Try it live -> spc-analyzer.streamlit.app — no install; the piston-ring
reference dataset below is in examples/.
A statistical process control (SPC) and process-capability analysis tool for manufacturing engineering — built to produce defensible engineering statements, not just charts:
"Cpk = 1.09 from within-subgroup sigma, but the individuals chart shows an N1 outlier and an N2 run — this Cpk is not trustworthy evidence of future performance until the process is stabilized."
It is intended for quality/manufacturing/process engineers who need SPC charts, Cp/Cpk vs Pp/Ppk analysis, stability assessment, and audit-ready reports from production measurement data.
- Control charts: X̄–R, X̄–S, and I–MR with proper 3σ limits from the published control-chart constants (never plain ±3·stdev of the plotted data).
- Run-rule engine: full Western Electric (4 rules) and Nelson (8 rules) sets, kept distinct (WE4 = 8-in-a-row ≠ Nelson N2 = 9-in-a-row), with structured violations: rule ID, name, affected observations, direction, explanation. Overlapping violations are preserved.
- Capability vs performance, never interchanged: Cp/Cpk from within-subgroup sigma (R̄/d₂, S̄/c₄, pooled, or MR̄/d₂ by regime); Pp/Ppk from overall sigma. One-sided specifications handled correctly (no fake Cp/Pp).
- Stability before capability: SPC rules are evaluated first, and every capability result carries the stability verdict. A high Cpk on an unstable process is explicitly caveated, never silently reported as "capable".
- Sigma level / DPMO (Z-bench from the normal model; the 1.5σ shift is an opt-in, clearly-labeled convention).
- Cpk confidence interval (Bissell 1990 / Montgomery approximation, flagged as approximate, with its assumptions attached).
- Deterministic interpretation engine — rule-based, reproducible, no LLM: statistical conclusions (facts) strictly separated from engineering recommendations (advice), each with a stable trigger code and severity.
- Reliability guards: small-sample, few-subgroups, zero-variation, non-normality (Shapiro–Wilk + Anderson–Darling), and instability warnings — advisory, never suppressing the numbers.
- Reporting: full-precision CSV, 7-sheet Excel workbook, professional PDF report with embedded charts, plus PNG/SVG chart export.
- Streamlit UI with data import (CSV/Excel/manual), cleaning transparency (every excluded row listed with its reason), and configurable thresholds.
Data input (CSV / Excel / manual)
↓
app/data validation & cleaning (per-row records, nothing silently dropped)
↓
app/statistics descriptive stats · control-chart constants · sigma estimators
↓
app/spc control limits (X̄–R, X̄–S, I–MR) · WE/Nelson rule engine
↓
app/capability Cp/Cpk (within σ) · Pp/Ppk (overall σ) · guards · Cpk CI
↓
app/interpret deterministic conclusions ⟂ recommendations
↓
app/reporting CSV · Excel · PDF serialization (no statistics recomputed)
↓
app/ui Streamlit presentation (thin; calls app/services only)
Dependencies point downward only. The statistical core (data,
statistics, spc, capability, interpret) is pure Python with no UI or
I/O dependency and is fully unit-tested. app/services orchestrates the
sequence-of-analysis (validate → charts → stability first → sigma →
capability → interpretation); app/charts renders validated results without
recalculating them; app/reporting serializes without recomputing. Each layer
has exactly one source of truth: limits come from app/spc, capability from
app/capability, charts from app/charts, interpretation from
app/interpret.
The complete statistical methodology — every formula, constant, assumption,
convention, and reference — is documented in
docs/FORMULAS.md.
Requires Python 3.10+. On Windows (PowerShell or git-bash):
git clone <repository-url> spc-analyzer
cd spc-analyzer
# Create and activate a virtual environment
python -m venv .venv
source .venv/Scripts/activate # git-bash
# .venv\Scripts\Activate.ps1 # PowerShell
# source .venv/bin/activate # Linux/macOS
# Install dependencies
pip install -r requirements.txtRun the application:
streamlit run app/ui/streamlit_app.pyThe app opens at http://localhost:8501.
A typical analysis session:
- Upload measurements (section 1) — CSV/Excel file or manual entry; select the measurement column. The cleaning summary shows exactly how many rows are usable and why any were excluded.
- Enter specifications (section 2) — USL and/or LSL, optional nominal, measurement unit, and traceability metadata (characteristic, part, machine, operator).
- Select the SPC chart (sidebar) — X̄–R, X̄–S, or I–MR, subgroup size, rule set (Nelson / Western Electric / both), and within-σ estimator.
- Review stability (section 5) — control charts with CL/UCL/LCL, flagged out-of-control points, and the violations table.
