A QUANTITATIVE QFD METHOD TAKING INTO ACCOUNT CO ST AND RISK FACTORS

A thesis submitted to the

Faculty of Graduate Studies and Research

in partial fulfillment of the requirements of the Master's degree of Engineering

Supervised by Professor Vince Thomson

Department of Mechanical Engineering

McGill University

Shan Zhao

Department of Mechanical Engineering, McGill University

Montreal, Quebec, Canada

June 2004 ©

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ACKNOWLEDGEMENTS

1 wish to express my special gratitude to Professor Vince Thomson for being a great

supervisor. 1 thank him for introducing me to research and keeping me interested. My

research and this the sis would not have been possible without his constant encouragement,

patience, assistance, teaching and wise instruction. 1 would like to thank the Department

of Mechanical Engineering, Mc Gill University, for offering me the opportunity to acquire

advanced knowledge and further my education. 1 dedicate this thesis to my parents for

pro vi ding me endless love, understanding, and support.

Thank you!

1

ABSTRACT

This thesis develops a quantitative quality function deployment (QFD) method and

integrates it with the methodologies of value engineering and risk analysis. Numerical

parameters and scales allow measurement of product development and onward

adjustment as the process unfolds. In QFD, customer requirements are usually defined in

terms of design characteristics. In the method developed here, customer requirements are

related to product functions. The use of value engineering then allows costs to be

quantified. The analysis of design uncertainties allows the determination of risk factors.

By introducing the new concepts of customer satisfaction value and an integrated

evaluation index, an appropriate balance between customer and enterprise satisfaction

can be attained in the final product. The new QFD method provides a quantitative

approach based on cost and risk for evaluating how weIl customer requirements are taken

into account in the development of a product.

2

RÉSUMÉ

Cette thèse propose une méthode quantitative de démarche qualité et l'intègre aux

méthodologies d'ingénierie de la valeur et d'analyse du risque. Les paramètres et échelles

numériques permettent de mesurer le produit et d'apporter des corrections tout au long du

déroulement du processus. Dans le cadre de la démarche qualité, les exigences des clients

se définissent habituellement en se fondant sur les caractéristiques de conception. Dans la

méthode élaborée ici, ces exigences sont reliées aux fonctions du produit. L'utilisation de

l'ingénierie de la valeur permet alors de quantifier les coûts. L'analyse des incertitudes

liée à la conception quant à elle permet de déterminer les facteurs de risque. En mettant

en place de nouveaux concepts de valeur de satisfaction du client et d'index d'évaluation

intégré, un juste équilibre entre la satisfaction du client et celle de l'entreprise peut être

atteint dans le produit final. La nouvelle méthode de démarche qualité propose une

approche quantitative fondée sur le coût et le risque pour évaluer jusqu'à quel point les

exigences du client sont prises en compte dans le développement d'un produit.

3

CONTENTS

ACKNOWLEDGEMENTS .......................................................................................................... 1 ABSTRACT ..•................................................................................................................................ 2 CHAPTER 1 .................................................................................................................................. 5 INTRODUCTION ......................................................................................................................... 5

1.1 Background ........................................................................................................................... 5 1.2 Objectives .............................................................................................................................. 6 1.3 Quality Function Deployment ............................................................................................... 7

1.3.1 History and Definition ................................................................................................... 7 1.3.2 House of Quality Matrix ................................................................................................ 9 1.3.3 QFD Models ................................................................................................................ 12 1.3.4 Itnpletnentation ............................................................................................................ 14

1.4 Value Engineering ............................................................................................................... 15 1.4.1 Definition ..................................................................................................................... 16 1.4.2 Implementation ............................................................................................................ 20

1.4.2.1 Information Phase ................................................................................................. 22 1.4.2.2 Function Ana1ysis Phase (or FAST Diagramming Phase) ................................... 22 1.4.2.3 Creative Phase ...................................................................................................... 24 1.4.2.4 Evaluation Phase .................................................................................................. 24 1.4.2.5 Developing and Reporting Phase ......................................................................... 25 1.4.2.6 Implementation Phase .......................................................................................... 25

1.5 Risk Management ............................................................................................................... 26 1.5.1 Risk .............................................................................................................................. 26 1.5.2 Ri sk Management Process ........................................................................................... 27

1.5.2.1 Risk Identification ................................................................................................ 28 1.5.2.2 Risk Analysis and Assessment.. ........................................................................... 29 1.5.2.3 Risk Control ......................................................................................................... 31

1.5.3 Integrated Risk Management in the Engineering Field ............................................... 32 CHAPTER 2 ................................................................................................................................ 39 OPTIMIZATION MODEL FOR IMPROVED QUANTITATIVE QFD .............................. 39

2.1 Background and Assumptions ............................................................................................. 39 2.2 Interpretation and Formulation of House of Quality Matrix ............................................... 41 2.3 Diagramming Function Analysis - the Function Tree ........................................................ 43 2.4 Constructing the Integrated Cost and Risk Analysis Matrix ............................................... 47

2.4.1 Cost and Risk Analysis Matrix 0 - Current Design ..................................................... 49 2.4.2 Cost and Risk Analysis Matrix n - New Designs ........................................................ 56

2.5 Building the Integrated Evaluation Matrix - Current Design and Different Solutions ....... 58 2.6 Deploying Development Matrix and Diagramming - Best Solution .................................. 61

CHAPTER 3 A CASE STUDY .................................................................................................. 65 3.1 HOQ Matrix ........................................................................................................................ 65 3.2 Function Tree and Cost and Risk Analysis Matrix 0 - CUITent Design .............................. 67 3.3 Cost and Risk Analysis Matrix 1 and Cost and Risk Analysis Matrix 2 - New Designs ... 73 3.4 Integrated Evaluation Matrix .............................................................................................. 77 3.5 Deve10pment Matrix and Diagram ...................................................................................... 81

CHAPTER 4 CONCLUSION AND FUTURE WORK ........................................................... 83 4.1 Conclusion .......................................................................................................................... 83 4.2 Constraints and Future Work .............................................................................................. 86

APPENDIX: P ARAMETER DEFINITION ............................................................................. 88 REFERENCES ............................................................................................................................ 91

4

1.1 Background

CHAPTERI

INTRODUCTION

"As the twig is bent, so grows the tree." This old saying is usually applied to children

as they grow to adulthood. It is also true for product concepts brought to life as products

in full production, or freshly introduced services or newly designed and implemented

processes. Earliest inputs must be right because they determine the direction of so many

activities that follow. Just de ci ding what concept will be worked on and developed

further defines and commits about 80% ofthe final costs (processing and assembly labor,

raw materials, processing and purchased parts and so on). It also determines the total

cycle time (product or service), the durability of the product, the ease of consistently

delivering the product or service, and several of the customers' perceptions about the

quality of the product, service or process. If the earliest activities in the design process

are well structured, it will definitely save waste and costly iterations later.

Everyday, a great variety of new or improved products are designed and

manufactured, which offer enough space for customers to become more selective in

making their choices. To survive the fierce competition in the market, companies have to

develop quality products and customer satisfaction becomes more and more of a concern

in the product design phase. As a well-known customer-oriented approach, quality

function deployment (QFD) is widely used not only to make the process possible to

translate the voice of customers into the language of designers, but also, it can assist in

making decisions during product design and development.

5

Generally, most of the existing methods and models used for QFD are able to pay

enough attention to customer requirements, and a higher customer satisfaction level is

regarded as the key to designing and developing products successfully. But these

techniques seldom consider the effect of resource constraints on the enterprise, such as

budget, time, labor. .. , nor do they usually take into account the impact of design

uncertainties during product development, for example, cost, risk, market and so on.

1.2 Objectives

In this thesis, a quantitative model is presented to offer a more practical means of

integrating value engineering and risk analysis methodologies with the QFD planning

process. In the new integrated QFD model, customer requirements are re1ated to product

functions and to how well customer requirements can be satisfied in order to evaluate a

design. Besides, financial factors and design uncertainties are considered during product

design by deploying detailed cost and risk analysis at the level of design functions. The

job plan in value engineering offers a systematic structure for analysis. Furthermore, by

introducing the new concepts of customer satisfaction value and an integrated evaluation

index, the best balance between customer and enterprise satisfaction can be achieved. In

summary, the objectives ofthis thesis are:

• to develop a quantitative QFD model and to integrate it with the methodologies of

value engineering and risk analysis in order to consider not only customer

satisfaction, but also enterprise satisfaction by making detailed cost and risk

analysis during design,

• to introduce the new concepts of customer satisfaction value and an integrated

evaluation index to assist in decision making,

6

• to propose a method for companies to determine the realistic target unit cost for

design in order to obtain the best balance between customer and enterprise

satisfaction with consideration of financial factors and design uncertainties.

The structure of this thesis is as follows. First, a general introduction of the concepts

and methodologies of QFD, value engineering and risk are made in Chapter 1. Next, the

theoretical description for the optimization model is given in Chapter 2. Moreover, in

Chapter 3, an example is provided to show how the model works. Finally, conclusions

and future work are summarized in Chapter 4.

In order to help readers to understand the approach better, different colors are

employed in the matrices in Chapter 2 and Chapter 3. Input information is indicated by

colorful (red, bIue or pink) letters or numbers (not in colorful shades). Output information

(or decision indicators) is highlighted in colorful shades, such as, light yellow, tE, Il,

or light turquoise, r'lill~t~~, light green. In addition, formulae are first defined

in the theory model and then expressed in Excel worksheets. Therefore, ca1culations can

be seen more easily and understanding the process is simplified.

1.3 Quality Function Deployment

1.3.1 History and Definition

Among various customer-oriented methodologies for product design and

development, Quality function deployment (QFD) is widely adopted. The concept of

quality function deployment was first proposed by Akao in 1966. Akao published the

idea as a system in an April 1972 magazine article under the name Hinshitsu Tenkai.

Again, Akao together with Shigeru Mizuno published the first book on QFD in 1978.

And the term "quality house" was presented during a Japan Standards Association

7

conference in 1979. Then, QFD was introduced into the entire Toyota Group. Four years

later, QFD was formally introduced to the United States by Furukawa, Kogure and Akao

during a four-day seminar for about 80 quality assurance managers from prominent U.S.

companies. And in 1984, Donald Clausing ofXerox introduced QFD to Ford. Moreover,

Ford suppliers developed the first case study on QFD outside Japan in 1987. In the next

year, Don Clausing published their article, "The House of Quality", in the May-June

1988 edition of the Harvard Business Review. In 1994, the QFD institute was founded by

Mr. Glenn H. Mazur together with Mr. Richard Zultner and Dr. John Terninko.

Over 40 years since the basic concepts and fundamental methodology of QFD have

been known, QFD has been defined in many different ways. According to the definition

by the American Supplier Institute (ASI) in 1987, QFD constitutes a system for

translating customer requirements into appropriate company requirements at every stage,

from research through production design and development, to manufacture, distribution,

installation, marketing, sales and services. Another definition is that by Akao (1990)

which states that "QFD is a method for developing a quality design aimed at satisfying

the customer and then translating the customer's demands into design targets and major

quality assurance points". It provides a systematic environment for product design based

on customer requirements and involves all members of the producer organization. What it

focuses on is to improve the efficiency of the initial product design. Nowadays, QFD

methodology is considered to be useful, particularly for its benefits in planning. In the

very near future, it could come to constitute the cohesive element within a group of

instruments able to create an integrated environment for decisional aids in the field of

design (Franceschini, 2002).

