Data-based Approach for Reducing Process Complexity in Parts Manufacturing

For industrial companies, rising product variety intensifies the dilemma between economies of scale and economies of scope, which needs to be overcome to secure a competitive advantage. The strategy of Mass Customization can support companies in achieving this objective but requires the identification and exploitation of complexity reduction potentials in production, e.g. through standardization. This paper presents a data-based approach to create transparency and derive recommendations for complexity reduction measures by applying cluster analysis methods to production data. It is validated using the case of a German parts manufacturer, creating insights about its production complexity by means of a Process Similarity Index and deriving complexity reduction potentials through the classification of the processes into the categories of Outliers, Representative Processes, and Cluster Processes.