ISO/TS 16355-6:2019

Applications of statistical and related methods to new technology and product development process — Part 6: Guidance for QFD-related approaches to optimization

This document provides guidance for QFD-related approaches to optimization through robust parameter design to ensure customer satisfaction with new products, services, and information systems. It is applicable to identify optimum nominal values of design parameters based on the assessment of robustness of its function at the product design phase. NOTE Some of the activities described in this document can be used at earlier and later stages. Other approaches to solve optimization problems in new technology and product development processes are listed in Annex B.

Application des méthodes statistiques et des méthodes liées aux nouvelles technologies et de développement de produit — Partie 6: Lignes directrices pour QFD et approches reliées pour l'optimisation

General Information

• Status: Published
• Publication Date: 05-Dec-2019
• ICS: 03.120.30 - Application of statistical methods
• Technical Committee: ISO/TC 69/SC 8 - Application of statistical and related methodology for new technology and product development
• Drafting Committee: ISO/TC 69/SC 8/WG 3 - Optimization
• Current Stage: 9093 - International Standard confirmed
• Completion Date: 01-Jul-2023

Standards Content (Sample)

TECHNICAL ISO/TS
SPECIFICATION 16355-6
First edition
2019-12
Applications of statistical and related
methods to new technology and
product development process —
Part 6:
Guidance for QFD-related approaches
to optimization
Application des méthodes statistiques et des méthodes liées aux
nouvelles technologies et de développement de produit —
Partie 6: Lignes directrices pour QFD et approches reliées pour
l'optimisation
Reference number
ISO/TS 16355-6:2019(E)
©
ISO 2019

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ISO/TS 16355-6:2019(E)

COPYRIGHT PROTECTED DOCUMENT
© ISO 2019
All rights reserved. Unless otherwise specified, or required in the context of its implementation, no part of this publication may
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ISO/TS 16355-6:2019(E)

Contents Page
Foreword .v
Introduction .vi
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Basic concepts of QFD . 1
5 Integration of QFD and robust parameter design . 2
5.1 Quality engineering . 2
5.1.1 General. 2
5.1.2 Loss function . 2
5.1.3 Types of factors which affect variability . 3
5.2 When to use quality engineering . 4
5.3 Robust parameter design, QFD, and TRIZ . 4
6 Types of QFD and robust design projects . 5
7 QFD and robust parameter design team membership . 6
7.1 QFD uses cross-functional teams . 6
7.2 Core team membership . 6
7.3 Subject matter experts . 6
7.4 QFD team leadership . 6
8 Robust parameter design . 6
8.1 General . 6
8.2 Signal-to-noise ratio . 6
8.2.1 General. 6
8.2.2 Signal . 6
8.2.3 Noise . 7
8.2.4 Three types of SN ratios . 7
8.3 Assessing robustness . 8
8.4 Two-step optimization . 8
8.4.1 General. 8
8.4.2 Design of experiments (DOE) . 8
8.5 Steps to robust parameter designed experiments . 8
8.5.1 General. 8
8.5.2 Step 1. Clarify the system’s ideal function . 9
8.5.3 Step 2. Select signal factor and its range . 9
8.5.4 Step 3. Select measurement method of output response . 9
8.5.5 Step 4. Develop a noise strategy, and select noise factors and levels . 9
