DaSPi — Process Analytics & Six Sigma in Python¶

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DaSPi helps engineers analyze and improve processes using statistical workflows.

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🎯 The Problem¶

Process analysis in practice is fragmented:

Excel is error-prone and hard to scale
Minitab / JMP are expensive and closed
Python tools (pandas, scipy, statsmodels) are powerful but disconnected

👉 Engineers spend more time combining tools than improving processes.

✅ The Solution¶

DaSPi provides integrated workflows for process analytics:

✅ Gage R&R analysis (MSA, repeatability, reproducibility)
✅ Capability analysis (Cp, Cpk)
✅ Root cause analysis (ANOVA, regression)
✅ Statistical process control (SPC)
✅ Professional visualization

All in one consistent and intuitive interface.

🚀 Four Flagship Workflows¶

DaSPi provides four ready-to-use workflows that cover the complete quality cycle. Each workflow produces visual output + interpretation in under 20 lines of code.

📏 Workflow 1: Gage R&R Analysis¶

Verify your measurement system is capable before analyzing process data.

import daspi as dsp

# Load data
df = dsp.load_dataset("grnr_layer_thickness")

# Step 1: Evaluate the gage itself (MSA Type 1)
gage = dsp.GageStudyModel(
    source=df,
    target="result_gage",
    reference="reference",
    u_cal=df["U_cal"][0],
    tolerance=df["tolerance"][0],
    resolution=df["resolution"][0]
)

# Step 2: Evaluate repeatability & reproducibility (MSA Type 2)
model = dsp.GageRnRModel(
    source=df,
    target="result_rnr",
    part="part",
    gage=gage,
    u_av="operator"  # Operator variation
)

# Visualize complete analysis
chart = dsp.GageRnRCharts(
    model, 
    stretch_figsize=True
).plot().stripes().label(
    fig_title="Gage R&R Analysis: Layer Thickness",
    info=True
)

chart.show()

Output: Comprehensive measurement system evaluation with repeatability (EV), reproducibility (AV), variance components, ANOVA tables, and capability indices (Cg, Cgk).

📊 Workflow 2: Process Capability Analysis¶

Evaluate if your process meets specifications.

import daspi as dsp

# Load data
df = dsp.load_dataset("drop_card")
spec_limits = dsp.SpecLimits(0, float(df.loc[0, "usl"]))

# Analyze capability
chart = dsp.ProcessCapabilityAnalysisCharts(
    source=df,
    target="distance",
    spec_limits=spec_limits,
    hue="method"
).plot().stripes().label(info=True)

chart.show()

Output: 5-panel analysis with Cp, Cpk, Pp, Ppk, distribution plots, and statistical interpretation.

🔍 Workflow 3: Root Cause Analysis¶

Identify which factors significantly impact your process.

import daspi as dsp

# Load data
df = dsp.load_dataset("painkillers-dissolution")

# Fit model with automatic factor selection
model = dsp.LinearModel(
    source=df,
    target="dissolution",
    factors=["employee", "brand", "catalyst"],
    covariates=["temperature"]
)
model.recursive_elimination()

# Visualize results
dsp.ResidualsCharts(model).plot().stripes().label(info=True)
dsp.ParameterRelevanceCharts(model).plot().stripes().label(info=True)

Output: Residual diagnostics + parameter effects with ANOVA tables and significance tests.

📈 Workflow 4: Statistical Process Control (SPC)¶

Monitor process stability and detect out-of-control conditions.

import daspi as dsp

# Load process data
df = dsp.load_dataset("grnr_spc")

# Create control chart
chart = dsp.SingleChart(
    source=df,
    target="layer_thickness",
    feature="sample"
).plot(
    dsp.Scatter
).stripes(
    mean=True,
    control_limits=True,  # UCL/LCL at 3-sigma
    spec_limits=dsp.SpecLimits(lower=45, upper=55),
    agreement=3
).label(
    fig_title="SPC Chart: Layer Thickness",
    sub_title="Control limits at ±3σ",
    info=True
)

chart.show()

Output: Control chart with mean, control limits (UCL/LCL), specification limits, and trend analysis.

🏭 Use Cases¶

Manufacturing: Monitor tolerances and reduce defects
Quality Engineering: Automate Six Sigma workflows (DMAIC)
Process Optimization: Identify key drivers of variation
Data Analysts: Unify statistics and visualization in one tool

🚀 Getting Started¶

Installation¶

pip install daspi

Next Steps¶

📖 User Guide — Complete tutorials for each workflow
🔧 API Reference — Detailed documentation
📊 3S Methodology — Structured problem-solving framework

💡 Why DaSPi?¶

Integrated Workflows¶

No more juggling multiple tools. Each workflow combines data loading, analysis, visualization, and interpretation in one seamless interface.

Professional Output¶

Publication-ready charts with automatic formatting, legends, and statistical annotations.

Six Sigma Ready¶

Built-in support for Cp/Cpk, ANOVA, DOE, Gage R&R, and control charts — everything you need for DMAIC projects

🔧 Technical Features¶

Centralized configuration — Manage language, username, and styles globally
Multivariate visualization — Explore complex relationships
Linear models & ANOVA — Statistical inference made simple
Hypothesis testing — Confidence intervals and p-values
Monte Carlo simulation — Assess uncertainty
Process capability — Cp, Cpk, Pp, Ppk calculations

⚙️ Built on Proven Libraries¶

DaSPi leverages the Python scientific stack:

pandas — Data manipulation
numpy — Numerical computing
matplotlib — Visualization
scipy — Statistical functions
statsmodels — Advanced statistics

🤝 Feedback & Contributions¶

If you use DaSPi in real-world process analysis:
👉 We would love to hear your use case.

Feedback, ideas, and contributions are very welcome.