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Enterprise solutions, specifically enterprise systems, have allowed companies to integrate enterprises’ operations throughout. The integration scope of enterprise solutions has increasingly widened, now often covering customer activities, activities along supply chains, and platform ecosystems. IS research has contributed a wide range of explanatory and design knowledge dealing with this class of IS. During the last two decades, many technological as well as managerial/organizational innovations extended the affordances of enterprise solutions—but this broader scope also challenges traditional approaches to their analysis and design. This position paper presents an enterprise-level (i.e., cross-solution) perspective on IS, discusses the challenges of complexity and coordination for IS design and management, presents selected enterprise-level insights for IS coordination and governance, and explores avenues towards a more comprehensive body of knowledge on this important level of analysis.
Yes, we can (?)
(2021)
The COVID-19 crisis has caused an extreme situation for higher education institutions around the world, where exclusively virtual teaching and learning has become obligatory rather than an additional supporting feature. This has created opportunities to explore the potential and limitations of virtual learning formats. This paper presents four theses on virtual classroom teaching and learning that are discussed critically. We use existing theoretical insights extended by empirical evidence from a survey of more than 850 students on acceptance, expectations, and attitudes regarding the positive and negative aspects of virtual teaching. The survey responses were gathered from students at different universities during the first completely digital semester (Spring-Summer 2020) in Germany. We discuss similarities and differences between the subjects being studied and highlight the advantages and disadvantages of virtual teaching and learning. Against the background of existing theory and the gathered data, we emphasize the importance of social interaction, the combination of different learning formats, and thus context-sensitive hybrid learning as the learning form of the future.
In response to the impending spread of COVID-19, universities worldwide abruptly stopped face-to-face teaching and switched to technology-mediated teaching. As a result, the use of technology in the learning processes of students of different disciplines became essential and the only way to teach, communicate and collaborate for months. In this crisis context, we conducted a longitudinal study in four German universities, in which we collected a total of 875 responses from students of information systems and music and arts at four points in time during the spring–summer 2020 semester. Our study focused on (1) the students’ acceptance of technology-mediated learning, (2) any change in this acceptance during the semester and (3) the differences in acceptance between the two disciplines. We applied the Technology Acceptance Model and were able to validate it for the extreme situation of the COVID-19 pandemic. We extended the model with three new variables (time flexibility, learning flexibility and social isolation) that influenced the construct of perceived usefulness. Furthermore, we detected differences between the disciplines and over time. In this paper, we present and discuss our study’s results and derive short- and long-term implications for science and practice.
In response to the impending spread of COVID-19, universities worldwide abruptly stopped face-to-face teaching and switched to technology-mediated teaching. As a result, the use of technology in the learning processes of students of different disciplines became essential and the only way to teach, communicate and collaborate for months. In this crisis context, we conducted a longitudinal study in four German universities, in which we collected a total of 875 responses from students of information systems and music and arts at four points in time during the spring–summer 2020 semester. Our study focused on (1) the students’ acceptance of technology-mediated learning, (2) any change in this acceptance during the semester and (3) the differences in acceptance between the two disciplines. We applied the Technology Acceptance Model and were able to validate it for the extreme situation of the COVID-19 pandemic. We extended the model with three new variables (time flexibility, learning flexibility and social isolation) that influenced the construct of perceived usefulness. Furthermore, we detected differences between the disciplines and over time. In this paper, we present and discuss our study’s results and derive short- and long-term implications for science and practice.
The design of qualitative, excellent teaching requires collaboration between teachers and learners. For this purpose, face-to-face teaching benefits from a long-standing tradition, while digital teaching is comparatively still at the beginning of its dissemination. A major developmental step toward the digitization of teaching was achieved in the context of university teaching during the Covid 19 pandemic in spring 2020, when face-to-face teaching was interrupted for months. During this time, important insights into the opportunities and limitations of digital teaching were gained. This paper presents selected results of a study conducted at four German universities and with 875 responses in spring 2020. The study uncovers opportunities and limitations of digital teaching from the students’ perspective and against the background of their experience in the completely digital semester. The results are used as a basis for deriving design guidelines for digital teaching and learning offerings. Based on a model for analyzing the design of teaching and learning formats, these indications are structured according to the elements learners, teachers, teaching content, environment and teaching style.