8.5.6 Step 5. Select control factors and their levels from design parameters .10
8.5.7 Step 6. Assign experimental factors to inner or outer array .10
8.5.8 Step 7. Conduct experiment and collect data .10
8.5.9 Step 8. Calculate the SN ratio (η) and sensitivity (S) .10
8.5.10 Step 9. Generate factorial effect diagrams on SN ratio and sensitivity .10
8.5.11 Step 10. Select the optimum condition .11
8.5.12 Step 11. Estimate the improvement in robustness by the gain .11
8.5.13 Step 12. Conduct a confirmation experiment and check the gain and
reproducibility .11
8.5.14 Conclusions.11
8.6 Case studies in robust parameter design .11
Annex A (informative) Integration of robust parameter design (RPD) with quality function
deployment (QFD) and theory of inventive problem solving (TRIZ) .12
Annex B (informative) Other optimization methods .13
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Bibliography .14
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Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
bodies (ISO member bodies). The work of preparing International Standards is normally carried out
through ISO technical committees. Each member body interested in a subject for which a technical
committee has been established has the right to be represented on that committee. International
organizations, governmental and non-governmental, in liaison with ISO, also take part in the work.
ISO collaborates closely with the International Electrotechnical Commission (IEC) on all matters of
electrotechnical standardization.
The procedures used to develop this document and those intended for its further maintenance are
described in the ISO/IEC Directives, Part 1. In particular the different approval criteria needed for the
different types of ISO documents should be noted. This document was drafted in accordance with the
editorial rules of the ISO/IEC Directives, Part 2 (see www .iso .org/ directives).
Attention is drawn to the possibility that some of the elements of this document may be the subject of
patent rights. ISO shall not be held responsible for identifying any or all such patent rights. Details of
any patent rights identified during the development of the document will be in the Introduction and/or
on the ISO list of patent declarations received (see www .iso .org/ patents).
Any trade name used in this document is information given for the convenience of users and does not
constitute an endorsement.
For an explanation of the voluntary nature of standards, the meaning of ISO specific terms and
expressions related to conformity assessment, as well as information about ISO's adherence to the
World Trade Organization (WTO) principles in the Technical Barriers to Trade (TBT), see www .iso .org/
iso/ foreword .html.
This document was prepared by Technical Committee ISO/TC 69, Applications of statistical methods,
Subcommittee SC 8, Application of statistical and related methodology for new technology and product
development.
A list of all parts in the ISO 16355 series can be found on the ISO website.
Any feedback or questions on this document should be directed to the user’s national standards body. A
complete listing of these bodies can be found at www .iso .org/ members .html.
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ISO/TS 16355-6:2019(E)

