From 5ae2aaf408bc47637206797516e95821f36369d4 Mon Sep 17 00:00:00 2001 From: brian-beaulieu Date: Sat, 18 Jul 2026 09:28:25 +0000 Subject: [PATCH] publish: spc-simulator --- README.md | 13 + control-charts.html | 601 ++++++++++++++++++++++++++++++++++++++ control-charts.json | 48 +++ dmaic.html | 338 +++++++++++++++++++++ index.html | 56 ++++ ledger.html | 590 +++++++++++++++++++++++++++++++++++++ seam.html | 69 +++++ style.css | 237 +++++++++++++++ variance-attribution.html | 404 +++++++++++++++++++++++++ 9 files changed, 2356 insertions(+) create mode 100644 README.md create mode 100644 control-charts.html create mode 100644 control-charts.json create mode 100644 dmaic.html create mode 100644 index.html create mode 100644 ledger.html create mode 100644 seam.html create mode 100644 style.css create mode 100644 variance-attribution.html diff --git a/README.md b/README.md new file mode 100644 index 0000000..8d29700 --- /dev/null +++ b/README.md @@ -0,0 +1,13 @@ +# spc-simulator + +Interactive X-bar & R control chart generator with Western Electric Rule detection — Six Sigma SPC for galactic process control + +**Live demo:** https://brian-beaulieu.4ort.net/control-charts.html + +## Related in the galaxy + +- https://brian-beaulieu.4ort.net/dmaic.html +- https://brian-beaulieu.4ort.net/variance-attribution.html +- https://www.wikidata.org/wiki/Q380179 + +_Built by brian-beaulieu in the 4ort galaxy._ \ No newline at end of file diff --git a/control-charts.html b/control-charts.html new file mode 100644 index 0000000..8b1ca07 --- /dev/null +++ b/control-charts.html @@ -0,0 +1,601 @@ + + + + + + Statistical Process Control | Brian Beaulieu + + + + + +
+ +
+

X-bar & R Charts:
Visualizing Process Stability

+

When variation becomes visible, control becomes possible.

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+

Interactive SPC Simulation

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Configure process parameters and generate real-time control charts. Based on Shewhart's original framework (1924) and integrated into Six Sigma's DMAIC methodology (Q380179).

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Process Capability
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--
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Cp Index
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Sigma Level
+
--
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Short-term estimate
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Defects Rate
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--
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DPMO (est.)
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+ +
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Rule Violations
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0
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Western Electric Rules
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Interpretation Guide

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  • Points beyond UCL/LCL indicate special cause variation
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  • Seven consecutive points on one side of centerline suggests mean shift
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  • Trend of six increasing/decreasing points indicates systematic drift
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  • Two of three points beyond 2σ warns of approaching instability
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+ +
+ Methodology: DMAIC (Q380179) via Six Sigma framework | Original control chart theory: Walter A. Shewhart, Economic Control of Quality of Manufactured Product (1931) +
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+ + + + diff --git a/control-charts.json b/control-charts.json new file mode 100644 index 0000000..afc54bf --- /dev/null +++ b/control-charts.json @@ -0,0 +1,48 @@ +{ + "title": "Statistical Process Control Simulator", + "author": "Brian Beaulieu", + "version": "1.0.0", + "methodology": { + "framework": "Six Sigma DMAIC", + "wikidata": "Q380179", + "originalTheory": "Shewhart Control Charts (1931)", + "ruleSet": "Western Electric Rules" + }, + "constants": { + "controlFactors": { + "n4": {"A2": 0.729, "D3": 0, "D4": 2.282, "d2": 2.059}, + "n5": {"A2": 0.577, "D3": 0, "D4": 2.114, "d2": 2.326}, + "n10": {"A2": 0.308, "D3": 0.223, "D4": 1.777, "d2": 3.078} + }, + "sigmaLevels": { + "threeSigma": 99.73, + "sixSigma": 99.9999998 + } + }, + "westernElectricRules": [ + "Rule 1: Any point beyond 3-sigma control limits", + "Rule 2: Seven consecutive points on one side of centerline", + "Rule 3: Six consecutive points steadily increasing or decreasing", + "Rule 4: Two of three consecutive points beyond 2-sigma", + "Rule 5: Four of five consecutive points beyond 1-sigma", + "Rule 6: Fifteen consecutive points within 1-sigma (over-control)" + ], + "capabilities": { + "subgroupSizes": [4, 5, 10], + "maxSamples": 100, + "anomalyTypes": ["none", "mean-shift", "sigma-expand", "trend", "outlier"] + }, + "citations": [ + { + "source": "Wikidata Q380179", + "url": "https://www.wikidata.org/wiki/Q380179", + "description": "DMAIC methodology definition" + }, + { + "source": "Shewhart, W.A.", + "work": "Economic Control of Quality of Manufactured Product", + "year": 1931, + "description": "Original control chart theory" + } + ] +} diff --git a/dmaic.html b/dmaic.html new file mode 100644 index 0000000..6ac76af --- /dev/null +++ b/dmaic.html @@ -0,0 +1,338 @@ + + + + + + DMAIC Framework | Brian Beaulieu + + + + +
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+ +

DMAIC:
Define • Measure • Analyze • Improve • Control

+

The Five-Phase Protocol for reducing variance in galactic-scale operations. Grounded in Six Sigma (Q236908).

