Classifying Data Mapping Techniques to Facilitate the Digital Thread and Smart Manufacturing

Representing manufacturing information and maintaining data interoperability are crucial to achieving Smart Manufacturing (SM), also referred to as Industry 4.0. Throughout the Product Lifecycle (PL), data standards have been developed in ``silos'' based on the specific needs of each PL stage. Dealing with multiple data standards, formats, and representations makes it challenging to transfer information throughout PL stages. Mapping techniques and data translation methods can aid in solving such data heterogeneity challenges. Recent works have demonstrated that general techniques for data mapping and analytics can facilitate more holistic viewpoints. Such viewpoints can help stakeholders make better decisions at the design phase, wherein cost and PL impacts such as those associated with environmental sustainability, are mostly fixed. This paper focuses on analyzing the potential of different types of mapping techniques to enable data interoperability between the multiple PL data formats and standards. We investigate the diversity of data formats in each of the PL data standards. Then, we focus on analyzing the challenges of successfully achieving PL interoperability. Finally, we define the data mapping and translation types with the goal of comparing different approaches to help realize interoperability across global PL management.