Introduction
Quality function deployment (QFD) is a method to assure customer or stakeholder satisfaction and
value with new and existing products by designing in, from different levels and different perspectives,
the requirements that are most important to the customer or stakeholder, and ensuring their quality
throughout the downstream activities of design, development, supply, building, commercializing,
support and retiring from the market. These requirements are well understood through the use
of quantitative and non-quantitative tools and methods to improve confidence of the design and
development phases that they are working on the right things. In addition to satisfaction with the
product, robust parameter design improves the process by which new products are developed and
produced.
Reported results of using QFD include improved customer satisfaction with products at time of launch,
improved cross-functional communication, systematic and traceable design decisions, efficient use of
resources, reduced rework, reduced time-to-market, lower life cycle cost, improved reputation of the
organization among its customers or stakeholders.
This document demonstrates the dynamic nature of a customer-driven approach. Since its inception
in 1966, QFD has broadened and deepened its methods and tools to respond to the changing business
conditions of QFD users, their management, their customers, and their products. Those who have used
older QFD models will find these improvements make QFD easier and faster to use. The methods and
tools shown and described represent decades of improvements to QFD; the list is neither exhaustive nor
exclusive. Users should consider the applicable methods and tools as suggestions.
Robustness assessment is performed as a consideration of overall loss during the product’s life
cycle. The overall loss is composed of costs and losses at each stage of the product’s life. It includes
all costs incurred during not only the production stage, but also the disposal stages. When a product
is not robust, the product causes many environmental and socioeconomic losses (including losses to
the manufacturer and the users) due to poor quality caused by functional variability throughout its
usable lifetime from shipping to final disposal. Product suppliers have responsibilities and obligations
to supply robust products to the market to avert losses and damages resulting from defects in the
products. The role of robust parameter in the QFD process is presented with examples and references
to other ISO documents and related materials.
The topics in this document are not exhaustive and vary according to industry, product, and markets.
They are considered a guide to encourage users of this document to explore activities needed to
accomplish the same goal for their products.
Users of this document include all organization functions necessary to assure customer satisfaction,
including business planning, marketing, sales, research and development (R&D), engineering,
information technology (IT), manufacturing, procurement, quality, production, service, packaging and
logistics, support, testing, regulatory, business process design, and other phases in hardware, software,
service, and system organizations.
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TECHNICAL SPECIFICATION ISO/TS 16355-6:2019(E)
Applications of statistical and related methods to new
technology and product development process —
Part 6:
Guidance for QFD-related approaches to optimization
1 Scope
This document provides guidance for QFD-related approaches to optimization through robust
parameter design to ensure customer satisfaction with new products, services, and information
systems. It is applicable to identify optimum nominal values of design parameters based on the
assessment of robustness of its function at the product design phase.
NOTE Some of the activities described in this document can be used at earlier and later stages. Other
approaches to solve optimization problems in new technology and product development processes are listed in
Annex B.
2 Normative references
The following documents are referred to in the text in such a way that some or all of their content
constitutes requirements of this document. For dated references, only the edition cited applies. For
undated references, the latest edition of the referenced document (including any amendments) applies.
ISO 16336:2014, Applications of statistical and related methods to new technology and product development
process — Robust parameter design (RPD)
ISO 16355-1:2015, Application of statistical and related methods to new technology and product
development process — Part 1: General principles and perspectives of Quality Function Deployment (QFD)
3 Terms and definitions
For the purposes of this document, the terms, definitions and symbols given in ISO 16336 and
ISO 16355-1 apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
— ISO Online browsing platform: available at https:// www .iso .org/ obp
— IEC Electropedia: available at http:// www .electropedia .org/
4 Basic concepts of QFD
The basic concepts of QFD are described in ISO 16355-1:2015, Clause 4.
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5 Integration of QFD and robust parameter design
5.1 Quality engineering
5.1.1 General
Dr. Genichi Taguchi, a pioneer in Japanese quality methods, developed a philosophy of quality
engineering based more on technology than theory in order to measure loss, maintain quality in
production, and improve quality continuously. The goal is to create high quality, low cost goods and
[3]
services that satisfy customer needs, a goal shared with quality function deployment (QFD) .
5.1.2 Loss function
Measuring loss can be explained by the concept of the loss function; any variability from the ideal
function of a product creates a loss:
[8]
a) to the customer, who is unable to fully enjoy their intended use for the expected life of the product) ;
NOTE Robust parameter design focuses on the customer’s quality loss due to variability in the function
[2]
or performance of the product. This has alternatively been called the cost of inferior quality .
b) to the organization, which can result from wasted activity, wasted materials, wasted time, rework,
[12]
scrap, warranty replacement, maintenance ;
[7]
c) to society, which can result from regulation, disposal, recovery, safety, hazards .
5.1.2.1 Taguchi's loss function vs. specification loss function
Measuring loss can be performed by calculating the cost to the customer, organization, and society
due to variability from the target design specification set to fully satisfy the customer. Defining this
target specification level is described in ISO 16355-5:2017, Clause 9, in the maximum value table, and in
ISO 16355-5:2017, 10.3.4.1 in both the unweighted and weighted design planning tables. Taguchi's loss
function considers any deviation from the target specification to be a loss to the customer, organization,
and society, and it can be quantified in terms of cost. Traditional loss function is a step function in that
as long as the product or component performance is within the lower and upper specification limits of
the nominal target value, there is no loss recognized, as shown in Figure 1.