- Review capability (section 6) — Cp/Cpk (within) beside Pp/Ppk (overall), sigma level, DPMO, the Cpk confidence interval, and all reliability warnings.
- Review the interpretation (section 7) — factual statistical conclusions and, separately, engineering recommendations with their triggers.
- Export (section 8) — CSV, Excel, PDF report, or PNG/SVG charts.
examples/pistonrings.csv holds 200 real piston-ring diameter measurements
(40 samples of 5) from an automotive forging process, taken from the qcc
package and originally published as Montgomery's worked X-bar/R example. Because
the reference results are published, the dataset doubles as an end-to-end check
on this implementation. Analyzed as X-bar/R with subgroup size 5:
| quantity | published | this application |
|---|---|---|
| x-bar-bar | 74.001 | 74.00118 |
| R-bar | 0.023 | 0.02276 |
| UCL (X-bar) | 74.014 | 74.01431 |
| LCL (X-bar) | 73.988 | 73.98804 |
Phase I (samples 1-25) is reported stable with no rule violations, Cp 1.70 /
Cpk 1.66; including the Phase II samples the drift is caught as N1/N5/N6 and the
capability verdict is caveated accordingly. See
examples/DATASETS.md.
The repository also ships examples/sample_measurements.csv (58 observations:
a stable process seeded with one gross outlier and a sustained shift at the
end). Analyzed as I–MR with USL = 11.5, LSL = 8.5 (mm), the application
reports:
- Stability: UNSTABLE — rules N1, N2, N4, N5, N6 triggered (the outlier and the tail shift are both caught).
- Capability: Cpk ≈ 1.09 (within), Ppk ≈ 0.88 (overall) — reported but explicitly caveated as unreliable while the process is out of control.
- Headline: "Process is statistically UNSTABLE; capability indices are retained but are not reliable evidence of future performance until the process is brought into control."
The chart image above is the actual output for this dataset.
python -m pytestThe suite currently contains 776 tests (all passing, on Python 3.10 / 3.11 / 3.12 in CI), covering:
- known-answer verification of every statistic against independently hand-computed values (constants are additionally cross-validated against their exact mathematical definitions),
- every WE/Nelson rule triggered by crafted deterministic sequences,
- the Cp/Cpk-vs-Pp/Ppk separation (including a test proving Pp/Ppk cannot be affected by the within-subgroup sigma),
- edge cases: missing/non-numeric values, duplicates, tiny samples, zero variation, one-sided and invalid specifications,
- chart-model fidelity (rendered values bit-identical to the calculators),
- interpretation scenarios asserting on structured codes, not prose,
- full-pipeline integration tests (CSV → analysis → interpretation → CSV/Excel/ PDF), with PDF content verified by text extraction,
- execution of the real Streamlit app via Streamlit's
AppTestharness.
Code-coverage percentages are not claimed because coverage is not currently measured.
- Stability and capability are different questions. SPC control charts ask "is the process statistically stable/predictable?"; capability indices ask "can the stable process meet the specification?". An unstable process should not be treated as reliably capable regardless of its Cpk.
- Capability indices assume the relevant statistical conditions — approximately normal data, rational subgrouping, and statistical control. The application tests and warns about these assumptions but cannot make an invalid assumption true.
- Capability thresholds (e.g. Cpk ≥ 1.33 "capable") are engineering conventions that vary by industry and customer standard; they are configurable in the app and are not universal statistical laws.
- The Cpk confidence interval is a large-sample approximation and is not exact.
- This software is an engineering analysis aid. It does not replace professional engineering judgment, customer-specific quality requirements, or formal measurement-system analysis.
Apache License 2.0 — see LICENSE and NOTICE.
Apache 2.0 was chosen over MIT deliberately: it carries an explicit patent grant (§3) and a fuller warranty/liability disclaimer (§§7–8), which matter for a tool whose output informs manufacturing quality decisions. Every runtime dependency is permissively licensed (BSD / MIT / PSF / Apache-2.0), so there is no copyleft obligation.
Montgomery, D.C., Introduction to Statistical Quality Control, 7th ed.
(Wiley, 2013) · AIAG, Statistical Process Control Reference Manual, 2nd ed.
(2005) · NIST/SEMATECH e-Handbook of Statistical Methods, §6 · David &
Nagaraja, Order Statistics, 3rd ed. (Wiley, 2003) · Bissell, A.F., "How
reliable is your capability index?", Applied Statistics 39(3), 1990 · ASTM,
Manual on Presentation of Data and Control Chart Analysis (STP-15D).
Per-formula citations: docs/FORMULAS.md.