TY - RPRT A1 - König, Mathias A1 - Winkler, Herwig T1 - Digitalisierung in der Montage mit dem Schwerpunkt des Einsatzes von Assistenzsystemen Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-62121 PB - Brandenburgische Technische Universität, IKMZ, Universitätsbibliothek CY - Cottbus ; Senfteberg ER - TY - GEN A1 - Berneis, Moritz A1 - Winkler, Herwig A1 - Abdelkafi, Nizar T1 - Insights from interviews with German supply chain managers: a study of supply chain transformations and emerging issues T2 - Procedia Computer Science N2 - To investigate current trends and challenges related to supply chain management in Germany, we conduct an interview-based study involving 16 experts. The study focuses on three areas: supply chain transformations, software challenges, and the adoption of emerging technologies. Supply chain transformations are influenced by increasing supply chain complexity, the growing prevalence of e-commerce, and the increased importance allocated by customers to trust and transparency. Complexity is, in part, due to disruptions caused by policy responses to the pandemic and to software challenges in terms of the adaptability of ERP systems and the cybersecurity of cloud-based solutions. This study is relevant for practitioners, as it sheds light on pressing issues in supply chain management, and for researchers as it uncovers potential topics that require further research in the future. KW - Supply Chain Management (SCM) Interviews KW - Emerging Technologies KW - German Companies KW - Digitalization KW - Trends KW - Interviews Y1 - 2024 UR - https://authors.elsevier.com/sd/article/S1877-0509(24)00106-6 U6 - https://doi.org/10.1016/j.procs.2024.01.106 SN - 1877-0509 VL - Vol. 232 SP - 1079 EP - 1088 ER - TY - GEN A1 - Singh, Amita A1 - Hayat, Mubashir A1 - Winkler, Herwig T1 - Deriving principles from participatory modelling to augment circularity and sustainability in EV battery lifecycle T2 - Procedia CIRP N2 - Very often circularity and sustainability go hand-in-hand. This paper is focused on understanding the key criteria of how circularity and hence sustainability can be increased in the field of electric vehicle (EV) batteries. Literature shows that only 2-5% of Lithium Ion Batteries (LIB) are recycled resulting in a huge number of not properly disposed batteries. This is harmful not only to the environment but also has adverse effects on the population of the area where the minerals are mined disturbing the triple bottom line of sustainability. Through this paper, we propose a holistic approach to tackle the problem by not only using technology but also including systematic input from different stakeholders through the lifecycle. We, thus, combine principles from participatory modelling and blockchain technology to bring transparency and accountability in the process. This approach is a step-by-step guideline for including circularity and sustainability through a continuous process of input collection from real life and validation in the real world thereby resulting in a practical and technologically adept solution yielding better results when compared to a solution developed in an isolated laboratory environment. In this paper, we discuss the guidelines for the process together with input data collection, method, process validation, and resulting document in each step. Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/j.procir.2024.10.120 SN - 2212-8271 IS - 130 SP - 498 EP - 503 ER - TY - GEN A1 - König, Mathias A1 - Winkler, Herwig T1 - Investigation of assistance systems in assembly in the context of digitalization: a systematic literature review T2 - Journal of Manufacturing Systems N2 - Assistance systems play a crucial role in enhancing working conditions and efficiency in industrial assembly. In the context of Industry 4.0, it is important to determine the types of assistance systems that contribute to assembly goals as well as their economic benefits. First, the significance of the topic will be introduced, and the research questions will be presented. Second, the basic technical terms will be defined, and third, the research methodology of a structured literature review (SLR) will be delineated. The fourth section presents an overview of the ergonomic and information assistance systems used in operational practice and academic test set-ups. It further explains the reasons for using assistance systems in assembly and their economic benefits, particularly in terms of reducing assembly times and errors. In the fifth section, the research perspectives of the respective publications are evaluated and summarized in both a qualitative and quantitative