8

1.3.2 House of Quality Matrix

QFD bridges the communications gap between the customer and the personnel

involved in choosing the concepts as weIl as designing and engineering the product to be

manufactured, distributed and maintained in the field (Revelle, Moran, Cox, 1998). The

linkages between the customer and the design are rationally structured in a series of

matrices, in which the most basic matrix used is the house of quality (HOQ). It assists in

the conversion of customer's requirements, the Whats to How the customer requirements

will be met (quality characteristics). Its matrix structure and visual nature gave both

discipline and guidance to the conversionprocess. A typical house of quality is shown in

Figure 1.1.

Section A contains a list of customer requirements. These are usually provided by the

marketing department after investigation with customers, which involves information

coIlected from actual customers of the product about their statements of their needs and

wants, and their prioritization of these desires. In this phase, it is important to maximize

customer involvement to determine product structure because any misunderstanding of

customer' s demands and needs will lead to wrong decisions about development process

characteristics, not to mention product characteristics. When all the information has been

collected, the list should be then refined and sorted; similar items need to be grouped into

one category, which will help focus the QFD process. Besides, the customers' own words

should be used so that the actual quality that customers really demand can be expressed

and understood. Generally, customers' desires are vague and not easily quantified.

9

1

Section A en

~ Customer ::t:

Requirements lS """ 0

1

01

WHATs t:: = 'fil =~ o <!) .• u

WHATI t: § - - ~

00 6 " .§ .... <!)

S .8 '" ;:l u

Section E Correlation Matrix ;;:

1

Section C 1 1

Design Requirements -~

1 HOWs

1 @

1

Section D Relationship Matrix

~

Symbols for Strength of Relationship: * = Strong o = Medium t. = Weak

Figure 1.1 House of Quality

1

1

N

~ @

Symb ols for Positive or Ive Relationship: Negaf

++= +=P

Strong positive ositive

- =Ne gative -- = St rong negative

Section B in the House of Quality can inc1ude recorded information for

• importance rating

• customer satisfaction levels towards the current product

• customer satisfaction levels towards competitors' current products

• setting the goal, de ci ding which level of customer satisfaction needs to be

attained, and

• setting values for every customer attribute.

This step collects data from customers as a basis for comparison. By figuring out the

importance rank and showing how well both the company and competitors meet

10

customer' s needs, it helps the design team to discover the strengths and weaknesses of

the product on both sides, capture additional customer' s requirement, and put more

emphasis on the most important attributes for using limited resources (money,

time, ...... ) more effectively.

Section C contains technical attributes (or design characteristics) of the product which

serve as design requirements. This part is the 'HOWs' with respect to the customers'

voice, and the 'WHATs' that emphasizes problem resolution. The criteria for this

technicallanguage are:

• global, solution-independent, and not constrained by any design alternatives or

approaches

• meaningful, i.e., subsequently actionable to drive the design process, and

• measurable, i.e., defining the target value.

Section D contains the relationships between customer requirements and design

requirements. One way is to put a value in each cell in the relationship section, where the

value reflects the extent to which the products' attributes (HOWs) contribute to meet the

customers' attributes (WHATs). Along with the importance ratings of the customer

attributes, section D establishes the contribution of the technical attributes to overall

customer satisfaction. This contribution also represents the importance of every technical

attribute, which is recorded in section C.

Section E contains correlations between technical attributes, which determine whether

each technical attribute supports or impedes the others. More effort needs to be put on the

negative relationship, where two technical attributes adversely affect each other, and

11

usually, tradeoffs need to be made; otherwise, additional research and development effort

may be needed.

The QFD chart helps the design team to set targets on issues which are the most

important to the customer, and how these can be achieved technically. The ranking of the

competitors' products can also be performed by technical and customer benchmarking

(Bouchereau and Rowlands, 2000). The QFD chart is a multifunctional tool that can be

used throughout the organization. For engineers, it is a way to summarize basic data in a

usable form. For marketing, it represents the customer's voice and general managers use

it to discover new opportunities (Clausing and Pugh, 1991).

1.3.3 QFD Models

QFD also provides a set of matrices that serves as both a structure and a graphic

representing the deployment process. These matrices are helpful towards understanding

relationships between inputs and outputs.

As shown in Figure 1.2, a typical four-phase model is composed of four matrices,

which divide the QFD process into four stages. They are:

• product planning (house of quality)

• design deployment (part deployment)

• manufacturing planning (process planning)

• production planning (production operation planning).

The output of every stage is the input of the next stage, which helps communicate

customer requirements from product design through to production.

12

Substitute Quality 1-

Characteristics Pro duet Planning

"E U ... ., s S .9 ,~ ~ b" o&! Part

Characteristics 1-

! HowMuch 1- Design Deployment ,è U ~ ,~

~ Customer Requirements LJ. a,~

~2 '~ j Manufacturing ~o Operations 1-

HowMuch 1-

" U Qualified Pl'Oduc u

'.jj ... ,~

LJ. '52 p.. n

"'-j Production

ts

Manuf ae turing Planning ()

Requil'ements

HowMuch -

J~ U 4-

-il ~ ~

Pro duetion Planning ~~ HowMuch

Figure 1.2 Four-Phase Model (Revelle, Moran, Cox, 1998)

The information needed to create the various matrices, especially the first matrix (the

house of quality), cornes from a wide variety of sources. Acquiring the information

typically requires inputs from marketing sources, technical sources, field service

personnel, customer service representatives, and most importantly, representatives from

several different sets of key customers. Most of these groups should be represented on the

QFD design team. The larger the area over which the information 'net' is cast, the more

13

likely the end result from the QFD design process will fully meet (or exceed) customers'

expectations.

Furthermore, when the QFD approach is used to consider all the multilevel analyses

and details associated with deploying the voice of the customer, it becomes apparent that

the four-phase model is not enough for many projects. So, the matrix of matrices can be

used to provide a more detailed understanding, which can contain several matrices at

every step of the deployment process. Determining the matrices that will be necessary

depends on the project's goals (or desired outputs) and what intermediate matrices will be

needed to obtain the goals.

1.3.4 Implementation

For QFD to be effective, it must be accepted as an integral part of the organization's

design process. Every organization takes its own approach to design, but aU design

processes share sorne basic activities. A QFD approach may help an organization

discover exciting and pertinent information previously overlooked. These discoveries are

a crucial part of connecting the voice of the customer to the design. The knowledge

gained gives the producer of the product a much better understanding of the needs of the

user and the environment where it will be used.

QFD emphasizes co-operation, convergent consensual decision making, and

systematic linkage of engineering activities (Clausing and Pugh, 1991). Therefore, the

interdisciplinary work team becomes an absolute premise for successful implementation.

It would include experts from design engineering, process (manufacturing) engineering,

research and development, marketing/sales, quality assurance and test, field and customer

service, manufacturing, in sorne cases distribution, and if the organization is project

14

driven, project engineering. If an organization is not project oriented, it is a good idea to

have a person with project management skills leading and facilitating the design team.

Besides, in order to extend QFD to large scale systems, attempts have been made to

integrate QFD with other design-supporting techniques. For instance, the research work

by Nilsson demonstrated how QFD can be linked to an integrated digital requirement and

concept model containing product-related information that evolves during the early

phases of the product development process. It is proposed that in order to gain maximum

benefit, QFD should be applied on a subsystem level and to subsystems that interact to a

large extent with the user. In addition, Bouchereau and Rowlands (2000) in their paper

de scribe efforts made to combine fuzzy logic, artificial neural networks, and Taguchi

methods with QFD. In another paper by Yamashina, Ito and Kawada (2002), a new

method named Innovative Product Development Process (IPDP) is proposed to integrate

QFD with TRIZ (a Russian acronym for Theory of Inventive Problem Solving); IPDP

enables the effective and systematic creation of technical innovation for new products.

1.4 Value Engineering

The value engineering (VE) methodology has been with us for a long time since the

original concept of value methodology was first proposed by Lawrence. D. Miles when

he was assigned to develop a plan to pro duce products for the General Electric Company

(GE) using fewer available materials during 1940s. In 1952, the first value analysis

seminar was conducted at GE. Two years later, the Navy Bureau of Ships, a division of

the Department of Defense, USA, established a value program and applied it at the

engineering stage. After that, due to the successful implementation of value engineering

in the Navy, the U.S. Army and Air Force also launched value engineering programs. In

15

1958, the Society of American Value Engineers (SAVE) was established as an

organization in Washington,. which defined VE methodology formally and which was

dedicated to strengthening its practice and development. Now, VE is popular worldwide.

In the field of value engineering, three terms are used to describe the application of

value techniques:

• Value engineering (VE) describes a value study on a project or product that is

being developed. It analyses the cost of the project as it is being designed.

• Value analysis (V A) describes a value study of a project or product that is already

built or designed and analyzes the product to see if it can be improved. Its

objective is to determine which alternative will pro duce the best value, for

example, which design will give the most reliable performance or the best sales

with the least cost. In many places, the terms VE and V A are used

interchangeably.

• Value management considers the value created in an organization and tries to

maximize the value added along the value chain. The term, value management, is

used to de scribe the entire field of value endeavors, and was first used by the

General Services Administration (USA) in 1974.

1.4.1 Definition

The SAVE international society defines Value Analysis/Engineering as follows.

Value AnalysislEngineering is the systematic application of recognized techniques

that

• identify the functions of a product or service

• establish a monetary value for the functions, and

16

• provide the required functions at the lowest overall cost.

In addition, the research by Del L. Y ounker (2003) defines VE in more detail. VE

means an analysis of the functions of a program, project, system, product, item of

equipment, building, facility, service, or supply of an executive agency performed by

qualified agency or contractor personnel, directed at improving performance, reliability,

quality, safety, and life cycle costs.

From the definitions, it is seen that the definition and analysis of functions are the

foundation of VE, which means VE focuses on what a product does for the customer

rather than what it is. VE defines two types of functions:

• A basic function is what products must do to satisfy customer's needs. It is the

main reason why products are produced. It is also regarded as the essential value

of products.

• A secondary function is what attracts customers to select the product among the

products with the same basic functions. Even though secondary functions can help

to provide customers convenience, attraction and satisfaction, they can be wanted

or unwanted.

The basic functions are the functions upon which secondary functions depend. For a

product, if the basic functions are not required, all the other dependent functions become

meaningless since they exist in order to support the basic functions.

Basically, a function is described by two words, a verb and a no un, which compose a

statement as short as possible. In the statement, the verb defines the required action and

the noun tells what is acted upon. Besides, the function should be measurable so that a

value can be established for the function. Furthermore, the proper definition of functions

17

should be able to help perceive the product in a new way, which means it will offer

opportunities for creativity "to break down the mental constraints and open minds to new

ideas".

Since VE supports cost reduction activities by relating the cost of components of a

product to their functions, function becomes one of the most important concepts of VE.

Defining and analyzing functions can help not only to find out what the product must do,

but also to figure out the most cost effective way of achieving attributes.

Another important term used in VE methodology is value. As Richard Park said

(1999): "A discussion of value is necessary because a measure of value makes it possible

to determine ifwe are spending our money wisely or ifwe are not getting good value." In

other words, the measurement of value can present the background for comparison, and

at the same time, give the direction for cost reduction.

According to the definitions by Webster's New International Dictionary,

• value is a fair return in goods, money, or services etc, for something exchanged,

• value can also be the monetary, estimated or assessed worth of a thing.

Although value is often defined in terms of dollars, it is not equal to cost. According

to Richard Park (1999), cost is a factor that is directly related to the product or service.

Value is an opinion and is related to a want or a need: what the product or service do es

for you or its performance relative to expectations. It is also important to recognize that

value is subject to rapid and measurable change as conditions change.

