Presents a deterministic process capability model showing improved output variability assessment in engineering, suggesting enhanced quality control methods.
Statistical Process Control (SPC) monitors the probabilistic behavior of measured outputs, yet in engineered processes output variability is commonly the manifestation of evolving deterministic system states observed at limited resolution. This paper formalizes a deterministic process capability framework by modeling hidden process states, deriving output variability as a projection of state uncertainty through a sensitivity map, and linking specification conformance to geometric containment of a state uncertainty ellipsoid between specification hyperplanes. A deterministic capability index is defined and compared with classical Cp / Cpk. The framework further yields a predictive early-warning indicator and a safe operating horizon remaining obtained by propagating state uncertainty forward in time. The result is a unified mathematicalbridge between SPC, state estimation, and predictive control, expressed in quality-engineering terms.
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Ozcan Ozok (2026) studied this question.
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