01 · 1920s–1980s
Statistical process-control foundations
Control charts, variation analysis and industrial quality methods established quantitative approaches to process performance.
ASSURANCE DNA · SIX SIGMA METHODS
ISO 13053
A structured Six Sigma improvement method: define, measure, analyse, improve and control, supported by appropriate quantitative tools.
Which measured process problem is being solved, and can the improvement survive control?
REFERENCEISO 13053-1:2011 and ISO 13053-2:2011CURRENT STATE · 26 JUL 2026Published parts covering the DMAIC methodology and supporting tools and techniques.BID ENTRYModel · Story · Evidence · Measure · Learn · Adapt
DMAIC is for improving an existing process with a measurable problem. It should not be forced onto invention, vague transformation or a process with unusable data. Source-bounded interpretation: no certification, protected text reproduction or award prediction.
LIVING EXPERIMENT · MICROSCOPE
The Process Microscope
The visitor changes magnification from claim to process step to variation. Unsupported averages blur; defined measures and controls sharpen the image.
MODEL STATESME · SENSE · APPLYDeterministic teaching model · no award prediction
ORIGIN × EVOLUTION × CURRENT PRACTICE
Why this reference exists—and what changed.
High-volume or repeatable manufacturing, transactional, service, health, logistics and public processes where variation and defects can be measured.
02 · 1980s–1990s
Six Sigma formalised
Motorola and later adopters popularised structured, project-based reduction of defects and variation.
03 · 2011
ISO 13053 published
Part 1 described DMAIC and project roles; Part 2 addressed supporting tools and techniques.
04 · 2021–2026
Confirmed references
ISO records the 2011 parts as confirmed, retaining applicability to manufacturing, services and transactional processes.
DMAIC is inappropriate where there is no stable process, measurable problem or sufficient data. Statistical language must never camouflage weak evidence.
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PUBLICLY DESCRIBABLE ANATOMY
Every domain gets a specific bid-management translation.
These are navigational interpretations, not substitute clauses. Use the official publication for normative wording.
- 01
Define the problem, customer and project
Define the buyer-relevant problem, customer, process boundary, objective and project authority.
- Evidence object
- DMAIC charter and critical-to-quality definition
- Decision test
- Is the problem observable without embedding a preferred solution?
- 02
Measure the current process and data quality
Establish operational definitions, measurement validity and a reproducible baseline.
- Evidence object
- Measurement plan and baseline data
- Decision test
- Would two observers record the same event in the same way?
- 03
Analyse causes and variation
Analyse variation and causes using evidence appropriate to the data and process.
- Evidence object
- Cause analysis and verified relationship
- Decision test
- Has the team separated association, mechanism and causal evidence?
- 04
Improve the process and test change
Design and test changes on a controlled scale before embedding them in the offer.
- Evidence object
- Change experiment and decision record
- Decision test
- What evidence would cause the proposed improvement to be rejected?
- 05
Control the improved state
Define control, ownership, limits and response so performance does not drift.
- Evidence object
- Control plan and response rule
- Decision test
- Who acts when the measure crosses its limit?
- 06
Roles, reviews and project governance
Govern project roles, phase reviews, decisions and benefit validation.
- Evidence object
- DMAIC gate and governance record
- Decision test
- Can a phase advance with unresolved data-quality weakness?
- 07
Supporting statistical and analytical tools
Select tools because they answer a question, not because they look sophisticated.
- Evidence object
- Method-selection rationale
- Decision test
- Can the analyst explain assumptions and limitations to a non-specialist buyer?
BEFORE × INTERVENTION × AFTER
Worked case · invoice exception reduction
A fictional shared-service bid proposes to reduce invoice exceptions from an unverified “18%” baseline.
Fictional analysis design; the numbers are teaching inputs, not claimed savings.
What the team can observe
Four teams define an exception differently; samples exclude rejected invoices and the proposed automation precedes cause analysis.
What changes in the operating system
The team standardises the definition, validates measurement, stratifies causes, tests two changes and creates response limits.
The commercial model uses a reproducible baseline and only includes the change whose controlled pilot evidence survives review.
PROPORTIONATE IMPLEMENTATION
Scale the control—not the integrity of the decision.
DMAIC is for improving an existing process with a measurable problem. It should not be forced onto invention, vague transformation or a process with unusable data.
WORKED CASEISO-13053–01Teaching scenario · no claimed outcome
Use a one-page DMAIC record with a stable operational definition and simple run chart.
Charter, baseline, tested change, control rule
Validate baseline and benefits before commercialising an improvement.
Data-quality test, causal analysis, approval
Operate phase gates, specialist review and independent benefit validation.
Project portfolio, control performance, lessons
SOURCE TRAIL · REVIEWED 26 JUL 2026
Trace each interpretation to its authority.
Publication status can change. Exact conformity questions belong with the current licensed publication and competent assurance.