Value may be measured by using a scale in dollars, time, power, or just by

determining the weight of one versus another. From different points of view, value can be

18

described in different ways. Usually, it can be given as a relation or a ratio. Two typical

equations were proposed by Robin Cooper and Regine Slagmulder (1997) as follows:

•

•

TI" l bene fit raue=-.-..::....-cost

P . d l perceivedbenefits ercelve va ue = "'---------'---

priee

(1.1 )

(1.2)

Equation (1.1) reflects the perspective of the producer and Equation (1.2) reflects the

perspective of the customer. Actually, the cost and price in the two equations play the

same kind of relative roles as the internaI (producer) and external (customer) agents for

the survival of the product. On the one hand, VE practices should be combined with

customer analysis and other techniques together to ensure customer satisfaction; on the

other hand, VE programs are concerned about how to improve value or reduce cost

without sacrificing function.

Generally, in the VE technique, values are set for functions, i.e., what the product

should and can do for customers. The research by Richard Park (1999) demonstrated

several categories for the value:

• use value - properties that make something work or sell

• esteem value - properties that make something desirable to own

• ex change value - properties that make it possible to exchange one thing for

another.

Related to functions, the best values can also be defined as the lowest cost to provide

the functions. The objective of VE is to optimize value, which can be achieved by

different ways, for instance:

• to accomplish basic functions at lower cost

19

• to reduce the cost of secondary functions

• if possible, to eliminate or merge as many secondary functions as possible in

order to lower cost.

Here, three points should be clarified. For the first, basis functions represent the

existence of a product. No matter what will be done to improve the product or reduce cost,

etc., basic functions must be achieved. Secondly, the measure of 'cost' does not have to

be the dollars; it may also be time, power, or any other proper scale unit which can

describe the value of a function. Thirdly, when talking about lowest cost for a given

benefit, customer satisfaction and quality must be ensured. As is well known, there is a

trade-off between customer satisfaction and cost. Lots of methods are initiated and

developed to deal with how to find the proper balance point between customer

satisfaction and cost, which lS also the key and objective for the successful

implementation of VE programs.

In summary, the objective of VE is to improve the value of functions, and the force

driving VE is to ensure that the product achieves its functions in a way that is able to

satisfy the customer at an acceptable cost. Greater value can be achieved by increasing

performance and holding cost the same, or decreasing cost and holding performance the

same, or improving both.

1.4.2 Implementation

A multidisciplinary team and a formaI job plan are two important elements for the

implementation of VE. The former is the organizational premise and context. The latter

provides the guideline and structure.

20

At first, a multidisciplinary team and efficient teamwork can be helpful to understand

the current situation thoroughly because the information and knowledge can be collected

from different views. AIso, the questions can be addressed from varied aspects, and

solutions may be proposed for the problems and combined if possible to be more

powerful. A multidisciplinary or cross-functional team should be selected from skillful

professionals with different work experiences and from different functional divisions,

including marketing, engineering, manufacturing, sales, finance, and so on. As the

members of a successful team, they should be responsible, knowledgeable, good at

communication, capable of making a commitment, and creative which is especially

important for VE.

Another important element of VE implementation is the job plan, which creates a

structure that leads a team through the process and at the same time, compensates for the

peculiarities of the individual (Richard Park 1999). Generally, the job plan involves

several phases:

• information phase

• function analysis phase (or FAST diagramming phase)

• creative phase

• evaluation phase

• developing and reporting phase

• implementation phase.

Following the six steps, analysis and creativity can be used at the proper place in the

process by a team to access and assess the problems, and then create new ideas and

propose solutions.

21

1.4.2.1 Information Phase

In this phase, the first thing to do is to select the project and its major components,

which may be a high cost project, a high risk project or a problem project. Then, the

required multidisciplinary team should be selected and constructed. In addition, one

should make sure that owner, team and other personnel involved understand the VE

process and are clear about the objectives for improving the project.

Next, more effort should be paid to collect an the pertinent information before the VE

study starts. The team can absorb the information in different ways (verbal, written,

visual, etc.) from different resources:

• documents or company agencies

• facilities where the products are designed, manufacture d, packaged, shipped and

returned

• marketing department with information about customer satisfaction

• finance department for the cost data, and

• department managers for trying to make sense of the project for the associated

items.

During the process, make sure that information collection will never stop, and that

information is always up to date. Sometimes, the latest information may change the

direction of the whole study. After obtaining enough information, it needs to be sorted

and summarized for further analysis in the next phase.

1.4.2.2 Function Analysis Phase (or FAST Diagramming Phase)

The heart of the function analysis phase is to define and analyze functions integrated

with cost analysis. The function analysis system technique (FAST) is employed to

22

structure the functions and cost information into a functional tree. A simple example of a

functional tree (Lucie Parrot, 2002) is shown in Figure 1.3.

HOW? ~

WHY?

!wt_!~~I~

Cost Worth Cost/Worth Push

Pass on mower $12.00 $13.00 0.92 The Lawn

Assist movement $18.00 $20.00 0.90

---Limit Muffle noise exhaust $6.00 $6.00 1.00

Store Supply fuel $3.00 $3.00 1.00 power

Collect solar energy $45.00 $30.00 1.50

Totals: $84.00 $72.00

Figure 1.3 An Example of a Function Tree

The tasks to be done in this phase inc1ude:

• to identify and determine functions

• to evaluate and c1assify functions as basic and secondary functions

• to set worth (desirable expense or target cost) per function

• to ascertain the co st of every function

• to deploy the process and the information in a functional tree

• to determine the value per function by dividing the cost by the worth of the

function.

During the process, special attention should be paid to:

23

• assessing customer requirements and attitudes when determining and analyzing

functions

• eliminating unneeded functions and combining functions if possible

• finding the functions with value (cost-to-worth ratio) higher than 1 for cost

improvement

• clarifying the opportunities or problems for the work in the next creative phase.

1.4.2.3 Creative Phase

In this phase, more effort is made to develop new ideas for solving the problems

defined in the previous phase for the functions with bad value. The key to creativity is to

encourage a free flow of ideas without considering costs. No judging during idea creation

is imperative.

Sorne methods are helpful to create ideas, such as, brainstorming, imagination,

positive thinking, accepting aIl ideas, free for opposite attitudes, listing ideas, and so on.

Then, by summarizing, sorting, and combining ideas (if possible), alternatives are

presented. Each of the alternatives should satisfy the basic functions and to sorne extent,

fulfill the secondary functions.

1.4.2.4 Evaluation Phase

The objective of this phase is to evaluate the possible alternatives generated in the

creative phase and find the best ones on which to focus effort for further development in

the next phase.

For each alternative, both advantages and disadvantages are listed. The alternative

with disadvantages that far outweigh advantages should be put aside or abandoned. AIso,

the evaluation matrix can be constructed to rank the alternatives by using the desirable

24

evaluation criteria. Furthermore, as Del L. Y ounker proposed (2003), if none of the

alternatives performs every criterion satisfactorily, return to the creative phase. Use the

knowledge gained in evaluation to create new alternatives.

1.4.2.5 Developing and Reporting Phase

During the development process, by organizing various ideas and comparing new

alternatives with the original situation, the best ideas from the evaluation phase should be

chosen and developed depending upon: feasibility, conditions for implementation,

investment and savings, effect of the changes on product and organization, risk analysis,

etc. Different worksheets can be designed to aid in deploying development and

assessment process systematically and avoid necessary information being overlooked. As

a result, practical project solutions are formed, and expected consequences of successful

implementation are forecast.

Moreover, a weIl prepared presentation should be given for approval. The report

should show how to implement the solutions, what the differences between the proposed

solutions and original situations are, advantages and disadvantages, cost analysis and

value improvement, and so on. The most important item is to clarify and propose the

instant actions which the audience should take to make the solution work.

1.4.2.6 Implementation Phase

At first, since each project is different and the people involved are different, a

different set of values will be discussed and the implementation method will also vary for

every project. Due to the diversity of methods, each project's implementation plan and

schedule should be established, and an effective monitoring system should be built to

ensure proper fulfillment.

25

The research by Del L. Younker (2003) proposed that the design professional and the

owner review the proposed alternatives independently and determine the applicability of

each alternative. The design professional documents the reason why specific alternatives

have not been implemented. The owner directs the design professional to implement

those alternatives that best meet his needs and requirements. In aU cases, the design

professional is responsible for determining the technical feasibility of an alternative, and

each alternative must be independently designed and confirmed before its implementation

into the project design.

1.5 Risk Management

GeneraUy, risk arises from uncertainty, which is defined as "doubt about our ability to

know". Since uncertainty is inherent in material strength, engineering design,

manufacturing pro cess, operating environments, etc. (Wang and Roush, 2000), risk

inevitably exists in each phase of the life cycle of the product or the project. Since the

term risk management emerged in the mid-1950s, companies have become more and

more conscious of risk and the means of managing risks.

1.5.1 Risk

Risk can also be defined as the "variation in outcomes around an expectation." In

other words, risk refers to how life differs from what is expected (Young and Tippins,

2001). In addition, according to Kliem and Ludin (1997), risk is the occurrence of an

event that has consequences for, or impacts on, projects. They categorized risks in

different ways:

• acceptable vs. unacceptable risks

• short-term vs. long-term risks

26

• positive vs. negative risks

• manageable vs. non-manageable risks

• internaI vs. external risks.

Aiso they pointed out the five key elements of rislc

1. The probability of occurrence of a risk. In a qualitative way, the question, 1s the

probability low, medium, or high? is asked and answered, and quantitatively,

should the probability be anywhere from a to 100 percent?

2. The frequency of occurrence of a risk, which means how often the event might

happen.

3. The impact of an occurrence of a risk. What consequence will it have?

4. The importance relative to other risks. Not all risks are equal and sorne have

greater importance than others for a project's outcome.

5. The exposure, or vulnerability, which is the impact of a risk on a product, system

or project. A risk can have different levels of exposures as well as varying

probabilities of occurrence based on given circumstances. Therefore, exposure is

simply the level of impact times the probability of occurrence.

1.5.2 Risk Management Process

As Young and Tippins (2001) define, risk management is any measure taken to

evaluate risk and to control its potential impact. Since uncertainties and risk are presented

at every stage of a project, risk management becomes a crucial strategy for any company

that wants to achieve its expected objectives successfully. Efficient risk management can

avoid unnecessary risk and control acceptable risk knowledgeably, and therefore,

27

minimize the negative consequences incurred. The risk management process is composed

of three steps which are risk identification, risk analysis and assessment, and risk control.

1.5.2.1 Risk Identification

As the first step of the risk management process, risk identification works as the

premise for both accurate risk analysis and assessment, and efficient risk control. The

identification process involves:

• systematically describing and researching the structure of a project or a product

based on the information collected from different resources, such as

environmental (physical, social, economic, political, legal, operational, etc.)

narration, financial statements, legal documents, jobsite inspections, interviews,

statistical analysis, benchmarking, consulting services, and so on.

• ascertaining the uncertainty factors and recognizing all the possible mechanisms

of failure. For engineering projects, some possible areas for risk include late

design changes and defects, manufacturing variability, the interface between

engineering design and manufacture, structural and technical failures (materials,

tools, process procedures, installation specification), wear-out failures, special

severe environment, human factors, etc. Furthermore, the research by Wang and

Roush (2000) presented four major types of uncertainties, which are inherent

uncertainty (due to variability inherent in the material or the environment),

statistical uncertainty (resulting from incompleteness of statistical data), modeling

uncertainty (resulting from assumptions made in analysis of stresses and strengths,

i.e., the use of simplified models), human error (including errors in calculation,

chosen wrong known data, inadequate design review, failed to calculated critical

28

conditions, poor quality fabrication, wrong materials used, poor judgment and

abuse by operators). AU this information will help to identify risk.

