Refine
Year of publication
Document Type
- Article (145)
- Monograph/Edited Volume (26)
- Part of a Book (22)
- Conference Proceeding (16)
- Postprint (15)
- Other (7)
- Contribution to a Periodical (5)
- Doctoral Thesis (1)
- Review (1)
Keywords
- knowledge management (7)
- Industrie 4.0 (5)
- Industry 4.0 (5)
- deep reinforcement learning (5)
- production control (5)
- ERP (4)
- Enterprise-Resource-Planning (4)
- digital learning (4)
- machine learning (4)
- systematic literature review (4)
- COVID-19 (3)
- CPPS (3)
- CPS (3)
- Digitale Plattformen (3)
- JSP (3)
- KMU (3)
- Marktübersicht (3)
- adaptability (3)
- business processes (3)
- evaluation (3)
- industry 4.0 (3)
- learning factories (3)
- learning factory (3)
- modular production (3)
- multi-agent system (3)
- neural networks (3)
- vocational training (3)
- Digitalisierung (2)
- ERP system (2)
- ERP-/PPS-Systeme (2)
- ERP-System (2)
- Enterprise Resource Planning (2)
- Geschäftsmodell (2)
- Hinweisreize (2)
- Internet of Things (2)
- Internet of things (2)
- Lernfabrik (2)
- Marktuntersuchung (2)
- Maschinen- und Anlagenbau (2)
- PPS (2)
- Refabrikation (2)
- Regionale Ansätze (2)
- Remanufacturing (2)
- Simulation (2)
- Strategie (2)
- TAM (2)
- Wandlungsfähigkeit (2)
- action problems (2)
- assessment (2)
- augmented reality (2)
- change management (2)
- deep learning (2)
- discipline differences (2)
- e-learning (2)
- intentional forgetting (2)
- job shop scheduling (2)
- knowledge transfer (2)
- knowledge transfers (2)
- market research (2)
- method comparision (2)
- multi-actor routines (2)
- multi-objective optimisation (2)
- organizational memory (2)
- process modelling (2)
- product generation engineering (2)
- production planning (2)
- production planning and control (2)
- serious game (2)
- situational strength (2)
- social network analysis (2)
- tacit knowledge (2)
- taxonomy (2)
- technology acceptance (2)
- technology-mediated teaching (2)
- university teaching (2)
- 4th industrial revolution (1)
- AI and business informatics (1)
- AI-based decision support system (1)
- Adaptation (1)
- Adaptivität (1)
- Altsyteme (1)
- Analytics (1)
- Anpassung (1)
- Anpassungsfähigkeit (1)
- Anwendungssystem (1)
- Anwendungssystemarchitekturen (1)
- Anwendungszentrum Industrie 4.0 (1)
- Architecture concepts (1)
- Assistenzsysteme (1)
- Audit (1)
- Auftragsabwicklung (1)
- Auftragsabwicklungssysteme (1)
- Augmented reality (1)
- Auswahlvorgehen (1)
- Automatisierung (1)
- Automobilzulieferer (1)
- Behavior (1)
- Bewertung (1)
- Blockchain (1)
- CO₂-Fußabdruck (1)
- Case Study (1)
- Change (1)
- Community (1)
- Coring (1)
- Cross-System (1)
- Crowdsourcing (1)
- Customer Relationship Management (1)
- Customization (1)
- Cyber-phyiscal system (1)
- Cyber-physical systems (1)
- Decentral Decision Making (1)
- Decentralized production control (1)
- Degree of autonomy (1)
- Digital Learning Factory (1)
- Digital Marketplaces (1)
- Digital Platforms (1)
- Digitalisierung von Produktionsprozessen (1)
- Digitization (1)
- Diskussion (1)
- ERP-/PPS-systems (1)
- ERP-/PPSsystems (1)
- ERP-Auswahl (1)
- ERP-Systeme (1)
- Enterprise Resource Planning (ERP) System (1)
- Enterprise System (1)
- Fabriksoftware (1)
- Factory operating system (1)
- Functions (1)
- Funktionsumfang (1)
- GHG Protocol (1)
- Generalized knowledge constructin axiom (1)
- Geschäftsmodelle (1)
- ISO 14067 (1)
- IT assessment (1)
- IT-Assessment (1)
- Industrial Analytics (1)
- Industrial IoT Competences (1)
- Industrieunternehmen (1)
- Informationssystemarchitektur (1)
- Intentional forgetting (1)
- Invidiuallösungen (1)
- KI (1)
- KI-ERP-Indikator (1)
- KVP (1)
- Kaizen (1)
- Klassifikationsschema (1)
- Kompetenzentwicklung (1)
- Konfigurator <Softwaresystem> (1)
- Konsens-Algorithmen (1)