Key
a
Y cost Lower specification limit.
b
1 loss Target specification.
c
2 no loss Upper specification limit.
Figure 1 — Taguchi (left) and specification (right) loss function
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5.1.2.2 Calculating the loss function
The farther from the nominal target specification the product or component performance varies, the
greater the Taguchi loss function is. Monetizing the average loss allows the QFD team to consider
different design alternatives with different costs and loss. The average loss can be calculated with a
[13]
quadratic formula :
2
2
 
Lk=+σ ()yT−
 
where

L is the loss to the customer, organization, or society,
2
k = money/Δ ,
where Δ is the tolerance, difference between the designed nominal value and the tolerance limit,
and “money” is loss when characteristic exceeds the tolerance Δ,
T is the target value of performance,
2
σ is the variance of performance, and
y
is the average performance.
1)
The determination of tolerance Δ is described in ISO 16337: — , 4.3.
NOTE 1 The above Taguchi loss function quadratic equation is commonly used in QFD when the target
specification is a nominal-the-best functional or non-functional requirement. Different equations for larger-the-
[11]
better and smaller-the-better specifications can also be used .
NOTE 2 In QFD applications, the value of k can be set to 1 since the monetary loss to customers would be the
[13]
same for all competitors .
NOTE 3 Competitive benchmarking of performance can be done in real-life environments (called gembas
in QFD) that represent key applications of key customers that were defined in the customer segments table
described in ISO 16355-2:2017, 9.2.2.2, and prioritized in the business goals - customer segments prioritization
matrix described in ISO 16355-2:2017, 9.2.3. If not possible, laboratory or computer simulations can be used as a
proxy. The results can be recorded in the maximum value table described in ISO 16355-5:2017, Clause 9, and in
either the unweighted and weighted design planning tables described in ISO 16355-5:2017, 10.3.4.1.
2
NOTE 4 The loss function for dynamic characteristic cases is defined as Lk==σηk/ , as described in
1)
ISO 16337:— , 4.3.
5.1.3 Types of factors which affect variability
[5]
The goal of robust parameter design is to minimize loss due to variation . There are different types of
factors to be considered on minimizing loss due to variation, as shown in Figure 2:
a) shifts in mean (B2 has higher mean than B1);
b) changes in variability (C2 has less variability than C1);
c) changes in costs, same mean or variability, but can lower cost by picking cheaper alternative
(D1 or D2);
d) trade-off between mean and variability (A1 versus A2).
1) Under preparation. Stage at the time of publication: ISO/DIS 16337:2019.
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Figure 2 — Types of factors
5.2 When to use quality engineering
[9]
Quality engineering can be used throughout the new product development process .
a) In the planning and development phase, it can assist technological research and feasibility studies
of project concepts as described in ISO 16355-2:2017, 9.1.2.8.2.
b) In the design phase, it can help structure simulation studies, failure mode and effects analysis
(FMEA), as described in ISO 16355-5:2017, 10.4.5.8, in testing specifications as described in
ISO/TR 16355-8:2017, 11.2, and in making design decisions.
c) In the product planning phase, it can influence process design as described in ISO/TR 16355-8:2017,
Clause 10, prototype development as described in ISO/TR 16355-8:2017, 11.5, standardization
as described in ISO/TR 16355-8:2017, 13.5, and supply chain decisions as described in
ISO/TR 16355-8:2017, 12.4.
d) In the production phase, it can improve process controls as described in ISO/TR 16355-8:2017,
13.2, tolerancing as described ISO/TR 16355-8:2017, 9.2, and inspection as described in
ISO/TR 16355-8:2017, 13.5.1.
e) In the sales and service phase, it can improve service procedures and technical bulletins as
described in ISO/TR 16355-8:2017, 15.5 and Clause 16, and with customer satisfaction with product
functions and performance as described in ISO 16355-3:2019 and ISO/TR 16355-8:2017, Clause 17.
5.3 Robust parameter design, QFD, and TRIZ
QFD is a framework for new product development quality assurance. This framework facilitates
integration with multiple quantitative and qualitative analytic tools, as described in the QFD tools
matrix in ISO 16355-1:2015, A.1. ISO 16355-5:2017, 10.4.3.4, describes the basic process for the theory