• simplifying the process. It is necessary, after identifying aH initial events, to

combine those that pose the same threat to hazard barri ers and require the same

mitigating functions of the process to prevent hazard exposure. (Modarres, 1993)

1.5.2.2 Risk Analysis and Assessment

The second step of risk management is risk analysis and assessment, in which the raw

data collected in the phase of risk identification is converted into the information

describing the five key elements of risk: probability, frequency, impact, importance, and

exposure. Generally, risk analysis consists of answers to the following questions:

1) What can happen that could cause exposure to hazard?

2) How likely is it to occur?

3) Suppose it happens, what are the consequences?

To answer these questions, first, a list of outcomes or a series of events leading to the

outcomes should be c1arified, and then, the likelihood of the occurrence should be

estimated. Finally, the consequence of each event should be described.

Besides, the research by Kliem and Ludin (1997) demonstrated the two categories of

risk analysis:

• quantitative techniques, which rely heavily on statistical approaches, such as

Monte Carlo simulation

• qualitative techniques, which rely more on judgment than on statistical

calculations, such as heuristics.

29

What's more, a number ofnew methods have been adopted to analyze and assess risk,

such as fuzzy arithmetic, intervai analysis, and artificiai intelligence based methods.

Regardiess of which method is chosen, sorne rules should be considered.

• There are no 100% correct models. Each different method has its own advantages

and disadvantages. Sometimes, integration of methods can get the best result.

• Because the environment can keep changing, data in a model will become "dated"

or incorrect from time to time. Risk is not constant and risk analysis and

assessment is a dynamic process. Therefore, updating information for a moving

target becomes more important.

• lndividual perception for risk may be different from pers on to person. Even the

same person may make a different assessment under similar conditions. In order

to minimize the influence ofhuman factors, a multi-functional team composed of

experienced engineers from different departments is necessary. Also, the

assumptions used shouid be clarified and agreed by team.

• Ranking risks based on their importance to the project is necessary for reasonable

allocation of risk management. Prioritization of risks can help find which risk can

be eliminated completely, which risk is so minor as not to de serve detailed

management effort, and also, which risk should be paid special attention because

of its high probability of occurrence or serious potential impact.

• Risk deals with exposure and uncertainty. Exposure occurs when a risk makes

something vulnerable. Different levels of the vulnerability manifest themselves in

the form of impacts. What complicates the situation is the level of uncertainty of

the risk occurring and its impact. (Kliem and Ludin 1997)

30

1.5.2.3 Risk Control

The essence of risk management is to manage risks in a manner that can create

designs with less chance of failure and so accomplish expected project objectives

effectively. Therefore, how to control risk effectively becomes the most important point.

Several ways are proposed:

• to accept tolerable risk, which can also be regarded as acceptable risk. According

to the research by Modarres (1993), an assessed risk from any controllable

activity should be required to be lower than risk thresholds in order to be defined

acceptable. The societal benefits and the cost trade-offs for risk reduction are

widely used guides to set and justify risk acceptability limits. By comparing the

risks and benefits associated with certain activities, fair, balanced and consistent

limits for risk acceptability can be set and institutional controls on risk can be

established.

• to prevent risk by reducing the occurrence of accidents (or the probability of their

occurrence). In other words, to delete the avoidable risks and minimize the

likelihood of unavoidable risks. It can be achieved by identifying sources of risks

or failure mechanisms, and then removing or improving the related parts or

processes based on the type of risks.

• to mitigate the impact of an undesired event when it does happen. On one hand, a

monitoring or waming system should be installed and assured to work efficiently

enough to detect the actual deviations from plans and to keep on monitoring.

Hence, the consequence of potential risk can be perceived as soon as it is

observed. On the other hand, emergency response measures should be prepared

31

and established to minimize the impact of failure if it does happen. Therefore,

catastrophic loss can be avoided or minimized.

• to transfer risks to insurance companies by contracting for insurance, or to

vendors and suppliers by lowering the price or shortening the delivery time, and

so on.

In summary, risk management is a continuous loop. Throughout the project or the

process, environment or conditions may vary from time to time, which can bring out new

uncertainty factors; therefore, the related risks should be continuously identified, assessed

and controUed throughout the project. Currently, many risk management methods are

available, such as Fault Tree Method (FTM), Event Tree Method (ETM), Decision Tree

Method (DTM), Failure Mode and Effect Analysis (FMEA), and so on, to help detect

potential failures and their possible impacts, and then to try to control risk although not

aU risks can be avoided.

1.5.3 Integrated Risk Management in the Engineering Field

In the engineering field, the process of product development is accompli shed by

making decisions. Each engineering decision has the possibility to make mistakes and to

have loss. The possibility of failure and loss exists almost everywhere and risk is inherent.

For engineering projects, risk can be defined as the deviation of project outcomes from a

mean or anticipated value. It can also be regarded as the chance of incurring a loss or gain

by investing in an engineering project (Wang and Roush 2000). A good engineer is clever

at making decisions that can maximize possible gain, and minimize possible loss;

therefore, risk management becomes a crucial part of aU engineering design decisions.

32

According to Wang and Roush (2000), the central features of any engineering project

are to produce a result that leads to customer satisfaction, to deliver on schedule, and to

deliver within budgeted cost. At the start of any project, there are uncertainties in each of

these factors and it is important to manage the associated ri sIc. This management can help

limit the potential for negative consequences arising from these uncertainties and

maximize the possibilities that results will be better than the target values.

First, related to the uncertainty of resources, technical performance, schedule

outcome, market performance, etc., the major risks involved in engineering projects can

be defined as cost risk, technical risk, schedule risk, and market risk. Engineers often

experiment with the possibilities of improving their products while lowering the cost of

manufacturing. The potential for over-budget cost is a major concern for every engineer.

Cost risk can be estimated in terms of probability and the extent by which final cost is

expected to exceed planned cost. In order to minimize cost risk and maximize

productivity and profitability, a thorough and accurate life-cycle cost analysis is

prerequisite to make reasonable decisions. Since the early design phase of a product or

process has the greatest impact on life-cycle cost and quality, more effort should be made

to optimize product design which can achieve expected cost savings and quality

improvement at the same time. The research by Wang and Roush (2000) demonstrated

how Taguchi's robust design method provides the designer with a systematic and

efficient approach for conducting experimentation to determine near optimum settings of

design parameters for performance and cost.

Can every scheduled manufacturing step follow the designed time table? Will the

final product be delivered on time? How bad will be the effects of the delay? ... . .. These

33

questions should be asked when schedule risk is concerned. It can be expressed in the

term of possibility and the extent by which the expected procedures will be behind the

planned schedule. Managing schedule risk means "arranging the project tasks, and their

resources (human and other) in sequence that facilitates their completion". Sometimes,

companies not only care that the project or product should be accompli shed on time, but

also they hope it could be done by using minimum resources. According to the research

of Wang and Roush (2000), managing schedule risk usually requires breaking the project

into component tasks, figuring out the order in which the tasks must be completed, and

assigning the necessary resources to each task. The simple st management method is

Gantt Charts developed by Henry Gantt, in which each activity necessary to complete a

project or a product is plotted and arranged in the sequence of time with duration marked.

Milestones and markers are provided to make sure every step is on schedule. Moreover,

as another powerful tool, Project Evaluation and Review Technique (PERT) is employed

not only to identify the tasks, their sequence and duration, but also to specify the

relationship between tasks and the resources needed to fulfill each task in required time

period. Once the network of tasks is determined, the Critical Path Method is used to

reallocate resources to find the shortest time in which the project can be completed and

what resources would be needed to do that.

When a new or improved product is introduced into the marketplace or a product is

presented to a new marketplace, such as, in different countries or with changed user

groups, market risk becomes unavoidable. It can be denoted by estimating the possibility

and the extent by which expected market introduction will fail to meet the planned

34

market outcome. In order to lower the influence of the market risks, several points are

necessary:

• to carefully describe the markets addressed (both original and new) and their

difference, and to investigate composition of the customer group in detail

• to identify the market distribution of similar products

• to benchmark customer satisfaction and technical characteristics, to understand

how customers perceive the products versus their competitors, and at the same

time, to identify the areas for competitive advantage and disadvantage

• to introduce sample products to the market and analyze the market outcome.

In addition, diversity and variability of the market entail the market risk itself.

Relatively, data and information must be refreshed and collected from a wide range of

sources to make an accurate evaluation of possible risks. This also increases the difficulty

of market risk management.

Development of society arouses the improvement of technology. In order to achieve

higher commercial and industrial objectives, efforts are made to improve the CUITent

materials, tools, methods, procedures, etc., which results in the renovated or new

technology being adopted. Broadly speaking, for a project, a new technology can be

regarded as any technology which is new. In the other words, not only can it be the one

which has never been used before for a similar process or to produce the same kind of

products, it can also be the one which is just new for the particular project or the

company. Generally, because of the lack ofknowledge and experience, anything new will

lead to something uncertainty. Therefore, how to manage related technology risks leads

to sorne questions about:

35

• description: What is the new technology? How is it used? What are the challenges

and problems?

• assessment: How new is it? (Was it used before in other projects? Is there any

knowledge available for successful experience or failure les sons?) What is the

consequence of success and possible failure?

Moreover, the additional investment used for the implementation of improved or new

technology should be evaluated carefully. Incurred risks should be analyzed thoroughly

and merged into cost risk if possible. It is proposed to use a 'technology directorate'

group to develop a technology, which compared with the old concept development phase

with a "technology development effort", can make goals and constraints to be defined

more clearly, avoid little public scrutiny, and lower the chance for failure.

Besides the four kinds of risk mentioned above, there could be other potential

uncertainty factors which may endanger the achievement of the expected goal. They can

be recognized and defined in terms of environmental risk, hazard risk, political risk,

organizational risk, and so on. Sometimes, the types of risks or their priority is not the

same for different projects or products. The major risk identified in one may be perceived

as a minor risk in another one. AIso, with increasing process complexity for a project or a

product, managing risk becomes harder. Therefore, efficient methods to concurrently

manage different risks are required, and the integration of all the risks expected will

provide the direction for rational decision making.

Integration will become difficult when different kinds of risk are handled. To sorne

extent, prioritization of risks can make risk integration possible, which offers a way to

36

combine different risks together by ranking aIl the risks involved and setting the scale for

different risks.

Wang and Roush (2000) proposed the integrated risk management approach to

manage cost risk, technical risk and schedule risk concurrently. In the other words, they

suggested management attention (and thus, resources) should be focused on the elements

of a project that need such attention in order to permit the project to achieve its teclmical,

schedule, and cost goals proactively. In their research, different methods were presented

for the integration, such as Present Worth (PM) method, Cash Flow Diagram, Network

Diagram. Furthermore, they proposed simulation techniques to solve complicated

problems.

Simulation is the modeling of reality to understand and solve problems. To perform a

simulation analysis for integrated risk management, the first step is to build an analytical

model by replicating the real world based on a set of assumptions and conceived models

of reality, which should be able to represent the actual decision situation. The second step

is to develop a probability distribution from subjective or historie data for each

uncertainty factor in the model, such as project schedule, budget constraints and technical

challenges. It is shown that a large amount of data relating to quality, quantity, schedule,

priees, manpower, overheads, materials, etc. are required to simulate the operation of an

engineering project. Although sorne limits exist, integrated risk management models, to

sorne extent, can simulate random variations to provide a range for the possible profit and

risk control for a project.

In addition, according to Wang and Roush (2000), risk implies opportunities for

improvement, and success in engineering benefits from the ability to recognize

37

opportunities. For engineering projects, there is always a trade-off between risk and

opportunity. A conservative engineer might select the conventional design to assure a

reasonable reliability, while an aggressive engineer might take the innovative design to

achieve better project performance. Anyway, a responsible engineer should be able to

make a balance between risk and opportunity based on the constraints and benefits

inherent in a project. On one side, uncertain factors (or risk) should be identified and

managed in a way to make designs with less chance of failure, and on the other side,

more effort should be focused on the area for further improvement to maximize gain.