- Kundenmanagement (1)
- Künstliche Intelligenz (1)
- Learning Factory (1)
- Lehr-Lernsituationen (1)
- Lernszenario (1)
- Literature Review (1)
- MES (1)
- Market Research (1)
- Marktanalyse (1)
- Meta-model (1)
- Mobile IIoT-Technologie (1)
- Mobile Software Ecosystems (1)
- Modellfabrik (1)
- Modification (1)
- Nachhaltigkeit (1)
- Open innovation (1)
- PAS 2050 (1)
- Portal (1)
- Problems (1)
- Process Mining (1)
- Process modeling (1)
- Production (1)
- Production system (1)
- Produktions-Routine (1)
- Produktkonfiguratoren (1)
- Professional Services Unternehmen (1)
- Projektmanagement (1)
- Prozessintegration (1)
- Prozessmanagement (1)
- Prozesswissen (1)
- Qualität (1)
- RFID (1)
- RPA (1)
- Regelkreismechanismus (1)
- Research Agenda (1)
- Retrieval cues (1)
- Roadmap (1)
- Robotic Process Automation (1)
- SECI-model (1)
- SMEs (1)
- Simulation process building (1)
- Spielifizierung (1)
- Student Training (1)
- Subject-oriented learning (1)
- Suchmaschine (1)
- Systematisches Vorgehen (1)
- Systemauswahl (1)
- Tailoring (1)
- Task realization strategies (1)
- Three-tier Architecture (1)
- Turbulenz (1)
- Unternehmen (1)
- Unternehmensberatung (1)
- Use cases Morphologic box (1)
- Variantenmanagement (1)
- Verbesserungen (1)
- Verbesserungsprozess (1)
- Verhalten (1)
- Veränderung (1)
- Vocational Training (1)
- Weiterbildung (1)
- Willentliches Vergessen (1)
- Wirtschaftsinformatik (1)
- Wissensmanagement (1)
- Zentrum Industrie 4.0 (1)
- adaptable software systems (1)
- advances in teaching and learning technologies (1)
- age-appropriate competence development (1)
- age-appropriate vocational training (1)
- application center Industrie 4.0 (1)
- application system architectures (1)
- artificial intelligence (1)
- assistance systems (1)
- automation (1)
- benefits (1)
- betriebliche Lernprozesse (1)
- betriebliche Weiterbildung (1)
- betriebliche Weiterbildungspraxis (1)
- big data analytics (1)
- business application (1)
- business model (1)
- business models (1)
- business process improvement (1)
- business process management (1)
- business process modeling (1)
- business process optimization (1)
- case-based reasoning (1)
- classification scheme (1)
- community (1)
- competence development (1)
- components suppliers (1)
- context-aware computing (1)
- control loop mechanism (1)
- conversion (1)
- conversion sequences (1)
- cooperative AI (human-in-the-loop) (1)
- copyright (1)
- coring (1)
- corona-sensitive data collection (1)
- creativity training (1)
- cross self-confrontation (1)
- cross-plant business processes (1)
- cyber-physical production systems (1)
- cyber-physical systems (1)
- data mining (1)
- data-driven artifacts (1)
- database (1)
- databases (1)
- decision-making (1)
- delegated proof of stake (1)
- demographic change (1)
- design science (1)
- design-science research (1)
- development of AI-based systems (1)
- didactic concept (1)
- didactic framework (1)
- digital learning factory (1)
- digital marketplaces (1)
- digital platform openness (1)
- digital platforms (1)
- digital teaching (1)
- digitization of production processes (1)
- discrete event simulation (1)
- discussion (1)
- distributed knowledge base (1)
- domain-specific language (1)
- eference Architecture Model (1)
- effectiveness (1)
- empirical evaluation (1)
- empirical examination (1)
- empirical studies (1)
- enhancement (1)
- enteprise-level (1)
- enterprise resource planning systems (1)
- enterprise system (1)
- enterprise systems (1)
- environmental footprint (1)
- errors in modeling (1)
- experience; (1)
- experiment (1)
- experimental design (1)
- explainability (1)
- factory software (1)
- federated industrial platform ecosystems (1)
- future (1)
- game-based learning (1)
- gamification (1)
- geographical distribution (1)
