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Data from production environments is now available in unprecedented volumes, making the problem-solving of incidents through root cause analysis straightforward. However, the root cause analysis process remains time-consuming. This study employs the Kitchenham standard systematic literature review methodology to explore how information models and deep learning can streamline this process. By conducting a comprehensive search across four major databases, we evaluate the current technological advancements and their application in root cause analysis. The aim of this study is to assesses the impact of information models for root cause analysis in a production environment. Our findings reveal that integrating knowledge graphs, association rule mining, and deep learning algorithms significantly improves the speed and depth of root cause analysis compared to traditional methods. Specifically, the use of neural networks in recent literature shows substantial advancements in analyzing complex datasets, facilitating large-scale data integration, and enabling automated learning capabilities. Comparing our findings with other recent studies highlights the advantages of using information modeling and deep learning technologies in root cause analysis. This comparison underscores the superior accuracy and efficiency of these advanced methodologies over traditional manual interpretation methods. The effective implementation of these technologies requires a robust foundation of clean, standardized data, giving rise to the concept of “Production IT.” Furthermore, it is crucial for this data to be openly available to facilitate academic research, thereby enabling the development of new methods for more efficient and effective root cause analysis.
The wireless communication technology has gained significant attention in the transportation industry over the recent years. Cellular Vehicle-to-Everything (V2X) communication facilitates the information exchange among road users (such as vehicles, pedestrians etc.) and the infrastructure with an intention to improve the overall road safety, driving comfort, traffic efficiency and save energy. Advanced use-cases aim towards enhancing key functionalities of vehicle automation by means of sensor data sharing and cooperative maneuver & trajectory planning. The introduction of the PC5 interface for sidelink (SL) communication within the mobile communication systems, supports direct exchange of messages between users, independent of the cellular network infrastructure.
Two types of radio resource allocation modes are supported in Cellular V2X: managed mode and the unmanaged mode. In the managed mode, a User Equipment (UE) remains connected to the cellular network and the process of resource allocation is coordinated by the base station. In the unmanaged mode, a UE selects its radio resources from a pre-configured resource pool without any assistance from the base station. Originally both these modes were developed by considering that the vehicles exchange periodic messages which are safety-critical in nature. The existing rule based radio resource allocation algorithms in both the modes are unable to adapt their selection parameters in the events of aperiodic data traffic patterns resulting from the diverse generation rules of different V2X messaging protocols.
We begin this PhD thesis by carrying out system level network simulations within the developed framework Artery-C, where we study the metrics and parameters that influence the performance of the rule-based radio resource allocation in the sidelink modes. In the first step, we derive the baseline conditions where each mode performs to its best efficiency. By varying the generation rules of the messaging protocols, we further analyze the behavior of the modes when V2X data traffic does not follow a specific pattern. Our studies have shown that both the modes suffer from frequent re-allocations because the messages are no longer periodic and the data sizes do not fit into the previously allocated radio resources. This results in poor utilization of the allocated resources. The unmanaged mode is particularly susceptible to radio resource collisions because the vehicles only have partial awareness about the resource selection decisions of other road traffic participants.
As a second contribution, we examine the criteria for sidelink mode selection and the possibilities for a mode switching operation within the sidelink modes and also between the sidelink and the cellular (Uu) modes. We have formulated the strategies for mode switching and calculate the latency in each phase of the mode switch procedure. Although the managed mode has shown advantages with regard to allocation and management of radio resources, it is to be noted that a vehicle cannot remain connected to a base station at all instants of time. Also, switching between different modes is not seamless considering the associated latencies in each phase. This leads us towards the goal of improving the efficiency of the allocation & scheduling of radio resources in the unmanaged mode.
After a careful review of the enhancements that can be implemented within the rule based algorithm in the unmanaged mode, it was found that there needs to be a mechanism where vehicles can continuously share their resource selection decisions, adapt their selection parameters and even re-evaluate them (if needed) within a grant period. Therefore, we investigated the Reinforcement Learning (RL) based Artificial Intelligence (AI) approaches that facilitate independent learning, adapting and decision making among spatially distributed vehicular agents. We have developed a fully decentralized multi agent networked Markovian Decision Process (MDP) model of the Cellular V2X communication network where each agent executes an AI based radio resource scheduler. By extending the actor-critic methodology of the RL, we have derived two variants - Independent Actor Critic (IAC) and Shared Experience Actor Critic (SEAC). The results of our evaluations have indicated that both these schedulers have a potential to achieve better radio resource utilization with a reduced risk of radio resource collisions among the agents. Subsequently, it brings about 15 − 20% improvement in the reliability of the communication link which we regard as a valuable contribution. To summarize, this PhD thesis investigates the performance of the rule based radio resource allocation algorithms in Cellular V2X and proposes the qualitative improvements that can be achieved by means of reinforcement learning.