Der Wandel zur automatisierten Produktion, die fortschreitende Digitalisierung der Wertschöpfungsprozesse sowie die stetige Implementierung von mobilen Industrial Internet of Things-Technologien (IIoT) in diese zur Unterstützung der Mitarbeiter stellen betriebliche Weiterbildung vor Herausforderungen. Komple-xere Anforderungen und veränderte Tätigkeitsprofile erfordern Handlungskom-petenzen bei Mitarbeitern im Sinne der Fähigkeit, in unbekannten Situationen auf Basis eigenen Könnens handlungsfähig zu bleiben. Jene sowie dafür notwendiges umfassendes Verständnis gegenüber digitalisierten Produktions-prozessen kann jedoch durch konventionelle Lehrmethoden nicht realisiert werden, da diese der erhöhten Anforderungskomplexität und den komplexen Rückkopplungen im Rahmen der Steuer- und Regelkreise nicht gerecht werden können. Diese Aspekte aufgreifend wird im Folgenden ein szenariobasierter Wei-terbildungsansatz für eine Lernfabrik vorgestellt, der insbesondere die Potenziale mobiler IIoT-Technologien zur Ausgestaltung dieser in den Blick nimmt.
Das Angebot digitaler Plattformen ist mittlerweile auch im Maschinen- und Anlagenbau weit verbreitet. Dabei konnte in den letzten Jahren der Trend verzeichnet werden, dass die Herstellerunternehmen von Maschinen und Anlagen nicht mehr ausschließlich physische Produkte veräußern, sondern zusätzliche auf das Produkt abgestimmte Dienstleistungen, wie bspw. digitale Services. Dieser Wandel kann einen großen Einfluss auf die Veränderung des Geschäftsmodells haben und je nach Komplexität der digitalen Plattformen unterschiedliche Ausmaße annehmen, die auch strategische Entscheidungen bestimmen können. In diesem Beitrag wird eine Klassifizierung der digitalen Plattformen im deutschen Maschinen- und Anlagenbau vorgenommen, mithilfe derer unterschiedliche Plattformtypen auf Grundlage ihrer Funktionszusammensetzung identifiziert werden. Demnach können bspw. Plattformen, über die lediglich grundlegende Funktionen wie die Verwaltung von Maschinen angeboten werden, von umfangreicheren Plattformen unterschieden werden, die eine höhere Komplexität aufweisen und somit einen größeren Einfluss auf die Veränderung des Geschäftsmodells haben. Diese Einteilung unterschiedlicher Plattformtypen kann Unternehmen im Maschinen- und Anlagenbau dabei unterstützen, strategische Entscheidungen bezüglich der Entwicklung und des Angebots digitaler Plattformen zu treffen und eine Einordnung ihrer digitalen Plattform im Wettbewerb vorzunehmen.
