@misc{SchmidWinkler, author = {Schmid, Stefan and Winkler, Herwig}, title = {Hybrid Production Management System in the Context of Industry 4.0}, series = {2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)}, journal = {2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)}, isbn = {978-1-6654-8687-3}, doi = {10.1109/IEEM55944.2022.9990000}, pages = {1573 -- 1577}, abstract = {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.}, language = {en} } @misc{SchmidWinkler, author = {Schmid, Stefan and Winkler, Herwig}, title = {Empirical Findings on the Need of Industrial Production Management Systems in the Context of Enhanced Digitalization}, series = {2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)}, journal = {2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {979-8-3503-2315-3}, doi = {10.1109/IEEM58616.2023.10406474}, pages = {1406 -- 1410}, abstract = {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.}, language = {en} } @misc{SchmidWinkler, author = {Schmid, Stefan and Winkler, Herwig}, title = {Empirical Findings on the Current State of Industrial Production Management Systems in the Context of Increasing Digitalization}, series = {Industrial Engineering and Applications - Europe : 11th International Conference, ICIEA-EU 2024, Nice, France, January 10-12, 2024}, journal = {Industrial Engineering and Applications - Europe : 11th International Conference, ICIEA-EU 2024, Nice, France, January 10-12, 2024}, publisher = {Springer Nature Switzerland}, address = {Cham}, isbn = {9783031581120}, issn = {1865-1348}, doi = {10.1007/978-3-031-58113-7_3}, pages = {23 -- 34}, abstract = {Increasing digitization and automation are currently bringing a revolution to many processes and sectors of the economy. The industry of production in particular is undergoing a fundamental transformation through the implementation of digital and networked system elements. In production control, traditionally based on established methods and experience, advanced technologies such as artificial intelligence are opening up entirely new options. Artificial intelligence's ability to analyze large amounts of data in real time and make instant decisions from it brings fresh perspectives to production control. Until now, there have been only a few surveys that examine the current status as well as future requirements for production control. Our research focuses on the current state of IT systems used to support decision-makers in production control. It also sheds light on the requirements needed to improve these systems, especially in light of advancing technological developments and increasing digitalization. The article is dedicated to the results and provides an overview of future expectations.}, language = {en} } @incollection{SchmidWinkler, author = {Schmid, Stefan and Winkler, Herwig}, title = {{\"U}berlegungen zur Konzeption eines smarten Produktionsleitsystems}, series = {Handbuch Industrie 4.0 und Digitale Transformation}, booktitle = {Handbuch Industrie 4.0 und Digitale Transformation}, publisher = {Springer Gabler}, address = {Wiesbaden}, isbn = {978-3-658-36874-6}, doi = {10.1007/978-3-658-36874-6_17-1}, pages = {23}, language = {de} } @misc{SchmidWinkler, author = {Schmid, Stefan and Winkler, Herwig}, title = {Evaluation of Future Enhancement Requirements in Production Control with Regard to the Deployment of Artificial Intelligence}, series = {Proceedings of the 11th International Conference on Industrial Engineering and Applications}, journal = {Proceedings of the 11th International Conference on Industrial Engineering and Applications}, publisher = {Springer Nature Singapore}, address = {Singapore}, isbn = {978-981-9764-92-1}, issn = {2195-4356}, doi = {10.1007/978-981-97-6492-1_7}, pages = {79 -- 91}, abstract = {In an age when digitization and the integration of artificial intelligence in production processes are becoming increasingly important, the selection and prioritization in the enhancement of production control functions has become a key challenge for companies. This article is dedicated to the application of the Analytic Hierarchy Process for the systematic selection and prioritization of enhancement options in production control, based on findings and data from empirical studies that examined the status quo and future needs. The focus is on structuring and weighting future enhancements. The findings are used for future considerations of a smart production management system that can be used for decision support as well as for auto-control.}, language = {en} } @techreport{SchmidWinkler, author = {Schmid, Stefan and Winkler, Herwig}, title = {Die Produktionssteuerung im digitalen Zeitalter aus Sicht der industriellen Praxis}, publisher = {Brandenburgische Technische Universit{\"a}t, IKMZ, Universit{\"a}tsbibliothek}, address = {Cottbus ; Senftenberg}, doi = {10.26127/BTUOpen-6500}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-65009}, pages = {110}, abstract = {-Im Zeitraum von M{\"a}rz bis Juni 2023 wurde vom Lehrstuhl f{\"u}r Produktionswirtschaft der BTU Cottbus Senftenberg die Studie mit dem Titel „Die Produktionssteuerung im digitalen Zeitalter" als Online Umfrage durchgef{\"u}hrt. 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 K{\"u}nstliche Intelligenz wird in sehr starkem Maße als sinnvoll erachtet, um die Effizienz am Shop Floor bei hoher Komplexit{\"a}t der Leistungsprozesse auch in Zukunft aufrechtzuerhalten. - Die ausgewerteten Daten zeigen, dass die aktuellen Systeme der Produktionssteuerung die Nutzer nicht in dem Ausmaß unterst{\"u}tzen, 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 Unterst{\"u}tzung bevorzugt, w{\"a}hrend eine vollautonome KI seltener favorisiert wird. Die Studienergebnisse zeigen eine wachsende Zustimmung und Offenheit f{\"u}r den Einsatz von KI als Werkzeug in der Produktionssteuerung, jedoch unter menschlicher Aufsicht und Beteiligung - Die Herausforderungen der Integration von K{\"u}nstlicher Intelligenz in die Mensch Maschine Interaktion umfassen technische und soziale Aspekte. Um diesen Herausforderungen zu begegnen, sind geeignete L{\"o}sungsans{\"a}tze notwendig, wie die Entwicklung sicherer Schnittstellen, robuste Datenschutzmaßnahmen aber auch Nutzerakzeptanzf{\"o}rderung durch Aufkl{\"a}rung und Schulung - Innerhalb eines hybriden Rahmens, wo der Mensch mit der KI arbeitet, m{\"u}ssen klare Regeln f{\"u}r die Interaktion definiert werden. Die KI soll die menschliche Entscheidungsfindung interaktiv unterst{\"u}tzen, neue Perspektiven bieten und alternative Vorschl{\"a}ge 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.}, language = {de} } @incollection{WinklerSchmid, author = {Winkler, Herwig and Schmid, Stefan}, title = {Empirical Findings on the Status Quo of Industrial Production Management Systems in the Context of Advancing Digitalization}, series = {Proceedings of the 10th International Conference on Industrial Engineering and Applications (ICIEA 2023), April 4-6, 2023, Phuket, Thailand}, booktitle = {Proceedings of the 10th International Conference on Industrial Engineering and Applications (ICIEA 2023), April 4-6, 2023, Phuket, Thailand}, editor = {Tang, Loon-Ching}, publisher = {IOS Press}, address = {Berlin}, isbn = {978-1-64368-408-6}, issn = {2352-751X}, doi = {10.3233/ATDE230029}, pages = {44 -- 54}, language = {en} }