Failure Analysis (FA) uses combinations of a multitude of methods to identify and localize failures in semiconductor devices. The performance or efficiency of a FA lab is often measured by throughput or the total time needed to complete an analysis. These KPIs (Key Performance Indicators) can be optimized, but sequences of executed methods might be long and, therefore, hard to understand and optimize. Processes executed in an ad hoc manner might block each other causing long queues for specific tools and overloading specialists working with them. These and other factors can lead to a significant decrease in lab performance. In this paper, we propose an approach to analyzing FA processes with a focus on Internal Physical Inspection (IPI) jobs. Specifically, we use machine learning and statistical methods to estimate (a) the workflow that engineers follow while completing IPI jobs and (b) the duration of each operation executed in the workflow. Our approach starts with data extraction and preprocessing, aiming at extracting features characterizing the workflow, like the package or technology of a sample, as well as providing information about the complexity of each task, thus, allowing us to predict their duration. The resulting tool allows lab management and team leads to analyze the execution of IPI jobs and optimize them. Moreover, the information provided by the tool can be used in automated scheduling methods providing recommendations to FA engineers about sequences of jobs improving utilization of the lab’s resources.