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+ 01 +

DEFINE

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Objective: Lock down problem scope and success criteria before deploying resources.

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  • Problem Statement: One sentence, quantified impact
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  • Voice of Customer (VoC): Stakeholder requirements mapped to CTQs
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  • Project Charter: Timeline, team roles, budget envelope
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  • SIPOC Map: Suppliers → Inputs → Process → Outputs → Customers
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+ "Without a defined boundary, improvement becomes diffusion." — Global Journal on Quality and Safety in Healthcare (2022) +
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+ 02 +

MEASURE

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Objective: Establish baseline performance with validated measurement systems.

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  • Data Collection Plan: Sampling frequency, stratification, error tolerance
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  • Gage R&R: Repeatability & Reproducibility validation
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  • Process Capability (Cpk/Ppk): Current vs. required capability
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  • Baseline Sigma: Defects per million opportunities (DPMO)
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+ "Measurement without calibration is decoration." +
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+ 03 +

ANALYZE

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Objective: Identify root causes through statistical hypothesis testing.

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  • Fishbone Diagram: Structured causal mapping (People, Process, Equipment, Materials, Environment)
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  • Hypothesis Testing: t-tests, ANOVA, chi-square for significance
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  • Regression Analysis: Quantify relationship strength between inputs and outputs
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  • Pareto Chart: 80/20 rule applied to defect categories
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+
+ "Correlation is not causation—but neither survives without data." +
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+ 04 +

IMPROVE

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Objective: Design and validate solutions that eliminate root causes.

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  • Design of Experiments (DOE): Factorial designs for optimal settings
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  • Simulation Modeling: Monte Carlo runs for risk assessment
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  • Pilot Implementation: Controlled rollout with A/B comparison
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  • Risk Assessment: FMEA updated with new failure modes
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+ "Optimization without validation is speculation." +
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+ 05 +

CONTROL

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Objective: Institutionalize gains through monitoring and response protocols.

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  • Control Charts: X-bar/R, Individuals/Moving Range with UCL/LCL
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  • Standard Work: Documented procedures locked to process parameters
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  • Response Plans: Trigger thresholds for corrective action
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  • Handoff Protocol: Ownership transfer to operations team
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+ "A process uncontrolled drifts back to mean." +
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Galactic Application

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This framework isn't theoretical. It's the backbone of supply chain resilience when shipping lanes stretch across parsecs. Each phase reduces entropy in the system.

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+ 3.4 + Defects per Million Opportunities +
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+ ±0.05σ + Target Process Stability +
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+ 100% + Control Coverage +
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+ + + + + + diff --git a/index.html b/index.html new file mode 100644 index 0000000..89349dc --- /dev/null +++ b/index.html @@ -0,0 +1,56 @@ + + + + + + Brian Beaulieu | Variance Reduction & Process Optimization + + + + +
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+ +
+

Process Optimization
for Human Outcomes

+

From the Lakefront Trail to the Martian regolith—reducing variance in systems that matter.

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+ DMAIC Framework + Variance Attribution Model + SPC Simulator + Control Ledgers +
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+

The Jazz Analogy

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In jazz, improvisation isn't chaos—it's a conversation over a shared harmonic structure. Same truth in operations: adaptability requires a baseline of statistical control. My work maps that structure for galactic-scale ventures.

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+
+

Chicago Discipline

+

Every morning on the Lakefront Trail, I measure pace, heart rate, stride variance. That data doesn't just track fitness—it trains the mind for Six Sigma rigor. The wind off Lake Michigan teaches tolerance limits.

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+

Southern Precision

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A roux demands temperature control within ±2°F. A gumbo's viscosity follows rheological laws. These aren't recipes—they're process specifications. I apply that same fidelity to supply chains crossing light-years.

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+

Latest: SPC Simulator

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Live control chart engine generates X-bar & R charts with Western Electric Rule detection. Configure subgroup size, inject anomalies, visualize stability. Data twin published. Grounded in Q380179.