Clustering and Anomaly Detection in Embedding Spaces for the Validation of Automotive Sensors
(2024)
This paper examines the relationship between corporate purpose and disaster response. To that aim, we use a unique dataset generated by Just Capital's COVID‐19 response tracker that characterizes the early responses of 928 companies included in the Russell 1000 Index generated by the COVID‐19 pandemic. We marry this data to the Business Roundtable Statement on the Purpose of a Corporation (BRT‐CPS) signatories list, which includes 210 firms that declared “a fundamental commitment to all stakeholders” as their corporate purpose. We estimate the differential impact of BRT‐CPS signees on the rest. After controlling our results with financial indicators and environmental, social, and governance (ESG) data, we convey that BRT‐CPS signees were more likely to develop strategies that benefited their surrounding communities and other stakeholders. Moreover, corporations with high ESG were less likely to have an early response that directly benefited stakeholders other than shareholders, except for layoffs and unpaid leave. Finally, we discuss the outcomes considering the symbolic management theory, corporate purpose literature, and its relation to stakeholder management.
A novel hybrid copper paste was developed for low temperature sintering of bare semiconductors. Cu(II) formate (Cu(for)) is complexed in amino-2-propanol (A2P) and added to a paste of etched brass micro flakes. A two-step sintering process is applied: The paste is printed and dried at 120 °C under formic acid (FA) enriched N2 atmosphere (FAN2) for 5 min. Afterwards, bare semiconductors are placed and sintered at 250 °C for 5 min applying a bonding pressure of 20 MPa/10 MPa. By the thermal decomposition of the Cu(for) atomic Cu is released and forms in-situ Cu-nanoparticles. An interconnect is realized with shear strength >100 MPa.
Invariant concept classes form the backbone of classification algorithms immune to specific data transformations, ensuring consistent predictions regardless of these alterations. However, this robustness can come at the cost of limited access to the original sample information, potentially impacting generalization performance. This study introduces an addition to these classes—the permutation-invariant linear classifiers. Distinguished by their structural characteristics, permutation-invariant linear classifiers are unaffected by permutations on feature vectors, a property not guaranteed by other non-constant linear classifiers. The study characterizes this new concept class, highlighting its constant capacity, independent of input dimensionality. In practical assessments using linear support vector machines, the permutation-invariant classifiers exhibit superior performance in permutation experiments on artificial datasets and real mutation profiles. Interestingly, they outperform general linear classifiers not only in permutation experiments but also in permutation-free settings, surpassing unconstrained counterparts. Additionally, findings from real mutation profiles support the significance of tumor mutational burden as a biomarker.
The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.e., anisokaryosis and/or shape) using a segmentation model. Its performance was evaluated on 46 cPC cases with comprehensive follow-up data regarding its accuracy in nuclear segmentation and its prognostic ability. Its assessment of NP was compared to manual morphometry and established prognostic tests (pathologists’ NP estimates (n = 11), mitotic count, histological grading, and TNM-stage). The standard deviation (SD) of the nuclear area, indicative of anisokaryosis, exhibited good discriminatory ability for tumor-specific survival, with an area under the curve (AUC) of 0.80 and a hazard ratio (HR) of 3.38. The algorithm achieved values comparable to manual morphometry. In contrast, the pathologists’ estimates of anisokaryosis resulted in HR values ranging from 0.86 to 34.8, with slight inter-observer reproducibility (k = 0.204). Other conventional tests had no significant prognostic value in our study cohort. Fully automated morphometry promises a time-efficient and reproducible assessment of NP with a high prognostic value. Further refinement of the algorithm, particularly to address undersegmentation, and application to a larger study population are required.
