ISO 16355-3:2019

INTERNATIONAL ISO
STANDARD 16355-3
First edition
2019-01
Applications of statistical and related
methods to new technology and
product development process —
Part 3:
Quantitative approaches for the
acquisition of voice of customer and
voice of stakeholder
Application des méthodes statistiques et des méthodes liées aux
nouvelles technologies et de développement de produit —
Partie 3: Acquisition quantitative du retour client (voice of customer)
ou du retour des parties prenantes (voice of stakholders)
Reference number
©
ISO 2019
© ISO 2019
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ii © ISO 2019 – All rights reserved

Contents Page
Foreword .vi
Introduction .vii
1 Scope . 1
2 Normative references . 1
3 Terms and definitions . 1
4 Basic concepts of QFD . 2
5 Integration of quantitative voice of customer (VOC) and voice of stakeholder (VOS)
acquisition with customer research methods . 2
6 Types of QFD projects . 2
7 QFD team membership . 2
7.1 QFD uses cross-functional teams . 2
7.2 Core team membership . 2
7.3 Subject matter experts . 2
7.4 QFD team leadership . 2
8 Types of information . 2
8.1 General . 2
8.2 Market strategy and trends . 2
8.2.1 General. 2
8.2.2 Analytic network process (ANP) . 3
8.2.3 Porter 5 force competitive analysis . 3
8.2.4 Market position analysis . 3
8.2.5 Project selection . 3
8.3 Market segments . 3
8.3.1 General. 3
8.3.2 Demographic market segmentation. 3
8.3.3 Attitudinal and cultural dimensions . 3
8.3.4 New Kano model studies . 3
8.3.5 Repertory grid technique . 3
8.4 Competitive space . 3
8.4.1 General. 3
8.4.2 Benchmarking . 4
8.4.3 Market position analysis . 4
8.4.4 Multidimensional scaling (MDS) . 4
8.4.5 Repertory grid technique . 4
8.5 Customer and stakeholder applications . 4
8.5.1 Frequency of use or application . 4
8.5.2 Robust parameter design . 4
8.6 Customer needs . 4
8.6.1 Functional needs using text analytics and text mining . 4
8.6.2 Emotional or attractive needs using kansei engineering . 4
8.7 Prioritization . 5
8.7.1 General. 5
8.7.2 Analytic hierarchy process (AHP) . 5
8.7.3 L-matrices . 5
8.7.4 Cluster analysis . 5
8.7.5 Analytic network process (ANP) . 5
8.7.6 Benchmarking . 5
8.8 Product requirements, feature sets, concept options . 5
8.8.1 Conjoint analysis . 5
8.8.2 Customer needs — Functional requirements matrix (house of quality) . 5
8.8.3 Quantification method III . 5
8.8.4 Regression analysis. 5
8.8.5 Repertory grid technique . 5
8.8.6 Text analytics and text mining. 6
8.9 Distribution, logistics and inventory, sales channels . 6
8.10 Customer satisfaction surveys and preference benchmarking . 6
8.10.1 Customer satisfaction surveys . 6
8.10.2 Factor analysis and covariance structure analysis . 6
8.10.3 Fuzzy set theory . 6
8.10.4 Net promoter score (NPS) . 6
8.10.5 Neural networks and artificial intelligence . 6
8.10.6 Regression analysis. 6
9 Tools for quantitative VOC and VOS acquisition and analysis . 8
9.1 Analytic network process (ANP) . 8
9.1.1 General. 8
9.1.2 Building and analyzing the network . 8
9.2 Artificial intelligence (AI) . 9
9.3 Conjoint analysis .10
9.3.1 General.10
9.3.2 Types of conjoint analyses used with QFD .10
9.3.3 Building the conjoint analysis survey .11
9.3.4 Case study of conjoint analysis and QFD .11
9.4 Cluster analysis .12
9.5 Cultural dimensions.12
9.5.1 General.12
9.5.2 Cultural dimension scores .13
9.5.3 Cultural dimensions and QFD .13
9.6 Factor analysis with covariance structure analysis .13
9.6.1 General.13
9.6.2 Factor analysis to classify functional requirements into satisfaction factors .14
9.6.3 Covariance structure analysis.14
9.7 Fuzzy set theory and multi-attribute utility theory .14
9.7.1 General.14
9.7.2 Difficulties in scoring customer satisfaction .14
9.7.3 Fuzzy sets .15
9.7.4 Crisp scores .15
9.7.5 Customer preferences by benchmarking competition .15
9.7.6 Failure mode and effects analysis using fuzzy multiple-objective decision
