Product Quality Optimization: Using Machine Learning to Reduce Waste and Improve Overall Facility Efficiency

Most manufacturers are sitting on the data that explains their defects. It's just scattered: machine parameters in one system, quality results in another, work orders in a third, none of them speaking to each other.

This white paper walks through how we connected those systems for a large plastics manufacturer running 59 molding machines across two facilities, then turned the unified data into predictions operators could act on mid-shift. The result was less scrap, less downtime, and a measurable gain in efficiency the plant could take straight to the bottom line.

What you'll learn:

• Turn disconnected plant data into a single source of truth. How we linked MES, SPC, and ERP data across molding and assembly into one training set.
• Find quick wins before you build a model. Early analysis caught defects at shift changes and a mold missing maintenance, fixed before any model went live.
• Predict quality, then explain it. How SHAP, PDP, and Friedman's H turned predictions into specific, trustworthy setting recommendations.
• Optimize the whole line, not just one machine. Recommendations balanced component quality against downstream assembly efficiency.

A 3% efficiency gain, no new sensors, built on data the plant already had. That's what happens when disconnected systems become a single predictive view of the floor. If your facility is sitting on the same untapped data, we'd like to help you put it to work.

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