Manufacturing companies worldwide recognized the high potential of Industrie 4.0 in order to increasing production efficiency. Key benefits include creation of integrated systems, networked products and improvement of service portfolios. However, for many companies deriving and evaluating necessary measures to use Industrie 4.0 potentials represents a major challenge. This paper introduces the "acatech Industrie 4.0 Maturity Index" as an approach to meet this challenge. The development of multidimensional maturity model intents to provide companies an assessment methodology. The aim is to capture the status quo in companies in order to be able to develop individual roadmaps for the successful introduction of Industrie 4.0 and manage the transformation progressively.

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The proposed workflow management system offers the possibility to embed CBIR conveniently into PACS environments. We achieve a chain of references that fills the information gap between acquisition and post-processing. Our approach takes into account the tight and solid organization of scheduled and performed tasks in clinical settings.

Data-driven transparency in end-to-end operations in real-time is seen as a key benefit of the fourth industrial revolution. In the context of a factory, it enables fast and precise diagnoses and corrections of deviations and, thus, contributes to the idea of an agile enterprise. Since a factory is a complex socio-technical system, multiple technical, organizational and cultural capabilities need to be established and aligned. In recent studies, the underlying broad accessibility of data and corresponding analytics tools are called “data democratization”. In this study, we examine the status quo of the relevant capabilities for data democratization in the manufacturing industry. (1) and outline the way forward. (2) The insights are based on 259 studies on the digital maturity of factories from multiple industries and regions of the world using the acatech Industrie 4.0 Maturity Index as a framework. For this work, a subset of the data was selected. (3) As a result, the examined factories show a lack of capabilities across all dimensions of the framework (IT systems, resources, organizational structure, culture). (4) Thus, we conclude that the outlined implementation approach needs to comprise the technical backbone for a data pipeline as well as capability building and an organizational transformation.

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