ÖPNV für Kinder
(2024)
Diese Bachelorarbeit befasst sich mit der nutzerzentrierten Konzeption hochautomatisierter Shuttlebusse im öffentlichen Personennahverkehr (ÖPNV) mit einem besonderen Fokus auf die Bedürfnisse von Kindern und deren Eltern. Ziel der Arbeit war es, herauszufinden, wie Shuttlebusse gestaltet werden müssen, um sie für Kinder optimal nutzbar zu machen und gleichzeitig das Vertrauen der Eltern in die Technologie zu gewinnen. Um die Perspektiven der zwei Zielgruppen besser nachvollziehen zu können, wurden Interviews mit Schulkindern und eine Onlineumfrage mit den Eltern durchgeführt. Basierend auf den Erkenntnissen wurden dann Prototypen entwickelt und in Nutzertests evaluiert. Die Ergebnisse der Studie mit Kindern zeigen, dass diese positiv und offen gegenüber der neuen Technologie eingestellt sind. Zudem lässt sich erkennen, dass den Kindern vor allem ein großes Medienangebot während der Fahrt wichtig ist. Die Studienergebnisse der Eltern haben gezeigt, dass durch erhöhte Level an Echtzeitinformationen, während der Fahrt der Kinder, die Zufriedenheit gesteigert wird. Zusätzlich dazu, wird der Trend erkennbar, dass durch die Bereitstellung vermehrter Informationen auch das Vertrauen in das System ansteigen kann. Die Arbeit liefert einige Empfehlungen dazu, wie die Bedürfnisse beider Zielgruppen miteinander vereint werden können.
This master thesis compares the environmental impacts of 4th-generation district heating (4GDH) and 5th-generation district heating and cooling (5GDHC) systems using different heat sources in Germany. The primary objective is to evaluate and compare the environmental performance of a geothermal-based 5GDHC system with a gas-combined heat and power (CHP) based 4GDH system.
The study follows DIN ISO 14040/14044 standards and employs the SimaPro software with the ecoinvent 3.10 database to perform the LCA. The analysis considers two scenarios: one using the German electricity grid and the other using photovoltaic (PV) panels as the electricity source. Key impact categories assessed include climate change, acidification, freshwater and marine eutrophication, human toxicity and resource depletion.
Results indicate that the 5GDHC system, despite its advanced technology, has higher overall environmental impacts compared to the 4GDH system in most categories, primarily due to the intensive resource use and higher greenhouse gas emissions during the drilling process and operational phases. However, 5GDHC shows lower impacts on human toxicity (cancer-related) and land use efficiency, highlighting areas where it performs better than 4GDH.
This comparative study underscores the need for further optimization and integration of renewable energy sources in 5GDHC systems to enhance their sustainability and reduce their environmental impacts. The findings provide valuable insights for policymakers and stakeholders in the energy sector aiming to achieve carbon neutrality and improve the environmental performance of district heating systems.
Keywords: Comparative life cycle assessment (LCA); Life cycle inventory (LCI); District heating system (DHS); 5th generation district heating and cooling system (5GDHC); 4th generation district heating system (4GDH); Geothermal energy; Combined heat and power (CHP); SimaPro; Ecoinvent
The presented work introduces a maturity model for evaluating Machine Learning implementations, with a primary focus on Production Planning and Control processes, as well as broader organizational and technical aspects in companies. This model emerges as a response to the research gap identified in the analysis of 14 existing maturity models, which served as foundational bases for the development of this novel approach. By examining success factors and obstacles at different maturity levels, categorized according to defined dimensions and overarching design fields, this model can serve as a catalyst for bridging the research gap between models demanded in practice and the scholary exploration of topics related to Machine Learning in corporate processes. Notably, the structured design of this maturity model ensures accessibility for small and medium sized enterprises (SMEs).
Lithium-Ion Batteries (LIBs) usually present several degradation processes, which include their complex Solid-Electrolyte Interphase (SEI) formation process, which can result in mechanical, thermal, and chemical failures. The SEI layer is a protective layer that forms on the anode surface. The SEI layer allows the movement of lithium ions while blocking electrons, which is necessary to prevent short circuits in the battery and ensure safe operation. However, the SEI formation mechanisms reduce battery capacity and power as they consume electrolyte species, resulting in irreversible material loss. Furthermore, it is important to understand the degradation reactions of the LIBs used in Electric Vehicles (EVs), aiming to establish the battery lifespan, predict and minimise material losses, and establish an adequate time for replacement. Moreover, LIBs applied in EVs suffer from two main categories of degradation, which are, specifically, calendar degradation and cycling degradation. There are several studies about battery degradation available in the literature, including different degradation phenomena, but the degradation mechanisms of large-format LIBs have rarely been investigated. Therefore, this review aims to present a systematic review of the existing literature about LIB degradation, providing insight into the complex parameters that affect battery degradation mechanisms. Furthermore, this review has investigated the influence of time, C-rate, depth of discharge, working voltage window, thermal and mechanical stresses, and side reactions in the degradation of LIBs.