38

CHAPTER2

OPTIMIZATION MODEL FOR IMPROVED QUANTITATIVE QFD

2.1 Background and Assumptions

The model to be developed in this thesis is concemed with how QFD can be

integrated with value engineering and risk analysis, given the product-related information

required during the early phases in the product development process. The core structure

of the model comprises three different objects which are customer requirements (CR),

design characteristics (DC), and design functions (DF). Design characteristics work as

the bridge to connect customer requirements with design functions. The interactions of

these entities are described by using quantitative scales in this model. The relationship

between CR and DC is discussed in section 2.2. The connection between DC and DF

during the decomposition process of product into design functions is discussed in section

2.3. Section 2.4 describes the resolution of problems into practical solutions which is

based upon the detailed analysis of cost and risk for every design function. For each of

these solutions, financial factors and design uncertainties are considered. In order to

identify the best solution, the method for comparison and evaluation of the current design

and the different solutions is shown in section 2.5. For this comparison, the new concepts

of customer satisfaction value and an integrated evaluation index are defined and used to

make a decision. Finally, the development of a best solution is discussed in section 2.6. A

method for companies to determine a realistic target unit cost for the best solution is

proposed with consideration of the best balance between customer and enterprise

satisfaction.

39

Three assumptions are made in the model. First, it is assumed that the correlations

between design characteristics and the interactions between design functions can be

ignored. In addition, customer requirements are assumed to be independent from each

other. For a product which is not very complex, it is feasible and reasonable. Therefore,

during the information collection and analysis process, effort is made to classify different

and independent items, and to combine similar and related items. Proper expressions are

given to have accurate descriptions. For example, a vacuum-insulated glass bottle is

analyzed. When the design characteristics are defined, since the shape of stopper may

affect the shape of top, the definition of relative design characteristic can be given as the

shape of top and stopper, in which the two related parts, i.e., top and stopper are

combined.

Second, it is assumed that the ratio of actual cost to planned cost for each function is

1 for solutions or new designs. This means that planned cost will be used to fulfill

functions; this is financiallyreasonable for optimum alternatives with the best value of

functions.

Third, it is assumed that sales are linear with customer satisfaction, ev en though an

increase in cost exists. Therefore, the model is more suitable for a product with a lower

cost or price that is more sensitive to change in unit cost. This means that a relatively

small increase in unit cost can visibly raise customer satisfaction, which causes increased

sales. It is easy to understand that customers pay more to get a better product. However,

for the product with a higher price, like an airplane, the difference in cost has to be

considered when the amount of sales is predicted.

40

2.2 Interpretation and Formulation of House of Quality Matrix

The model starts with the collection of customer requirements (CR) and the first

matrix used here is the house of quality matrix (HOQ). In HOQ, the voice of the

customer is translated into the language of the designer, and this is named the design

characteristics (DC). The relation between CRs and DCs will be discussed further.

At the beginning, who the customer is must be determined and the customer

requirements must be defined properly. The customer's own words will be used so that

the real demands of the us ers can be fully expressed and so that the expression can be

clearly explained and weIl understood. In addition, the type of market is chosen as the

point of reference. Market surveys are made and as much information as possible can be

gathered. By analyzing questionnaires, claims, and attitudes regarding a product or

service, customer requirements are determined and related data is summarized to give

relative importance to customer requirements.

From a technical point of view, design characteristics are determined for the product

or service in order to meet customer requirements. At least one design characteristic is

identified for each customer requirement. Measurable and controllable technical

specifications not only help to exhaustively evaluate the product or service, but also to

reflect customer perception concerning quality. This is a particularly ex acting step

because it implies translating the market model as expressed in subjective terms by the

customer's words into objective factors of a technical nature (performance

characteristics), that is, into a description of the product or service expressed in the

designer's own language (the so-called voice of the engineer) (Franceschini, 2002).

41

The central part of HOQ is the relationship matrix, which indicates the impact of

design characteristics on the satisfaction level of each customer requirement. The

knowledge and experience of engineers who are proficient in the design of the product or

service being developed provide the foundation for the accurate definition of the

relationship between customer requirements and design characteristics. For every element

of the matrix, the question is asked and answered: to what extent can the design

characteristics of a product or service affect the quality expected by the customers in

terms oftheir degree of satisfaction (Franceschini, 2002)7 The relationships between CRs

and DCs are expressed in a quantitative manner.

Table 2.1 House of Quality Matrix

Customer Requirement (CR)

Relative Weight of Importance of CR (C)

AlI the information mentioned above is rationalized and organized into the house of

quality matrix. As shown in Table 2.1, customer requirements and the relative weight of

importance of CR are input into the leftmost columns, and the design characteristics are

located on the right side at the top. The relationship matrix is in the body. Based on the

relative weight of importance of the CRs and the information contained in the

relationship matrix, the weight of importance of the DCs can be derived. The priorities of

the DCs are then determined. The symbols and formulas used in the related matrix are:

CRi: ith customer requirement

42

Ci: relative weight of importance of the ith CR,

(2.1)

Rij : relationship value, indicating the strength of relationship between ith customer

requirement and the /h design characteristic, and the scale 1,2,3,4,6,7,8,9,10 is

used to denote relationship. The bigger the value is, the stronger the relationship

IS.

DCj : jth design characteristic.

Dj: weight of importance of /h DC.

m

D j = ICi * Ru') = 1,2, ... ,n (2.2) i=1

Dt: the sum of the total weight of importance of n design characteristics.

n

Dt =ID; (2.3) )=1

dj: relative weight of importance of /h DC,

d D; . 12 .=--,]= , , ... ,n .1 Dt

(2.4)

fi

Id=l .1 (2.5)

;=1

2.3 Diagramming Function Analysis - the Function Tree

Function identification and analysis should be performed by a multidisciplinary team,

which is requisite for a complete understanding of all aspects of a product. Before

starting team actions, it is necessary to obtain all available information regarding the

project. For a product or a process, this should inc1ude detailed costs, technical drawings,

43

operation sheets, marketing surveys' reliability and warrant y information, a set of parts or

an assembly, and any other available information. If the project is in the concept stage,

the information may be sketchy or be only a set of specifications (Park, 1999). Many

methods can be used to identify functions, such as intuitive research, environmental

analysis, sequential analysis, reference product analysis, regulation analysis, and so on.

The functions should show what a product can do for the customer. Each function can be

expressed by an active verb and a measurable noun. AlI functions are c1arified and

organized into a function tree. When diagramming the function tree, functional

relationships are arranged in accordance with the answers to why/haw logic questions

with 'how a function will be done' to the left and 'why a function exists' to the right. See

Figure 2.1 for an example. In addition, the arrangement of functions which have a time

sequence relationship are arranged such that the functions happening earlier appear at the

right side to answer haw questions, and the functions occurring later are found at the left

to answer why questions.

In this model, the function tree not only provides a hierarchical structure of design

functions (DF), but also indicates the relationships between design characteristics and

design functions. As shown in Figure 2.1, as the oth level function, the basic goal is first

set, which is a general task that the product itself should accomplish, for example, please

customer. By considering the contents of the product, the oth level function can be

decomposed into the 1 st level functions, which are mainly composed of design

characteristics, for example, improve nth design characteristic. It is easy to understand the

cause-and-effect relationships between oth level and 1 st level functions. As an example,

from the left to the right: haw ta please custamer? is asked, and the answer is the design

44

HOW? .. ·'Ji~,,~(qii;·?l

<.;

Improve Design - Characteristics (1)

(DC1)

......

Please Improve Design Customer Characteristics 0)

1 (DCj)

Basic

Goal

......

--- Improve Design

Characteristics (n) (DCn)

r---

1 _J

1 __ -

..... WH Y?

Design Function (11)

(DF 11)

Design Function (12) (DF 12)

1 Design Function (j(k-1»

;-- (DF j(k-1»

1 _ L _ Design Function (jk)

1 (DF jk)

1 !.. _ ~ Design Function (j(k+1»

(DF j(k+1»

Design Function (n(x-1»

1-1 (DF n(x-1»

1 - L _ ~ Design Function (nx)

(DFnx)

Totals:

Figure 2.1 Function Tree

45

Relative Weight of Importance of OF

(f)

f11

f12

f j(k-1}

f jk

fj(k+1)

f n(x-1)

f nx

ft

Actual Cost of OF

(AC) ($)

AC 11

AC12

AC j(k-1)

ACjk

AC j(k+1)

AC n(x-1)

AC nx

ACt

R

s

K

A

N

A

L

y

s

s

characteristics shauld be impraved. Inversely, why the design characteristics shauld be

impraved? The purpose is ta please custamer.

Similarly, corresponding to every characteristic, 1 st level functions can be

decomposed into lower level functions, such as 2nd level, 3rd level, ... , nth level, ....

During the process, two-word function language (noun-verb) is used. Haw and why

questions are asked and answered. Following the above procedure, the function tree is

constructed.

At the deepest level, design functions are classified as basic or secondary functions,

and for each function, the relative weight of importance (f) is given. The bigger the value

of f, the more important the related function is. It means the basic functions have the

higher value of f than the secondary functions related to the same design characteristic.

Moreover, aIl the defined basic functions must be attained in aU solutions proposed later.

Next, the information from historical data and actuallife-cycle cost of the product are

provided by the owner as weIl as design and manufacture professionals. Actual cost for

every function (AC) is derived and put into the right column in function tree.

In summary, during the function analysis phase, what should be performed includes

that:

• design functions are identified and defined by the noun-verb function language

• the connections between design characteristics and design functions are clarified

• design functions are classified as basic or secondary functions by using relative

weight of importance, f

• financial factors are considered and the actual cost of each function is given

46

• the function tree diagram provides a foundation for analyzing cost and risk in

detail in the next cost and risk analysis matrix.

Definition of symbols and formulas related are expressed as follows:

DFjk: kth design function which is related to the /h design characteristic (DCÙ, and it is

from the deepest level functions on the function tree, which is then reordered and

arranged in sequence in the foUowing cost and risk analysis matrices

f jk: relative weight of importance of DF jk

x

If 'k = l,) = l,2, ... ,n k=l .1

(2.6)

ACjk: the actual cost required to achieve DFjk

ACt: total actual cost required to achieve aU the DFs.

n x

ACt =I IACjk (2.7) j=1 k=1

2.4 Constructing the Integrated Cost and Risk Analysis Matrix

Based on the information collected in the function tree, cost and risk analysis matrices

are developed, in which the cost model and risk model are integrated with function

analysis. Financial factors and design uncertainties are considered and identified at the

level of every design function. Actual product data is put into the matrix.

In this model, in order to analyze different cases or designs, a series of cost and risk

analysis matrices are built, which are named cost and risk analysis matrix 0 (CRAM 0)

for the current design, cost and risk analysis matrix 1 (CRAM 1) for solution 1, cost and

risk analysis matrix 2 (CRAM 2) for solution 2, ... , cost and risk analysis matrix n

(CRAM n) for new designs or solutions.

47

Before deploying matrices, target unit costs (TC) must be determined. The definition

of TC is expressed in the equation below:

T arg etUnitCost = T arg etUnitSale Pr ice - T arg etUnit Pr ofitM arg in (2.8)

Historical data and market analysis can help to set a realistic target unit sale price

(TS). Market conditions have to be carefully considered. Enough attention must be paid

to get a proper value for TS, since it is essential for achieving an accurate overall analysis.

In order to help understand how TS can be determined, an example from Cooper and

Slagmulder (1997) is followed. Nissan determines the target price by considering a

nurnber of internaI and external factors. The internaI factors include the position of the

model in the product matrix and the strategic and profitability objectives of top

management for that model. The external factors include the corporation' s image and

level of customer loyalty in the model's niche, the expected quality level and

functionality of the model compared to competitive offerings, the model' s expected

market share, and the expected price of competitive models.