of inventive problem solving, abbreviated in Russian as TRIZ. Like robust parameter design, TRIZ
examines functions and their ability to satisfy customer needs. When TRIZ is conducted first and
identifies multiple solutions to a problem, robust parameter design can be used to select and further
[1],[6].
improve the most robust
[14]
The following steps integrate QFD, TRIZ, and Taguchi's robust parameter design . Their relationships
are shown in Annex A.
1) Project level
i) Identify and prioritize customer segments as described in ISO 16355-2:2017, 9.2.
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ii) Identify future technology trends that address future customer problems as described in
ISO 16355-5:2017, 10.4.3.5.1.1.
2) Customer level
i) Understand the customer's usage environment (gemba) as described in the customer segments
table in ISO 16355-2:2017, 9.2.2.2, the customer process model described in ISO 16355-2:2017,
9.2.5.2.3, and gemba visit table described in ISO 16355:2:2017, 9.2.5.2.4.
[9]
ii) Taguchi methods for robust design can be adapted for dynamic customer needs .
iii) TRIZ looks for available system or environment of use resources to contribute to ideality (high
function, low cost), historical constraints, and useful or harmful functions as described in the
innovative situation questionnaire in ISO 16355-5:2017, 10.4.3.4.1.2.
iv) Taguchi methods would look for sources of variation due to the environment or user as
described in 8.5.5.
3) Transfer voice of customer into voice of engineer
i) QFD uses the maximum value table as described in ISO 16355-5:2017 or the customer needs –
functional requirements matrix (house of quality) as described in ISO 16355-5:2017, 9.3.6, to
transfer the voice of the customer into technical product requirements.
ii) TRIZ looks to minimize technical and physical contradictions without trading off target values
as described in ISO 16355-5:2017, 10.4.3.4.2.2, and ISO 16355-5:2017, A.3.
iii) Taguchi methods can take advantage of positive interactions as described in 8.5.6.
iv) Taguchi's loss function is an effective way to technically benchmark competitive products as
described in 8.2.
4) Technology concept level
i) TRIZ develops many solution concepts for functions and performance measures described in
the inventive principles in ISO 16355-5:2017, 10.4.3.4.2.3 and A.4.
ii) TRIZ identifies patterns of evolution that lead to exciting products described in
ISO 16355-5:2017, 10.4.3.5.1.1.
iii) Taguchi methods determine best values for robust design for each concept under consideration
as described in 8.2.
5) Manufacturing level
i) TRIZ can improve manufacturing equipment and processes by examining their patterns of
evolution described in ISO 16355-5:2017, 10.4.3.5.1.1.
ii) TRIZ can broaden application of manufacturing processes to other products.
iii) TRIZ can improve the manufacturing workflow.
[3][12]
iv) Taguchi methods can make manufacturing more robust to variation .
v) Taguchi design of experiments and QFD house of quality can build a knowledge database for
the future as describe in ISO 16355-5:2017, 9.3.7.
6 Types of QFD and robust design projects
QFD projects encompass new developments, as well as generational improvements to existing products.
The types of QFD projects are described in ISO 16355-1:2015, Clause 6, and ISO 16355-2:2017, Clause
6 notes.
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7 QFD and robust parameter design team membership
7.1 QFD uses cross-functional teams
Cross-functional teams are described in ISO 16355-1:2015, 7.1.
7.2 Core team membership
Core team membership is described in ISO 16355-1:2015, 7.2.
7.3 Subject matter experts
Subject matter experts involvement is described in ISO 16355-1:2015, 7.3.
7.4 QFD team leadership
QFD team leadership is described in ISO 16355-1:2015, 7.4.
NOTE Robust parameter design projects are typically led by the engineering department.
8 Robust parameter design
8.1 General
Robust parameter design in the design phase of QFD can minimize defects, failures, and quality
problems due to functional variability that can occur during the use of the product. In robust parameter
design, optimum nominal values of the product’s design parameters are considered control factors that
can be studied and made more robust under certain noise factors. The use of robust parameter design
at development and design stages help the QFD team determine optimum design specifications that
lead to better product quality in application.
8.2 Signal-to-noise ratio
8.2.1 General
The function of a product's system is to convert an input into an output. Any input variable to the
system that is intentional
...