- gewerkschaftlich unterstützte Weiterbildungspraxis (1)
- higher education (1)
- human-machine-interaction (1)
- humans-in-the-loop (1)
- hybrid simulation (1)
- improvement (1)
- industrial innovation (1)
- industrielle Innovationen (1)
- information system architecture (1)
- information systems research (1)
- intentionales Vergessen (1)
- intermediaries (1)
- internet of things and services (1)
- intervention (1)
- interventions (1)
- job-shop scheduling (1)
- knowledge engineering (1)
- kognitive Assistenzsysteme (1)
- labour union education (1)
- learning (1)
- learning environment (1)
- learning scenario (1)
- learning scenario for manufacturing (1)
- learning scenario implementation (1)
- manipulation (1)
- manufacturing systems (1)
- market survey (1)
- metadata (1)
- mobile IIoT-technologies (1)
- mobile software ecosystems (1)
- modeling language (1)
- morphologic box (1)
- morphological analysis (1)
- music industry (1)
- new product development (1)
- notation (1)
- personalised learning (1)
- portal (1)
- problems (1)
- process integration (1)
- process knowledge (1)
- process of modeling (1)
- process-oriented knowledge acquisition (1)
- product carbon footprint (1)
- product configurators (1)
- product development (1)
- production engineering computing (1)
- production networks (1)
- production routine (1)
- programming skills (1)
- proof of stake (1)
- proof of work (1)
- quality (1)
- recording of workplaces (1)
- regional network (1)
- remanufacturing (1)
- requirements (1)
- research challenges (1)
- retrieval cues (1)
- retrofit (1)
- risks (1)
- routines (1)
- scenario modeling (1)
- search engine (1)
- simulation (1)
- smart automation (1)
- smart grid (1)
- smart production (1)
- software engineering (1)
- standardization (1)
- subject differences (1)
- subject-oriented learning (1)
- sustainability (1)
- task realization strategies (1)
- teaching and learning model (1)
- technologies (1)
- terminology (1)
- training (1)
- triple bottom line (1)
- turbulence (1)
- unlearning (1)
- variant management (1)
- various applications (1)
- virtual learning (1)
- werksübergreifende Geschäftsprozesse (1)
- ökologischer Fußabdruck (1)
Institute
- Wirtschaftswissenschaften (139)
- Fachgruppe Betriebswirtschaftslehre (86)
- Wirtschafts- und Sozialwissenschaftliche Fakultät (4)
- Hasso-Plattner-Institut für Digital Engineering GmbH (3)
- Fachgruppe Politik- & Verwaltungswissenschaft (2)
- Sozialwissenschaften (2)
- Bürgerliches Recht (1)
- Department Psychologie (1)
- Extern (1)
- Forschungsbereich „Politik, Verwaltung und Management“ (1)
Openness indicators for the evaluation of digital platforms between the launch and maturity phase
(2024)
In recent years, the evaluation of digital platforms has become an important focus in the field of information systems science. The identification of influential indicators that drive changes in digital platforms, specifically those related to openness, is still an unresolved issue. This paper addresses the challenge of identifying measurable indicators and characterizing the transition from launch to maturity in digital platforms. It proposes a systematic analytical approach to identify relevant openness indicators for evaluation purposes. The main contributions of this study are the following (1) the development of a comprehensive procedure for analyzing indicators, (2) the categorization of indicators as evaluation metrics within a multidimensional grid-box model, (3) the selection and evaluation of relevant indicators, (4) the identification and assessment of digital platform architectures during the launch-to-maturity transition, and (5) the evaluation of the applicability of the conceptualization and design process for digital platform evaluation.
