usable data from robots are rather scarce. The present work describes an approach to capture data from robots, which can be applied to both legacy and current state-of-the-art manufacturing systems. The described approach is developed using Sequence Planner – a tool for modelling and analyzing production systems – and is currently implemented at an automotive company as a pilot project to visualize and examine the ongoing process. By exploiting the robot code structure, robot actions are converted to event streams that are abstracted into operations. We then demonstrate the applicability of the resulting operations, by visualizing the ongoing process in real-time as Gantt charts, that support the operators performing maintenance. And, the data is also analyzed off-line using process mining techniques to create a general model that describes the underlying behaviour existing in the manufacturing station. Such models are used to derive insights about relationships between different operations, and also between resources.