Within the data.pilot.3 initiative, the Institute of Lightweight Engineering and Polymer Technology at TU Dresden is researching how to enhance Life Cycle Assessment (LCA) by using primary manufacturing data from machines and sensors. Instead of relying mainly on generic default values, we aim to calculate manufacturing-related footprints using measured data such as machine energy consumption, cycle times, and scrap rates. To do this, we are defining the requirements for a unified data management approach that can handle complex engineering environments.

Our technical focus is to connect diverse sources, such as manufacturing equipment, sensors, and monitoring software, into a shared, consistent data foundation. A key aspect is not just collecting data, but validating its quality before it enters the managed data store and attaching metadata (context such as where, when, and how the data was generated). This enables transparent documentation and traceability, so we can track where each value came from and how it was processed. In the long term, this granular data foundation will enable more sustainable and cost-effective decisions, including the prediction of economic and ecological indicators. For example, reliable data streams could allow production schedules to be adjusted using weather forecasts to better align energy-intensive steps with times of high renewable electricity availability.

To support transparency and reuse, we will document and publish data and workflows following the FAIR principles (Findable, Accessible, Interoperable, Reusable). Solutions developed in this project will be transferred into data.knowledge as best-practice guidance for the wider research community.