After setting the target unit sale price, the target unit profit margin (TP) needs to be

established based on the company' s experience and on evaluation of competitive

companies with the similar products. Besides, a realistic value of TP should be

determined so as to assure that the expected profitability of the product across its life is

adequate, which means that enough profit margin must be left so that an adequate return

can be earned by the company for a given product.

It is easy to understand that setting a reasonable target unit cost (TC) is a premise for

successfully deploying a cost model. Based on the analysis and definitions above, several

points are clarified to help predict a realistic target unit cost.

48

• TC cost goals must be established before detailed design starts.

• A thorough study of historical data and market information lS necessary.

Customer wants and company profitability requirements must be carefuIly

analyzed and weIl balanced.

• Benchmarking can help a company identify its position compared to its

competitors in the chosen market.

• Target cost should cover aIl costs incurred, both inside and outside, and both

direct and indirect. From another viewpoint, only the product that can guarantee

the proper target profit margin when sold at the target unit price is the meaningful

design for a company to invest to make the product.

2.4.1 Cost and Risk Analysis Matrix 0 - Current Design

CRAM 0 is composed oftwo main parts which are cost analysis and risk analysis. AlI

necessary information about functions, cost, and risk are collected and put into the

current design. Cost and risk analysis are performed in detail. Problems that exist in the

current design are figured out. Possible areas on which more attention should be focused

in the next stage are determined.

As shown in the Table 2.2, design characteristics (DC) and relative weight of

importance of DC( d) from the house of quality matrix are used, and design functions (DF)

and the relative weight of importance of DF(f) are derived from the function tree. Once

the target unit cost is set, based on the value of d and f, the target cost can be prorated to

each design function, and so planned cost of design function (PC) can be obtained. This

can also be regarded as the target function cost by which the necessary design functions

can be achieved. In addition, direct market research, by asking customers to estimate how

49

\

Table 2.2 Cost and Risk Analysis Matrix 0

Product (Current Design) --> Design Characteristics - Functions: Cost and Risk Analysis Target Unit Cost (TC): TC 0 ($)

50

much they would pay for a given function, can also help set a realistic value for the

planned cost of each function. The definition of the pararneters and the related formula

are denoted as follows:

PCjk: the planned cost required to achieve DFjk

PC 'k = d.* f, * TCO ,U = 1,2, ... ,n),(k = 1,2, ... , x) ./ ./ ./k

(2.9)

TCO: target unit cost for product in the current design.

For every design function, the actual cost is given in the function tree. So the ratios of

actual cost ofDFs to planned cost ofDFs can be calculated as follow:

ACjk . V'k = ,} = 1,2, ... ,n,k = 1,2, ... ,x., ./ PCk

./

(2.10)

Vjk: ratio of the actual cost required to achieve DFjk to the planned cost required to

achieve DFjk, the best value of Vjk is equal to 1.

The comparison of the actual cost to planned cost of each function can clearly

determine the places to concentrate creative efforts for maximum benefit. In the other

words, a value of Vjk greater than 1: 1 indicates the opportunity for cost improvement.

The greater the ratio, the greater is the chance for improvement on the potential areas.

Thus, improvement opportunities for the related design functions are identified for

current design, which chart the direction for work in the next phase.

Many variables (factors) can be identified which impact the current design, such as

cost, time, location, level of knowledge, extent of effort, internaI or external change

conditions, performance, etc. Design uncertainties exist in these factors. All of these

uncertainties are considered as risk attributes (RA) in this model. Their definition and the

relative weight of importance of each attribute (RA) should be determined by a

multidisciplinary team which is made up of experts from different departments. They

51

should be familiar with specifications for design and manufacturing. Their expertise can

pro vide the foundation for a thorough and accurate analysis. The parameters and formula

used in matrix are expressed as follows:

RAi: ith risk attribute

Rai: relative weight of importance of ith risk attribute

(2.11)

Once the risk attributes and their relative weights of importance are settled, risk

analysis is deployed in the second part of the matrix and the risk assessment levels are

defined:

Pjki : the risk assessment level for jkth design function DFjk to satisfy the ith risk

attribute RAb which is based on the probability of success for DFjk to fulfill RAi.

The higher the Pjki is, the more probable the jkth design function will fulfill the ith

risk attribute successfuIly, which means the better the result is for

accomplishment, 0:::; PJki :::; 100% .

FPjk: the risk assessment level for jkth design function DFjk to satisfy aIl the risk

attributes, which is based on the average probability of success for FPjk to fulfill

aIl the risk attributes.

y

FPJk = LPJki* RŒ,(j = 1,2, ... ,n),(k = 1,2, ... ,x) (2.12) i=!

DPj: the risk assessment level for the jth design characteristic DCj to satisfy aIl the risk

attributes, which is based on the average probability of success for DCj to fulfill

aIl the risk attributes.

52

DPt: the total risk assessment level for aIl design characteristics, which is also the

average risk assessment level for aIl the design functions.

In order to obtain the risk assessment level (P) for every function, the definition of

success and failure is given for every risk attribute. In addition, some questions are asked

and answered to help get the value of P. For example, when cost is treated as RA, the

definition of success is: the planned cost of the function is enough to achieve the function,

which can definitely satisfy the customer requirement weIl. In this case, it is called the

function can satisfy the risk attribute (cost) successfully and Pis equal to 1 or 100%. On

the other hand, the definition of failure is: when extra money, which is more than 50

percent of planned cost of the function, has to be spent on the function, but it still cannot

attain the desired customer satisfaction, and so P is O. The questions asked are: how much

money is needed to perform the design function? how weIl has the required function been

performed? or what is the chance of success for the function to be achieved? The answers

are given a value between 0 and 1.

Another example of risk is for technique. The definition of success is: when the same

kind of practice has been successfully dealt with before and there is no main technical

challenge now; p is 1 or 100%. On the other hand, the definition of failure is: when a

serious technical problem exists and the solutions are hard to figure out, or the pro cess

has never been tried before; p is O. The questions asked are: what is the technical problem?

to what extent can the function be accompli shed? or what is the opportunity of success

for the function?

As above, after analyzing the chance of success for every function to fulfill each risk

attribute, the risk assessment level of every design function (P) is determined. Then the

risk assessment level for every design characteristic (DP) is computed as follows:

53

x

Dp· = I.FP" * f. ,j = 1,2, ... ,n .1 "=1 .1 .1"

(2.13)

FinaUy, the risk assessment level for aU design characteristics (DPt) is summed,

n

DPt = I.DP*d .1 .1 (2.14)

j=1

The result also glVes the chance of success of the whole design taking into

consideration the given risk attributes. At the same time, by observing the data given in

the risk analysis, more attention should be given to the design functions with lower risk

assessment level or lower chance for success. The potential problems related to the risk

attributes need to be determined. This can give sorne hints for the next stage where as

many new ideas as possible will be created to get the solutions.

As stated above, in cost and risk analysis matrix 0,

• design characteristics and design functions of the product for the current design

are deployed, and their relationships are discussed,

• cost is elaborated at the function level in the first part of the matrix, and the ratio

of actual cost to planned cost is calculated for each function,

• in another part of the matrix, risk attributes are identified and risk assessment

levels for each design function are determined; furthermore, the risk assessment

levels for every design function and aU design characteristics to fulfiU aU risk

attributes are calculated,

• problems and are as for improvement are identified, and more effort should be

paid to the items which show the larger difference between actual cost and

planned cost, and/or the items with lower risk assessment level.

54

Table 2.3 Cost and Risk Analysis Matrix n

Product (Solution n) --> Design Characteristics - Functians: Cast and Risk Analysis Target Unit Cost: TCn ($)

55

2.4.2 Cost and Risk Analysis Matrix n - New Designs

Starting from the issues determined in the preceding phase, as many new ideas as

possible are developed to improve the defined functions. Sorne creativity methods, like

brainstorming, can be used to stimulate creativity for possible alternatives. AlI the

information and data are collected. Created ideas are classified and all the ideas with the

greatest potential are combined to give sorne practical and realistic solutions. For every

solution, the information about cost and design uncertainties is gathered and deployed in

the new cost and risk analysis matrix. This is called the cost and risk analysis matrix n

(CRAM n), n = 1,2,3, ....

As shown in Table 2.3, CRAM n looks quite similar to CRAM O. Since the processes

for building the different matrices are almost the same, there is no need to spend time

repeating the same description. Sorne key points are highlighted below.

• Every proposed solution should be defined carefully and clearly before

constructing matrices. All related information should be collected and analyzed

by multidiscipline team. If necessary, new function trees should be created.

• When setting the target unit cost for solutions or new models, the cost of any

design changes or manufacturing modifications should be considered carefulIy.

• In CRAM n, there are no columns for actual cost of DF (AC) and the ratio of

actual cost to planned cost (V). Planned cost of DF is supposed to fuifill every

function for the new model, which is also the ideal financial case for the optimum

solutions with the best value of functions; then, risk is analyzed as before.

• By building CRAM n, the distribution of cost is shown. Risk assessment levels

for each function to fulfill every risk attribute are carefully laid out. The risk

assessment levels for design functions and design characteristics are calculated.

56

Table 2.4 Integrated Evaluation Matrix

Solutions --> Customer Satisfaction Value + Integrated Evaluation Index

Customer Requirement (CR)

57

• As a result, the average risk assessment level for all design characteristics to

fulfill aU risk attributes is determined for each solution.

2.5 Building the Integrated Evaluation Matrix - Current Design and Different Solutions

In order to filter out the best solution from different solutions, an integrated

evaluation matrix (IEM) is built. In IEM, the current design and different possible

solutions are compared by using customer requirements as the evaluation criteria;

moreover, the value of total customer satisfaction for each design, and the integrated

evaluation index with consideration of cost and risk are determined to assist decision

making.

As shown in Table 2.4, on the top part of the matrix, customer requirements and

relative weight of importance of CR are given in the left columns. Then, the brief

description for current design and different solutions are listed on the right at the top. The

value of customer satisfaction is defined as follows:

Mij : customer satisfaction rank value 1-9, 9=high, which indicates how well the

product is made by using CUITent design or the /h solution satisfies the ith customer

requirement CRi. The higher Mij is, the better the customer requirements are

satisfied.

Mtf the value of total customer satisfaction for the CUITent design or each possible

solution. This indicates how well the product made by using the CUITent design or

the /h solution satisfies aIl the customer requirements.

By giving the value ofMij in the center part of the matrix, each solution is ranked from 1

to 9 against the current design by using every customer requirement as evaluation criteria.

The value of total customer satisfaction for different designs is computed:

58

m

Mt} = ICi * Mij,(j = 1,2, ... ,n) (2.15) i~l

The solution with the higher value of Mtj is the one which can achieve better customer

satisfaction. Also, it implies that the solution is the design that customers really like.

