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Driven by digital transformation, manufacturing systems are heading towards autonomy. The implementation of autonomous elements in manufacturing systems is still a big challenge. Especially small and medium sized enterprises (SME) often lack experience to assess the degree of Autonomous Production. Therefore, a description model for the assessment of stages for Autonomous Production has been identified as a core element to support such a transformation process. In contrast to existing models, the developed SME-tailored model comprises different levels within a manufacturing system, from single manufacturing cells to the factory level. Furthermore, the model has been validated in several case studies.
Process quality has reached a high level on mass production, utilizing well known methods like the DoE. The drawback of the unterlying statistical methods is the need for tests under real production conditions, which cause high costs due to the lost output. Research over the last decade let to methods for correcting a process by using in-situ data to correct the process parameters, but still a lot of pre-production is necessary to get this working. This paper presents a new approach in improving the product quality in process chains by using context data - which in part are gathered by using Industry 4.0 devices - to reduce the necessary pre-production.
In recent years, machine learning algorithms have made a huge development in performance and applicability in industry and especially maintenance. Their application enables predictive maintenance and thus offers efficiency increases. However, a successful implementation of such solutions still requires high effort in data preparation to obtain the right information, interdisciplinarity in teams as well as a good communication to employees. Here, small and medium sized enterprises (SME) often lack in experience, competence and capacity. This paper presents a systematic and practice-oriented method for an implementation of machine learning solutions for predictive maintenance in SME, which has already been validated.
The chemical synthesis of polysiloxanes from monomeric starting materials involves a series of hydrolysis, condensation and modification reactions with complex monomeric and oligomeric reaction mixtures. Real-time monitoring and precise process control of the synthesis process is of great importance to ensure reproducible intermediates and products and can readily be performed by optical spectroscopy. In chemical reactions involving rapid and simultaneous functional group transformations and complex reaction mixtures, however, the spectroscopic signals are often ambiguous due to overlapping bands, shifting peaks and changing baselines. The univariate analysis of individual absorbance signals is hence often only of limited use. In contrast, batch modelling based on the multivariate analysis of the time course of principal components (PCs) derived from the reaction spectra provides a more efficient tool for real time monitoring. In batch modelling, not only single absorbance bands are used but information over a broad range of wavelengths is extracted from the evolving spectral fingerprints and used for analysis. Thereby, process control can be based on numerous chemical and morphological changes taking place during synthesis. “Bad” (or abnormal) batches can quickly be distinguished from “normal” ones by comparing the respective reaction trajectories in real time. In this work, FTIR spectroscopy was combined with multivariate data analysis for the in-line process characterization and batch modelling of polysiloxane formation. The synthesis was conducted under different starting conditions using various reactant concentrations. The complex spectral information was evaluated using chemometrics (principal component analysis, PCA). Specific spectral features at different stages of the reaction were assigned to the corresponding reaction steps. Reaction trajectories were derived based on batch modelling using a wide range of wavelengths. Subsequently, complexity was reduced again to the most relevant absorbance signals in order to derive a concept for a low-cost process spectroscopic set-up which could be used for real-time process monitoring and reaction control.
Der Anspruch an Energieversorger wird wachsen: in Zukunft gewinnen vor allem Aufgaben wie die Entwicklung digitalisierter Produkte/Dienstleistungen sowie ökologische Aktivitäten an Relevanz. Dies zeigt die Hochschule Reutlingen in ihrer aktuellen Untersuchung unter Aufsichtsräten, Geschäftsführern und Führungskräften. Trotz der erwarteten Veränderungen: die Aufsichtsräte sind sich zwar ihrem Druck zu mehr Professionalisierung bewusst, scheinen aktuell aber nur mäßig für die künftigen Herausforderungen des Unternehmens gerüstet. Besonders relevant dabei: die Professionalisierung der Gremienarbeit in kommunalen EVU ermöglicht einen höheren wahrgenommenen Unternehmenserfolg. So die Studie des Reutlinger Energiezentrums and der Hochschule Reutlingen im Auftrag von fünf Unternehmen der Branche.
nKV in action: accelerating KVstores on native computational storage with NearData processing
(2020)
Massive data transfers in modern data intensive systems resulting from low data-locality and data-to-code system design hurt their performance and scalability. Near-data processing (NDP) designs represent a feasible solution, which although not new, has yet to see widespread use.
In this paper we demonstrate various NDP alternatives in nKV, which is a key/value store utilizing native computational storage and near-data processing. We showcase the execution of classical operations (GET, SCAN) and complex graph-processing algorithms (Betweenness Centrality) in-situ, with 1.4x-2.7x better performance due to NDP. nKV runs on real hardware - the COSMOS+ platform.
Customer orientation should be the core engine of every organisation while IT can be considered as the enabler to generate competitive advantages along customer processes in marketing, sales and service. Research shows that customer relationship management (CRM) enables organisations to perform better and experience indicates that organisations that focus on customer orientation are more successful. With marketplace organisations such as Amazon, Alibaba or Conrad shaping the future of customer centricity and information technology, German B2B organisations need to shift their value contribution from product-centric to customer-centric. While these organisations are currently attempting to implement CRM software and putting their customers more into focus, the question remains how organisations are approaching the implementation of CRM and whether these attempts are paying off in terms of business performance.
Dieser Beitrag gibt einen Überblick über die verschiedenen Möglichkeiten der Bilanzierung einens Initial Coin Offerings (ICO) beim Emittenten auf der Passivseite nach den Regelungen der IFRS. Ziel ist es, die bilanzielle Einordnung anhand verschiedenenr Arten von Token zu erörtern und den Emittenten bei der Ausgestaltung der Token sowie der anschließenden Bilanzierung zu unterstützen. Die Ergebnisse zeigen, dass die Standards für die bilanzielle Einordnung von ICO-Token zwar ausreichen, allerdings eine große Bandbreite der Bilanzierung zu berücksichtigen ist und eine detaillierte Regelung durch einen eigenen IFRS daher schwierig erscheint.
Das Value-Engineering in der Kundenkommunikation ist eine strukturierte Methode, Kommunikationsprozesse zwischen Unternehmen zu verbessern. Das Konzept greift bewährte Elemente der technischen Wertanalyse und der Gemeinkosten-Wertanalyse auf und überträgt sie auf die Kundenkommunikation. Der Ansatz bietet eine systematische Vorgehensweise, Kommunikationsprozesse zwischen Anbieter und Kunde zu durchleuchten und neu zu gestalten. Value-Engineering in der Kundenkommunikation schafft somit Wettbewerbsvorteile durch eine Optimierung der Kommunikation.
Our paper investigates the response of acquiring firms’ stock returns around the announcement date in cross-border mergers and acquisitions (M&A) between listed Chinese acquirers and German targets. We apply an event study methodology to examine the shareholder value effect based on a sample of M&A deals over the most recent period of 2012-2018. We apply a market model event study based on the argumentation of Brown and Warner (1985) and use short-term observation periods according to Andrade, Mitchell, and Stafford (2001) as well as Hackbarth and Morellec (2008). The results indicate that the announcement of M&A involving German targets results in a positive cumulative abnormal return of on average 2.18% for Chinese acquirers’ shareholders in a five-day symmetric event window. Furthermore, we found slight indications of possible information leakage prior to the formal announcement. Although it shows that the size of acquiring firms is not necessarily correlated with the positive abnormal returns in the short run, this study suggests that Chinese acquirers’ shareholders gain higher abnormal returns when the German targets are non-listed companies.