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In today’s interconnected world, the pace of change is increasing gradually and the effects of an event can propagate and disrupt industries, organizations or companies more dramatically and quickly. Therefore, having a comprehensive overview of the environment is a precious asset for resilience and sustainable growth. One enabler of the above-mentioned interconnectedness is the rapid flow and vast availability of information in text form, which can be also used as the fundamental resource to understand the shifting environment. Hence, actors can be able to become aware of changes at an early stage. The underlying patterns to filter relevant information can be detected by learning from data, or more specifically machine learning. Natural language processing (NLP) techniques can be applied because text data is analyzed. However, to embed the expertise and perspective of the user into the initial model, data should be labeled. This requires valuable expert time from the organization for the labeling, thus it should be minimized. This study aims to present an efficient and user-friendly solution for data labeling. To achieve this, a modularized Active Learning-based backend is combined with an intuitive interface. The output of this labeling process will be used further to train a model for environment analysis. Nevertheless, the main focus of this paper is the development of a solution to maximize efficiency during data labeling for environment analysis. After an introduction to the problem, the overview of the suggested solution accompanied by a prototype will be demonstrated.

Aufgrund der vielfältigen und überlappenden Einflüsse aus dem Unternehmensumfeld (z. B. Pandemie, Chip-Krise, Naturkatastrophen) müssen Entscheidungen sowohl auf Basis von unternehmensinternen Daten (z. B. Liquidität) als auch Umfeldinformationen (z. B. Technologie-trends) getroffen werden. Anschließend sind Maßnahmen abzuleiten und - neben den regulären Unternehmensprozessen - umzusetzen. In diesem Beitrag wird ein Ansatz für die Informationsvernetzung und -bereitstellung für agiles Prozessmanagement vorgestellt.

Viele Unternehmen bringt die Krise um den Coronavirus SARS-CoV-2 in existenzielle Nöte. Der plötzliche Ausfall von Zulieferern und Kunden oder eine kurzfristige Änderung von Regularien (z. B. Kurzarbeitsregeln) stellen Betriebe vor Herausforderungen. Hinzu kommen sich laufend ändernde Einflüsse und Informationsquellen. Ein gemeinsames Lagebild bietet Einsatzkräften im Sicherheitsbereich seit langem Orientierung und wird nun auch für Unternehmen relevant. Die Verknüpfung und Visualisierung von externen Informationen aus dem Unternehmensumfeld (z. B. Corona-Fallzahlen in der Region eines Hauptzulieferers) mit den internen Fähigkeiten eines Unternehmens (z. B. Verfügbarkeit der Mitarbeiter) bleibt jedoch ein ungelöstes Problem. Dieser Beitrag zeigt, wie ein Lagebild auch für Unternehmen zu einem zielgerichteten Handeln beitragen kann.

In recent years, the framework conditions on the Chinese market for machine and equipment manufacturing have changed significantly. By now, products are expected to be customized and at the same time offered at low prices with fast delivery times. Thereby, customer-specific production processes are becoming increasingly important in manufacturing. In addition, German small and medium-sized companies are confronted with a progressively turbulent business environment. For example, Chinese customers expect that orders can still be changed after they had been placed. Hence, companies must be able to identify shortterm changes and individual requirements at an early stage. In order to withstand the competitive pressure, alternative courses of action must be created by generating new process variants quickly and reliably. To address this challenge, an approach for the automated generation and realization of processes based on an integrated enterprise model for supporting agile shop floor management as part of an agile process management system is being developed in the German-Chinese joint research project “MAP” and proposed in this paper. Starting with an introduction into the subject matter, the development gap is demonstrated and an approach as well as an early prototype is presented, followed by an outlook on further development in the research project.

Currently, companies are becoming increasingly vulnerable towards unforeseen disruptive events. One example of such an event is the COVID-19 pandemic, in which many companies are facing existential difficulties. The sudden loss of suppliers and customers as well as frequent changes in regulations at short notice present companies with considerable challenges. Potential uncertainties and complexity complicate the planning and the implementation of measures. A fast and effective overview and the benefit of measures is usually missing. Situation awareness monitors have long provided orientation for first responders and rescue operators within the field of crisis management and can be just as relevant for companies. Identifying, gathering, interpreting and visualizing external information from the corporate environment in relation to relevant disruptions remains an unsolved problem. In addition to the decision to implement effective measures before, during and after a crisis, an approach for the simulation of the disruptive impact and the effect of resilience measures on the company’s performance as well as liquidity is required. In this paper, a Situation Awareness Monitor (SAM) for resilience management is presented to address the aforementioned issues. First, the model-based IT architecture for the monitor is depicted. The enterprise model is coupled with an approach to assess critical processes of small and medium enterprises. Statistical models are applied to draw up the balance between single components, like input, output, supplier, storage and production. The result shows a continuous performance-based liquidity assessment to evaluate the resilience against disruptive events. Finally, single enhancement measures in alignment to the resilience phases – prepare, prevent, protect, respond and recover – can be analyzed.

Ziel der vorliegenden Arbeit ist die Beurteilung der Kondylenposition durch eine Umstellungsosteotomie des Ober- und/oder Unterkiefers. Zusätzlich erfolgte eine Befragung bezüglich der subjektivem Beschwerdesymptomatik im Bereich der Kiefergelenke. Hierzu wurden Patienten untersucht, bei denen im Zeitraum von 01.01.2009 bis 31.12.2017 eine Unterkieferrückverlagerung oder Unterkiefervorverlagerung, bzw. in beiden Kiefern, also „bimaxillär“ oder auch „bignath“, eine Umstellungsosteotomie durchgeführt wurde. Die Untersuchung erfolgte durch Analyse der Fernröntgenseitbilder, welche vor, unmittelbar nach der Operation und ca. sechs Monate postoperativ als Verlaufskontrolle erstellt wurden. Zusätzlich wurden den Patienten Fragebögen ausgehändigt, mit denen die subjektiven Beschwerden im Bereich der Kiefergelenke spezifiziert werden sollten., The aim of this study was to evaluate the condyle position after mandibular or two-jaw orthognathic surgery. Additionally, a questionnaire was sent out to assess the subjective pain symptoms. Patients undergoing isolated bilateral sagittal split osteotomy or combined with a Le-Fort-I-Osteotomy from 01.01.2009 to 31.12.2017 were included. Lateral cephalometric X-rays were obtained preoperative, postoperative and as a follow-up after six months.