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 - TY - RPRT A1 - Winkler, Herwig A1 - Hayat, Mubashir A1 - Rüster, Moritz T1 - KI-basierte Instandhaltungsplanung und –steuerung im öffentlichen Verkehr: Status quo und konzeptionelle Überlegungen N2 - Durch eine sorgfältige Instandhaltung von Zügen, Bussen und Flugzeugen lässt sich die Verfügbarkeit, Betriebssicherheit und Wirtschaftlichkeit dieser Verkehrsmittel maßgeblich sichern. Bislang wurden die dafür relevanten planungsintensiven Prozesse in Zugwerkstätten mit herkömmlichen statischen Planungssystemen durchgeführt. Diese Systeme bilden zwar eine Grundlage für die Organisation der Instandhaltungsarbeiten, sind jedoch nicht in der Lage, optimierte Instandhaltungsprogramme zu erstellen. Infolgedessen treten während der tatsächlichen Durchführung der Instandhaltungsmaßnahmen häufig ungeplante Zusatzaufgaben auf, die den Arbeitsablauf in den Instandhaltungswerkstätten stören und sowohl die wirtschaftliche als auch die betriebliche Effizienz beeinträchtigen. Es hat sich gezeigt, dass solche Störungen häufig zu erhöhten Instandhaltungskosten, längeren Durchlaufzeiten und einer verminderten Anlageneffizienz führen. Daher besteht ein wachsender Bedarf, bei der Instandhaltungsplanung und -steuerung im öffentlichen Verkehrssektor von statischen zu dynamischeren, datengesteuerten Ansätzen überzugehen. Künstliche Intelligenz (KI) bietet in dieser Hinsicht ein erhebliches Potenzial, da sie die Entwicklung von Vorhersagefähigkeiten, dynamischer Planung und intelligenter Ressourcenzuweisung erleichtert. Zu diesem Zweck werden in dieser Studie bestehende Forschungsarbeiten zu KI Ansätzen für die Wartungsplanung und -steuerung in diesen Bereichen des öffentlichen Verkehrs systematisch untersucht und bewertet. Anhand einer deskriptiven Analyse werden neue Forschungstrends vorgestellt, während eine detaillierte Inhaltsanalyse die Ergebnisse der relevantesten Artikel in diesem Bereich zusammenfasst. Die Analyseergebnisse zeigen, dass sich die meisten Forschungsarbeiten isoliert auf bestimmte Aspekte wie die Vorhersage der verbleibenden Nutzungsdauer und die Fehlerdiagnose konzentrieren. In der Praxis umfasst die Wartungsplanung und -steuerung jedoch eine komplexe Koordination vieler Aktivitäten (z B Inspektion, Ressourcenzuweisung und Personal und Aufgabenplanung usw.). Daher sind die Autoren der Ansicht, dass noch holistischere Lösungen erforderlich sind, die Instandhaltungsaktivitäten und Werkstattbeschränkungen integrieren und über Einzelkomponentenvorhersagen hinaus zu einer Optimierung der Instandhaltung auf Systemebene führen, die in realen Instandhaltungsbetrieben validiert werden kann. Darüber hinaus wird in diesem Studienbericht ein konzeptioneller Rahmen vorgestellt der die Planung und Steuerung von Instandhaltungsaufträgen in Instandhaltungswerkstätten des öffentlichen Nahverkehrs verbessern soll. Der vorgeschlagene Rahmen dient als Grundmodell für den Einsatz von KI Technologien zur Reduzierung von Ineffizienzen, zur Verbesserung der Terminplanung und letztlich zur Unterstützung einer zeitnahen und kosteneffizienten Instandhaltungsplanung und -steuerung für Züge. N2 - Maintenance of public transport assets e.g., trains, buses, and airplanes has a major impact on their availability, safety and cost-effectiveness. To date, planning-intensive processes in train maintenance workshops have been carried out with traditional static planning systems. While these systems have provided a foundation for organizing maintenance work, they are unable to generate optimized maintenance programs. As a result, unplanned additional tasks frequently arise during the actual execution of the maintenance operations, thereby disrupting workflows in maintenance workshops and reducing both economic and operational efficiency. It has been demonstrated that such disruptions often lead to increased maintenance costs, longer lead times, and reduced plant efficiency. Therefore, there is a growing need to shift from static to more dynamic, data-driven approaches for maintenance planning and control in public transport sectors. Artificial Intelligence (AI) offers significant potential in this regard, as it facilitates the development of predictive capabilities, dynamic scheduling, and intelligent resource allocation. To this aim, this study therefore systematically reviews and examines existing research on AI approaches for maintenance planning and control across these public transport sectors. Descriptive analysis is performed to present emerging research trends, while detailed content analysis synthesizes the findings of the most relevant articles in the domain. The analysis results show that most of the research focuses on the specific aspects such as predicting the remaining useful life and faults diagnoses, etc., in isolation. However, in practice, maintenance planning and control involves a complex coordination of many activities (e.g., inspection, resource allocation, and workforce & tasks scheduling, etc.). Therefore, the authors believe that there is still need for more holistic solutions that can integrate maintenance activities and shopfloor constraints, moving beyond single-component predictions to system-level