TY - JOUR A1 - Ullrich, André A1 - Weber, Edzard A1 - Gronau, Norbert T1 - Regionale Refabrikationsnetzwerke BT - Potenziale und Herausforderungen der lokalen Wiederaufarbeitung von Produkten JF - Industrie 4.0 Management : Gegenwart und Zukunft industrieller Geschäftsprozesse N2 - Die Herstellung von Produkten bindet Energie sowie auch materielle Ressourcen. Viel zu langsam entwickeln sich sowohl das Bewusstsein der Konsumenten sowie der Produzenten als auch gesetzgebende Aktivitäten, um zu einem nachhaltigen Umgang mit den zur Verfügung stehenden Ressourcen zu gelangen. In diesem Beitrag wird ein lokaler Remanufacturing-Ansatz vorgestellt, der es ermöglicht, den Ressourcenverbrauch zu reduzieren, lokale Unternehmen zu fördern und effiziente Lösungen für die regionale Wieder- und Weiterverwendung von Gütern anzubieten. KW - Refabrikation KW - Regionale Ansätze KW - Remanufacturing Y1 - 2023 U6 - https://doi.org/10.30844/IM_23-2_11-14 SN - 2364-9208 VL - 39 IS - 2 SP - 11 EP - 14 PB - GITO mbH Verlag CY - Berlin ER - TY - GEN A1 - Ullrich, André A1 - Weber, Edzard A1 - Gronau, Norbert T1 - Regionale Refabrikationsnetzwerke BT - Potenziale und Herausforderungen der lokalen Wiederaufarbeitung von Produkten T2 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe N2 - Die Herstellung von Produkten bindet Energie sowie auch materielle Ressourcen. Viel zu langsam entwickeln sich sowohl das Bewusstsein der Konsumenten sowie der Produzenten als auch gesetzgebende Aktivitäten, um zu einem nachhaltigen Umgang mit den zur Verfügung stehenden Ressourcen zu gelangen. In diesem Beitrag wird ein lokaler Remanufacturing-Ansatz vorgestellt, der es ermöglicht, den Ressourcenverbrauch zu reduzieren, lokale Unternehmen zu fördern und effiziente Lösungen für die regionale Wieder- und Weiterverwendung von Gütern anzubieten. T3 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe - 183 KW - Refabrikation KW - Regionale Ansätze KW - Remanufacturing Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-604510 SN - 2364-9208 SN - 1867-5808 IS - 2 ER - TY - CHAP A1 - Thim, Christof A1 - Gronau, Norbert A1 - Haase, Jennifer A1 - Grum, Marcus A1 - Schüffler, Arnulf A1 - Roling, Wiebke A1 - Kluge, Annette ED - Shishkov, Boris T1 - Modeling change in business processes T2 - Business modeling and software design N2 - 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. KW - intentional forgetting KW - routines KW - business processes KW - unlearning Y1 - 2023 SN - 978-3-031-36756-4 SN - 978-3-031-36757-1 U6 - https://doi.org/10.1007/978-3-031-36757-1_1 SP - 3 EP - 17 PB - Springer Nature CY - Cham ER - TY - JOUR A1 - Teichmann, Malte A1 - Vladova, Gergana A1 - Gronau, Norbert T1 - Conception of subject-oriented learning BT - ameso-didactic design framework for learning scenarios for manufacturing JF - SSRN eLibrary / Social Science Research Network N2 - 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. KW - subject-oriented learning KW - learning scenario for manufacturing KW - didactic framework KW - action problems KW - didactic concept Y1 - 2023 U6 - https://doi.org/10.2139/ssrn.4457995 SN - 1556-5068 PB - Social Science Electronic Publ. CY - [Erscheinungsort nicht ermittelbar] ER - TY - CHAP A1 - Teichmann, Malte A1 - Busse, Jana A1 - Gonnermann, Jana A1 - Reimann, Daniela A1 - Ritterbusch, Georg David A1 - Langemeyer, Ines A1 - Gronau, Norbert ED - Nitsch, Verena ED - Brandl, Christopher ED - Häußling, Roger ED - Roth, Philip ED - Gries, Thomas ED - Schmenk, Bernhard T1 - Konzeption, Erstellung und Evaluation von VR-Räumen für die betriebliche Weiterbildung in KMU BT - Erfahrungen und Handlungsempfehlungen aus dem Forschungsprojekt API-KMU T2 - Digitalisierung der Arbeitswelt im Mittelstand 3 N2 - Der Beitrag adressiert die Erstellung von Virtual-Reality gestützten (Lehr- und Lern-) Räumen für die betriebliche Weiterbildung im Rahmen eines Forschungsprojektes. Der damit verbundene Konzeptions- und Umsetzungsprozess ist mit verschiedenen Herausforderungen verbunden: einerseits ist Virtual-Reality ein vergleichsweise neues Lehr- und Lernmedium, womit wenig praktische Handreichungen zur praktischen Umsetzung