Obwohl Handelsplattformen zunehmend an Bedeutung gewinnen, besteht im deutschsprachigen Raum ein Mangel an umfassenden Marktübersichten. Dadurch fehlt es Verkäufern, potenziellen Plattformbetreibern und Kunden an einer soliden Grundlage für fundierte Entscheidungen. Das ändern wir mit folgendem Beitrag. Erfahren Sie hier das Wichtigste über den rasant wachsenden Markt der Handelsplattformen.
Enhancing economic efficiency in modular production systems through deep reinforcement learning
(2024)
In times of increasingly complex production processes and volatile customer demands, the production adaptability is crucial for a company's profitability and competitiveness. The ability to cope with rapidly changing customer requirements and unexpected internal and external events guarantees robust and efficient production processes, requiring a dedicated control concept at the shop floor level. Yet in today's practice, conventional control approaches remain in use, which may not keep up with the dynamic behaviour due to their scenario-specific and rigid properties. To address this challenge, deep learning methods were increasingly deployed due to their optimization and scalability properties. However, these approaches were often tested in specific operational applications and focused on technical performance indicators such as order tardiness or total throughput. In this paper, we propose a deep reinforcement learning based production control to optimize combined techno-financial performance measures. Based on pre-defined manufacturing modules that are supplied and operated by multiple agents, positive effects were observed in terms of increased revenue and reduced penalties due to lower throughput times and fewer delayed products. The combined modular and multi-staged approach as well as the distributed decision-making further leverage scalability and transferability to other scenarios.
Enterprise Resource Planning (ERP) system customization is often necessary because companies have unique processes that provide their competitive advantage. Despite new technological advances such as cloud computing or model-driven development, technical ERP customization options are either outdated or ambiguously formulated in the scientific literature. Using a systematic literature review (SLR) that analyzes 137 definitions from 26 papers, the result is an analysis and aggregation of technical customization types by providing clearance and aligning with future organizational needs. The results show a shift from ERP code modification in on-premises systems to interface and integration customization in cloud ERP systems, as well as emerging technological opportunities as a way for customers and key users to perform system customization. The study contributes by providing a clear understanding of given customization types and assisting ERP users and vendors in making customization decisions.
Terminology is a critical instrument for each researcher. Different terminologies for the same research object may arise in different research communities. By this inconsistency, many synergistic effects get lost. Theories and models will be more understandable and reusable if a common terminology is applied. This paper examines the terminological (in)consistence for the research field of job-shop scheduling by a literature review. There is an enormous variety in the choice of terms and mathematical notation for the same concept. The comparability, reusability and combinability of scheduling methods is unnecessarily hampered by the arbitrary use of homonyms and synonyms. The acceptance in the community of used variables and notation forms is shown by means of a compliance quotient. This is proven by the evaluation of 240 scientific publications on planning methods.
Competence development must change at all didactic levels to meet the new requirements triggered by digitization. Unlike classic learning theories and the resulting popular approaches (e.g., sender-receiver model), future-oriented vocational training must include new learning theory impulses in the discussion about competence acquisition. On the one hand, these impulses are often very well elaborated on the theoretical side, but the transfer into innovative learning environments - such as learning factories - is often still missing. On the other hand, actual learning factory (design) approaches often concentrate primarily on the technical side. Subject-oriented learning theory enables the design of competence development-oriented vocational training projectsin learning factories in which persons can obtain relevant competencies for digitization. At the same time, such learning theory approaches assume a potentially infinite number of learning interests and reasons. Following this, competence development is always located in an institutional or organizational context. The paper conceptionally answers how this theoryimmanent challenge is synthesizable with the reality of organizationally competence development requirements.