maintenance optimization that could be validated in real maintenance facilities. Furthermore, this study report presents conceptual framework designed to enhance maintenance order planning and control in public transport maintenance workshops. The proposed framework serves as a foundational model for utilizing the AI technologies to reduce inefficiencies, improve scheduling, and ultimately support timely and cost-effective maintenance planning and control strategies for trains. T2 - AI-based maintenance planning and control of public transport : status quo and conceptual considerations KW - Instandhaltungsplanung KW - Instandhaltungssteuerung KW - KI KW - Öffentliche Verkehrsmittel KW - AI KW - Maintenance control KW - Maintenance planning KW - Public transportation Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:co1-opus4-71612 PB - Brandenburgische Technische Universität Cottbus-Senftenberg CY - Cottbus ER - TY - GEN A1 - Ahatsi, Emmanuel A1 - Winkler, Herwig A1 - Olanrewaju, Oludolapo T1 - Modified teaching-learning based optimisation for resilience and sustainability in the renewable energy industry T2 - 2025 IEEE 13th International Conference on Smart Energy Grid Engineering (SEGE) N2 - Renewable energy sector resilience ensures sustainability and reliability together with cybersecurity features for present-day power networks. Growing wind and solar and hybrid distributed generation (RHDG) systems create numerous network problems, including power system instability, cyber security risks, and operational efficiency problems. The implementation of Modified Teaching-Learning Based Optimisation (MTLBO) serves as an improvement method for resolving power flow management issues and enhancing fault tolerance while ensuring energy security. MTLBO serves to maximize renewable energy deployment while minimizing power losses and improving resistance against cyber-physical security threats. The integration of decentralized microgrid design, realtime monitoring, and artificial intelligence to identify anomalies enhances system resilience. Multiple smart grid security measures are implemented through Blockchain technology to combat potential cyberattacks which endanger grid operations. The research demonstrates how implementing MTLBO and advanced cybersecurity strategies for OPF enables sustainable development of renewable energy infrastructure. The proposed method delivers secure, efficient, and adaptive power management that enables a resilient renewable energy power infrastructure for future readiness. KW - Optimal Power Flow (OPF) KW - RHDG KW - Particle Swarm Optimisation (PSO) KW - Modified TLBO (MTLBO) KW - Distributed Energy Resources (DER) KW - Teaching-Learning Based Optimisation (TLBO) Y1 - 2025 U6 - https://doi.org/10.1109/SEGE65970.2025.11203381 SP - 225 EP - 230 PB - Institute of Electrical and Electronics Engineers (IEEE) CY - Piscataway, NJ ER - TY - CHAP A1 - Abdulghani, Tamer A1 - Winkler, Herwig T1 - Artificial intelligence as a driver for the digital transformation of business models T2 - Smart production for sustainability : proceedings of the International Conference for Production Research Asia Pacific Region 2025 N2 - Digital transformation is forcing companies to adapt their existing business mod-els to new technological developments and market requirements. A significant challenge is the effective application of innovation digital technologies to enhance operational efficiency and achieve competitive advantage. This paper investigates the impact of digitalization and artificial intelligence (AI) on business models, highlighting the key success factors for transformation and addressing the chal-lenges arising throughout the process. Empirical insights from semi-structured in-terviews with relevant actors from the research and practice are presented in this paper. The Gioia method was used to analyse the interview data in order to obtain a meaningful data structure. The findings identify three primary aggregate dimen-sions that influence the transformation of business models, namely operational ef-ficiency and technological feasibility, organizational culture change, and strategic competitiveness and future security. Digital technologies have the potential to in-crease efficiency and facilitate the transformation of existing business models. However, challenges related to data management, process optimization and cost concerns are faced by companies. In addition, the digital transformation process is frequently accompanied by a cultural shift that is often accompanied by unreal-istic expectations, resistance to change, and identified skills gaps. This necessi-tates effective leadership and change management to facilitate the navigation of these obstacles. Moreover, AI can serve as a powerful competitive advantage, fa-cilitating scalable innovation. These technological, cultural and strategic factors require addressing in order to facilitate the successful integration of AI in a com-pany's transformation strategy. KW - Artificial intelligence KW - Digital transformation KW - Business model Y1 - 2026 SN - 978-981-95-5819-3 U6 - https://doi.org/10.1007/978-981-95-5819-3_11 SN - 2731-4170 SP - 78 EP - 83 PB - Springer Nature CY - Singapore ET - 1 ER - TY - GEN A1 - Abdulghani, Tamer A1 - Winkler, Herwig T1 - Assessing the maturity of digital decarbonization in the manufacturing sector T2 - LogForum : scientific journal of logistics N2 - Organizations face the challenge of making strategic decisions to build a carbon-neutral and competitive future through the adoption of digital technologies. This pressure arises from rapidly evolving global market conditions and the need to achieve required emission reductions. For a successful deployment of Industry 4.0 technologies to support decarbonization, it is crucial for organizations to assess their competencies and establish a clear path forward. Digital maturity models are effective frameworks for measuring an organization's digital capabilities, and several such models have been developed. However, existing models often overlook environmental dimensions, focusing primarily on profit-driven growth. To address this gap, a Digital Decarbonization Maturity Model has been developed. This paper aims to evaluate the model’s effectiveness and applicability by assessing the current state of digital decarbonization within organizations and identifying opportunities for advancement, drawing on empirical insights from a focus group. KW - Digital decarbonization KW - Maturity model KW - Manufacturing sector KW - Case study Y1 - 2026 U6 - https://doi.org/10.17270/J.LOG.001282 SN - 1734-459X VL - 22 IS - 1 SP - 15 EP - 27 PB - Wyzsza Szkola Logistyki CY - Poznan ER - TY - GEN A1 - Abdulghani, Tamer A1 - Tugra Yilmaz, Oguzkan A1 - Winkler, Herwig T1 - The role of information sharing in supply chain collaboration : impacts and emerging opportunities T2 - Procedia computer science N2 - Effective supply chain management is essential for competitive advantage in an increasingly complex global marketplace, and information sharing between partners is becoming a strategic necessity. This paper aims to investigate information sharing practices within the supply chain of the fast-moving consumer goods sector. Specifically, it explores the challenges associated with these practices and their influence on collaboration among partners. Additionally, it analyzes the practical effects of technology and Industry 4.0 on these processes, emphasizing their impact on enhancing or reconfiguring supply chain collaboration. As a methodological approach, semi-structured interviews were conducted with three key supply chain actors: a supplier, a logistics service provider, and a retailer. The Gioia method was employed to analyze the interview data, thereby generating a meaningful data structure. This resulted in a conceptual model demonstrating the relationship between information sharing and collaborative supply chain performance, providing practical insights for enhancing integration. KW - Information sharing KW - Supply chain collaborations KW - Opportunities KW - Conceptual model Y1 - 2026 U6 - https://doi.org/10.1016/j.procs.2026.02.224 SN - 1877-0509 VL - 277 SP - 1859 EP - 1868 PB - Elsevier BV CY - Amsterdam ER - TY - GEN A1 - Hayat, Mubashir A1 - Rüster, Moritz A1 - Winkler, Herwig T1 - AI-based maintenance order planning and control of trains : status quo and conceptual considerations T2 - Procedia computer science N2 - Maintenance of trains has a major impact on their availability, safety and cost-effectiveness. To date, planning-intensive processes in train maintenance workshops have been carried out with traditional static planning systems. While these systems have provided a foundation for organizing maintenance work, they are unable to generate optimized maintenance programs. As a result, unplanned additional tasks frequently arise during the actual execution of the maintenance operations, thereby disrupting workflows in maintenance workshops and reducing both economic and operational efficiency. It has been demonstrated that such disruptions often lead to increased maintenance costs, longer lead times, and reduced plant efficiency. Therefore, there is a growing need to shift from static to more dynamic, data-driven approaches for maintenance planning and control in the railway sector. Artificial Intelligence (AI) offers significant potential in this regard, as it facilitates the development of predictive capabilities, dynamic scheduling, and intelligent resource allocation. To this aim, this work presents a conceptual framework designed to enhance maintenance order planning and control in train maintenance workshops. The proposed framework serves as a foundational model for utilizing the AI technologies to reduce inefficiencies, improve scheduling, and ultimately support timely and cost-effective maintenance planning and control strategies for trains. The framework remains conceptual at this stage, which is the main limitation of the work; however, as future work, its practical effectiveness will be explored through implementation in trains maintenance workshop and empirical validation. KW - Artificial intelligence KW - Maintenance planning KW - Maintenance control KW - Conceptual framework KW - Train maintenance Y1 - 2026 U6 - https://doi.org/10.1016/j.procs.2026.02.286 SN - 1877-0509 VL - 277 SP - 2495 EP - 2504 PB - Elsevier BV CY - Amsterdam ER -