AI-assisted continuous improvement
Turn process friction into a reviewable improvement system.
Lean AI Ops helps teams structure unclear operational problems, separate evidence from assumptions, choose the right analysis tools, and carry improvement work through DMAIC.
5 DMAIC phases8 analytics areas3 evidence states5 export formats
PROJECTSupplier Change Request Intake
SampleDDefine
MMeasure
AAnalyze
IImprove
CControl
Supported18Inferred6Missing4
Project workspace
Follow the work, phase by phase.
This sample is a client-side explainer based on the repository's deterministic example. It does not call an AI service or send your input anywhere.
CURRENT VIEW
DEFINEDefine the problem precisely
Problem framing
SupportedWhat is happening?
WHY
Evidence health
Define viewDeliverables
phase outputsBrowser-only demo
Frame a process problem without sharing data.
This helper creates a transparent starter brief in your browser. Full assessment generation remains in the Python application.
STARTER BRIEF
DeterministicAdd a process problem to begin
WORKING PROBLEM STATEMENT
Your description will be normalized here without inventing facts.
SUGGESTED FIRST MEASURES
- Baseline recommendations appear here.
EVIDENCE GAPS TO RESOLVE
- Evidence gaps appear here.
BEST NEXT TOOL
Select a concern
Tool rationale appears here.
Analytics workbench
Start with the question, then choose the statistic.
The repository includes capability, MSA, hypothesis testing, SPC, FMEA, regression, DOE, and benefits/COPQ.
SPC control chart
- Use when
- Watch for
- Repository area
System map
A thin interface over explicit assessment and analysis layers.
Select a component to inspect its responsibility. The map describes the current architecture and intended boundaries without hiding the prototype's limitations.
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Project intake
Read architecture notes →Learn & inspect
Documentation that exposes the reasoning boundaries.
Use the public interface for orientation, then move into the repository docs for detailed contracts and implementation.
ARCHITECTURE
Assessment boundaries
How input, orchestration, modes, and output contracts fit together.
GENERATIONAssessment flow
How project information becomes a reviewable improvement package.
EVIDENCEFact vs. hypothesis
How supported, inferred, and missing states prevent false certainty.
UXNon-technical usage
How statistical and LSS concepts are translated for practical users.
Run the full application
Use the Python app for real assessments and analytics.
The public site is an explainer and deterministic demo. Streamlit remains the runtime for generation, saved projects, statistical analysis, and exports.