way. The present mixed-methods study is not generalizable due to its limitations such as a small sample size, the geographical scope of the study, type of databanks, time of publication and language of the reviewed articles, and methods of data collection. It does, however, identify potential areas for future research and provide recommendations for further investigation. KW - AssemblyIndustry 4.0Assistance systemsDigitalization Y1 - 2025 U6 - https://doi.org/10.1016/j.jmsy.2024.11.015 SN - 0278-6125 VL - 78 SP - 187 EP - 199 PB - Elsevier BV ER - TY - GEN A1 - Mielke, Jonas A1 - Winkler, Herwig T1 - A reference architecture for the application of machine leaning algorithms in production planning and control T2 - Procedia CIRP N2 - The organization of complex production systems in enterprises poses major challenges for decision-makers in production planning and control (PPC). Especially the volatility of planning scenarios and the associated demand for short-cycle adaptation of decisions requires methods that enable fast and reliable evaluations. This article presents a system architecture that addresses these challenges holistically. The system architecture uses real production data to generate scenario-based value stream simulations, which are subsequently used to train machine learning algorithms. The trained models enable a holistic evaluation of new production scenarios. Finally, decisions can be made for the real production environment based on reliable knowledge. KW - Production planningcontrolmachine learningvalue stream simulationproduction datasystem architecturefast decision-making processes Y1 - 2024 U6 - https://doi.org/10.1016/j.procir.2024.10.059 VL - 130 SP - 79 EP - 85 PB - Elsevier BV ER - TY - GEN A1 - Mielke, Jonas A1 - Winkler, Herwig T1 - Application of machine learning algorithms for the evaluation of employee qualification matrices in complex production systems T2 - Procedia CIRP N2 - Machine learning (ML) methods are powerful tools for the rapid and targeted evaluation of large data volumes. Especially in the area of production planning and control (PPC), there is huge potential for making the operational and strategic organization of production systems more efficient. However, in order to achieve these objectives, practical research contributions are required that focus on the actual boundary conditions of PPC. This article presents the practical evaluation of qualification matrices using the gradient boosting method and cross-validation to determine the influence of data splitting and selection on prediction accuracy to demonstrate the usability of the system. KW - machine learningproduction planningcontrolpersonnel qualificationcontrol systemcomplex production systemsproduction data Y1 - 2024 U6 - https://doi.org/10.1016/j.procir.2024.10.058 SN - 2212-8271 VL - 130 SP - 72 EP - 78 PB - Elsevier BV ER - TY - GEN A1 - Abdulghani, Tamer A1 - Winkler, Herwig A1 - Abdelkafi, Nizar A1 - Lange, Hans-Rüdiger T1 - Paving the Green Path: a Maturity Model for Digital Decarbonization T2 - Procedia CIRP N2 - Digital transformation has been employed across all industries to enhance business operations. Its potential lies in optimising existing business models and creating innovative ones based on various technologies, making them fit for the future by reducing greenhouse gas emissions and fulfilling future decarbonization goals. In order to ensure a successful digital transformation towards decarbonization, managers have to assess the capabilities of their organisation and competently oversee their journey by defining a clear path employing digital maturity models. For this purpose, the analysis focused on published literature concerning digital maturity models developed to guide organisations in navigating their digital transformation journey. A systematic literature review of 36 selected digital maturity models is presented in this paper. These models overlook decarbonisation measures and focus solely on demonstrating digital capabilities, efficiency and profit growth. Therefore, a maturity model for digital decarbonization encompassing dimensions of decarbonization goals, business model innovation, customers and suppliers, products and services, employees, collaboration, processes and activities, data, and IT-infrastructure, -security and -technology has been developed. It assists organisations in reducing their carbon footprint by strategically and effectively leveraging digital technologies. The proposed model has five maturity levels: assess and align, engage and refine, innovate and collaborate, optimize and personalize, and transform and decarbonize. This paper aims