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About

mfgQC is built and maintained by Brantner Solutions.

It exists to give manufacturing practitioners quality-control analysis they can trust and defend — not just compute. The three design pillars are invariants, not preferences:

  1. Statistical guardrails. Analyses check their own assumptions and report the outcome; they never silently switch methods.
  2. Practitioner-oriented. The public surface assumes domain knowledge, not a statistics or programming background.
  3. Auditable by construction. Results are immutable and carry a hash-chained provenance history, so a reported number can be traced back to raw data and verified against tampering.

Provenance of the methods themselves

mfgQC is validated in two independent layers. Regression tests pin it to its build oracles (Montgomery; AIAG MSA 4th ed.; Lawson, Design and Analysis of Experiments with R). A separate correctness suite pins each analysis to an independent source it was not built against — the NIST/SEMATECH e-Handbook and StRD certified datasets, the R qcc/SixSigma packages, and scipy/statsmodels computed in-test. No expected value in that suite is ever taken from a prior mfgQC run. See the Bibliography for the full source list.

License

MIT. See LICENSE.