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 - Schmid, Stefan A1 - Winkler, Herwig T1 - Evaluation of Future Enhancement Requirements in Production Control with Regard to the Deployment of Artificial Intelligence T2 - Proceedings of the 11th International Conference on Industrial Engineering and Applications N2 - 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. Y1 - 2024 SN - 978-981-9764-92-1 U6 - https://doi.org/10.1007/978-981-97-6492-1_7 SN - 2195-4356 SP - 79 EP - 91 PB - Springer Nature Singapore CY - Singapore 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 -