to assess the current level of digital decarbonization and determine the position of the organizations. Additionally, it endeavours to identify the specific opportunities for growth that digitalization presents, thereby enabling the creation of a new digital carbon-free existence. KW - Maturity Model KW - Digital Decarbonization KW - Digital Transformation KW - Review Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/j.procir.2024.10.201 SN - 2212-8271 VL - 130 SP - 1020 EP - 1027 ER - TY - GEN A1 - Hayat, Mubashir A1 - Winkler, Herwig T1 - Blockchain-based Order Management Process in Decentralized Production Networks: A Conceptual Framework T2 - Procedia CIRP N2 - In today's world of decentralized production, original equipment manufacturers (OEMs) are facing more than ever an increasing complexity in their order management processes. On the one hand, they deal with issues related to data transparency, traceability, and security in constant back-and-forth communication exchange between customers and sub-contractors having to confirm the order status. On the other hand, there are significant obstacles when it comes to integrating their traditional order management systems with the partner organization's information systems within the production network. These complexities tend to decrease the ability of OEMs to remain flexible and profitable. Therefore, they are constantly looking for feasible solutions to overcome these issues and improve their order management processes. To this aim, this work proposes a novel blockchain-based conceptual framework for order management that intends to establish a secure environment for efficient communication and data integrity. The decentralized and immutable nature of blockchain makes it capable of providing real-time data as well as ensuring data security and transparency. Furthermore, this work also set up basic level SQLite3 databases and design prototypical on-chain and off-chain smart contract logics that can be used to automate various operations and decisions making in order management, thereby, improving the overall order processing efficiency. According to the authors knowledge, there is no such framework available in research. Therefore, it is suggested that organization can utilize the proposed framework to develop their own blockchain based order management systems. Y1 - 2024 U6 - https://doi.org/https://doi.org/10.1016/j.procir.2024.10.218 SN - 2212-8271 IS - 130 SP - 1137 EP - 1143 ER - TY - GEN A1 - Eichenseer, Patrick A1 - Hans, Lukas A1 - Winkler, Herwig T1 - A data-driven machine learning model for forecasting delivery positions in logistics for workforce planning T2 - Supply Chain Analytics N2 - Workforce planning in logistics is a major challenge due to increasing demands and a dynamic environment. The number of delivery positions is a key factor in determining staffing requirements. This is often predicted subjectively based on employee assessments. To improve decision making and increase both the efficiency of this important forecasting process and the use of resources in the production system, i.e. shopfloor logistics, a data-driven machine learning model with a forecasting horizon of 5 working days was developed and validated in a practical case study in a company. The results show that the novel and specifically developed model outperforms both the manual forecasting approach in practice and auto machine learning models in terms of accuracy. The outperformance is particularly strong in the short term. Based on the predicted delivery positions, an optimised workforce planning was subsequently carried out in the case study company. Limitations of the model include the fact that it was validated in only one company and that the number of picks may need to be derived for more accurate scheduling. These two aspects also represent potential for future research. KW - Delivery Positions KW - Forecasting KW - Logistics KW - Workforce Planning KW - Machine Learning KW - Picks Y1 - 2025 U6 - https://doi.org/10.1016/j.sca.2024.100099 SN - 2949-8635 VL - 9 (2025) PB - Elsevier BV ER - TY - GEN A1 - Abdulghani, Tamer A1 - Winkler, Herwig T1 - Unlocking the power of digital technology for fostering sustainability T2 - IEEE Student Conference on Research and Development N2 - In the pursuit of economic, social and environmental sustainability, digital technologies have the potential to become powerful tools that provide innovative solutions for the reduction of greenhouse gas emissions and the mitigation of the effects of climate change. Therefore, it is essential to understand how digital technologies can drive or inhibit the sustainability