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Industrie 4.0 is all around us today: in politics, in the media, and on the agendas of researchers and entrepreneurs. Smarter, faster, more personalized, more efficient, more integrated – those are just some of the promises of this new industrial era. The potential, especially for Germany ́s mechanical
engineering industry and plant engineering sector, is indeed great, both for providers and for users of technologies across the spectrum of Industrie 4.0.
But there are still many unresolved questions, uncertainties, and challenges. Our readiness study seeks to address this need and offer insight. Because Industrie 4.0 will not happen on its own.
This study is intended to bring the grand vision closer to the business reality. We also highlight the challenging milestones that many companies must still pass on the road to Industrie 4.0 readiness.
The study examines where companies in the fields of mechanical and plant engineering currently stand, focusing on what motivates them and what holds them back, and on the differences that emerge between small and medium enterprises on the one hand and large enterprises on the other.
The results make it possible for the first time to develop a detailed, systematic picture of Industrie 4.0 readiness in the engineering sector.
The study concludes with recommendations for action in the business community, complementing the diverse suite of programs and activities offered by VDMA’s Forum Industrie 4.0. We would like to take this opportunity to thank the two sponsors of this project from the VDMA Forum, Dietmar Goericke and Dr. Christian Mosch, whose efforts played a critical role in making this study a success.
We are convinced that Industrie 4.0 can become a success story for Germany’s engineering sector. May our “Industrie 4.0 Readiness” study do its part in this effort.
Die Facetten und Potenziale der Entwicklungen rund um Industrie 4.0 sind genauso vielfältig wie die Anwendungsfälle. Die Fachgruppe Produktionsregelung des FIR befasst sich unter anderem mit der kurzfristigen Planung von Produktionsaufträgen und der Reaktion auf ungeplante Abweichungen. Im Zuge dessen haben die Mitglieder der Fachgruppe zur Erzeugung von Rückmeldedaten eine Umgebung aufgebaut, die einfache Logistik- und Montagetätigkeiten ermöglicht. Mithilfe verschiedener Informations und Kommunikationstechnologien können diese digital nachverfolgt werden.
With big data-technologies on the rise, new fields of application appear in terms of analyzing data to find new relationships for improving process under-standing and stability. Manufacturing companies oftentimes cope with a high number of deviations but struggle to solve them with less effort. The research project BigPro aims to develop a methodology for implementing counter measures to disturbances and deviations derived from big data. This paper proposes a methodology for practitioners to assess predefined counter measures. It consists of a morphology with several criterions that can have a certain characteristic. Those are then combined with a weighting factor to assess the feasibility of the counter measure for prioritization.
Failure management in the production area has been intensely analyzed in the research community. Although several efficient methods have been developed and partially successfully implemented, producing companies still face a lot of challenges. The resulting main question is how manufacturers can be assisted by a sustainable approach enabling them to proactively detect and prevent failures before they occur. A high-resolution production system based on analyzed real-time data enables manufacturers to find an answer to the main question. In this context, Big Data technologies have gained importance since the critical success factor is not only to collect real-time data in the production but also to structure the data. Therefore, we present in this paper the implementation of Big Data technologies in the production area using the example of an actual research project. After the literature review, we describe a Big Data based approach to prevent failures in the production area. This approach mainly includes a real-time capable platform including complex event processing algorithms to define appropriate improvement measures.
Im Forschungsprojekt BigPro wird die Frage beantwortet, wie Big Data aus der Produktion genutzt werden können, um das Störungsmanagement zu unterstützen. Dazu wurde ein Vorgehen entwickelt, das sicherstellt, dass die erforderlichen Informationen in der richtigen Form zu Verfügung stehen und das System zielgerichtet auf- und eingesetzt werden kann. Das Projekt „BigPro“ wird
über das Bundesministerium für Bildung und Forschung (BMBF) im Rahmen des Förderprogramms IKT 2020 – Forschung für Innovationen mit dem Förderkennzeichen 01IS14011 gefördert