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Section IX — Design for Six Sigma (DFSS) ~4%

DFSS is the smallest section of the 2022 Body of Knowledge, about 6 of the 150 scored questions. DMAIC improves a process that already exists; DFSS designs a new product, service or process so it meets customer needs from the start. Questions test which framework fits which situation and what the main design tools do.

DMAIC or DFSS?

Situation	Use	Why
An existing process misses its targets and the cause is unknown	DMAIC	There is a baseline to measure and a cause to find
A new product, service or process is being designed	DFSS	There is no baseline; quality has to be designed in
An existing process has been improved repeatedly and still can't meet the requirement	DFSS (redesign)	The current design has hit its entitlement; incremental fixes won't close the gap

DFSS roadmaps

DMADV: Define, Measure, Analyze, Design, Verify. The most common roadmap. Measure here means measuring customer needs and turning them into CTQs, not measuring an existing process.
DMADOV: DMADV with an Optimize step before Verify, to tune design parameters (for example with designed experiments or tolerance design).
IDOV: Identify, Design, Optimize, Validate (some sources say Verify). An alternative roadmap you may see in other references; the BoK focuses on DMADV and DMADOV.

Exam trap: the last step verifies or validates the design against customer requirements, usually through pilots and prototypes. It is not a control plan for a running process.

Core DFSS tools

The BoK’s DFSS section centers on the DMADV/DMADOV roadmaps, design for X and robust design. QFD, TRIZ and Pugh concept selection are standard companion tools; QFD is also the main way VOC is translated into requirements in Define.

QFD (house of quality): translates customer needs (the "whats") into technical requirements (the "hows"), with a relationship matrix, importance ratings and a correlation "roof" between requirements.
Design for X (DFX): designing for a chosen life-cycle goal, such as manufacturability and assembly (DFM/DFA), reliability, serviceability, testability, cost or the environment.
Robust design: choosing design parameters so performance is insensitive to noise (variation you can't or won't control). Taguchi's parameter design and signal-to-noise ratios are the classic approach.
TRIZ: a theory of inventive problem solving built from patterns in patents; it resolves technical contradictions (improving one parameter worsens another) with inventive principles.
Pugh concept selection: scores alternative concepts against a datum concept on each criterion (+, −, S) to pick and combine the strongest ideas.

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