DaSPi — Process Analytics & Six Sigma in Python

DaSPi helps engineers analyze and improve processes using statistical workflows.

🎯 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:

• Capability analysis (Cp, Cpk)
• Root cause analysis (ANOVA, regression)
• Statistical process control (SPC)
• Professional visualization

All in one consistent and intuitive interface.

🚀 Three Flagship Workflows

DaSPi provides three ready-to-use workflows that cover the most common process analysis tasks. Each workflow produces visual output + interpretation in under 20 lines of code.

📊 Workflow 1: 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 2: 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 3: 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 DMAIC workflows
• Process Optimization: Identify key drivers of variation
• Data Analysts: Unify statistics and visualization in one tool

📊 Example Outputs

Process Capability Analysis

Root Cause Analysis (ANOVA)

Multivariate Visualization

🚀 Installation

pip install daspi

📚 Documentation

• User Guide — Complete tutorials for each workflow
• API Reference — Detailed documentation
• 3S Methodology — Structured problem-solving

🔧 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

👤 About

DaSPi is created and maintained by Reto Jäggli, Data Scientist at Festo Microtechnology AG.

The project is driven by a passion to make process analytics and Six Sigma workflows more accessible in Python.

⚠️ Disclaimer

DaSPi is under active development and may contain bugs.
Results should be validated with trusted statistical tools when required.

🤝 Feedback & Contributions

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

Feedback, ideas, and contributions are very welcome.