models .16
9.8 Market position analysis .16
9.8.1 General.16
9.8.2 Types of market positioning .16
9.9 Market segmentation using cross tabulations .17
9.9.1 General.17
9.9.2 Types of cross tabulations .17
9.9.3 Uses of cross tabulations .18
9.10 Multidimensional scaling (MDS) .18
9.10.1 General.18
9.10.2 Conducting the MDS study .18
9.10.3 Case study on toothpaste .19
9.11 Net promoter score (NPS) .20
9.11.1 General.20
9.11.2 NPS survey .20
9.11.3 NPS survey results .20
9.12 Neural networks (NN) .21
9.12.1 General.21
9.12.2 Preparing the surveys.21
9.12.3 Interpreting the NN output .22
9.12.4 Using the NN output in a QFD study .22
iv © ISO 2019 – All rights reserved

9.13 Quantification methods (QM) .22
9.13.1 General.22
9.13.2 Quantification method III (QM III) .23
9.13.3 Applying QM III to a 2-dimensional QFD matrix .23
9.14 Regression analysis .27
9.14.1 General.27
9.14.2 Regression analysis in QFD .28
9.14.3 Regression data .28
9.15 Repertory grid technique .30
9.15.1 General.30
9.15.2 The repertory grid technique process.30
9.16 Text analytics and text mining .31
9.16.1 General.31
9.16.2 Text clustering .31
9.16.3 Topic modelling . .32
10 Deployment to next stage .33
10.1 Customer needs related information .33
10.2 Product related information.33
Annex A (informative) Using sampling surveys .34
Bibliography .42
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
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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
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.org/iso/foreword .html.
This document was prepared by 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.
vi © ISO 2019 – All rights reserved

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. These requirements can be
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, QFD improves the process by which new products are developed.
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 lifecycle cost, and 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 find these improvements make QFD easier and faster to use. The methods and tools
shown and referenced in the standard represent decades of improvements to QFD; the list is neither
exhaustive nor exclusive. Users can consider the applicable methods and tools as suggestions, not
requirements.
This document is descriptive and discusses current best practice, it is not prescriptive by requiring
specific tools and methods.
INTERNATIONAL STANDARD ISO 16355-3:2019(E)
Applications of statistical and related methods to new
technology and product development process —
Part 3:
Quantitative approaches for the acquisition of voice of
customer and voice of stakeholder
1 Scope
This document describes quantitative approaches for acquisition of the voice of customer (VOC) and
voice of stakeholder (VOS) and its purpose, and provides recommendations on the use of the applicable
tools and methods. It is not a management system standard.
NOTE It does not provide requirements or guidelines for organizations to develop and systematically
manage their policies, processes, and procedures in order to achieve specific objectives.
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, and other phases in hardware, software, service, and system
organizations.
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 10004:2018, Quality management — Customer satisfaction — Guidelines for monitoring and measuring
ISO 3534-1:2006, Statistics — Vocabulary and symbols — Part 1: General statistical terms and terms used
in probability
ISO 3534-4:2014, Statistics — Vocabulary and symbols — Part 4: Survey sampling
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)
ISO 20252:2012, Market, opinion and social research — Vocabulary and service requirements
3 Terms and definitions
For the purposes of this document, the terms and definitions given in ISO 3534-1, ISO 3534-4,
ISO 16355-1, ISO 10004 and ISO 20252 apply.