Furthermore, in the bottom part of the matrix, the different designs are given

evaluations with consideration of financial factors and design uncertainties. Parameters

and formulae are:

Sj: average sale units per year for the current design or the /h solution, which is

assumed to be the liner ratio with the total customer satisfaction value for each

solution (MtÙ. The more the customers are satisfied, the more likely it is that they

want to buy the products,

Sj = .Mti , (j = 1,2, ... , n) SO MtO

(2.16)

uej: unit cost for the /h solution, which is the actual unit cost for the current design or

the target unit cost for /h solution

Ctj: total cost ofproduct per year for the current design or the jth solution

Ct· = UC· * S,(j = O,1,2, ... ,n) J J J

(2.17)

Pricej: actual sale priee per unit for the current design or target sale price per unit for the

/h solution

Bj: benefits obtained from making one unit of the product by the company when

using current design or /h solution

B· = Pr ice·,(j = O,1,2, ... ,n) J J

(2.18)

YBj : yearly benefits for current design or /h solution

59

YB· = Pr ice· * S .,(j = 0,1,2, ... ,n) .1 .1.1

(2.19)

(BIUC)j: the ratio of bene fit per unit to cost per unit for current design or lh solution

( BJ Bj. UC . = UCj,(j = 0,1,2, ... ,n)

.1

(2.20)

NVj : integrated evaluation index taking into account cost and risk factors for current

design or lh solution

NV· = [(~J l * D Pt·, (j =O,I,2, ... n) .1 UC. .1

.1

(2.21)

The solution with the higher value ofNV, i.e., the one which can meet the integrated

criteria of cost and risk better, is more beneficial one for enterprise satisfaction.

As described above, different criteria (customer requirements, financial factors,

design uncertainties) are used to make evaluation on each design. The large lists of

solutions are narrowed to the best one or two which will undergo further development in

next phase. Doubtlessly, the solution with the highest value of both Mt and NV will be

the best one. But sometimes, for the solutions with higher Mt, its NV is lower. And

similarly, the solutions with higher NV have Mt in the middle range from 1 to 9. In this

situation, the objective value of Mt and/or NV should be set by benchmarking and careful

analysis. Comparing aIl solutions with the value of Mt (and/or NV) more than the

objective value of Mt (and/or NV), the one with the highest value of NV (and/or Mt) is

the best one. For example, the objective value of Mt is first set at 7. Three solutions are

discussed. Their Mt and NV are given as:

60

NVI = 0.9, NV2 = 0.85, NV3 = 0.7

Since Mtl is less than 7 (objective value), solution 1 is rejected. Besides, Mt2 and Mt3 are

both more than the objective value of7, and at the same time, NV2 is bigger than NV3; so,

Solution 2 is the best one, although Solution 3 has bigger Mt than Solution 2. This means

that the final product foIlowing the proposed design should not only satisfy customers

weIl, but also, should be the one the company can afford.

2.6 Deploying Development Matrix and Diagramming - Best Solution

The best solution deserves further development and the development matrix (DM) is

deployed. Given different target unit costs for the proposed design, customer satisfaction

is analyzed and integrated evaluation indexes are determined. In addition, a diagram is

constructed to show the relation between target unit costs and customer satisfaction, and

the relation between target unit costs and integrated evaluation indexes. The combination

of the matrix and the diagram can show the trend for improvement. Companies can get

what they want, and a reasonable target unit cost for the best solution can be determined

for the proper balance between customer and enterprise satisfaction.

In the development matrix, almost aIl the parameters have been defined before.

Related data can be derived from the matrices illustrated in preceding phases. Here, unit

cost for the best solution (UC) is used to represent the different target unit cost which is

set for the best solution. UC should not be confused with the target unit cost defined for

the different solutions in the cost and risk analysis matrix. Take UC2 as an example. It

means that the planned unit cost for the best solution is UC2 in the development matrix or

the target unit cost (TC) for the best solution is set as UC2. Of course, the value of TC for

the best solution could also be assumed to be UCI, UC3, etc. Now, TC2 means the target

61

Table 2.5 Development Matrix

Best Solution: Unit Cost --> Customer Satisfaction? Evaluation Index?

62

unit cost for solution 2 in the cost and risk analysis matrix 2. If solution 2 is not the best

solution, the value of TC2 is assumed to be constant and to be used in CRAM2. If

solution 2 is found to be the best solution and needs further development, different value

(UC I , UC2, UC3, etc.) can be given as the target unit cost for the best solution, i.e.

solution 2, in the development matrix.

The development matrix comprises two separated parts. In the first part of DM,

different planned unit costs are given as target unit costs for the best solution, and each

planned cost for one product unit is distributed to every design function. The process of

cost analysis is the same as the one elaborated in cost and risk analysis matrix n.

Similarly, the risk analysis for the best solution with different planned unit cost can be

performed at the level of design functions based on the given risk attributes. The process

is the same as the one described in cost and risk analysis matrix n. As a result, total risk

assessment levels are obtained for the best solution with different planned unit costs.

The second part of DM looks quite like the integrated evaluation matrix, in which the

best solution with different planned unit cost is ranked by using criteria for customer

satisfaction, financial factors and design uncertainties. As a result, the value of total

customer satisfaction value and the integrated evaluation index with consideration of cost

and risk are determined for the design with different planned unit cost. Based on their

value, the diagram below can be constructed.

As shown in Figure 2.2, the x axis represents the planned unit cost, the left y axis

denotes total customer satisfaction, and the right y axis shows the integrated evaluation

index with consideration of cost and risk. The pink curve demonstrates the relationship

between customer satisfaction and planned unit cost for the best solution. The dark blue

63

Unit Cost (UC) ($)

Figure 2.2 Unit Cost versus Customer Satisfaction and Evaluation Index Diagram

curve depicts the relationship between the integrated evaluation index and planned unit

cost for the best solution. When the junction points of these two curves are taken, the

proper balance between customer and company satisfaction can be achieved in the final

product. Based on the concrete data and the objective value, the realistic target unit cost

can be determined for the best solution.

Generally, spending more money can make a better product which can achieve higher

customer satisfaction. But sometimes, a perfect product is not only unaffordable, but also

it cannot achieve a high sales level, which means that even interested customers don't

like to spend that much money on the product compared to the same kind of product from

other companies at a lower price. Therefore, it is necessary to find the proper balance

between customer and enterprise satisfaction. An example is illustrated in Chapter 3 to

show how the model works.

64

CHAPTER3

ACASESTUDY

In order to show the performance of the model, an example is presented. Parameters

and related data are illustrated in a series of matrixes. AIl calculations are performed

automatically through the use of spreadsheets based on the equations formulated in

Chapter 2. Solutions and results are discussed at the end of the chapter.

3.1 HOQ Matrix

A company pro duces a hot & cold vacuum-insulated glass bottle (HCVGB). It is

composed of five main parts: plastic mug top, plastic stopper, bottle body (glass interior

wall, vacuum insulation layer, glass exterior wall and the plastic cover), plastic handle,

and plastic base. At first, based on the information collected, customer requirements are

defined and related design characteristics are identified. During this pro cess, similar and

related items are combined together, and different and independent items are separated.

As a result, every customer requirement is independent from each other. Similarly, for

design characteristics with different definitions, their correlations are ignored.

According to a market survey, nine major customer requirements (CR) are gathered

and relative weights of importance of CR (C) are identified. They are: (CRI) good

insulation to keep items hot or cold (C 1=O.35), (CR2) durability (unbreakable) (C2=O.15),

(CR3) easy to fill and serve (C3=O.1), (C~) easy to open and close (C4=O.09), (CRs)

comfortable handle (Cs=O.08), (CR6) stable base (C6=O.08), (CR7) does not spill

(C7=O.06), (CRs) nice look (appearance) (Cs=O.05), (CR9) easy to clean (C9=O.04).

65

Table 3.1 House of Quality Matrix

Customer Requirement Handle 1 Base

66

AIso, there are seven design characteristics (DC), i.e., shape and material of top and

stopper (DCI), shape and dimension of mouth (DC2), attainable vacuum for insulation

layer (DC3), bottle body material (DC4), handle shape and material (DC5), base shape and

material (DC6), exterior color and texture (DC7). The relationship between CRs and DCs

is indicated by the relationship value (Rij), and the scale 1, 2, 3,4, 6, 7, 8, 9, 10 is used.

The bigger the value, the stronger the relationship is. AlI the information above is

illustrated in the HOQ matrix in Table 3.l.

Based on the formulae given in Section 2.2, calculation is done automatically. The

relative weight of importance of design characteristics (d) is the output, which shows the

relative importance of each design characteristic for the whole unit. For example, d3 has

the highest value 0.215, and d7 has the lowest value 0.081, which me ans that DC3 is more

important than DC7, and this somewhat indicates that DC3 deserves more attention than

DC7.

3.2 Function Tree and Cost and Risk Analysis Matrix 0 - Current Design

The functional analysis for the hot & cold vacuum-insulated glass bottle is developed

in the function tree, detailed cost and risk analysis for the glass bottle are processed, and

then put into the cost and risk analysis matrix O.

At first, in the function tree, the basic goal is set, which is to please the customer.

Then, the sequential relationships are arranged in accordance with the answers to

hawlwhy logic questions. For example, from left to the right, the question is haw ta

please the custamer?; from right to the left, the answer is ta imprave design

characteristics. Step by step, aIl design characteristics are connected with design

functions (DF), which are identified as verb-noun descriptions, i.e., keep hot or cold, stop

67

interior contents, grip bottle, and so on. Moreover, actual costs are glven for each

function and their value is put into the right column in the function tree.

Relative Weight Actual HOW? WHY?

~ i::;;;ii{:~''",,;, ' , L~V:èl, ; ;;;f4

-..' ,,~v-..

of Importance of DF Cos! of OF . If) (AC) ($)

~. ;:1!~v&it~(; 1 Stop interior contents (OF1'l) 1 t;:ÎlvIiIO Improve

0.30 $O.[iO

...... 1 Allow mouth open or close (OFI2) 1 0.30 $0.50 R

ri Top and stopper ~ r Keep hot or cold (DF13) l (OCll

0.20 $0.30

r Con Iain c~mtents (DF14) l 0.10 $0.13 S

Satisfy temp. & press. (OF 15) 0.10 $0.15 1<

1 Mouth (DC2) Allow contents to move in and out (DF26) 0.50 $0.50 l\

1 Avoid spill (DF27) 1 0.50 $0.30 N

/\ 1 Altainable vacuum (OC3) 1 r Keep' hot or cold (Df'38) l 1.00 $1.75

1 Avoid breakage and spoilar)El (DF49) 0.50 $1.80 y

1 Picage rI-- -1 Bottle body material ~ H Satisfy temp., press. and light weigl1t 1 Customer (OC4) (OF4(10))

0.30 $0.72 S

1

1 Basic 1 1 clean easily (OF4(11) Goal

0.15 $0.30 S

Keep hot or cold (DF4(12») 0.05 $0.01

r Handle (OC5) 1 1 Grip boUle (OF5( 13)) 0.70 $0.50

1 Be cornforlable (DF5(14») 1 0.30 $0.15

Base (DC6) 1 r Hold boUle stably (DF6(15)) l 1.00 $0.52

-1 Exterior color and texture Attract uservisuallv OF7(16») (DC7)

0.60 $0.12

Satisfv ("elinIJ oftouch (DF?(17)) 0.40 $0.26

Totals: 7.00 $8.61

Figure 3.1 Function Tree

Furthermore, design functions are analyzed and classified, so the relative weight of

importance of DF is determined. The bigger the value of f, the more important is the

function compared with the other functions for the same design characteristics, i.e., for

the same design characteristic, the basic functions should have a higher value of f

compared to the secondary functions. Take Del (top and stopper) as an example, for

which there are five DFs related. Basic functions are DFll, stop interior contents, DF12,

allow mouth to be open or closed, DF 13, keep hot or cold, and secondary functions

68

include DF 14, contain contents, DF 15, satisfy temperature and pressure. Respectively, the

value offll , f12, f13, f14, fl5 are 0.3, 0.3, 0.2, 0.1, 0.1. As shawn, the model also provides a

way ta demonstrate the slight difference between the basic functions in terms of their

importance. For example, DF\\, DF12 and DF13 are determined ta be the basic functions

for DCI. DF Il and DF 12 are more important than DF 13; therefore, fil and f l2 are given the

higher value of 0.3 compared ta f13 with a value of 0.2. The quantitative description for

the importance of DF makes it possible ta prioritize design functions for their related

design characteristic. The most important thing is that the basic functions must be

achieved in the product by using any design or solution. They are: DFII, DF12, DF13 for

DCI, DF26 (allow contents ta move in and out) and DF27 (avoid spill) for DC2, DF38 (keep

hot or cold) for DC3, DF49 (avoid breakage and spoilage) and DF4(1O) (satisfy temperature,

pressure and light weight) for DC4, DF5(13) (grip bottle) for DC5, DF6(l5) (hold bottle

stably) for DC6, DF7(16) (attract user visualIy) for DC7.

