@misc{AbdulghaniWinkler, author = {Abdulghani, Tamer and Winkler, Herwig}, title = {A SWOT analysis approach for assessing the sustainable transition in the coal energy sector in Germany}, series = {Procedia computer science}, volume = {263}, journal = {Procedia computer science}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {1877-0509}, doi = {10.1016/j.procs.2025.07.010}, pages = {76 -- 81}, abstract = {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.}, language = {en} } @incollection{WinklerSeebacher, author = {Winkler, Herwig and Seebacher, Gottfried}, title = {Verbesserung der Robustheit von Supply Chains durch Einsatz eines hierarchischen Flexibilit{\"a}tsmanagements}, series = {Risikomanagement in Supply Chains : Compliance und resiliente Lieferketten - Realisierung von Chancen durch Risikosteuerung}, booktitle = {Risikomanagement in Supply Chains : Compliance und resiliente Lieferketten - Realisierung von Chancen durch Risikosteuerung}, editor = {Bungartz, Oliver}, edition = {3., v{\"o}llig neu bearbeitete Auflage}, publisher = {Erich Schmidt Verlag}, address = {Berlin}, isbn = {978-3-503-24068-5}, doi = {10.37307/b.978-3-503-24069-2}, pages = {268 -- 291}, abstract = {Das aktuelle Unternehmensumfeld weist eine hohe Volatilit{\"a}t bei vielen Faktoren auf, gepaart mit Technologiespr{\"u}ngen und erheblichen Diskontinuit{\"a}ten in den Kunden- und Lieferantenbeziehungen. Dies f{\"u}hrt zu vielf{\"a}ltigen Risiken entlang der gesamten Wertsch{\"o}pfungskette. Die auftretenden Risiken verursachen bei den Unternehmen einer bestimmten Supply Chain betr{\"a}chtliche Probleme zur Aufrechterhaltung der geplanten Performance. Aufgrund verketteter Prozesse in Produktion und Logistik f{\"u}hren Probleme bei den Wertsch{\"o}pfungsaktivit{\"a}ten einzelner Partner zu starken Beeintr{\"a}chtigungen im gesamten Betriebsgeschehen. In dieser Situation erlangt die Robustheit der Supply Chain eine zentrale Bedeutung. Robustheit kann allgemein als eine F{\"a}higkeit angesehen werden, die dazu beitr{\"a}gt, dass durch St{\"o}rungen von innen oder außen, keine oder nur geringe Abweichungen bei der geplanten Performance entstehen. Die Robustheit ist eine komplexe Gr{\"o}ße in der sich Flexibilit{\"a}t, Agilit{\"a}t, Resistenz und Resilienz vereinen. Nach unserem Verst{\"a}ndnis spielt vor allem die Flexibilit{\"a}t eine besondere Rolle f{\"u}r Supply Chains, um eine hohe Robustheit zu erlangen. Wir wollen in diesem Beitrag schwerpunktm{\"a}ßig den konzeptionellen Aufbau eines hierarchischen Flexibilit{\"a}tsmanagement von der Supply Chain {\"u}ber die einzelnen Unternehmen bis in die Produktionssysteme vorstellen, mit dem die Robustheit ganzheitlich verbessert werden kann.}, language = {de} } @misc{ThomasWinkler, author = {Thomas, Kevin and Winkler, Herwig}, title = {Extended reality for process visualization in cloud manufacturing : a systematic review}, series = {Procedia CIRP}, volume = {136}, journal = {Procedia CIRP}, editor = {Mourtzis, Dimitris}, publisher = {Elsevier B.V.}, address = {Amsterdam}, issn = {2212-8271}, doi = {10.1016/j.procir.2025.08.111}, pages = {648 -- 653}, abstract = {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.}, language = {en} } @techreport{WinklerHayatRuester, author = {Winkler, Herwig and Hayat, Mubashir and R{\"u}ster, Moritz}, title = {KI-basierte Instandhaltungsplanung und -steuerung im {\"o}ffentlichen Verkehr: Status quo und konzeptionelle {\"U}berlegungen}, publisher = {Brandenburgische Technische Universit{\"a}t Cottbus-Senftenberg}, address = {Cottbus}, organization = {BTU Cottbus - Senftenberg, Lehrstuhl f{\"u}r Produktionswirtschaft}, doi = {10.26127/BTUOpen-7161}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-71612}, pages = {84}, abstract = {Durch eine sorgf{\"a}ltige Instandhaltung von Z{\"u}gen, Bussen und Flugzeugen l{\"a}sst sich die Verf{\"u}gbarkeit, Betriebssicherheit und Wirtschaftlichkeit dieser Verkehrsmittel maßgeblich sichern. Bislang wurden die daf{\"u}r relevanten planungsintensiven Prozesse in Zugwerkst{\"a}tten mit herk{\"o}mmlichen statischen Planungssystemen durchgef{\"u}hrt. Diese Systeme bilden zwar eine Grundlage f{\"u}r die Organisation der Instandhaltungsarbeiten, sind jedoch nicht in der Lage, optimierte Instandhaltungsprogramme zu erstellen. Infolgedessen treten w{\"a}hrend der tats{\"a}chlichen Durchf{\"u}hrung der Instandhaltungsmaßnahmen h{\"a}ufig ungeplante Zusatzaufgaben auf, die den Arbeitsablauf in den Instandhaltungswerkst{\"a}tten st{\"o}ren und sowohl die wirtschaftliche als auch die betriebliche Effizienz beeintr{\"a}chtigen. Es hat sich gezeigt, dass solche St{\"o}rungen h{\"a}ufig zu erh{\"o}hten Instandhaltungskosten, l{\"a}ngeren Durchlaufzeiten und