ISO and IEC maintain terminological databases for use in standardization at the following addresses:
— IEC Electropedia: available at http: //www .electropedia .org/
— ISO Online browsing platform: available at https: //www .iso .org/obp
4 Basic concepts of QFD
The basic concepts of QFD are described in ISO 16355 1:2015, Clause 4.
5 Integration of quantitative voice of customer (VOC) and voice of stakeholder
(VOS) acquisition with customer research methods
Integration of quantitative voice of customer (VOC) and voice of stakeholder (VOS) acquisition with
customer research methods is described in ISO 16355-1:2015, 8.2 and ISO 16355-2:2017, Clause 5.
6 Types of QFD 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.
7 QFD 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 It is common for quantitative VOC and VOS acquisition to be led by market researchers. This can be an
internal department or outsourced to a third-party organization.
8 Types of information
8.1 General
Quantitative methods and tools are used in QFD to understand, structure, prioritize, and analyze voice
of customer and stakeholder. These are summarized in Table 1.
8.2 Market strategy and trends
8.2.1 General
Quantitative information is useful in setting strategies and projects for new product development. It
is the basis for both the objective and subjective decision making described in ISO 16355-2:2017, 9.1.2.
2 © ISO 2019 – All rights reserved

8.2.2 Analytic network process (ANP)
The ANP is used to prioritize alternative plans for achieving strategic objectives that require input from
[1]
multiple stakeholders . ANP is described in 9.1.
8.2.3 Porter 5 force competitive analysis
This is used in strategic planning to give a high level view of future market opportunities and threats.
Its use in QFD is described in ISO 16355-2:2017, 9.1.2.3.
8.2.4 Market position analysis
This is used to show current and trending changes in markets. Its use in QFD is described in 9.8.
8.2.5 Project selection
Quantification is used to identify both objective and subjective criteria that can be used to synthesize
a prioritized project portfolio. The analytic hierarchy process (AHP) is used throughout QFD for this
purpose. Its use in QFD is described in ISO 16355-2:2017, 9.1.2.8.
8.3 Market segments
8.3.1 General
Quantitative information is useful in identifying potential market segments and applications during
new product development which is described in ISO 16355-2:2017, 9.2.2.
8.3.2 Demographic market segmentation
Market segments can be analyzed according to demographic attributes using cross tabulations. Its use
in QFD is described in 9.9.
8.3.3 Attitudinal and cultural dimensions
Demographics, attitudes, and culture can affect how customers respond to surveys and visits.
Quantification of these factors and its use in QFD is described in 9.5.
8.3.4 New Kano model studies
Using knowledgeable consumers to respond to Kano model satisfaction surveys, the new Kano model
can be used to reveal hidden market segments. Its use in QFD is described in ISO 16355-5:2017,
10.3.4.4.8.1.
8.3.5 Repertory grid technique
In addition to the obvious physical characteristics of a product, for example shape or color, there can
also be unconsciously perceived customer-specific characteristics. The repertory grid technique
helps customers and stakeholders reveal their personal constructs by organizing and scoring product
characteristics. Its use in QFD is described in 9.15.
8.4 Competitive space
8.4.1 General
In new product development, it is important to understand the alternative choices that customers can
make. This can include both similar products as well as new technologies. The following tools are useful
in ISO 16355-2:2017 and ISO 16355-4.
8.4.2 Benchmarking
Benchmarking is used to capture customer perceptions about current and competitive products, as
well as to plan future product placement in QFD. Customer perception benchmarking is used in the
quality planning table described in ISO 16355-4:2017, 12.2.
8.4.3 Market position analysis
Market position analysis is used to understand how competing products are perceived by customers.
Its use in QFD is described in 9.8.
8.4.4 Multidimensional scaling (MDS)
MDS is used to graphically display visual maps of competitive market space and opportunities. Its use
in QFD is described in 9.10.
8.4.5 Repertory grid technique
Repertory grid technique is used to capture customer perceptions regarding competing products. Its
use in QFD is described in 9.15.
