PLM data transformation: A mesoscopic scale perspective and an industrial case study

Abstract

Structured enterprise information systems such as Enterprise Resources Planning (ERP) and Product Lifecycle Management (PLM) have reached a maturity plateau and are storing up to hundreds of millions of objects and links. Such data is crucial for enterprise processes and operations. They are frequently the target of data transformation such as migration to a new data system, re-organisation according to new business paradigms, cleansing, purge and archive, etc. To make data transformation manageable, iterative, and achievable, it requires a divide and conquer strategy therefore producing loosely coupled data packages. Most data migration methods recommend divide and conquer strategy but do not explain how to produce these loosely coupled data packages. This paper outlines there exist two different approaches relying on a wide range of algorithms: clustering and community detection. Also, data package must be PLM business meaningful and fit into a mesoscopic scale to provide operational and achievable options for data transformation. Finally, a PLM specific algorithm is proposed for pre-processing data before clustering. A multi-pass tooled-up method able to combine and sequence data clustering approaches/algorithms has been developed for this purpose: Data Systemizer (D6). Using graph-based clustering metrics will help to assess the benefit of multi-pass data clustering approach and provide some principles to select right clustering approaches/algorithms chain.