DMAIC Define · Measure · Analyze · Improve · Control
Classical Six Sigma improvement cycle for existing processes with measurable output.
Data & Tools
Data preparation, visualisation and generation tools — usable across every phase.
Data Collection
Spreadsheet for data entry and management
XY Plot
Create scatter plots from worksheet columns
Pie Chart
Pie/donut chart from worksheet columns or manual data
Contour Plot
Visualize response surfaces z = f(x, y) as contour diagrams
Histogram
Frequency distribution with boxplot and statistics
Boxplot
Horizontal boxplots for comparing distributions
Probability Plot
Normal probability plot for checking normality of a sample
REST API
Centrally manage and test REST API endpoints
Random Generator
Generate reproducible samples from statistical distributions
Model Data Generator
Generate synthetic datasets from configurable regression models
Calculator
Scientific calculator with statistics and 6σ mode
Unit Converter
Precise unit conversion (18 categories + timezones)
Data Transformation
Transform data (Box-Cox, Johnson, Log, …) for normality
Define
Phase 1 of the DMAIC cycle — define problem, goals, scope and stakeholders.
Project Charter
Project mandate: problem statement and measurable goals
Calendar
Description
Todo
Task list with status, due date, and owner
Stakeholder Analysis
Identify, assess, and plan communication with project stakeholders
VoC → CTx Tree
Translate Voice of Customer into measurable CTx requirements
RACI Matrix
Responsibility Assignment Matrix: roles per activity and stakeholder
SIPOC
Supplier-Input-Process-Output-Customer diagram
5-Why Analysis
Root cause analysis with branching question paths
Measure
Phase 2 of the DMAIC cycle — map the process, collect data, validate measurement systems.
Process Map
Visual process flow with inputs and outputs per step
MSA Type 1
Measurement System Analysis Type 1 — Repeatability (Cg) and Bias (Cgk)
MSA Type 2
Measurement System Analysis Type 2 — Gage R&R (Repeatability & Reproducibility)
Process Capability
Process Capability Analysis — Cp, Cpk, Pp, Ppk, PPM, Sigma Level
Analyze
Phase 3 of the DMAIC cycle — surface root causes, test hypotheses, uncover patterns.
C&E Matrix
Cause and Effect Matrix
Ishikawa 6M
Root cause analysis with 6M categories and expert scoring
Correlation Analysis
Pearson, Spearman, and Kendall correlation between variables
Distribution Fit
Fit data to multiple distributions and rank by Goodness-of-Fit test
FMEA
Failure Mode and Effects Analysis with RPN calculation
Hypothesis Test
Variance and mean tests with automatic normality assessment and power analysis
Sample Size
Calculate the required sample size for variance and mean tests
Pairwise Comparison
Prioritize criteria through systematic pairwise comparison. Every criterion is judged against every other.
Improve
Phase 4 of the DMAIC cycle — design, test and prove out improvements.
DoE Advisor
Overview of DoE designs and a guided wizard for design selection
DoE Planner
Design of Experiments: Full Factorial, Fractional, Plackett-Burman, CCD, Box-Behnken, Taguchi
Regression Analysis
Polynomial regression (degree 1–3, interactions), Exponential, Logarithmic, Power
Regression (Attributive)
Binary logistic, Poisson, and negative binomial regression for attributive/count data
Control
Phase 5 of the DMAIC cycle — lock in gains and make improvements stick.