endeavors of firms. For this purpose, we systematically analyze the literature published during the last five years to feature how digitalization could contribute to the achievement of a more sustainable future. The study provides key findings on the potential applications of digital technologies in promoting and contributing to sustainability and presents a roadmap for adopting digital technologies. It also highlights the challenges and barriers faced by different industries in realizing the full potential of these technologies as drivers of sustainable development as well as identifies future directions. KW - Sustainability KW - Digitalization KW - Digital Technology KW - Industry 4.0 KW - A Systematic Literature Review Y1 - 2024 UR - https://ieeexplore.ieee.org/document/10563721 SN - 979-8-3503-1882-1 U6 - https://doi.org/https://doi.org/10.1109/SCOReD60679.2023.10563721 SP - 57 EP - 64 ER - TY - GEN A1 - Abdulghani, Tamer A1 - Winkler, Herwig T1 - Managing the decarbonization of coal-based energy value chain: empirical findings from Lusatia, Germany T2 - IEEE Student Conference on Research and Development N2 - Decarbonization is causing structural changes that exert pressure not only on the companies and their employees in the lignite industry, but also on other companies along the value chain across multiple sectors. As a result, companies have an urgent need for change. They are challenged to take action to build a sustainable new existence as the markets collapse. For this purpose, we investigate the structural changes due to decarbonization and what factors can be part of a successful business transformation to manage the decarbonization of the coal-based energy value chain. This paper presents empirical insights from several semi-structured interviews with the key actors i.e., energy companies, service providers, logistics companies, suppliers, etc. in the Lusatia mining region as well as a conceptual model for a successful business transformation in the region. We used the Gioia method to analyze the interview data in order to obtain a meaningful data structure. This resulted in a conceptual model that shows how success factors have to be combined to manage the decarbonization measures within a region. KW - Decarbonization KW - Coal Phase-out KW - Energy Value Chain KW - Structural Change KW - Case Study KW - Digitalization Y1 - 2024 UR - https://ieeexplore.ieee.org/document/10563936 U6 - https://doi.org/https://doi.org/10.1109/SCOReD60679.2023.10563936 SP - 307 EP - 315 ER - TY - GEN A1 - Eichenseer, Patrick A1 - Winkler, Herwig T1 - A data-oriented shopfloor management in the production context: a systematic literature review T2 - The International Journal of Advanced Manufacturing Technology N2 - AbstractData not only plays an essential role in traditional shopfloor management, but it is also becoming even more important in Industry 4.0, particularly due to the increasing possibilities offered by new digital and data technologies and developments. In this context, the literature often refers to digital shopfloor management, the next generation shopfloor or other evolutionary synonyms. This raises the question of how to differentiate the content of data-oriented shopfloor management from digital shopfloor management. This paper discusses the state of the art — in terms of both data and digital perspectives — using a systematic literature review. Due to the complexity of the topic, three different levels of consideration — technology, organisation and people — are examined and discussed. Existing conceptual approaches are analysed in terms of conclusions and research gaps. It was found that the area of technology, including dedicated applications, is very well represented and researched in the literatur Y1 - 2024 U6 - https://doi.org/10.1007/s00170-024-14238-8 SN - 0268-3768 VL - 134 SP - 4071 EP - 4097 PB - Springer Science and Business Media LLC ER - TY - RPRT A1 - Berneis, Moritz A1 - Winkler, Herwig T1 - Untersuchung von aktuellen Trends und Herausforderungen im Supply Chain Management in Deutschland N2 - ▪Im Zeitraum von November 2022 bis Januar 2023 wurde am Lehrstuhl für Produktionswirtschaft der BTU Cottbus-Senftenberg die Studie mit dem Titel „Untersuchung von aktuellen Trends und Herausforderungen im SCM deutscher Unternehmen“ durchgeführt. Es wurden dazu Interviews mit über 20 Experten durchgeführt. 