PPAP (Production Part Approval Process)

PPAP is the gateway between product development and production. No automotive supplier ships production parts without an approved PPAP package. The 18 elements range from design records and material certifications to dimensional results and process capability studies.

Elements 8 (MSA) and 9 (dimensional results with capability studies) are where statistical analysis matters most. Customers typically require Cpk ≥ 1.33 for standard characteristics and Cpk ≥ 1.67 for safety/critical characteristics. These numbers must come from a statistically valid process capability study — not a cherry-picked sample.

The pressure to achieve these thresholds creates a real temptation to manipulate data: sorting out-of-spec parts before measurement, increasing sample sizes until the numbers look good, or assuming normality when the data clearly is not. A robust statistical method that produces honest capability indices from whatever data the process actually generates makes PPAP submissions both faster and more defensible.

EntropyStat addresses the two biggest pain points in PPAP capability studies. First, production trials for PPAP often involve limited runs — sometimes fewer than 30 parts for a new process. Traditional Cpk requires large samples for stable estimates, forcing suppliers to either produce extra parts or submit indices with wide confidence intervals. EntropyStat's EGDF produces reliable capability estimates from as few as 5–8 measurements, making small-run PPAPs practical.

Second, PPAP dimensional data is frequently non-normal. Injection-molded dimensions often skew toward the mold parting line, machined features drift with tool wear, and assembled dimensions follow distributions driven by stack-up tolerances. EntropyStat computes capability indices from the actual learned distribution, not a forced Gaussian fit — so the Cpk you submit reflects real process performance.

Because EntropyStat layers on top of existing SPC/MES systems, suppliers can run their standard data collection workflows and add entropy-based analysis as a validation step. No need to replace the QMS infrastructure — just supplement it with more accurate statistical methods where the data warrants it.