prof.Dr.-Ing. S. Thiede (Sebastian)

I´m interested in the planning and operation of manufacturing systems/factories. This includes the interaction of different processes over the process chain but also with other factory elements like technical building services. Besides technological developments, digital methods and tools play an important role. Thereby, obviously the technical and economic performance but also environmental and social perspectives need to be taken into account:

Smart manufacturing solutions based on Cyber Physical Production System (CPPS) framework (e.g. data acquisition strategies, machine learning and factory simulation, decision support and control)

Methods for factory planning and operation (e.g. layout planning, planning and control, quality management, lean management)

Innovative manufacturing systems/factory concepts (e.g. flexible production systems, scale up scenarios)

Sustainable manufacturing (e.g. energy and resource efficiency)

Human factors in manufacturing systems (e.g. occupational health and safety, ergonomics, workplace design)

Application areas range from mechanical/mechatronic products in general, batteries (see also https://www.utwente.nl/tcabt), surface technologies but also food industry.

A more extensive overview can be found here: https://www.utwente.nl/en/research/researchers/featured-scientists/thiede/

I´m also the chairman for the department Design, Production and Management in the Engineering Technology faculty, https://www.utwente.nl/en/et/dpm/

Engineering & Materials Science

# Energy Efficiency
# Environmental Impact
# Industrial Plants
# Industry
# Life Cycle
# Lithium-Ion Batteries
# Planning
# Sustainable Development

Faculty of Engineering Technology (ET), Manufacturing Systems (MS)

Thiede, S. (2023). Advanced energy data analytics to predict machine overall equipment effectiveness (OEE): a synergetic approach to foster sustainable manufacturing. Procedia CIRP, 116, 438-443. https://doi.org/10.1016/j.procir.2023.02.074

Berger, M. , & Thiede, S. (2023). Circularity of water - good for conscience or the environment? A deeper look into the water footprint of factories. Procedia CIRP, 116, 125-130. https://doi.org/10.1016/j.procir.2023.02.022

Flick, D., Vruna, M., Bartos, M., Ji, L., Herrmann, C. , & Thiede, S. (2023). Machine learning based internal and external energy assessment of automotive factories. CIRP annals : manufacturing technology, 72(1), 21-24. https://doi.org/10.1016/j.cirp.2023.04.038

Zietsch, J., Kulaga, R., Held, H., Herrmann, C. , & Thiede, S. (2023). Multi-layer edge resource placement optimization for factories. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-022-02071-3

Bienia, S., Demes, M., Dreger, J., Dröder, K. , & Thiede, S. (2022). Functional analysis of an Optical Real Time Locating System in production environments. Procedia CIRP, 107, 1107-1111. https://doi.org/10.1016/j.procir.2022.05.116

Tumbajoy, L. M., Muñoz-Añasco, M. , & Thiede, S. (2022). Enabling Industry 4.0 impact assessment with manufacturing system simulation: an OEE based methodology. Procedia CIRP, 107, 681-686. https://doi.org/10.1016/j.procir.2022.05.045

Chemweno, P. , Sullivan, B. P., Bermperidis, G. , & Thiede, S. (2022). Exploring the Added-Value of Integrating Real-Time Location Systems for Tracking Critical Maintenance Tools. Procedia CIRP, 107, 902-907. https://doi.org/10.1016/j.procir.2022.05.082

Sullivan, B. P., Yazdi, P. G., Suresh, A. , & Thiede, S. (2022). Digital Value Stream Mapping: Application of UWB Real Time Location Systems. Procedia CIRP, 107, 1186-1191. https://doi.org/10.1016/j.procir.2022.05.129

Gebler, M., Juraschek, M. , Thiede, S., Cerdas, F., & Herrmann, C. (2022). Defining the “Positive Impact” of socio-technical systems for absolute sustainability: a literature review based on the identification of system design principles and management functions. Sustainability Science, 17, 2597-2613. https://doi.org/10.1007/s11625-022-01168-1

Thiede, S. (2022). Cyber Physical Production Systems and Their Role for Decarbonization of Industry. In O. Inderwildi, & M. Kraft (Eds.), Intelligent Decarbonisation: Can Artificial Intelligence and Cyber-Physical Systems Help Achieve Climate Mitigation Targets? (pp. 75-86). ( Lecture Notes in Energy (LNE); Vol. 86). Springer. https://doi.org/10.1007/978-3-030-86215-2_8

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