TY - GEN A1 - Schmid, Stefan A1 - Winkler, Herwig T1 - Empirical Findings on the Need of Industrial Production Management Systems in the Context of Enhanced Digitalization T2 - 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) N2 - Since the framework conditions of manufacturing companies change dynamically, production control must react to this and be adaptive and dynamically designed. Our article addresses the need of industrial production management systems in the course of enhanced digitization. The aim is to examine the extent to which traditional systems for controlling and optimizing production systems have been supplemented by Industry 4.0 concepts. In the course of the scarcity of resources and the shortage of labor, the human factor is once again coming to the fore. Against this background, the interaction between users / humans and artificial intelligence applications will be the main focus. The result should give an indicator how this connection must be considered in the future and what should artificial intelligence do in the context of production control. The findings will be the basis for future considerations of a smart production management system, which can be used for decision support as well as for auto-control. KW - Production Management System KW - Production Control KW - Manufacturing Control KW - Production KW - Industry 4.0 Y1 - 2023 SN - 979-8-3503-2315-3 SN - 979-8-3503-2316-0 U6 - https://doi.org/10.1109/IEEM58616.2023.10406474 SP - 1406 EP - 1410 PB - IEEE CY - Piscataway, NJ 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 - Schmid, Stefan A1 - Winkler, Herwig T1 - Hybrid Production Management System in the Context of Industry 4.0 T2 - 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) N2 - In our contribution, we consider how the Digital Twin of the production system can be combined with Artificial Intelligence methods. The result of these considerations should be a hybrid production management system, which can be used for decision support as well as for self-control. On the one hand, the Digital Twin is providing information and simulation for the optimal decision-making to the decision support system (human interaction). On the other hand, Artificial Intelligence is taking over some elements and tasks with automatic control (independent of human interaction). If the result is out of tolerance or if a random sample of the result is audited, the decision support system is validating the Artificial Intelligence result for execution (by human interaction). The hybrid production management system is to be used to control production processes and generates learning effects. The extension by methods of Artificial Intelligence opens possibilities to process and to master certain tasks independently of human interaction. The presented approach should relieve and support decision makers in production. By performing various actions automatically and achieving partial auto-control, the reactivity of linked processes can increase. The provision of decisive information favors decision quality and time-effectiveness. Y1 - 2022 SN - 978-1-6654-8687-3 SN - 978-1-6654-8688-0 U6 - https://doi.org/10.1109/IEEM55944.2022.9990000 SP - 1573 EP - 1577 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 -