Statistical Process Control (SPC)

SPC is the backbone of manufacturing quality management. Every IATF 16949-certified facility runs SPC on critical dimensions, and most MES/ERP systems include SPC modules. The core idea is simple: track your process on a control chart, and react only when special causes appear.

The problem is that traditional SPC was designed in the 1920s for high-volume, normally distributed processes. Modern manufacturing often involves short runs, mixed product families, and process data that does not follow a normal distribution. Applying Shewhart rules to non-normal data generates excessive false alarms, leading to alarm fatigue and wasted investigation time.

Effective SPC requires a statistical foundation that matches your actual data characteristics — not one that forces assumptions onto the data and hopes for the best.

EntropyStat reimagines SPC by replacing the normality assumption with entropy-based distribution fitting. Instead of computing control limits as mean ± 3σ (which only works for normal data), EntropyStat derives control limits directly from the EGDF — the actual learned distribution of your process.

This eliminates the most common failure mode of traditional SPC: false alarms on non-normal data. When your process distribution is skewed (as with surface roughness, concentricity, or cycle times), entropy-based control limits match the real tail behavior rather than imposing symmetric limits that trigger unnecessary investigations.

Because the EGDF is robust to outliers, a single aberrant measurement does not distort the entire control limit calculation. Traditional SPC recalculates limits including outliers unless an engineer manually removes them — a subjective step that entropy-based methods avoid entirely.