Background
Anti-vascular endothelial growth factor (VEGF) monoclonal antibodies (mAbs) are widely used for tumor treatment, including metastatic colorectal cancer (mCRC). So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab.
Patients and methods
15 German and Austrian centers prospectively recruited 50 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. Plasma samples were collected every two weeks until radiologic progression (RECIST 1.1) as determined by CT scans performed every 2 months. 102 pre-selected CAFs were centrally analyzed using a cytokine multiplex assay (Luminex, Myriad RBM).
Results
Using random forests, we developed a predictive machine learning model that discriminated between the situations of “no progress within 100 days before radiological progress” and “progress within 100 days before radiological progress”. We could further identify a combination of ten out of the 102 CAF markers, which fulfilled this task with 78.2% accuracy, 71.8% sensitivity, and 82.5% specificity.
Conclusions
We identified a CAF marker combination that indicates treatment resistance to FOLFOX plus bevacizumab in patients with mCRC within 100 days prior to radiologic progress.
Die Unternehmensberatung OSCAR GmbH und der Autor dieses Beitrags führen im Rahmen einer Kooperationsveranstaltung regelmäßig gemeinsam ein Case Study-Training für ein Assessment Center mit Studierenden der Betriebswirtschaft durch. Die Veranstaltung findet in einem rein digitalen Format live via Zoom statt und wird anschließend um ein Peer Review-Verfahren via Moodle ergänzt, bei dem die Studierenden ihre Gruppenlösungen zur Case Study gegenseitig beurteilen. Nach einem Überblick über das Gesamtkonzept skizziert der vorliegende Beitrag die Vorbereitung in Moodle, die Umsetzung in der Haupt-Session und den Breakout Sessions in Zoom und schließlich die Überführung der Gruppen mit ihren Arbeitsergebnissen von Zoom nach Moodle, um hier die gegenseitige Beurteilung zu ermöglichen. Der Beitrag wendet sich nicht nur an Interessierte im Themenfeld Assessment Center, sondern auch generell an Lehrende, die an einem kombinierten Einsatz von Zoom und Moodle im Kontext von Gruppenarbeit und Feedback interessiert sind.
Digitalisierung – Motor für innovative Geschäftsmodelle im Umfeld des hochautomatisierten Fahrens
(2024)
Digital Trust Management
(2024)
Cooperative intelligent transport systems (C-ITSs) are mass-produced and sold in Europe, promising enhanced safety and comfort. Direct vehicle communication, known as vehicle-to-everything (V2X) communication, is crucial in this context. Drivers receive warnings about potential hazards by exchanging vehicle status and environmental data with other communication-enabled vehicles. However, the impact of these warnings on drivers and their inclusion in accident reconstruction remains uncertain. Unlike sensor-based warnings, V2X warnings may not provide a visible reason for the alert, potentially affecting reaction times and behavior. In this work, a simulator study on V2X warnings was conducted with 32 participants to generate findings on reaction times and behavior for accident reconstruction in connection with these systems. Two scenarios from the Car-2-Car Communication Consortium were implemented: “Stationary Vehicle Warning—Broken-Down Vehicle” and “Dangerous Situation—Electronic Emergency Brake Lights”. Volkswagen’s warning concept was utilized, as they are the sole provider of cooperative vehicles in Europe. Results show that V2X warnings without visible reasons did not negatively impact reaction times or behavior, with average reaction times between 0.58 s (steering) and 0.69 s (braking). No significant distraction or search for warning reasons was observed. However, additional information in the warnings caused confusion and was seldom noticed by subjects. In this study, participants responded correctly and appropriately to the shown false-positive warnings. A wrong reaction triggering an accident is possible but unlikely. Overall, V2X warnings showed no negative impacts compared with sensor-based systems. This means that there are no differences in accident reconstruction regarding the source of the warning (sensors or communication). However, it is important that it is known that there was a warning, which is why the occurrence of V2X warnings should also be saved in the EDR in the future.
