House of Quality (QFD)

What is the House of Quality?

The House of Quality is the first matrix in Quality Function Deployment (QFD), a methodology that connects customer needs (the "Whats") to Engineering characteristics or measurable Critical-to-Quality (CTQ) parameters (the "Hows"). It resembles a house with a correlation matrix as the roof, hence the name.

QFD Cascade Methodology: The House of Quality is commonly used as the first matrix in a multi-stage QFD deployment process. Traditional QFD literature often describes four cascading matrices translating customer requirements into product characteristics, component specifications, manufacturing processes, and production controls. However, the number of deployment matrices varies by industry and project complexity.

Historical Development: QFD methodology was developed in Japan during the late 1960s by Yoji Akao and Shigeru Mizuno. Originally developed in Japanese heavy industry and widely applied in shipbuilding at companies such as Mitsubishi’s Kobe Shipyard, Akao and Mizuno recognized that design decisions made early in development dramatically impact final quality and cost. Their structured approach to linking customer requirements with technical specifications revolutionized product development and became foundational to modern Design for Six Sigma (DFSS) practices.

Structured Decision Weighting: Unlike subjective design prioritization based on individual engineer preferences, HOQ supports structured decision weighting using numerical relationships and importance calculations. This semi-quantitative approach reduces political decision-making and ensures customer value drives engineering effort allocation.

Customer-Driven Design: Supports design decisions by systematically translating customer requirements into engineering priorities, reducing reliance on internal assumptions and improving customer alignment. Critical for DMAIC Define/Design phases and DFSS projects.

Engineering Effort Alignment: HOQ aligns engineering effort with customer value prioritization by calculating which technical requirements contribute most to satisfying important customer needs. High-importance customer requirements with strong technical relationships receive development priority, ensuring limited engineering resources maximize customer satisfaction impact.

DFSS and Early Lifecycle Support: HOQ supports DFSS (Design for Six Sigma) and early product lifecycle design decisions by providing objective prioritization before significant design commitments. Early application prevents costly late-stage changes when customer requirements were misunderstood during initial concept development.

Analytical Interpretation

Propagation Example: Consider a customer requirement "Long battery life" rated importance 5 (critical). If technical requirement "Battery capacity" has a Strong relationship (9) and "Power management efficiency" has Medium relationship (3), the calculated importance scores are 45 and 15 respectively. This propagation shows that improving battery capacity delivers three times more customer value than improving power management for this specific requirement.

Technical Importance Calculation: Sum contributions across all customer requirements to determine which technical specifications deserve highest development priority. Technical requirements with high importance scores but poor current performance indicate critical improvement opportunities.

Methodology Context

Automatic Calculation Support: The tool's automatic calculations support technical prioritization by computing weighted importance scores and identifying high-impact technical requirements. These calculations eliminate manual computation errors and enable rapid scenario analysis.

Engineering Validation Required: While the tool assists analysis, engineering validation is required for final decisions. Relationship strengths depend on technical judgment. Calculated priorities guide but should not override fundamental physics, regulatory constraints, or safety requirements.

Semi-Quantitative Scoring: Relationship strength scoring is semi-quantitative and depends on engineering judgment. The 1-3-9 scale (Weak-Medium-Strong) or 1-5-9 alternatives provide relative weighting rather than precise physical relationships. Teams should document rationale for strong relationships to ensure consistency.

Cross-Functional Collaboration: Relationship ratings require cross-functional collaboration for accuracy. Marketing provides customer importance ratings. Engineering assesses technical feasibility relationships. Manufacturing inputs production capability considerations. Single-function assessments produce biased matrices that misallocate development resources.

Positive Correlations: Positive correlations in the roof indicate design synergy—improving one technical requirement simultaneously improves another. These synergies represent design efficiencies where single engineering efforts deliver multiple benefits.

Negative Correlations and Trade-offs: Negative correlations indicate trade-offs requiring optimization or innovation strategies. Classic examples include "Lightweight" vs. "Durable" or "Low Cost" vs. "High Performance." The roof identifies these conflicts but does not resolve them automatically. Engineering teams must develop innovative solutions, accept suboptimal compromises, or prioritize based on customer importance weightings.

Decision Support, Not Resolution: The correlation matrix supports decision-making by making trade-offs visible. Without explicit documentation, teams might unknowingly optimize one requirement while severely damaging another. Visibility enables conscious, customer-value-based trade-off decisions.

Structured but Not Guaranteed: HOQ supports structured prioritization but does not guarantee product success. Excellent QFD analysis on a fundamentally flawed concept still produces a failed product. Market timing, pricing, and competitive responses remain critical success factors.

VOC Sensitivity: The model is highly sensitive to inaccurate customer requirement data. If importance ratings reflect vocal minority opinions rather than target market needs, HOQ optimizes for the wrong customers.

Feasibility Limitations: HOQ does not replace detailed engineering feasibility analysis. A technical requirement may score highest on importance yet be physically impossible or economically unviable. HOQ prioritizes; engineering validates.

Validation Requirements: HOQ requires follow-up validation using testing or DOE (Design of Experiments). Assumed relationships between customer needs and technical characteristics must be empirically verified through experimentation.

Relationship strength scoring relies heavily on expert judgment and team consensus. Cognitive bias, groupthink, or dominant stakeholders may influence scoring results. Facilitated workshops and cross-functional participation improve scoring reliability.

What HOQ Accomplishes: House of Quality creates a visible, numerical bridge between what customers say they want ("comfortable car") and what engineers must design ("seat cushion density 45 kg/m³, suspension damping coefficient 0.35"). It prevents the common failure mode where engineering teams optimize technical metrics that don't actually improve customer satisfaction.

When to Apply QFD: Beginners should apply QFD when developing new products, entering new markets, or when current products receive poor customer satisfaction scores despite meeting internal technical specifications. If customers complain but engineers insist the product meets specs, you need HOQ to realign technical priorities with actual customer values.

Real-World Example: A smartphone manufacturer learns customers value "long battery life" (importance 5) and "thin profile" (importance 4). Engineering identifies "battery thickness" strongly supports battery life (9) but conflicts with thin profile (-9). HOQ reveals this trade-off explicitly. Rather than compromising blindly, the team innovates a denser battery chemistry that maintains capacity with reduced thickness—resolving the conflict through technology rather than compromise.