16 Interviews wurden ausgewertet. ▪ Im ersten Themenkomplex wurden aktuelle Ereignisse untersucht. Die häufigsten Herausforderungen sind demnach die Ressourcenverfügbarkeit, lange Lieferzeiten, der Fachkräftemangel und preisgetriebene Themen. Experten wurden zu aktuellen Ereignissen befragt, wie Rohstoffpreisschwankungen, der Zero-Covid-Strategie von China und Knappheiten in der Logistik. Preisschwankungen betrafen die meisten Unternehmen, während Krieg und Sanktionen nur indirekt wirkten. Die Zero-Covid-Strategie führte zu Vertrauensverlust in China und Knappheiten in der Logistik zu höheren Transportkosten. Der Fachkräftemangel wurde auch als aktuelles Ereignis genannt. ▪ Der zweite Themenkomplex ist Veränderungen im SCM gewidmet. Dabei wurde insbesondere der Wandel in komplexere Netzwerke, der Aufstieg des E-Commerce und die Bedeutung von Vertrauen und Transparenz angeführt als wesentliche Veränderungen. Supply Chains sind gewachsen und komplexer geworden, aber auch die Qualität der Vernetzung hat sich verbessert. E-Commerce für Standardprodukte wird weiter an Relevanz gewinnen. Kein Trend zur Deglobalisierung wurde festgestellt. Die Experten sind sich einig, dass Supply Chains "too lean" geworden sind und das Risikomanagement wichtiger ist. Das oberste Ziel sollte sein, die Produktion und Lieferfähigkeit sicherzustellen. ▪ Der dritte Themenkomplex befasst sich mit Nachhaltigkeit, einschließlich des Lieferkettengesetzes (LkSG), Dekarbonisierung und Nachhaltigkeitsnachweisen. Die meisten Unternehmen erfüllen die Anforderungen des LkSG und nehmen das Gesetz als Klausel in Verträgen auf. Bürokratie in Deutschland führt zu hohen Kosten, besonders für kleine Unternehmen. Kosteneinsparungen sind der größte Treiber bei der Dekarbonisierung. Nachhaltigkeit wird immer wichtiger und kann über Zertifikate nachgewiesen werden. ▪ Im vierten Themenkomplex Software wurde die Anpassungsfähigkeit von ERP-Systemen bemängelt und automatische Bestellungen wurden kontrovers betrachtet. Die meisten Experten verzichten aus Sicherheitsgründen auf Cloud-basierte Lösungen aufgrund des hohen Risikos für Cyberangriffe und möglicher Gefahren für die Supply Chain. ▪ In Themenkomplex fünf wurden neue Technologien im SCM besprochen. AR wird für die Wartung von Maschinen eingesetzt und VR für Simulation und Planung. IoT wird für predictive Maintenance genutzt, Treiber der Automatisierung ist der Fachkräftemangel. Big Data & AI sollen die Vorhersage der Nachfrage verbessern. Additive Fertigung hat Potenzial für Automatisierung und sinkende Kosten. Die Blockchain gilt als komplex und kann als geteilte Datenbank, Zahlungssystem und zur Rechnungsteilung dienen. ▪ Im sechsten Themenkomplex Sichtbarkeit halten Experten Transparenz für wichtig, sie ist noch zu gering aber bietet Potenzial zur Erkennung von Risiken. Tracking und Tracing ist intern verbreitet, international gibt es noch Probleme. Eine höhere Zahlungsbereitschaft der Kunden für Sichtbarkeit besteht nicht, diese entsteht nur für einen besseren Service. KW - Herausforderungen KW - Logistik KW - Logistics KW - Supply Chain Management KW - Trends KW - Challenges KW - Interview Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-62950 PB - Brandenburgische Technische Universität, IKMZ, Universitätsbibliothek CY - Cottbus ; Senftenberg ER - TY - RPRT A1 - Hoffmann, Stefanie A1 - Thomas, Kevin A1 - Winkler, Herwig T1 - Technologische Trends und Entwicklungen bei alternativen Energiesystemen Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-64847 PB - Brandenburgische Technische Universität, IKMZ, Universitätsbibliothek CY - Cottbus ; Senftenberg ER - TY - RPRT A1 - Schmid, Stefan A1 - Winkler, Herwig T1 - Die Produktionssteuerung im digitalen Zeitalter aus Sicht der industriellen Praxis N2 - -Im Zeitraum von März bis Juni 2023 wurde vom Lehrstuhl für Produktionswirtschaft der BTU Cottbus Senftenberg die Studie mit dem Titel „Die Produktionssteuerung im digitalen Zeitalter“ als Online Umfrage durchgeführt. 125 Personen mit operativen, planerischen und strategischen Funktionen in der Produktion oder des produktionsnahen Umfeldes beantworteten den Fragebogen - Die Studie zeigt, dass bei vielen Praktikern ein sehr klares Bild vorherrscht, was die Produktionssteuerung der Zukunft leisten muss - Die Erweiterung der Produktionssteuerung um Künstliche Intelligenz wird in sehr starkem Maße als sinnvoll erachtet, um die Effizienz am Shop Floor bei hoher Komplexität der Leistungsprozesse auch in Zukunft aufrechtzuerhalten. - Die ausgewerteten Daten zeigen, dass die aktuellen Systeme der Produktionssteuerung die Nutzer nicht in dem Ausmaß unterstützen, wie es erwartet wird. Die Nutzer sind gefordert, ihr Wissen und ihre Kompetenzen einzusetzen, um funktionale Defizite der Anwendungen zu kompensieren - Die Kooperation von Mensch und KI in der Produktionssteuerung wird als sinnvoll angesehen. Dabei wird eine Kombination aus