First Pass Yield (FPY)

Final yield can be deceiving. A process with 99% final yield sounds excellent — but if 20% of units required rework to achieve that number, the process is consuming 20% extra labor, materials, and cycle time. FPY exposes the hidden factory that final yield conceals.

FPY is a leading indicator of cost and delivery performance. Low FPY means unpredictable cycle times (rework adds variable delay), higher work-in-process inventory (rework units clog the line), and quality risk (reworked units may have different characteristics than first-pass units).

For multi-step processes, the rolled throughput yield (RTY = FPY₁ × FPY₂ × ... × FPYₙ) reveals the cumulative impact. Five steps at 95% FPY each produce an RTY of only 77% — nearly one in four units needs rework somewhere along the line. RTY is the single most important metric for lean manufacturing initiatives targeting waste reduction.

EntropyStat improves FPY prediction by providing accurate per-characteristic conformance probabilities. Each dimensional or performance characteristic contributes an independent probability of first-pass failure, and the product of all conformance probabilities estimates the overall FPY. This calculation is only as accurate as the individual probability estimates.

Using the EGDF, EntropyStat estimates each characteristic's nonconformance probability from its actual distribution shape, not a normal approximation. For a 15-characteristic part where three characteristics are non-normal, the normal-based FPY estimate might be 92% while the EGDF-based estimate is 87% — a 5-point difference that significantly impacts production planning.

The ELDF adds diagnostic power to FPY analysis. When FPY drops, the question is: which characteristic is responsible, and why? The ELDF can detect whether a historically capable characteristic has developed subpopulations (perhaps from tool wear creating bimodal dimensions), providing root cause direction that aggregate FPY tracking alone cannot offer.