# Risk-Calibrated Process Capability Approval with Finite Samples

arXiv: https://arxiv.org/abs/2603.14479 · HTML: https://mlsanity.com/paper/2603.14479/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2026.

## Built on by (most cited first, 1 of 1)

- [Bayesian Deployment Approval for Learned Landing Controllers under Finite Rollout Validation](https://mlsanity.com/paper/2605.27720/) (2026)

## Abstract (arXiv)

Process capability indices such as Cpk are widely used in manufacturing to support supplier qualification, pilot-build release, and production approval. In practice, approval decisions are often based on deterministic threshold rules of the form widehatCpk ≥ C0. Because widehatCpk is estimated from finite samples, however, such decisions are inherently stochastic, especially when the true capability lies near the approval threshold. This paper develops a risk-calibrated decision framework for process capability approval that explicitly accounts for estimation uncertainty and asymmetric operational loss. Capability approval is formulated as a binary statistical decision problem, leading to a rule of the form widehatCpk ≥ C0 + k,SE(widehatCpk), where the calibration constant k is determined either by a tolerable failure probability or by a false-accept/false-reject cost ratio. The resulting formulation unifies several commonly used procedures, including deterministic thresholding, lower confidence bound rules, and probability-based approval rules, and naturally extends them to cost-sensitive decision rules derived from asymmetric operational loss. Simulation experiments and an industrial case study show that risk calibration primarily affects near-threshold decisions, improves approval stability, and can substantially reduce expected operational loss when false acceptance is more costly than false rejection.