Das Angebot digitaler Plattformen ist mittlerweile auch im Maschinen- und Anlagenbau weit verbreitet. Dabei konnte in den letzten Jahren der Trend verzeichnet werden, dass die Herstellerunternehmen von Maschinen und An- lagen nicht mehr ausschließlich physische Produkte veräußern, sondern zusätzliche auf das Produkt abgestimmte Dienstleistungen, wie bspw. digitale Services. Dieser Wandel kann einen großen Einfluss auf die Veränderung des Geschäftsmodells ha- ben und je nach Komplexität der digitalen Plattformen unterschiedliche Ausmaße annehmen, die auch strategische Entscheidungen bestimmen können. In diesem Bei- trag wird eine Klassifizierung der digitalen Plattformen im deutschen Maschinen- und Anlagenbau vorgenommen, mithilfe derer unterschiedliche Plattformtypen auf Grundlage ihrer Funktionszusammensetzung identifiziert werden. Demnach können bspw. Plattformen, über die lediglich grundlegende Funktionen wie die Verwaltung von Maschinen angeboten werden, von umfangreicheren Plattformen unterschieden werden, die eine höhere Komplexität aufweisen und somit einen größeren Einfluss auf die Veränderung des Geschäftsmodells haben. Diese Einteilung unterschiedli- cher Plattformtypen kann Unternehmen im Maschinen- und Anlagenbau dabei unter- stützen, strategische Entscheidungen bezüglich der Entwicklung und des Angebots digitaler Plattformen zu treffen und eine Einordnung ihrer digitalen Plattform im Wettbewerb vorzunehmen.
Increasingly fast development cycles and individualized products pose major challenges for today's smart production systems in times of industry 4.0. The systems must be flexible and continuously adapt to changing conditions while still guaranteeing high throughputs and robustness against external disruptions. Deep rein- forcement learning (RL) algorithms, which already reached impressive success with Google DeepMind's AlphaGo, are increasingly transferred to production systems to meet related requirements. Unlike supervised and unsupervised machine learning techniques, deep RL algorithms learn based on recently collected sensor- and process-data in direct interaction with the environment and are able to perform decisions in real-time. As such, deep RL algorithms seem promising given their potential to provide decision support in complex environments, as production systems, and simultaneously adapt to changing circumstances. While different use-cases for deep RL emerged, a structured overview and integration of findings on their application are missing. To address this gap, this contribution provides a systematic literature review of existing deep RL applications in the field of production planning and control as well as production logistics. From a performance perspective, it became evident that deep RL can beat heuristics significantly in their overall performance and provides superior solutions to various industrial use-cases. Nevertheless, safety and reliability concerns must be overcome before the widespread use of deep RL is possible which presumes more intensive testing of deep RL in real world applications besides the already ongoing intensive simulations.
Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.
Shortening product development cycles and fully customizable products pose major challenges for production systems. These not only have to cope with an increased product diversity but also enable high throughputs and provide a high adaptability and robustness to process variations and unforeseen incidents. To overcome these challenges, deep Reinforcement Learning (RL) has been increasingly applied for the optimization of production systems. Unlike other machine learning methods, deep RL operates on recently collected sensor-data in direct interaction with its environment and enables real-time responses to system changes. Although deep RL is already being deployed in production systems, a systematic review of the results has not yet been established. The main contribution of this paper is to provide researchers and practitioners an overview of applications and to motivate further implementations and research of deep RL supported production systems. Findings reveal that deep RL is applied in a variety of production domains, contributing to data-driven and flexible processes. In most applications, conventional methods were outperformed and implementation efforts or dependence on human experience were reduced. Nevertheless, future research must focus more on transferring the findings to real-world systems to analyze safety aspects and demonstrate reliability under prevailing conditions.
The digitalization of value networks holds out the prospect of many advantages for the participating compa- nies. Utilizing information platforms, cross-company data exchange enables increased efficiency of collab- oration and offers space for new business models and services. In addition to the technological challenges, the fear of know-how leakage appears to be a significant roadblock that hinders the beneficial realization of new business models in digital ecosystems. This paper provides the necessary building blocks of digital participation and, in particular, classifies the issue of trust creation within it as a significant success factor. Based on these findings, it presents a solution concept that, by linking the identified building blocks, offers the individual actors of the digital value network the opportunity to retain sovereignty over their data and know-how and to use the potential of extensive networking. In particular, the presented concept takes into account the relevant dilemma, that every actor (e. g. the machine users) has to be able to control his commu- nicated data at any time and have sufficient possibilities for intervention that, on the one hand, satisfy the need for protection of his knowledge and, on the other hand, do not excessively diminish the benefits of the system or the business. Taking up this perspective, this paper introduces dedicated data sovereignty and shows a possible implementation concept.
