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)
Already successfully used products or designs, past projects or our own experiences can be the basis for the development of new products. As reference products or existing knowledge, it is reused in the development process and across generations of products. Since further, products are developed in cooperation, the development of new product generations is characterized by knowledge-intensive processes in which information and knowledge are exchanged between different kinds of knowledge carriers. The particular knowledge transfer here describes the identification of knowledge, its transmission from the knowledge carrier to the knowledge receiver, and its application by the knowledge receiver, which includes embodied knowledge of physical products. Initial empirical findings of the quantitative effects regarding the speed of knowledge transfers already have been examined. However, the factors influencing the quality of knowledge transfer to increase the efficiency and effectiveness of knowledge transfer in product development have not yet been examined empirically. Therefore, this paper prepares an experimental setting for the empirical investigation of the quality of knowledge transfers.
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.
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.
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 reinforcement 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 sensorand 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.
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.
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.
This paper presents an exploratory study investigating the influence of the factors (1) intermediary participation, (2) decision-making authority, (3) position in the enterprise, and (4) experience in open innovation on the perception and assessment of the benefits and risks expected from participating in open innovation projects. For this purpose, an online survey was conducted in Germany, Austria and Switzerland. The result of this paper is an empirical evidence showing whether and how these factors affect the perception of potential benefits and risks expected within the context of open innovation project participation. Furthermore, the identified effects are discussed against the theory. Existing theory regarding the benefits and risks of open innovation is expanded by (1) finding that they are perceived mostly independently of the factors, (2) confirming the practical relevance of benefits and risks, and (3) enabling a finer distinction between their degrees of relevance according to respective contextual specifics.