How to find correlations in high amounts of categorical variables visually?

Logistical data from industrial production processes spanning across many process steps or intermediate products contain a wealth of categorical variables. Each step or intermediate product adds his own data about used tools, consumables, materials, suppliers and operators to the process history of a complex product.

If downstream test data show a relevant discrete structure such as stratification into multiple groups, the task of root cause analysis is to find the few – ideally just one - categorical upstream variables that explain this structure best. In the simplest case this can be phrased as the question: “What do the bad units have in common that the good units do not have?”

This research report introduces a novel visualization called Multi-Category-Chart. It explains the motivation, compares it with other common visualizations and describes the benefits of the new approach.

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