existieren. Andererseits existieren theoretisch-konzeptionelle Ansätze zur Gestaltung digitaler Lehr- und Lernarrangements, die jedoch 1) oft Gefahr laufen, an den realen Bedürfnissen der Praxis „vorbei“ zu gehen und 2) zumeist nicht konkret Virtual-Reality bzw. damit verbundene Lehr- und Lernumgebungen adressieren. In dieser Folge sind Best-Practice Beispiele basierend auf erfolgreichen Umsetzungsvorhaben, die nachfolgenden Projekten als „Wegweiser“ dienen könnten, äußerst rar. Der Beitrag setzt an dieser Stelle an: basierend auf zwei real existierenden betrieblichen Anwendungsfällen aus den Bereichen Natursteinbearbeitung sowie Einzel- und Sondermaschinenbau werden Herausforderungen und Lösungswege des Erstellungsprozesses von Virtual-Reality gestützten (Lehr- und Lern-)Räumen beschrieben. Ebenfalls werden basierend auf den gemachten Projekterfahrungen Handlungsempfehlungen für die gelingende Konzeption, Umsetzung und Evaluation dieser Räume formuliert. Betriebliche Beschäftigte aus den Bereichen Aus- und Weiterbildung, Management oder Human Ressources, die in eigenen Projekten im Bereich Virtual Reality aktiv werden wollen, profitieren von den herausgestellten praktischen Handreichungen. Forschende Personen sollen Anregungen für weiterführende Forschungsvorhaben erhalten. Y1 - 2023 SN - 978-3-662-67023-1 SN - 978-3-662-67024-8 U6 - https://doi.org/10.1007/978-3-662-67024-8_5 SP - 155 EP - 204 PB - Springer Vieweg CY - Berlin ER - TY - JOUR A1 - Rojahn, Marcel A1 - Weber, Edzard A1 - Gronau, Norbert T1 - Towards a standardization in scheduling models BT - assessing the variety of homonyms JF - International journal of industrial and systems engineering N2 - 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. KW - job-shop scheduling KW - JSP KW - terminology KW - notation KW - standardization Y1 - 2023 UR - https://publications.waset.org/10013137/pdf SN - 1748-5037 SN - 1748-5045 VL - 17 IS - 6 SP - 401 EP - 408 PB - Inderscience Enterprises CY - Genève ER - TY - CHAP A1 - Rojahn, Marcel A1 - Gronau, Norbert T1 - Digital platform concepts for manufacturing companies BT - a review T2 - 10th International Conference on Future Internet of Things and Cloud (FiCloud) N2 - 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. Y1 - 2023 SN - 979-8-3503-1635-3 U6 - https://doi.org/10.1109/FiCloud58648.2023.00030 SP - 149 EP - 158 PB - IEEE CY - [Erscheinungsort nicht ermittelbar] ER - TY - CHAP A1 - Rojahn, Marcel A1 - Ambros, Maximilian A1 - Biru, Tibebu A1 - Krallmann, Hermann A1 - Gronau, Norbert A1 - Grum, Marcus ED - Rutkowski, Leszek ED - Scherer, Rafał ED - Korytkowski, Marcin ED - Pedrycz, Witold ED - Tadeusiewicz, Ryszard ED - Zurada, Jacek M. T1 - Adequate basis for the data-driven and machine-learning-based identification T2 - Artificial intelligence and soft computing N2 - 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. KW - data mining KW - knowledge engineering KW - various applications Y1 - 2023 SN - 978-3-031-42504-2 SN - 978-3-031-42505-9 U6 - https://doi.org/10.1007/978-3-031-42505-9_48 SP - 570 EP - 588 PB - Springer CY - Cham ER - TY - GEN A1 - Panzer, Marcel A1 - Bender, Benedict A1 - Gronau, Norbert T1 - A deep reinforcement learning based hyper-heuristic for modular production control T2 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe - 173 KW - production control KW - modular production KW - multi-agent system KW - deep reinforcement learning KW - deep learning KW - multi-objective optimisation Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-605642 SN - 1867-5808 ER - TY - JOUR A1 - Panzer, Marcel A1 - Bender, Benedict A1 - Gronau, Norbert T1 - A deep reinforcement learning based hyper-heuristic for modular production control JF - International journal of production research N2 - 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. KW - production control KW - modular production KW - multi-agent system KW - deep reinforcement learning KW - deep learning KW - multi-objective optimisation Y1 - 2023 U6 - https://doi.org/10.1080/00207543.2023.2233641 SN - 0020-7543 SN - 1366-588X SN - 0278-6125 SP - 1 EP - 22 PB - Taylor & Francis CY - London ER -