8.5 Customer and stakeholder applications
8.5.1 Frequency of use or application
How often a customer uses a product for a certain problem or opportunity can influence design choices.
Cross tabulations can be used to gather and analyze this information. Its use in QFD is described in 9.9.
8.5.2 Robust parameter design
Robust parameter design can incorporate the impact of customer usage and application as well as
environmental influences in order to make the product more robust to these factors. Its use in QFD is
described in ISO/TS 16355-6 and ISO 16336.
8.6 Customer needs
8.6.1 Functional needs using text analytics and text mining
Text analytics of big data is used to augment interview- and observation-derived customer needs, as
described in 9.16. Customer needs are defined in ISO 16355-1:2015, 3.3 and ISO 16355-4:2017, 9.1.3.3.
The use of AHP to prioritize them is described in ISO 16355-4:2017, 11.2. The house of quality is
described in ISO 16355-5:2017, 9.3.6.
8.6.2 Emotional or attractive needs using kansei engineering
In QFD, customer needs are described as the benefit to the customer of their problem solved, their
opportunity enabled, or their image (self or to others) enhanced. Problem solved and opportunity
enabled are functional needs as described in 8.6.1. Image enhanced to self or to others are addressed
using kansei engineering. Kansei engineering uses quantitative techniques such as the semantic
differential, factor analysis, and multivariate analysis to explain what components at what performance
level drive design elements that lead targeted customers to experience specific emotions. This is
explained in ISO/TR 16355-8:2017, Clause 8. For category-type variables, the quantification methods
can be used, as described in 9.13.
4 © ISO 2019 – All rights reserved

8.7 Prioritization
8.7.1 General
Prioritization is used throughout QFD to focus the efforts of the design team. Modern QFD uses the
analytic hierarchy process (AHP) in voice of customer and stakeholder analysis to structure and
prioritize customer needs.
8.7.2 Analytic hierarchy process (AHP)
Prioritize business and project goals as well as prioritize market segments using AHP. (ISO 16355-2:2017,
9.1.2.8 and 9.1.3). Prioritize customer needs using AHP. (ISO 16355-4:2017, 11.2)
8.7.3 L-matrices
Structure and prioritize customer segments using L-matrices and AHP. (ISO 16355-2:2017, 9.2.3)
8.7.4 Cluster analysis
Structure customer needs using cluster analysis (9.4) instead of affinity diagram and hierarchy
diagram. (ISO 16355-4:2017, 10.2 and 10.3)
8.7.5 Analytic network process (ANP)
Prioritize customer needs using the analytic network process (ANP) (9.1).
8.7.6 Benchmarking
Benchmark customer perceptions of current and competitive products (ISO 16355-4:2017, 12.2).
8.8 Product requirements, feature sets, concept options
8.8.1 Conjoint analysis
Conjoint analysis is used to determine what combination of product attributes and performance levels
are most preferred by customers. Its use in QFD is described in 9.3.
8.8.2 Customer needs — Functional requirements matrix (house of quality)
The house of quality is used to transfer prioritized customer needs into prioritized functional
requirements in QFD. Its use in QFD is described in ISO 16355-5:2017, 9.3.6.
8.8.3 Quantification method III
This variation on correspondence analysis helps uncover hidden use cases and appropriate concepts to
address them. Its use in QFD is described in 9.13.2.
8.8.4 Regression analysis
Regression analysis is used to predict the effects of product performance on customer evaluations. Its
use in QFD is described in 9.14.
8.8.5 Repertory grid technique
Repertory grid technique is used to what product features are desired by customers. Its use in QFD is
described in 9.15.
8.8.6 Text analytics and text mining
Text analytics of big data helps extract product features that are referenced frequently in online
reviews. Its use in QFD is described in 9.16.
8.9 Distribution, logistics and inventory, sales channels
New Lanchester strategy can be used to take competitive advantage of distribution and sales channels.
Its use in QFD is described in ISO 16355-2:2017, 9.1.2.6.