menschlicher Arbeit und KI Unterstützung bevorzugt, während eine vollautonome KI seltener favorisiert wird. Die Studienergebnisse zeigen eine wachsende Zustimmung und Offenheit für den Einsatz von KI als Werkzeug in der Produktionssteuerung, jedoch unter menschlicher Aufsicht und Beteiligung - Die Herausforderungen der Integration von Künstlicher Intelligenz in die Mensch Maschine Interaktion umfassen technische und soziale Aspekte. Um diesen Herausforderungen zu begegnen, sind geeignete Lösungsansätze notwendig, wie die Entwicklung sicherer Schnittstellen, robuste Datenschutzmaßnahmen aber auch Nutzerakzeptanzförderung durch Aufklärung und Schulung - Innerhalb eines hybriden Rahmens, wo der Mensch mit der KI arbeitet, müssen klare Regeln für die Interaktion definiert werden. Die KI soll die menschliche Entscheidungsfindung interaktiv unterstützen, neue Perspektiven bieten und alternative Vorschläge unterbreiten. - Insgesamt betonen die Ergebnisse der Umfrage die Bedeutung von Sicherheit, Transparenz, Kontrolle und die Validierung von KI Entscheidungen durch menschliche Akteure. Es zeigt sich, dass die Kombination von Mensch und KI nicht nur technologische Vorteile bietet, sondern auch dazu beitragen kann, nachhaltig wirtschaftlichere Ergebnisse zu erzielen. Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-65009 PB - Brandenburgische Technische Universität, IKMZ, Universitätsbibliothek CY - Cottbus ; Senftenberg ER - TY - CHAP A1 - Winkler, Herwig A1 - Schmid, Stefan ED - Tang, Loon-Ching T1 - Empirical Findings on the Status Quo of Industrial Production Management Systems in the Context of Advancing Digitalization T2 - Proceedings of the 10th International Conference on Industrial Engineering and Applications (ICIEA 2023), April 4–6, 2023, Phuket, Thailand Y1 - 2023 SN - 978-1-64368-408-6 SN - 978-1-64368-409-3 U6 - https://doi.org/10.3233/ATDE230029 SN - 2352-751X SN - 2352-7528 SP - 44 EP - 54 PB - IOS Press CY - Berlin ER - TY - GEN A1 - Abdulghani, Tamer A1 - Winkler, Herwig T1 - Transition Towards a Green Future: Upgrading a Digital Decarbonization Maturity Model T2 - Procedia Computer Science N2 - Successful digital transformation, driven by structural changes resulting from climate change mitigation, requires a strategic approach to help organizations assess their current position in both digitalization and decarbonization efforts. Through expert focus groups, the “Digital Decarbonization Maturity Model” we developed in a previous study, has been upgraded. Additionally, measures and approaches have been identified within each dimension of the model. This model covers nine dimensions: decarbonization goals, business model innovation, customers and suppliers, products and services, employees, collaboration, processes and activities, data, and IT-infrastructure, -security and -technology, that present a holistic approach to reduce CO2 emissions in organizations. These dimensions are assessed against five maturity levels: beginner, adopter, integrator, innovator, and transformer, indicating various degrees of maturity within each dimension. These levels provide a pathway from beginner to more advanced level with respect to adopting digital technologies for the decarbonization goal. KW - Digital Decarbonization KW - Maturity Model KW - Digital Transformation Y1 - 2025 U6 - https://doi.org/https://doi.org/10.1016/j.procs.2025.01.192 SN - 1877-0509 VL - 253 SP - 1303 EP - 1312 ER - TY - GEN A1 - Eichenseer, Patrick A1 - Winkler, Herwig T1 - Data-oriented shopfloor management : an inductively developed holistic conceptual model T2 - The international journal of advanced manufacturing technology N2 - Data plays a fundamental role in shopfloor management in order to make better decisions and increase efficiency throughout the production system. The idea of data-oriented shopfloor management has been discussed in the literature as an innovative concept, but unlike traditional or digital shopfloor management, it has not been conceptualised from a holistic perspective. For this reason, this paper used qualitative research in the form of expert interviews to collect holistic conceptual requirements for data-oriented shopfloor management. The data were analysed using the Gioia Methodology and transferred into a data structure to derive conceptual requirements at different levels of aggregation. On the basis of the empirical data, data-oriented shopfloor management was modelled, consisting of the system elements shopfloor, management approach with four subsystems and information system. Specifically, two conceptual models were developed for data-oriented shopfloor management as a distinction