In mid-November, experts from TU Wien and the Center for Digital Production (CDP) delivered inspiring insights on how data analysis and AI can unlock a deeper understanding of processes and drive meaningful improvements.

Companies that utilize data and AI to enhance their processes anticipate tangible improvements: reduced scrap and rework, fewer unplanned downtime incidents, and faster, more reliable implementation of data and AI projects. With the procan.do method, organizations can quickly uncover critical weak points in their process and data landscape and create a robust basis for successful analytics and AI initiatives.

These possibilities were demonstrated in practice at the workshop Understanding and Improving Processes with Data Analysis and AI at the Excellence Center for Digital Production (CDP). Participants from production, process consulting, quality management, and administration took part, including both decision-makers and quality assurance officers for data and AI projects. The event was jointly organized by the CDP, TU Wien Informatics, and TU Wien’s Funding Support & Industry Relations (FÖWI).

Keynote presentations showed how data analysis reveals hidden dependencies in complex processes. Using examples from past projects, speakers illustrated reductions in scrap, shorter lead times, improved throughput, stabilized quality, and robust AI applications—core goals of manufacturing analytics. The participants then worked hands-on with procan.do, analyzing a production waste issue: available data, missing information, process mapping. They asked if it was systematic or an outlier, addressing pain points such as recurring quality issues, rising scrap costs, and stalled root-cause analysis due to fragmented data.