einer verminderten Anlageneffizienz f{\"u}hren. Daher besteht ein wachsender Bedarf, bei der Instandhaltungsplanung und -steuerung im {\"o}ffentlichen Verkehrssektor von statischen zu dynamischeren, datengesteuerten Ans{\"a}tzen {\"u}berzugehen. K{\"u}nstliche Intelligenz (KI) bietet in dieser Hinsicht ein erhebliches Potenzial, da sie die Entwicklung von Vorhersagef{\"a}higkeiten, dynamischer Planung und intelligenter Ressourcenzuweisung erleichtert. Zu diesem Zweck werden in dieser Studie bestehende Forschungsarbeiten zu KI Ans{\"a}tzen f{\"u}r die Wartungsplanung und -steuerung in diesen Bereichen des {\"o}ffentlichen Verkehrs systematisch untersucht und bewertet. Anhand einer deskriptiven Analyse werden neue Forschungstrends vorgestellt, w{\"a}hrend 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 Aktivit{\"a}ten (z B Inspektion, Ressourcenzuweisung und Personal und Aufgabenplanung usw.). Daher sind die Autoren der Ansicht, dass noch holistischere L{\"o}sungen erforderlich sind, die Instandhaltungsaktivit{\"a}ten und Werkstattbeschr{\"a}nkungen integrieren und {\"u}ber Einzelkomponentenvorhersagen hinaus zu einer Optimierung der Instandhaltung auf Systemebene f{\"u}hren, die in realen Instandhaltungsbetrieben validiert werden kann. Dar{\"u}ber hinaus wird in diesem Studienbericht ein konzeptioneller Rahmen vorgestellt der die Planung und Steuerung von Instandhaltungsauftr{\"a}gen in Instandhaltungswerkst{\"a}tten des {\"o}ffentlichen Nahverkehrs verbessern soll. Der vorgeschlagene Rahmen dient als Grundmodell f{\"u}r den Einsatz von KI Technologien zur Reduzierung von Ineffizienzen, zur Verbesserung der Terminplanung und letztlich zur Unterst{\"u}tzung einer zeitnahen und kosteneffizienten Instandhaltungsplanung und -steuerung f{\"u}r Z{\"u}ge.}, language = {de} } @misc{AhatsiWinklerOlanrewaju, author = {Ahatsi, Emmanuel and Winkler, Herwig and Olanrewaju, Oludolapo}, title = {Modified teaching-learning based optimisation for resilience and sustainability in the renewable energy industry}, series = {2025 IEEE 13th International Conference on Smart Energy Grid Engineering (SEGE)}, journal = {2025 IEEE 13th International Conference on Smart Energy Grid Engineering (SEGE)}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {Piscataway, NJ}, doi = {10.1109/SEGE65970.2025.11203381}, pages = {225 -- 230}, abstract = {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.}, language = {en} } @incollection{AbdulghaniWinkler, author = {Abdulghani, Tamer and Winkler, Herwig}, title = {Artificial intelligence as a driver for the digital transformation of business models}, series = {Smart production for sustainability : proceedings of the International Conference for Production Research Asia Pacific Region 2025}, booktitle = {Smart production for sustainability : proceedings of the International Conference for Production Research Asia Pacific Region 2025}, edition = {1}, publisher = {Springer Nature}, address = {Singapore}, isbn = {978-981-95-5819-3}, issn = {2731-4170}, doi = {10.1007/978-981-95-5819-3_11}, pages = {78 -- 83}, abstract = {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.}, language = {en} } @misc{AbdulghaniWinkler, author = {Abdulghani, Tamer and Winkler, Herwig}, title = {Assessing the maturity of digital decarbonization in the manufacturing sector}, series = {LogForum : scientific journal of logistics}, volume = {22}, journal = {LogForum : scientific journal of logistics}, number = {1}, publisher = {Wyzsza Szkola Logistyki}, address = {Poznan}, issn = {1734-459X}, doi = {10.17270/J.LOG.001282}, pages = {15 -- 27}, abstract = {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.}, language = {en} } @misc{AbdulghaniTugraYilmazWinkler, author = {Abdulghani, Tamer and Tugra Yilmaz, Oguzkan and Winkler, Herwig}, title = {The role of information sharing in supply chain collaboration : impacts and emerging opportunities}, series = {Procedia computer science}, volume = {277}, journal = {Procedia computer science}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {1877-0509}, doi = {10.1016/j.procs.2026.02.224}, pages = {1859 -- 1868}, abstract = {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.}, language = {en} } @misc{HayatRuesterWinkler, author = {Hayat, Mubashir and R{\"u}ster, Moritz and Winkler, Herwig}, title = {AI-based maintenance order planning and control of trains : status quo and conceptual considerations}, series = {Procedia computer science}, volume = {277}, journal = {Procedia computer science}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {1877-0509}, doi = {10.1016/j.procs.2026.02.286}, pages = {2495 -- 2504}, abstract = {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.}, language = {en} }