Overview - ISO/TS 16355-6:2019 (QFD-related optimization)
ISO/TS 16355-6:2019 provides guidance for integrating Quality Function Deployment (QFD) with robust parameter design (RPD) to optimize design parameters so products, services, and information systems meet customer needs consistently. The technical specification focuses on identifying optimum nominal values of design parameters by assessing the robustness of a product’s function during the design phase. It supports customer-driven design decisions and links QFD matrices to statistical and experimental approaches for design optimization.
Key topics and technical requirements
- Scope and applicability: Guidance applies to new product, service, and information-system development to ensure customer satisfaction by reducing functional variability.
- Integration of QFD and RPD: Practical methods to combine QFD’s customer requirement translation with Taguchi-style robust parameter design for improved design decisions.
- Quality engineering concepts: Use of the loss function to quantify customer/organizational/societal loss from variability, and emphasis on reducing life‑cycle loss.
- Robust parameter design techniques:
- Signal‑to‑noise (SN) ratio concepts (signal, noise, three SN ratio types).
- Design of experiments (DOE) and two‑step optimization approaches.
- Steps for RPD experiments (clarify ideal function, select signal/noise/control factors, run experiments, compute SN ratios/sensitivity, select optimum, confirm improvements).
- Project and team structure: Cross‑functional team membership, roles, and leadership for QFD + RPD projects.
- Supporting material: Annex A (integration with TRIZ) and Annex B (other optimization methods).
- Normative references: Links to ISO 16336:2014 (RPD) and ISO 16355-1:2015 (QFD principles).
Practical applications and users
Who benefits:
- Product designers, R&D, engineering, quality managers, manufacturing, procurement, IT, and marketing teams involved in product development.
- Organizations aiming to optimize design parameters, reduce variability, shorten time‑to‑market, lower life‑cycle costs, and improve customer satisfaction at launch.
Practical uses:
- Translate customer requirements into robust nominal design settings.
- Structure DOE to reduce sensitivity to environmental/usage noise.
- Quantify improvement using SN ratios, sensitivity metrics, and gain/confirmation experiments.
- Integrate with innovation methods (TRIZ) and other optimization tools (see Annex B) for complex problems.
Related standards
Keywords: ISO/TS 16355-6:2019, QFD, robust parameter design, quality function deployment, design optimization, Taguchi, DOE, signal-to-noise ratio, loss function, robustness.