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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.
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.
The article studies a novel approach of inflation modeling in economics. We utilize a stochastic differential equation (SDE) of the form dXt=aXtdt+bXtdBtH, where dBtH is a fractional Brownian motion in order to model inflationary dynamics. Standard economic models do not capture the stochastic nature of inflation in the Eurozone. Thus, we develop a new stochastic approach and take into consideration fractional Brownian motions as well as Lévy processes. The benefits of those stochastic processes are the modeling of interdependence and jumps, which is equally confirmed by empirical inflation data. The article defines and introduces the rules for stochastic and fractional processes and elucidates the stochastic simulation output.
Resilienz und Stabilität? Weichenstellungen im Banken- und Finanzsystem in der Corona-Pandemie
(2020)
Seit der globalen Finanzkrise 2008/2009 hat es keine vergleichbare Herausforderung wie die Corona-Krise für das Finanz- und Bankensystem mehr gegeben.
Schwache Profitabilität, ungelöste Regulierungs-herausforderungen und steigende Konkurrenz im Digitalbereich stellen die Banken vor weitere Heraus-forderungen.
Die Stabilität des Finanzsystems und der Zugang zu den Finanzmärkten war während der Pandemie nicht gefährdet. Durch gemeinsame Bemühungen und bes-sere Bankenkapitalisierung ist das Finanzsystem heute widerstandsfähiger als zu Zeiten der Finanzkrise.
Sofern die Zuschüsse und Kredite im „Next Genera-tion EU“-Fund zielgerichtet für Strukturreformen und Zukunftsinvestitionen eingesetzt werden, dürfte dies einen Vertrauens- und Wachstumsimpuls darstellen.
Weitere Verbesserungen der Finanzstabilität, wie erhöhte Eigenkapitalunterlegungen, Regulierung von Schattenbanken oder Reformen im Bereich der Finanzaufsicht, sind jedoch von Nöten.
Since the global financial crisis of 2008/2009, there has been no challenge to the financial and banking system comparable to that during the Corona crisis.
Weak profitability, unresolved regulatory challenges and increasing competition in the digital sector pose further challenges for banks.
The stability of the financial system and access to financial markets was not at risk during the pandemic. Through joint efforts and better bank capitalisation, the financial system is now more resilient than during the financial crisis.
Provided that grants and loans in the “next generation EU” fund are well targeted for structural reforms and investments in the future, this should boost confi-dence and growth.
However, further improvements in financial stability, such as increased capital requirements, regulation of shadow banks or reforms in financial supervision, are needed.
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.