Email tracking allows email senders to collect fine-grained behavior and location data on email recipients, who are uniquely identifiable via their email address. Such tracking invades user privacy in that email tracking techniques gather data without user consent or awareness. Striving to increase privacy in email communication, this paper develops a detection engine to be the core of a selective tracking blocking mechanism in the form of three contributions. First, a large collection of email newsletters is analyzed to show the wide usage of tracking over different countries, industries and time. Second, we propose a set of features geared towards the identification of tracking images under real-world conditions. Novel features are devised to be computationally feasible and efficient, generalizable and resilient towards changes in tracking infrastructure. Third, we test the predictive power of these features in a benchmarking experiment using a selection of state-of-the-art classifiers to clarify the effectiveness of model-based tracking identification. We evaluate the expected accuracy of the approach on out-of-sample data, over increasing periods of time, and when faced with unknown senders. (C) 2018 Elsevier B.V. All rights reserved.
This study aims to compare online vs. offline flirting and dating behavior using the example of the location-based real-time dating (LBRTD) app Tinder, a popular dating platform. We focus on persons' self-descriptions like self-esteem, social desirability, state social anxiety, and adjustment behavior on Tinder and the perceived data privacy of the app. Data was gathered using a survey approach with Tinder users reporting their behavior in offline and online settings. The comparison between offline and online behavior was made using Response Surface Analysis. The results suggest that the different conditions of the natural and digital worlds do not influence the individual's behavior and emotional perception. The results are analyzed and discuss gender, age, motivation to use the app, and the user's relationship status.
Expanding modeling notations
(2021)
Creativity is a common aspect of business processes and thus needs a proper representation through process modeling notations. However, creative processes constitute highly flexible process elements, as new and unforeseeable outcome is developed. This presents a challenge for modeling languages. Current methods representing creative-intensive work are rather less able to capture creative specifics which are relevant to successfully run and manage these processes. We outline the concept of creative-intensive processes and present an example from a game design process in order to derive critical process aspects relevant for its modeling. Six aspects are detected, with first and foremost: process flexibility, as well as temporal uncertainty, experience, types of creative problems, phases of the creative process and individual criteria. By first analyzing what aspects of creative work modeling notations already cover, we further discuss which modeling extensions need to be developed to better represent creativity within business processes. We argue that a proper representation of creative work would not just improve the management of those processes, but can further enable process actors to more efficiently run these creative processes and adjust them to better fit to the creative needs.
Expanding modeling notations
(2022)
Creativity is a common aspect of business processes and thus needs a proper representation through process modeling notations. However, creative processes constitute highly flexible process elements, as new and unforeseeable outcome is developed. This presents a challenge for modeling languages. Current methods representing creative-intensive work are rather less able to capture creative specifics which are relevant to successfully run and manage these processes. We outline the concept of creative-intensive processes and present an example from a game design process in order to derive critical process aspects relevant for its modeling. Six aspects are detected, with first and foremost: process flexibility, as well as temporal uncertainty, experience, types of creative problems, phases of the creative process and individual criteria. By first analyzing what aspects of creative work modeling notations already cover, we further discuss which modeling extensions need to be developed to better represent creativity within business processes. We argue that a proper representation of creative work would not just improve the management of those processes, but can further enable process actors to more efficiently run these creative processes and adjust them to better fit to the creative needs.
As Industry 4.0 infrastructures are seen as highly evolutionary environment with volatile, and time-dependent workloads for analytical tasks, particularly the optimal dimensioning of IT hardware is a challenge for decision makers because the digital processing of these tasks can be decoupled from their physical place of origin. Flexible architecture models to allocate tasks efficiently with regard to multi-facet aspects and a predefined set of local systems and external cloud services have been proven in small example scenarios. This paper provides a benchmark of existing task realization strategies, composed of (1) task distribution and (2) task prioritization in a real-world scenario simulation. It identifies heuristics as superior strategies.