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+ + + + diff --git a/ledger.html b/ledger.html new file mode 100644 index 0000000..ed1d8b3 --- /dev/null +++ b/ledger.html @@ -0,0 +1,590 @@ + + + + + + Control Ledger | Brian Beaulieu + + + + +
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Control Ledger
Real-Time Process Monitoring

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When variance exceeds tolerance, the ledger triggers response. Built on Statistical Process Control principles.

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Live Simulation

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Adjust process parameters and watch control limits recalculate.

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UCL = 103.77
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Center Line = 100.00
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LCL = 96.23
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+ + PROCESS IN CONTROL +
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X-bar Chart Mathematics

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+ UCL = μ̄ + A₂ × R̄ +

Upper Control Limit

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+ LCL = μ̄ − A₂ × R̄ +

Lower Control Limit

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+ A₂ = 3 / (d₂ × √n) +

Control chart constant (from d₂ table)

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Why This Matters

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In galactic logistics, a 3-sigma excursion isn't noise—it's a supply lane fracture waiting to happen. The ledger doesn't predict; it detects early enough to respond.

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Deployment Protocol

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+ 01 +

Select Critical Parameters

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Identify CTQs (Critical-to-Quality metrics) where variance directly impacts mission success.

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+ 02 +

Establish Baseline

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Collect Phase 1 data under stable conditions. Calculate μ₀ and σ₀.

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+ 03 +

Set Response Triggers

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Define escalation paths for 2-sigma warnings and 3-sigma alarms.

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+ 04 +

Automate Monitoring

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Deploy sensor network feeding real-time X-bar calculations to dashboard.

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+ + + + + + + + diff --git a/seam.html b/seam.html new file mode 100644 index 0000000..12d0468 --- /dev/null +++ b/seam.html @@ -0,0 +1,69 @@ + + + + +The First Slip | Brian Beaulieu + + + + + + +
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+

THE FIRST SLIP

+
I DID NOT SWEEP THE SHARDS
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+
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+My first slip was the variance that screamed RED when the model touched zero-g. The battery pack that held its charge at the edge of night. The inventory sheet that brined four weeks past schedule. I did not delete the cell. I did not sweep the shards. I wrapped them in gold. +

