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.

Please download the complete document below.

Please select the desired PDF-file(s). It/They will be send to you via e-mail afterwards.

Cornerstone Download


By clicking on "Submit (Anfordern)", you agree that camLine can use your entered data (name and email address) for customer care and internal analyzes. Your information will be stored on a server in Germany. In no case, your data will be disclosed to third parties.