Digital Leadership
(2024)
Reliable Trajectory Prediction and Uncertainty Quantification with Conditioned Diffusion Models
(2024)
Digitale Transformation
(2024)
Digital Communication – Systematisches Social-Media-Marketing auf der Basis des Social-Media-Zyklus
(2024)
Digital Procurement
(2024)
The conversion from water-based to solvent-based paint systems, particularly Ultra High Solid (UHS) formulations, is a unique and significant undertaking for enhanced efficiency, sustainability, and environmental responsibility within industrial paint shop operations. This study outlines a comprehensive conversion concept explicitly tailored for the MAN Truck & Bus SE cab paint shop, emphasizing its unique energy-saving strategies, sustainability measures, and environmental considerations.
The development of the conversion concept actively integrates scientific principles, advanced technologies, and strategic methodologies to optimize energy utilization throughout the paint application and curing processes. Key elements of the concept actively include the implementation of energy-efficient equipment, such as infrared drying systems and regenerative thermal oxidizers, to minimize energy consumption and actively reduce greenhouse gas emissions associated with paint curing.
Furthermore, sustainability objectives are addressed through adopting eco-friendly practices, such as using low-VOC (Volatile Organic Compound) solvent formulations and integrating closed-loop solvent recovery systems to minimize waste and promote resource efficiency. Life cycle assessment methodologies are employed to evaluate the environmental impacts of the conversion process, enabling informed decision-making regarding material selection, process optimization, and waste management strategies.
The proposed conversion concept is underpinned by a holistic approach to energy management, sustainability, and environmental stewardship, aligning with MAN Truck & Bus SE's commitment to corporate social responsibility and ecological sustainability. By implementing energy-saving measures, adopting sustainable practices, and adhering to stringent environmental regulations, the conversion to a solvent-based (UHS) paint system not only enhances operational efficiency and product quality but also contributes to reducing the carbon footprint and environmental impact associated with automotive paint manufacturing.
Developing an integrated conversion concept from water to solvent-based paint (UHS) system in the MAN Truck & Bus SE cab paint shop signifies a significant step towards achieving energy efficiency, sustainability, and environmental responsibility in industrial paint shop operations. This conversion concept fosters sustainable growth and drives positive ecological change within the automotive manufacturing sector by prioritizing energy-saving initiatives, embracing sustainable practices, and mitigating environmental risks.
Gerade in Zeiten des Fachkräftemangels ist es wichtig, neue Strategien zur Gewinnung von Fachkräften zu implementieren. Die vorliegende Arbeit widmet sich daher dem Thema der Relevanz von Social Media Kanälen in der Gewinnung von Auszubildenden im speziellen im Bereich der Kindertageseinrichtungen. Hierbei werden unter anderem die Themen Ausbildungsmarketing, Social Media und Social Media Recruiting in Bezug auf aktueller Fachliteratur dargestellt, um einen theoretischen Überblick über das Thema zu gewährleisten. Im Rahmen einer quantitativen Datenerhebung wurde untersucht, welche Relevanz aktuelle Erzieherauszubildende in der Nutzung von Social Media Kanälen sehen. Sowohl die theoretische Darlegung des Themas als auch die Ergebnisse der erhobenen Daten sollen als Entscheidungs- und Implementierungshilfe für Kindertageseinrichtungen dienen.
Markov reward models are commonly used in the analysis of systems by integrating a reward rate to each system state. Typically, rewards are defined based on system states and reflect the system’s perspective. From a user’s point of view, it is important to consider the changing system conditions and dynamics while the user consumes a service. The key contributions of this paper are proper definitions for (i) system-centric reward and (ii) user-centric reward of the Erlang loss model M/M/n-0 and M/M(x)/n with state-dependent service rates, as well as (iii) the analysis of the relationships between those metrics. Our key result allows a simple computation of the user-centric rewards. The differences between the system-centric and the user-centric rewards are demonstrated for a real-world cloud gaming use case. To the best of our knowledge, this is the first analysis showing the relationship between user-centric rewards and system-centric rewards. This work gives relevant and important insights in how to integrate the user’s perspective in the analysis of Markov reward models and is a blueprint for the analysis of other services beyond cloud gaming while also considering user engagement.
Optimizing High Voltage Battery Manufacturing Supply Chains: A Comprehensive Analysis Of Product Carbon Footprint And Carbon Tax Of Three Different Manufacturer’s Batteries.
