Units_of_Measure_Harmonization-intelligence-platform

Why we included this project

Unit of measure errors are a quiet source of real losses in manufacturing and procurement, and this KNIME workflow goes after them directly. It reads CSV or Excel files, runs an XGBoost classifier over 60+ engineered features to flag suspicious values, then checks each one against NIST-compliant physics-based conversion rules before correcting it. A Q-learning agent handles the routine fixes on its own, while the built-in dashboard shows confidence scores, root-cause analytics, and anything that still needs a human reviewer. That pairing of ML detection with rule-based verification is what makes the results defensible, which matters when you need auditable data cleaning rather than a black-box model. It runs in KNIME Analytics Platform 4.5+, so teams already comfortable with visual workflow tooling can pick it up quickly, and the approach carries over to domains beyond manufacturing.