8.10 Customer satisfaction surveys and preference benchmarking
8.10.1 Customer satisfaction surveys
Customer satisfaction surveys are described in ISO 10004. Sample survey development guidance is
described in Annex A. These can be used to produce customer satisfaction surveys based on sample
survey methods.
8.10.2 Factor analysis and covariance structure analysis
Customer satisfaction and dissatisfaction surveys can be analyzed to help developers prioritize which
product attributes and functional requirements are strongly associated with customer excitement or
basic expectations. Its use in QFD is described in 9.6.
8.10.3 Fuzzy set theory
Customer scoring on a linear satisfaction scale is difficult when the scores are not crisp. Fuzzy set
theory can be used to improve the process. Its use in QFD is described in 9.6.
8.10.4 Net promoter score (NPS)
Net promoter score is used to measure customer loyalty in terms of likelihood of recommending a
product to others. Its use in QFD is described in 9.11.
8.10.5 Neural networks and artificial intelligence
Neural networks are computer models that use surveys to create and test a hypothesis of customer
satisfaction and preferences. Its use in QFD is described in 9.12 and 9.2.
8.10.6 Regression analysis
Regression analysis is used to predict the effects of product performance on customer evaluations of
current, competitive, and proposed products. Its use in QFD is described in 9.14.
Table 1 — Quantitative voice of customer tools used in QFD
New product
Method or tool Detailed information
development phase
8.2 Market strategy and Analytic network process (ANP) 9.1
trends
Porter 5 force competitive analysis ISO 16355-2:2017, 9.1.2.3
Market position analysis 9.8
Project selection ISO 16355-2:2017, 9.1.2.8
6 © ISO 2019 – All rights reserved

Table 1 (continued)
New product
Method or tool Detailed information
development phase
8.3 Market segments Demographics using cross tabulation 9.9
Attitudinal and cultural dimensions 9.5
New Kano model ISO 16355-5:2017, 10.3.4.4.8.1
Repertory grid technique 9.15
8.4 Competitive space Benchmarking ISO 16355-4:2017, 12.2
Market position analysis 9.8
Multidimensional scaling (MDS) 9.10
New Lanchester strategy ISO 16355-2:2017, 9.1.2.6
Repertory grid technique 9.15
8.5 Customer and stake- Frequency of use or application 9.9
holder applications
Robust parameter design ISO/TS 16355-6, ISO 16336, ISO 16337
8.6 Customer needs Functional needs using text analytics and
9.16
text mining
Emotional or attractive needs using kansei ISO/TR 16355-8:2017, Clause 8
engineering
8.7 Prioritization Analytic hierarchy process (AHP) ISO 16355-2:2017, 9.1.2.8 and 9.1.3
L-matrices ISO 16355-2:2017, 9.2.3
Cluster analysis 9.4
Analytic network process (ANP) 9.1
Benchmarking ISO 16355-4:2017, 12.2
8.8 Product require- Conjoint analysis 9.3
ments, feature sets, con-
Fuzzy multiple-objective decision models
9.7.6
cept options
for FMEA
House of quality ISO 16355-5:2017, 9.3.6
Quantification method III and factor analysis 9.13.2
Regression analysis 9.14
Repertory grid technique 9.15
Text analytics and text mining 9.16
8.9 Distribution,
logistics and inventory, New Lanchester strategy ISO 16355-2:2017, 9.1.2.6
sales channels
8.10 Customer satisfaction Customer satisfaction surveys ISO 10004 and Annex A
surveys and preference
Factor analysis with covariance structure 9.6
benchmarking
analysis
Fuzzy set theory 9.7
Net promoter score (NPS) 9.11
Neural networks/artificial intelligence 9.12, 9.2
Regression analysis 9.14
9 Tools for quantitative VOC and VOS acquisition and analysis
9.1 Analytic network process (ANP)
9.1.1 General
The analytic network process (ANP) is used for multi criteria decision making when there are
dependencies among the criteria (a network), unlike the AHP which assumes independence. Judgments
in ANP represent the influence of one element over another in relationship to a third element in the
[2]
network. This creates a supermatrix of
...