Researcher  Ole Preisig

Goal

Develop digital models and AI-supported optimisation methods that enable manufacturers to analyse and improve energy efficiency, productivity, and emissions simultaneously.
The overarching goal is to support data-driven decision making for economically and environmentally sustainable manufacturing systems.

Research focus

Manufacturing is under increasing pressure to reduce energy consumption and emissions while maintaining productivity and profitability. However, energy optimisation in factories is complex because energy use depends not only on individual machines but also on the interaction between processes, production planning, and system design.

My research focuses on developing data-driven methods to model, analyse, and optimise energy consumption in manufacturing systems. By combining real energy data, discrete event simulation, and artificial intelligence, I aim to identify how production system design and operational strategies influence energy efficiency and productivity. The goal is to enable transparent decision-support tools that help engineers and managers optimise factory operations while reducing costs and environmental impact.