Clustering and Classification of Manufacturing Enterprises Regarding Their Industry 4.0 Reshoring Incentives.

Abstract Due to drivers like Industry 4.0, reshoring recently receives more attention. In order to increase understanding, in this novel field of research, k-means clustering is performed to find groups of enterprises, which differ regarding their reshoring incentives. Based on these clusters, manufacturing enterprises are classified by the combination of an intra variance analysis and prior knowledge. Therefore, an own enlarged sample, encompassing 94 German industrial enterprises with global sourcing and production activities is used. It is investigated that five clusters segment the sample optimally and that the importance of innovation as well as trust and sustainability are decisive for the classification of German manufacturing enterprises regarding their reshoring incentives. These findings contribute to the body of knowledge about reshoring incentives in terms of methodology and content, since unsupervised learning is used for the first time within that context and enables insight into previously unexplored structures of the reshoring phenomenon.