The Digital Shadow as Enabler for Data Analytics in Product Life Cycle Management

Abstract

The high availability of data as well as intelligent analysis methods are central points of “Industrie 4.0”. However, companies often miss their opportunities because they are not able use available information. In times of a dynamic market environment, it is essential for companies to build up a detailed understanding of the influences on their business based on data. Especially in product development and life cycle management, the use of data-generated knowledge can significantly increase the productivity. The problem is not a lack of information but an information overload, as companies are generating more data than they are actually able to use. At the same time, it is crucial that decision-makers have access to the right information for the right purpose at the right time. Which means, the primarily reason for a lack of data-based decisions is the availability of suitable data. Especially the identification and preparation of data for certain analyses causes a high delay and therefore a decrease of decision quality. A multi-perspective and database-overarching information model of products is necessary to overcome incomprehensibility of information, undefined data structures and inflexibility of systems. The methodology presented in this paper describes the derivation of such an information model, which is collecting and merging selected information from existing databases for business intelligence applications. Key element of the method is the choice of a suitable data source for the required information. Therefore, a data-information-fit-indicator is introduced to enable the assignment of data sources to information of the information model.