Handbuch der ERP-Auswahl
(2023)
The authors propose that while tacit knowledge is a valuable resource for developing new business models, its externalization presents several challenges. One major challenge is that individuals often don’t recognize their tacit knowledge resources, while another is the reluctance to share one’s knowledge with others. Addressing these challenges, the authors present an application-oriented serious game-based haptic modeling approach for externalize tacit knowledge, which can be used to develop the first versions of business models based on tacit knowledge. Both conceptual and practical design fundamentals are presented based on elaborated theoretical approaches, which were developed with the help of a design science approach. The development of the research process is presented step by step, whereby we focused on the high accessibility of the presented research. Practitioners are presented with guidelines for implementing their serious game projects. Scientists benefit from starting points for their research topics of externalization, internalization, and socialization of tacit knowledge, development of business models, and serious games or gamification. The paper concludes with open research desiderata and questions from the presented research process.
Neben kleineren, meist automatisierten Update-Aktivitäten zur Fehlerbehebung und zur Steigerung der Performance von ERP-Systemen, gibt es größere Updates mit umfangreicheren Aktualisierungen und Erweiterungen der Software – auch „Updateprojekte“ oder „Upgrades“ genannt. Ein ERP-Upgrade beschreibt einen größeren Änderungsprozess, der die Nutzung neuer Technologien ermöglicht und das System mit (neuen) Geschäftsstrategien in Einklang bringt. Upgrades tragen zur Verbesserung der Software bei und sind klar zu unterscheiden von geringfügigen Änderungen innerhalb einer Version eines ERP-Systems.
Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research.
Umstieg auf S/4HANA
(2023)
Sehr viele langjährige R/3-Nutzer stehen derzeit vor der Frage, wie sie sich hinsichtlich des Upgrades auf S/4HANA entscheiden sollen. Die Wartung für R/3 endet absehbar in den nächsten Jahren, und mit S/4 kommen neue Möglichkeiten, aber auch neue Herausforderungen. Dieser Beitrag beschreibt die Entscheidungen, die bei der Prüfung eines Umstiegs auf S/4HANA getroffen werden müssen und zeigt diese am Beispiel eines Multi-Channel-Handelsunternehmens.
Großer Marktüberblick
(2023)
Der Beratermarkt ist ähnlich undurchsichtig wie der ERP-Markt selbst. Daher veröffentlichen wir in dieser Ausgabe einen umfassenden Marktüberblick zu ERP-Beratungen mit 24 Unternehmen vom Spezialisten zum Generalisten, aber immer fokussiert auf das Thema ERP. Sehr spannend ist z. B. die Bandbreite der Antworten, mit der ERP-Berater den Nutzen von ERP bei der ERP-Auswahl bewerten. Auch Auswahlportale stoßen nicht überall auf große Gegenliebe. Manche Antworten mussten aus Platzgründen leider gekürzt werden. Alle Abonnenten von ERP Management finden die vollständigen Antworten zum Download im Webauftritt.
This meta-analysis synthesizes 332 effect sizes of various methods to enhance creativity. We clustered all studies into 12 methods to identify the most effective creativity enhancement methods. We found that, on average, creativity can be enhanced, Hedges’ g = 0.53, 95% CI [0.44, 0.61], with 70.09% of the participants in the enhancement conditions being more creative than the average person in the control conditions. Complex training courses, meditation, and cultural exposure were the most effective (gs = 0.66) while the use of cognitive manipulation drugs was the least and also noneffective, g = 0.10. The type of training material was also important. For instance, figural methods were more effective in enhancing creativity, and enhancing converging thinking was more effective than enhancing divergent thinking. Study effect sizes varied considerably across all studies and for many subgroup analyses, suggesting that researchers can plausibly expect to find reversed effects occasionally. We found no evidence of publication bias. We discuss theoretical implications and suggest future directions for best practices in enhancing creativity. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
Digital Platforms (DPs) has established themself in recent years as a central concept of the Information Technology Science. Due to the great diversity of digital platform concepts, clear definitions are still required. Furthermore, DPs are subject to dynamic changes from internal and external factors, which pose challenges for digital platform operators, developers and customers. Which current digital platform research directions should be taken to address these challenges remains open so far. The following paper aims to contribute to this by outlining a systematic literature review (SLR) on digital platform concepts in the context of the Industrial Internet of Things (IIoT) for manufacturing companies and provides a basis for (1) a selection of definitions of current digital platform and ecosystem concepts and (2) a selection of current digital platform research directions. These directions are diverted into (a) occurrence of digital platforms, (b) emergence of digital platforms, (c) evaluation of digital platforms, (d) development of digital platforms, and (e) selection of digital platforms.
