Research Centre of Digital Labs & Production
The research group Digital Labs & Production connects people, spaces and processes in the life sciences. From mixed-reality digital twins via progressive web applications to machine-to-machine interfaces, we connect physical and digital worlds through data and analytics.
The centre combines specific methodological and technological expertise in the digitization and virtualization of laboratories, processes and production facilities in the life sciences. This includes, on the one hand, the networking of devices, processes and people using interfaces, data pipelines and data management and, on the other hand, the mapping of physical systems and infrastructures to models and simulation environments. Digital twins are a good example of how these topics interact.
Three research groups are active in this field.
Strategic, tactical and operational process optimization using modelling and simulation tools are the focus of the research group. This includes the modelling and simulation of the dynamics of heterogeneous, complex systems as well as the investigation, optimization and control of their behaviour.
The research group specializes in the development of systems for data aggregation, transformation and management. Processing pipelines are designed and implemented to take data from their sources (e.g. graphic user interfaces, wearable sensors, measuring probes) through preparation steps (incl. quality control and homogenization) to storage solutions, analysis and visualization of results and insights.
The research group combines dynamic physical structures with digital environments using sensor, actuators, and edge computing. The group supports automation and decentralized intelligent data processing in the life sciences by connecting people, machines, and contexts.
PE(K)O Sustain – Physically modified oils as sustainable alternative to tropical fats for the baking and sweet goods industry
Predictive Analytics for Hospital Supply Chain Management
Preliminary/feasibility study for a comprehensive AI-based solution in hospitals for demand-oriented assortment and efficient inventory Management ordering with a focus on security of supply and cost Efficiency planning of reprocessing planning of personnel deployment cost and income planning ...
Designing Business Models for the IoT
This project aims at developing a business model simulation software for evaluating IoT business models. The holistic approach leverages advanced simulation methods and will create new revenue opportunities for Swiss manufacturing companies.
A top-down indicator of lean-green alignment in small and medium-sized enterprises
To address the challenge from global warming, the UNFCCC has given rise to several initiatives to channel financial capital into decarbonization efforts. Among investors, demand increasing for investment vehicles that offer both environmental sustainability as well as economic performance benefits. We aim to design ...
Predicitve Waste Management for SBB Train Stations
We develop a system to optimize the waste collection and disposal on SBB's train stations. The new system will use a container fill level sensor network, a novel waste accumulation forecasting algorithm, and state of the art methods for simulation-based tour-planning.
2021(2), pp. 5.
Available from: https://doi.org/10.21256/zhaw-23748
Igwe, Kay C.; Lao, Patrick J.; Vorburger, Robert S.; Banerjee, Arit; Rivera, Andres; Chesebro, Anthony; Laing, Krystal; Manly, Jennifer J.; Brickman, Adam M.,
Magnetic Resonance Imaging.
85, pp. 71-79.
Available from: https://doi.org/10.1016/j.mri.2021.10.007
2020(2), pp. 5.
Available from: https://doi.org/10.21256/zhaw-21455
Meier, Irene B.; Lao, Patrick J.; Gietl, Anton; Vorburger, Robert; Gutierrez, José; Holland, Christopher M.; Guttmann, Charles R.G.; Meier, Dominik S.; Buck, Alfred; Nitsch, Roger M.; Hock, Christoph; Unschuld, Paul G.; Brickman, Adam M.,
Cerebral Circulation - Cognition and Behavior.
Available from: https://doi.org/10.1016/j.cccb.2020.100001
Lebensmittel-Industrie: Fachmagazin für das Management der Nahrungsmittel- und Getränkeindustrie.
2020(5/6), pp. 18-19.
Available from: https://doi.org/10.21256/zhaw-20194