Incorporating process and data heterogeneity in enterprise architecture: Extended AMA4EA in an international manufacturing company

Abstract

The heterogeneity of production processes is a serious problem faced by international manufacturing companies. The transformation towards Industry 4.0 and the adoption of Internet-of-Things (IoT) have produced huge amounts of heterogeneous data. The production processes and data from sites across the world cannot be shared and compared at the enterprise level. Therefore, companies cannot improve their production processes and the current state-of-the-art of enterprise architecture (EA) cannot address this heterogeneity problem. To mitigate and address this heterogeneity problem, we extend the automated modelling with abstraction for EA (AMA4EA). We demonstrate the extension using the processes and data of an international manufacturing company in Denmark. The results show that the extended AMA4EA addresses the process heterogeneity problem by automatically creating EA models that relate and compare production processes from different sites. In addition, the extended AMA4EA extracts value from heterogeneous data and visualizes them in EA models. The extended AMA4EA exhibits a novel method in EA to incorporate process and data heterogeneity. This is a significant advance to EA research because it supports EA in modelling the different realities of companies. In addition, the extended AMA4EA demonstrates how production managers can jointly analyse production processes from different sites. As a result, managers can identify potential opportunities for improvement across production sites. Through EA models, they can access data and documentation stored on different enterprise systems. These contributions pave the foundation for understanding and improving the performance of heterogeneous production processes for international manufacturing companies.