Developing a new product generation requires the transfer of knowledge among various knowledge carriers. Several factors influence knowledge transfer, e.g., the complexity of engineering tasks or the competence of employees, which can decrease the efficiency and effectiveness of knowledge transfers in product engineering. Hence, improving those knowledge transfers obtains great potential, especially against the backdrop of experienced employees leaving the company due to retirement, so far, research results show, that the knowledge transfer velocity can be raised by following the Knowledge Transfer Velocity Model and implementing so-called interventions in a product engineering context. In most cases, the implemented interventions have a positive effect on knowledge transfer speed improvement. In addition to that, initial theoretical findings describe factors influencing the quality of knowledge transfers and outline a setting to empirically investigate how the quality can be improved by introducing a general description of knowledge transfer reference situations and principles to measure the quality of knowledge artifacts. To assess the quality of knowledge transfers in a product engineering context, the Knowledge Transfer Quality Model (KTQM) is created, which serves as a basis to develop and implement quality-dependent interventions for different knowledge transfer situations. As a result, this paper introduces the specifications of eight situation-adequate interventions to improve the quality of knowledge transfers in product engineering following an intervention template. Those interventions are intended to be implemented in an industrial setting to measure the quality of knowledge transfers and validate their effect.
While Information Systems (IS) Research on the individual and workgroup level of analysis is omnipresent, research on the enterprise-level IS is less frequent. Even though research on Enterprise Systems and their management is established in academic associations and conference programs, enterprise-level phenomena are underrepresented. This minitrack provides a forum to integrate existing research streams that traditionally needed to be attached to other topics (such as IS management or IS governance). The minitrack received broad attention. The three selected papers address different facets of the future role of enterprise-wide IS including aspects such as carbonization, ecosystem integration, and technology-organization fit.
Business processes are regularly modified either to capture requirements from the organization’s environment or due to internal optimization and restructuring. Implementing the changes into the individual work routines is aided by change management tools. These tools aim at the acceptance of the process by and empowerment of the process executor. They cover a wide range of general factors and seldom accurately address the changes in task execution and sequence. Furthermore, change is only framed as a learning activity, while most obstacles to change arise from the inability to unlearn or forget behavioural patterns one is acquainted with. Therefore, this paper aims to develop and demonstrate a notation to capture changes in business processes and identify elements that are likely to present obstacles during change. It connects existing research from changes in work routines and psychological insights from unlearning and intentional forgetting to the BPM domain. The results contribute to more transparency in business process models regarding knowledge changes. They provide better means to understand the dynamics and barriers of change processes.
Faced with the triad of time-cost-quality, the realization of production tasks under economic conditions is not trivial. Since the number of Artificial-Intelligence-(AI)-based applications in business processes is increasing more and more nowadays, the efficient design of AI cases for production processes as well as their target-oriented improvement is essential, so that production outcomes satisfy high quality criteria and economic requirements. Both challenge production management and data scientists, aiming to assign ideal manifestations of artificial neural networks (ANNs) to a certain task. Faced with new attempts of ANN-based production process improvements [8], this paper continues research about the optimal creation, provision and utilization of ANNs. Moreover, it presents a mechanism for AI case-based reasoning for ANNs. Experiments clarify continuously improving ANN knowledge bases by this mechanism empirically. Its proof-of-concept is demonstrated by the example of four production simulation scenarios, which cover the most relevant use cases and will be the basis for examining AI cases on a quantitative level.