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Kurzfassung Die Anwendungspotenziale für Digitale Zwillinge smarter Produkte in verfügbarkeitsorientierten Geschäftsmodellen erstrecken sich über den gesamten Produktlebenszyklus und erweiterte Wertschöpfungsnetzwerke. Das in diesem Beitrag vorgestellte Framework zur Konzeption digitaler Produktzwillinge wurde im Kontext des BMBF-Forschungsprojekts InnoServPro für Anwendungsfälle der Landmaschinenindustrie angewendet und liefert konkrete Beispiele für wertschöpfungsfördernde digitale Produktzwillinge.

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Der Data Model Canvas (DMC) unterstützt methodisch und informationstechnisch den Aufbau der für ein durchgängiges und interdisziplinäres Engineering benötigten fachlichen Datengrundlage und deren Abbildung in den betreffenden IT-Systemen. Basierend auf konkreten Analyse-Szenarien erfolgt eine Modellierung der erforderlichen Datenvernetzung, die wiederum die explizit benötigten Datenquellen umfasst. Im Mittelpunkt dieses Ansatzes steht die Entwicklung eines fachlichen Verständnisses über die zur Analyse notwendigen roduktdaten. Unterstützt wird der Ansatz durch ein Softwaretool zur Erstellung der benötigten Datenmodelle.

Industrial data analytics needs well-structured and linked data from different data sources. The increasing mass of data, scattered IT-structures and a lack of knowledge, especially in small and medium-sized companies (SMEs) are factors that hinder the usage of data analytics. The goal of the research project AKKORD is to build a toolkit for companies to facilitate distributed and integrated industrial data analytics inside valueadding networks. A core part of this toolkit is a data backend system, which collects and links data from different source systems together in a single meta-model. This paper describes the requirements analysis of the data backend system by conducting structured interviews and workshops.

Industrial Data Analytics needs access to huge amounts of data, which is scattered across different IT systems. As part of an integrated reference kit for Industrial Data Analytics, there is a need for a data backend system that provides access to data. This system needs to have solutions for the extraction of data, the management of data and an analysis pipeline for those data. This paper presents an approach for this data backend system.

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The increasing industrial relevance of connected smart product systems, enhanced individualized customer requirements, regulatory boundary conditions as well as advanced value networks and volatile international markets determine a new level of engineering complexity. Current engineering processes are enabled by a large number of IT applications, which are partially orchestrated by product lifecycle management solutions. Considering the dramatically increasing complexity, the IT applications are often not adequately connected as a whole, leading to interruptions in the process chains and finally resulting in quality problems, time expenditure, and additional costs.To support seamless engineering processes, a sufficiently linked information system structure is necessary. Due to the often-existing best-of-breed solutions and historically grown IT infrastructures, the direct point-to-point coupling of IT systems among each other requires disproportionately high efforts. As an approach to improve the existing situation, integration platforms utilizing loose coupling of individual heterogeneous engineering IT applications by semantic web technologies could be used to create a comprehensive, linked engineering data structure.This paper presents a conceptual IT-Service approach that utilizes this linked data structure to address problems in the area of information retrieval, data exchange, and data quality. A fundamental service function enables easy access to the information scattered throughout the whole organization. This approach could contribute to a high level of transparency within the company's internal processes and relationships between data across individual engineering IT applications. The proposed concepts for IT platform services facilitate the exchange of data between different software applications and the standardized checking and documentation of data quality.