With the growing digitalization and automation in the manufacturing domain, an increasing amount of process data and error reports become available. To minimize the number of errors and maximize the efficiency of the production line, it is important to analyze the generated error reports and find solutions that can reduce future errors. However, not all errors have the equal importance, as some errors may be the result of previously occurred errors. Therefore, it is important for domain experts to be able to find out the correlations among the errors. A visual analytics approach may help visualize and understand how the errors relate to each other. The goal of this thesis is to develop a concept that helps the analysts understand the cause-effect relations of reported errors. The concept of this thesis is based on Markov model, which helps to find that relations from a production line data. At first, to understand the source of the errors and position of that source in the production line, data overview visualizations like treemap, block diagram are initiated. Then, for the purpose of detailed analysis for any source of the errors and to show the correlations among them, visualizations like heatmaps, rooted trees, and bar charts are used. The adapted visual analytics approaches show that it is possible to visualize the cause-effect correlations among the errors with an appropriate model and thus may help reduce the number of errors in the production line.