MaaSiveTwin
MaaSiveTwin aims to develop a digital platform to address the complex challenges associated with the production and logistics of critical raw materials for clean energy technologies.
Factsheet
- Schools involved School of Engineering and Computer Science
- Institute(s) Institute for Intelligent Industrial Systems (I3S)
- Research unit(s) I3S / Prozessoptimierung in der Fertigung
- Funding organisation Europäische Union
- Duration (planned) 01.04.2024 - 31.03.2028
- Head of project Michael Stalder
-
Project staff
Michael Stalder
Jon Kunz
Yanis Lupberger -
Partner
ELEVENES DOO SUBOTICA
ROCK TECH CONSULTING GMBH
Battronics sp.z.o.o (Leading House)
Technische Universität Hamburg
POLYTECHNEIO KRITIS
RTD TALOS LIMITED - Keywords Manufacturing as a Service, Discrete Event Simulation, Probabilistic modelling, Critical Raw Materials
Situation
The increasing demand for critical raw materials, driven by emerging technologies such as rechargeable batteries and wind turbines, highlights the urgent need to optimise supply chains, anticipate mismatches in demand and supply, and ensure sustainable and transparent practices. The project aims to develop a real-time monitoring and prediction platform that can identify and mitigate supply chain disruptions caused by various factors, including geopolitical events and policy changes, by leveraging advanced digital tools and predictive analytics. By facilitating Manufacturing as a Service (MaaS) for critical raw material processing and production, MaaSiveTwin intends to boost supply chain efficiency, minimise downtime and facilitate the shift towards a more sustainable and robust economy, in accordance with EU regulations and green initiatives.
Course of action
The project involves systematic collection of data from primary sources covering supply and demand, as well as on ethical and sustainability aspects, geopolitical events, and tariffs. This data is integrated into a digital infrastructure designed to enable the automated collection, storage, and sharing of data across stakeholders. Based on this foundation, the project will develop a digital representation of critical raw material supply chains. Multiple modelling approaches are applied, including deterministic, probabilistic, and machine learning-based techniques, to enable simulation, forecasting, and scenario analysis. Within this framework, BFH is responsible for developing probabilistic models based on Discrete Event Simulation. These models support the analysis of supply chain dynamics, helping to assess uncertainties, identify risks, and evaluate the impact of disruptions on the overall system.
