SPC Quality AI

A Quality Management System for Statistical Process Control (SPC), Measurement System Analysis (MSA), and Process Capability — powered by AI agents.

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Overview

Quality engineers spend too much time clicking through interfaces and manually interpreting charts. SPC Quality AI automates the tedious parts of quality analysis — just upload your data and ask questions in plain English.

No more:

• Guessing which control chart to use
• Manually calculating control limits
• Repeating the same MSA interpretations

Instead, the AI agents handle:

• Automatic detection of the correct chart type
• Identification of out-of-control points
• Plain-language interpretation of results
• Professional report generation

They’re not perfect, but they’re remarkably efficient assistants for modern quality engineers.

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Features

Statistical Process Control (SPC)

• Control Charts: I-MR, Xbar-R, Xbar-S, P, NP, C, and U charts
• Automatic Chart Selection based on dataset characteristics
• Out-of-Control Detection with statistical rules
• Data Quality Validation before analysis
• Interactive Visualizations via Plotly

Measurement System Analysis (MSA)

• Gage R&R Studies (ANOVA method)
• Bias Studies with significance testing
• Linearity Studies for accuracy verification
• Stability Studies for long-term consistency
• Comprehensive MSA Reports with interpretation

Process Capability Analysis

• Capability Indices: Cp, Cpk, Pp, Ppk, Cpm
• Normality Testing: Anderson–Darling, Shapiro–Wilk, Kolmogorov–Smirnov
• Process Centering and Variation Analysis

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Quick Start

1. Installation

# Clone the repository
git clone https://github.com/M1ndSmith/SPC-AI.git
cd SPC-AI

# Create and activate virtual environment
python -m venv spc_env
source spc_env/bin/activate  # On Windows: spc_env\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Set up your API key
cp env.example .env
# Edit .env and add your GROQ_API_KEY (or key for your chosen LLM)

2. Configure LLM (Optional)

The system uses Groq by default (free tier available). To switch providers, edit agent_config/config.yaml:

llm:
  provider: "groq"        # or "openai", "anthropic"
  model: "llama-3.1-8b-instant"

3. Start the API Server

uvicorn api.main:app --host 0.0.0.0 --port 8000

Server available at: http://localhost:8000

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Using the CLI

The CLI (./spc) provides the easiest way to interact with the agents. It automatically activates the virtual environment.

Basic Commands

# Check server health
./spc health

# Run control chart analysis
./spc chat control-charts "Analyze this data" -f your_data.csv

# Run MSA study
./spc chat msa "Run a Gage R&R study" -f measurement_data.csv

# Run capability analysis
./spc chat capability "Calculate Cp and Cpk with USL=10.5, LSL=9.5" -f process_data.csv

Available Agents

Agent	Purpose
control-charts	Control chart and SPC analysis
msa	Measurement System Analysis
capability	Process Capability Analysis

CLI Options

Option	Short	Description	Default
--file	-f	CSV file to upload	None
--thread-id	-t	Conversation thread ID	cli_session
--user-id	-u	User identifier	cli_user
--timeout		Request timeout (seconds)	120
--verbose	-v	Verbose output with metadata	False
--output	-o	Save response to JSON file	None
--url		API base URL	http://localhost:8000

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CLI Examples

Control Charts

# Basic analysis
./spc chat control-charts "Analyze this data" -f data.csv

# Verbose output
./spc chat control-charts "Check for out-of-control points" -f data.csv -v

# Save results
./spc chat control-charts "Generate full report" -f data.csv -o report.json

MSA Example

./spc chat msa "Conduct a Gage R&R study" -f gage_data.csv

Capability Example

./spc chat capability "Assess capability with USL=10.5, LSL=9.5, target=10.0" -f process_data.csv

Conversational Threads

# Initial message
./spc chat control-charts "Analyze this data" -f data.csv -t analysis_001

# Follow-up queries using same thread
./spc chat control-charts "What are the main issues?" -t analysis_001
./spc chat control-charts "Give me recommendations" -t analysis_001

Advanced Usage

Custom API URL:

./spc --url http://remote-server:8000 chat msa "Run study" -f data.csv

Extended Timeout:

./spc chat control-charts "Analyze large dataset" -f big.csv --timeout 300

Batch Automation:

#!/bin/bash
for file in data/*.csv; do
  echo "Processing $file..."
  ./spc chat control-charts "Analyze and report" -f "$file" -t batch_$(date +%Y%m%d)
done

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API Usage

You can also call the API directly.