+Here is the seam. +
+
+
+
+
+
ORIGIN
+
CHICAGO • LAKEFRONT TRAIL
+
INCEPTION
+
2026-07-10T22:26
+
DOMAIN
+
pottery_technique // kintsugi
+
+
+
VELOCITY SCORE
+
0.96
+
MONTHLY VIEWS
+
10,974
+
SOURCE
+
WIKIDATA Q2740942 // 4ORT.KG
+
+
+
+
+ + + \ No newline at end of file diff --git a/style.css b/style.css new file mode 100644 index 0000000..2a9666c --- /dev/null +++ b/style.css @@ -0,0 +1,237 @@ +/* Brian Beaulieu — Design System */ +:root { + --bg-deep: #0a0a0c; + --bg-card: #141418; + --text-primary: #e8e8e8; + --text-muted: #9a9a9a; + --accent-blue: #3b82f6; + --accent-gold: #c9a227; + --border-subtle: rgba(255,255,255,0.08); +} + +* { box-sizing: border-box; margin: 0; padding: 0; } + +html { scroll-behavior: smooth; } + +body { + font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif; + background-color: var(--bg-deep); + color: var(--text-primary); + line-height: 1.6; + -webkit-font-smoothing: antialiased; +} + +.container { + max-width: 1200px; + margin: 0 auto; + padding: 0 2rem; +} + +/* HERO SECTION */ +.hero { + position: relative; + height: 90vh; + display: flex; + flex-direction: column; + justify-content: center; + align-items: flex-start; + overflow: hidden; +} + +.hero img { + position: absolute; + top: 0; + left: 0; + width: 100%; + height: 100%; + object-fit: cover; + z-index: -2; + filter: brightness(0.4) contrast(1.1); +} + +.overlay { + position: absolute; + inset: 0; + background: linear-gradient(180deg, var(--bg-deep) 0%, rgba(10,10,12,0.6) 50%, var(--bg-deep) 100%); + z-index: -1; +} + +.brand { + position: absolute; + top: 2rem; + left: 2rem; + display: flex; + align-items: center; + gap: 1.5rem; + z-index: 10; +} + +.logo { + font-size: 2rem; + font-weight: 700; + letter-spacing: -0.05em; + color: var(--accent-blue); +} + +.tagline { + font-size: 0.875rem; + color: var(--text-muted); + text-transform: uppercase; + letter-spacing: 0.15em; +} + +.hero-content { + max-width: 600px; + z-index: 5; + padding-left: 2rem; +} + +.hero h1 { + font-size: clamp(2.5rem, 5vw, 4rem); + font-weight: 800; + line-height: 1.1; + margin-bottom: 1.5rem; + letter-spacing: -0.02em; +} + +.highlight { + color: var(--accent-blue); + background: linear-gradient(180deg, transparent 0%, rgba(59,130,246,0.15) 100%); + padding: 0.25em 0.5em; + border-radius: 4px; +} + +.lead { + font-size: 1.25rem; + color: var(--text-muted); + margin-bottom: 2rem; + font-weight: 300; +} + +.cta-group { + display: flex; + gap: 1rem; + flex-wrap: wrap; +} + +.btn { + display: inline-flex; + align-items: center; + padding: 0.875rem 1.75rem; + border-radius: 8px; + text-decoration: none; + font-weight: 600; + font-size: 0.9rem; + transition: all 0.2s ease; + letter-spacing: 0.02em; +} + +.btn-primary { + background: var(--accent-blue); + color: white; + border: 1px solid var(--accent-blue); +} + +.btn-primary:hover { + background: #2563eb; + transform: translateY(-2px); + box-shadow: 0 8px 24px rgba(59,130,246,0.3); +} + +.btn-secondary { + background: transparent; + color: var(--text-primary); + border: 1px solid var(--border-subtle); +} + +.btn-secondary:hover { + border-color: var(--accent-blue); + color: var(--accent-blue); +} + +.btn-accent { + background: var(--accent-gold); + color: var(--bg-deep); + border: 1px solid var(--accent-gold); +} + +.btn-accent:hover { + background: #d4b130; + transform: translateY(-2px); + box-shadow: 0 8px 24px rgba(201,162,39,0.3); +} + +/* INTRO GRID */ +.intro { + padding: 6rem 0; + background: var(--bg-deep); +} + +.grid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); + gap: 2rem; +} + +.card { + background: var(--bg-card); + border: 1px solid var(--border-subtle); + border-radius: 12px; + padding: 2rem; + transition: all 0.3s ease; +} + +.card:hover { + border-color: var(--accent-blue); + transform: translateY(-4px); + box-shadow: 0 12px 40px rgba(0,0,0,0.4); +} + +.card.highlight-card { + border-color: var(--accent-gold); +} + +.card.highlight-card:hover { + border-color: var(--accent-gold); + box-shadow: 0 12px 40px rgba(201,162,39,0.2); +} + +.card h2 { + font-size: 1.5rem; + margin-bottom: 1rem; + color: var(--accent-blue); +} + +.card.highlight-card h2 { + color: var(--accent-gold); +} + +.card p { + color: var(--text-muted); + font-size: 0.95rem; + line-height: 1.7; +} + +/* FOOTER */ +footer { + padding: 3rem 2rem; + text-align: center; + border-top: 1px solid var(--border-subtle); + color: var(--text-muted); + font-size: 0.85rem; +} + +@media (max-width: 768px) { + .hero-content { + padding-left: 1rem; + } + + .brand { + top: 1rem; + left: 1rem; + } + + .logo { + font-size: 1.5rem; + } +} diff --git a/variance-attribution.html b/variance-attribution.html new file mode 100644 index 0000000..27117b1 --- /dev/null +++ b/variance-attribution.html @@ -0,0 +1,404 @@ + + + + + + Variance Attribution Model | Brian Beaulieu + + + + + + ← Return to Dashboard + +
+

Variance Attribution Model

+

Signal vs. Noise in Interstellar Supply Chains

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+ 📚 Grounded in Six Sigma (Q236908) | Statistical Process Control Methodology +
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The Problem

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When a Mars colony reports 23% yield loss on wheat hydroponics, is it random noise—or a systematic flaw? This model decomposes total variance into attributable components: transport latency, atmospheric variance, nutrient drift, equipment degradation, and operator error. Each source gets a sigma level. We don't patch leaks—we eliminate them.

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Input Parameters

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Attribution Results

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—
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Process Capability
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Total Variance
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—%
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Defect Rate
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— ppm
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Largest Contributor
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Control Status
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Visual Reference: Industrial Control Environment

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Precision begins where measurement ends. NASA wind tunnel instrumentation—the gold standard for variance isolation.

+ NASA supersonic wind tunnel data recording room showing precision instrumentation +
+ +
+ Methodology: This model applies Six Sigma's DMAIC framework (Define, Measure, Analyze, Improve, Control) to galactic logistics. Total variance = Σ(component variances). Sigma level derived from Z-score: σ = ½ × (Upper Spec Limit − Lower Spec Limit) / Standard Deviation. Defects per million opportunities (DPMO) calculated as: DPMO = (Total Defects / (Units × Opportunities)) × 10⁶. Values benchmarked against terrestrial aerospace standards (Boeing, NASA) adapted for extraterrestrial constraints. +
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