was made between episodic configuration and strategic adaptation and the persistent operating mode. Both conceptual models represent the reciprocal relationships and interdependencies of the system elements within data-oriented shopfloor management. Looking to the future, this paper provides the basis for further research to refine the conceptual models based on the empirically collected conceptual requirements with a higher level of detail. KW - Data · Shopfloor · Production · Management concept · Industry 4.0 · Empirical study Y1 - 2025 UR - https://link.springer.com/article/10.1007/s00170-025-15571-2 U6 - https://doi.org/10.1007/s00170-025-15571-2 SN - 1433-3015 VL - 2025 SP - 1 EP - 23 PB - Springer CY - Wiesbaden ER - TY - GEN A1 - Abdulghani, Tamer A1 - Winkler, Herwig T1 - A SWOT analysis approach for assessing the sustainable transition in the coal energy sector in Germany T2 - Procedia computer science N2 - The coal energy sector plays a significant role within the German economy, providing a key source of growth and energy supply. However, it is also a major contributor to climate change. In order to address this issue, Germany has adopted a policy to phase-out coal by 2038 at the latest. This paper employs a SWOT (strengths, weaknesses, opportunities, and threats) analysis approach to evaluate the sustainable transition of Germany's coal energy sector. A case study using semi-structured interviews with key actors was conducted in the second largest mining region in Germany, the Lusatia region. The analysis shows that the strengths of the area include its energy expertise, existing infrastructure with renewable energy potential, and availability of land for industrial development and settlement. However, dependence on lignite, internal resistance to change, lack of competence in digital technologies, and complex bureaucracy for implementing new projects represent some of the key weaknesses that companies must address in order to succeed. Some opportunities arise from the structural changes currently occurring, highlighting the potential of a growing renewable energy market and the development of innovative business models, as well as the creation of new employment opportunities and the enhancement of the region's attractiveness for global corporations. Nevertheless, a number of factors present a threat to the sustainable transition, including concerns about energy security, high green fuel costs, social resistance, a shortage of skilled labor, labor migration, and the loss of cooperation partners. KW - Sustainable Transition KW - Sustainability KW - Energy Sector KW - Coal Phase-out KW - Decarbonization KW - SWOT Analysis KW - Energy Value Chain Y1 - 2025 U6 - https://doi.org/10.1016/j.procs.2025.07.010 SN - 1877-0509 VL - 263 SP - 76 EP - 81 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Thomas, Kevin A1 - Winkler, Herwig ED - Mourtzis, Dimitris T1 - Extended reality for process visualization in cloud manufacturing : a systematic review T2 - Procedia CIRP N2 - The advent of cloud-based processes represents a novel opportunity within the manufacturing industry. However, a lack of common understanding of integration persists. The virtualization of manufacturing and logistics processes provides the foundation for subsequent stages of visualization. Conversely, the visualization of processes facilitates a deeper comprehension of the value streams involved, thereby enabling improvements to be made. In this regard, the objective of this research is to assess process virtualization techniques in cloud manufacturing, with a particular focus on existing academic literature in the field. Consequently, this investigation also encompasses an exploration of immersive visualization techniques in cloud manufacturing environments. For the purpose of this research, a systematic literature review is conducted in the SCOPUS, Web of Science, and Science Direct databases. Various methods and implications for the virtualization of cloud manufacturing processes available in the literature are identified, analyzed, and summarized. Furthermore, the potential for visualizing data related to cloud manufacturing processes in extended reality is also investigated. The findings of this research offer scientists the prospect of further investigation into the cloud manufacturing paradigm, for example in the context of virtual learning factories. KW - Cloud manufacturing KW - Web-based manufacturing KW - Virtualization KW - Extended reality KW - Literature review Y1 - 2025 U6 - https://doi.org/10.1016/j.procir.2025.08.111 SN - 2212-8271 VL - 136 SP - 648 EP - 653 PB - Elsevier B.V. CY - Amsterdam ER -