Python Example

import requests

response = requests.post(
    "http://localhost:8000/chat/control-charts",
    data={"message": "Analyze this data", "thread_id": "my_session", "user_id": "engineer1"},
    files={"file": open("data.csv", "rb")}
)
print(response.json()["response"])

curl Example

curl -X POST http://localhost:8000/chat/control-charts \
  -F "message=Analyze this data" \
  -F "thread_id=session1" \
  -F "user_id=user1" \
  -F "[email protected]"

Endpoints

Method	Endpoint	Description
GET	/	API overview
GET	/health	Health check
POST	/chat/{agent_id}	Chat with an agent (control-charts, msa, capability)

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Data Format

Each CSV should include the relevant measurement columns. The system will auto-detect column roles but supports explicit naming.

Control Charts

date,measurement
2024-01-01,10.2
2024-01-02,10.1
2024-01-03,9.9

Subgroup Data

subgroup,measurement
1,10.2
1,10.1
1,9.9
2,10.0
2,10.3

MSA (Gage R&R)

Part,Operator,Measurement,Trial
1,A,10.2,1
1,A,10.1,2
1,B,10.3,1
1,B,10.2,2

Process Capability

measurement
10.2
10.1
9.9
10.0

The system is tolerant of alternative column names and will infer structure automatically.

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Sample Data

Control Charts

• spc_individual_in_control.csv
• spc_individual_out_of_control.csv
• spc_subgroup_data.csv
• spc_np_chart_data.csv
• spc_c_chart_data.csv

MSA Studies

• msa_gage_rr_excellent.csv
• msa_gage_rr_poor.csv
• msa_bias_study.csv
• msa_linearity_study.csv
• msa_stability_study.csv

Process Capability

• capability_excellent.csv
• capability_off_center.csv
• capability_high_variation.csv
• capability_skewed_data.csv

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Output

Each analysis produces:

• Detailed statistical summaries
• HTML report with charts
• Plain-language recommendations
• Out-of-control detection (SPC)
• Acceptance criteria (MSA)
• Capability indices (Cp, Cpk, Pp, Ppk)

Reports are stored in temp_uploads/.

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Project Structure

├── agent_config/               # LLM and agent settings
│   ├── config.yaml             # Model provider config
│   └── agent_prompts/          # Agent prompt templates
├── api/                        # FastAPI server
├── control_chart_system/       # SPC logic
├── msa_system/                 # MSA logic
├── process_capability_system/  # Capability analysis logic
├── data_samples/               # Example datasets
├── spc_cli.py                  # Command-line tool

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Example Workflow

# 1. Start the server
uvicorn api.main:app --host 0.0.0.0 --port 8000 &

# 2. Validate measurement system
./spc chat msa "Conduct Gage R&R study" -f gage_data.csv

# 3. If MSA passes, monitor process
./spc chat control-charts "Check process stability" -f process_data.csv

# 4. If process is stable, check capability
./spc chat capability "Calculate Cp and Cpk with USL=10.5, LSL=9.5" -f process_data.csv

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Use Cases

Manufacturing

• Monitor process stability using control charts
• Validate measurement systems for precision
• Assess process capability for continuous improvement

Research & Development

• Verify measurement reliability before experiments
• Ensure data integrity in prototypes or trials
• Automate statistical exploration of test results

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Conversation Memory

Agents retain context within a session (--thread-id), allowing natural multi-step discussions:

./spc chat control-charts "Analyze this" -f data.csv -t project_001
./spc chat control-charts "Explain those out-of-control points" -t project_001
./spc chat control-charts "Give improvement suggestions" -t project_001

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Contributing

This is a work in progress project. Contributions are welcome — code, documentation, or ideas.

1. Open an issue describing your suggestion or bug
2. Fork and submit a pull request

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Credits

Built with:

• LangGraph — Agent orchestration
• FastAPI — REST API
• Plotly — Visualization
• scipy, numpy, pandas — Statistical backbone

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Disclaimer: This tool uses AI (LLMs) for statistical interpretation. Always verify results with standard software and domain expertise before making decisions. The AI assists quality engineers — it doesn’t replace them.