This thesis presents a comprehensive analysis of the carbon footprints associated with the manufacturing supply chains of three vehicle batteries. With data provided by A2mac1 Company and conducted for both industry and academic purposes at Technical Hochschule Ingolstadt, the study focuses on optimizing battery production processes and transportation to reduce environmental impact.
In addition to analyzing carbon footprints within the battery manufacturing supply chains, the research extends its scope to evaluate the most favorable locations in terms of carbon tax implications across USA regions, Chinese states, and European cities. By integrating predictions of future emissions and carbon tax scenarios, this study provides holistic insights into the environmental sustainability of battery manufacturing practices.
Through detailed assessments of production and transportation factors, including raw material sourcing, manufacturing processes, and distribution networks, this study identifies key drivers of carbon emissions and evaluates their impact on overall environmental sustainability. The findings underscore the importance of supply chain optimization and the adoption of greener practices to mitigate carbon emissions and promote sustainability within the automotive industry.
Keywords: Battery manufacturing, Supply chain optimization, Carbon footprint analysis, Production factors, Transportation, Environmental sustainability, Automotive industry, Carbon tax implications
Diese Masterarbeit befasst sich mit der Entwicklung eines vereinfachten Modells eines elektromagnetisch betätigten Relais unter Verwendung des Simulationsprogramms COMSOL Multiphysics. Sie bietet eine detaillierte Einleitung in die mathematischen und physikalischen Konzepte der verwendeten Bauelemente sowie in die Berechnungsverfahren elektromagnetischer Felder und der Finite-Elemente-Methode (FEM). Dies verschafft ein tiefgehendes Verständnis für die Berechnung der magnetischen Flussdichte, der elektromagnetischen Kraft und der Verluste. Zudem stellt die Arbeit Strategien vor, wie der Rechenaufwand durch gezielte Vereinfachungen effizient reduziert werden kann. Die erstellte Simulation modelliert einen zeitabhängigen Schaltvorgang im dreidimensionalen Raum und visualisiert die dynamische Natur des Prozesses.
In einer Ära zunehmender Arbeitsmobilität und eines verschärften Wettbewerbs
um hochqualifizierte Arbeitskräfte erlangen anteilsbasierte
Vergütungsprogramme zunehmend an Bedeutung. Unternehmen sehen sich
vermehrt mit der Herausforderung konfrontiert, die langfristige Bindung und
Motivation ihrer Mitarbeiter zu gewährleisten. Die Integration anteilsbasierter
Vergütungsprogramme ermöglicht es Unternehmen, ihre Mitarbeiter unmittelbar
am Erfolg des Unternehmens zu beteiligen, indem sie Aktienoptionen oder
andere Beteiligungsrechte gewähren. Hierbei wird nicht nur ein finanzieller Anreiz
geboten, sondern folglich eine Anerkennung für die Leistungen der Mitarbeiter
am Unternehmenserfolg signalisiert. Trotz der Potenziale, die anteilsbasierte
Vergütungssysteme bieten, gehen mit ihnen auch Herausforderungen einher. Für
eine effiziente Umsetzung ist es unerlässlich, neben der Konzeption und der
darauffolgenden Implementierung eine akribische Planung und Abstimmung mit
den Unternehmenszielen vorzunehmen. Hierbei stellt die Berücksichtigung
bilanzieller Aspekte einen fundamentalen Aspekt dar, um die potenziellen
Auswirkungen und Effekte auf die Vermögens-, Finanz- und Ertragslage
angemessen zu analysieren. Ein zentraler Fokus dieser Untersuchung liegt auf
den regulatorischen Anforderungen des IFRS 2, welcher Unternehmen dazu
verpflichtet, anteilsbasierte Vergütungstransaktionen transparent in ihren
Finanzberichten darzustellen und somit die finanziellen Auswirkungen
offenzulegen. Dabei werden neben den grundlegenden Modalitäten der
Ausgestaltungsmöglichkeiten, miteinhergehend auch die Bewertungs- und
Bilanzierungsrichtlinien ausführlich erläutert. Durch eine Analyse der
Implementierung und Auswirkungen dieser Programme die in den DAX gelisteten
Unternehmen wird ein umfassendes Verständnis für deren Bedeutung und
Funktionalität in der Unternehmenspraxis angestrebt. Diese Erkenntnisse sollen
dazu beitragen, gegenwärtig eingesetzte Praktiken zu identifizieren und
Potenziale zu erschließen, um die Effektivität und Transparenz der Programme
weiter zu verbessern.