Once the function tree is created for the current design, the cast and risk analysis

matrix 0 (CRAM 0) needs ta be built ta give a more systematic description. CRAM 0

comprises two major parts, and in each part, cast and risk of design functions are

elaborated separately for the current design.

By studying the information from history, market, and benchmarking, the target unit

cast for one glass bottle is given as $8.00. With the formulae given in Section 2.4.1, the

unit cast is allocated ta every function and planned cast of each design function is

determined. Then, the ratios of actual cast of functions ta planned cast of functions are

calculated. AlI the information and data are organized in seven columns for DC, d, DF, f,

planned cast of DF (PC), actual cast of DF (AC), actual cast 1 planned cast (V).

69

Table 3.2 Cost and Risk Analysis Matrix 0

~i~"'" "" .... , CC" "W"'"""""·"~V' on """,~, ... ,···c··.,,~v,,~··, "_',,"'w""""'" """,,., ......... , ·"w,"""~, 'v'cu'"""c "v",~""v· .. ", .... · "",,".,~

'Cu.rre.ntJ1!lsi gn.: §J~S'SJlottl!l .::?':J1il5Ï9,!Çh ilra ct!l~~st.i.cs:-F.I,I.'!t:lio,!s:.Cllst an~. RJ~~.!!.~IYS!!i Unit .Cost (TCO) . $8.00

70

From the data in column (V), it is seen that for DF 49, DF 4(10) and DF 4(11), they have higher

values of V: 2.75, 1.84, 1.53, which are much more than 1. Contrarily, for DF27, DF4(12),

DFs(13), DFs(14), DF6(lS), DF7(16), V is equal to 0.54, 0.15, 0.73, 0.51, 0.59, 0.31

respectively, which are much less than 1. The ratios more or less than 1 mean that the

difference exists between planned cost of DF and actual cost of DF. In other words, the

opportunities for cost improvement exist for their related design functions and design

characteristics, which can be easily figured out from the relative columns on the left. The

higher the value of V, the larger the chance for improvement is. Thus, in the cost analysis

part, the comparison of the actual cost with planned cost of each function clearly

indicates potential areas of improvement.

In another part of the matrix, design uncertainties are first considered and defined as

three risk attributes (RA), which are cost (RAI), technique (RA2), and market (RA3).

Corresponding to every risk attribute, the value of relative weight of importance of RA

(Ra) is decided. Next, in order to give the risk assessment level (P) for every function, the

definition of success and failure are given for every risk attribute. Besides, sorne

questions are asked and answered to help get the value of P. For example, for RAI, cost,

the definition of success is that planned cost is high enough to achieve the function,

which can definitely satisfy the customer requirement weIl. In this case, it is defined that

the function can easily satisfy the risk attribute (co st) successfully; then, P is equal to 1 or

100%. On the other hand, the definition offailure is such that after spending extra money,

which is more than 50 percent of planned cost, the design function cannot be achieved

and P is O. Then, the questions are asked: how much money will it cost to perform the

design function? how well has the required function been performed? or what is the

71

chance of success for the function to be achieved? Take DF49 (avoid breakage and

spoilage) as an example. The planed cost is $0.65 and the actual cost is $1.80, which

means extra money, more than 50 percent of planed cost, is consumed to avoid the body

being broken and damaged. However, because the internaI body of the bottle is made of

glass, breakage cannot be absolutely avoided and the chance for the current design to

keep it unbreakable is 0.30 or 30%. In other words, the risk assessment level for DF49 to

satisfy the RAI (cost) is 0.30 for the CUITent design.

As above, after analyzing the chance of success for every function to satisfy each risk

attribute, the risk assessment level of every design function (P) is determined. Then, by

using the formulae described in Section 2.4.1, the risk assessment level for every function

(FP) and the risk assessment level for every design characteristics (DP) to satisfy aIl risk

attributes are computed in sequence. FinaIly, the risk assessment level for aIl design

characteristics (DPt) to satisfy aIl risk attributes is summed, which also gives the average

chance of success for the current design to satisfy the given risk attributes. For the glass

bottle, the risk assessment level for aIl DCs is 0.602.

By observing the data given in the risk analysis part in CRAM 0, it is found that DF49,

DF4(IO), DF4(1l), DF4(12), DFs(14), DF6(IS), DF7(16) have a lower chance ofsuccess, which are

recognized as the areas needing more attention.

Based on cost and risk analysis in CRAM 0, the design functions showing the larger

difference between planned cost and actual cost, and/or having the lower risk assessment

level, are identified and defined as the areas for improvement. Potential problems are

determined and conclusions can be made for the current design. In summary, a new

alternative body material should be found, which should be stronger and more

72

unbreakable, have better ability for special temperature and pressure, be light weight,

have good insulation, and be easily cleaned. AlI of the above functions must be achieved

at a reasonable cost. In addition, it is indicated that more attention should be paid to the

mouth, handle, base and exterior, and more money should be spent to make the necessary

improvements and assure the requisite quality. What's more, effort should be made to

increase the chance of success for mouth, body, handle, base, and exterior to achieve the

required functions when three risk attributes are considered.

3.3 Cost and Risk Analysis Matrix 1 and Cost and Risk Analysis Matrix 2 - New Designs

In the creativity phase of the value engineering process, a wide variety of ideas is

brought out. By gathering and grading all the ideas, two solutions are chosen.

For solution 1, lightweight stainless steel is used as the bottle body material. The steel

bottle is comprised of a mug top made of stainless steel and plastic, plastic stopper,

stainless steel bottle body (interior wall, exterior wall and vacuum insulation layer), a

handle made of plastic and steel, and a steel base.

For solution 2, plastic becomes the bottle body material. What's more, instead of the

vacuum insulation layer used in the original design, a new composite material is added

between the interior wall and the exterior wall of the body as the insulation layer. The

plastic bottle consists of a plastic mug top, a plastic stopper, a plastic bottle body (plastic

interior wall, plastic exterior wall and composite insulation layer), a plastic handle, and a

plastic base.

When constructing the matrices for these two solutions, a detailed market survey and

careful research of historical data are crucial for a good analysis. As shown in the

matrices for each solution, the design characteristics, design functions, and risk attributes

73

are given. The data are inputted for d, f, target unit cost, Ra, P. The processes for cost and

risk analysis at the function level are almost the same as the one explained in Section 3.2.

PC, FP, DP, and DPt are calculated. New areas for improvement are indicated.

Solution 1 has merit, almost satisfying all of the requirements. Its main attraction is

that the new bottle body material, stainless steel, is rugged, rustproof and unbreakable. In

addition, the desired insulation level for the vacuum layer can be achieved, and the new

model improves the design for sorne design characteristics, i.e., mouth, base and exterior

color. To a great extent, the related design functions can be accomplished by the planned

cost. But for the handle, although it has convenient features with a folding plastic handle

grip and the steel linkage with steel bottle body, the risk assessment level for the function

(DF5(14)) is somewhat lower at 0.615. This me ans that there is an opportunity for

improvement here. Another shortcoming is that since steel can transfer heat, and since the

exterior and interior walls are made of a whole piece of steel, there is a heat bridge

between the two walls. Practically, this is hard to avoid. Fortunately, the connection part.

is thin and the heat transfer is small. It is acceptable. This is why in cost and risk analysis

matrix 1, for DF4(12), keep hot or cold, the risk assessment level P4(l2)3 (for market) has a

higher value (0.7) than the risk assessment level P4(12)2 (for technique) which is 0.6. After

calculation, the total risk assessment level for aIl design characteristics for solution 1 is

0.796.

Unlike solution 1, the plastic bottle design in solution 2 looks quite similar to the

glass bottle in the current design except that the material and structure of bottle body are

different from each other. Solution 2 makes sorne improvement on the design for the

parts, i.e., mouth, handle, base and exterior color. Instead of using steel in solution 1,

74

Table 3.3 Cost and Risk Analysis Matrix 1

Solution 1: Stainless Steel Bottle --> Design Characteristics - Functions: Cost and Risk Analysis Target Unit Cost (TC1): $11.00

Stop inlerior contents Allow mouth open or close

Keep hot or cold Contain contents

75

Table 3.4 Cost and Risk Analysis Matrix 2

Solution 2: Plastic Bottle --> Design Characteristics - Functions: Cost and Risk Analysis Target Unit Cost (TC2): $7.00

76

plastic is used, which is appealing for color and texture. However, because composite

insulation materials are needed to fiU the space between the plastic exterior and interior

waUs, there are sorne constraints on the shape of the bottle mouth. For manufacturability,

it is hard to make the mouth with a flexible shape and its thickness has to be bigger,

which is disadvantageous for good fluid flow, and this will cause spillage sometimes. In

addition, the composite material has to satisfy the requirements for use with high

temperature and high pressure, and it has to be light. This type of material is expensive.

But in this example, the planned cost is required in the low to medium range. In addition,

even the lightest insulation material is heavier than a vacuum layer. As mentioned above,

aU the information is shown in the matrix. The total risk assessment level for aU design

characteristics is computed as 0.667 for solution 2.

3.4 Integrated Evaluation Matrix

In the integrated evaluation matrix, aU the customer requirements are input as the

criteria for evaluation. U sing a ranking procedure, the current design and two solutions

are ranked on how well they meet the criteria or satisfy customer requirements. A ranking

scale from 1 to 9 is used and the customer satisfaction rank values (M) are inputted. The

higher the value, the better the customer requirements are satisfied. The total customer

satisfaction values (Mt) are 6.33, 8.14, and 7.04 respectively for the current design,

solution 1 and solution 2. It is seen that compared with the glass and plastic bottles, the

steel bottle satisfies customers best.

Next, more financial information is gathered. The average amount of sales for glass

bottles per year is given as 200,000. Then, if the predicted sales per year for steel and

plastic bottles are linear with customer satisfaction value, the sales are calculated:

77

for solution 1: 8.14 S2 6.33 200000 '

for solution 2: 7.04 S3 -- = 6.33 200000

Thus, the average sales per year for steel and plastic bottles are determined as follows:

S2 = 257188 (for steel bottles), S3 = 222433 (for plastic bottles).

The unit cost and sales price for the different designs are:

UCO = $8.61, UCI= $11.00, UC2 = $7.00

PriceO = $11.00, Price) = $14.50, Price2 = $9.00.

Then, the ratio of the bene fit to the cost is calculated for the designs and the results are

shown in the matrix as:

BO/UCO = 1.28, B)/UCI =1.32, B2/UC2 = 1.29

Combined with the total risk assessment levels which are determined from cost and risk

analysis matrices,

DPtO = 0.602, DPtl = 0.796, DPt2 = 0.667,

The integrated evaluation index with consideration of cost and risk (NV) is calculated for

the designs:

NVO = 1.28 * 0.602 = 0.77,

NV I = 1.32 * 0.796 = 1.05,

NV2 = 1.29 * 0.667 = 0.86

Compared to the original design and solution 2, solution 1 not only has the highest

customer satisfaction value, but also it is ranked best when cost and risk factors are

considered on the company side. Therefore, solution 1 is regarded as the best idea, and

will be given further development in the next phase.

78

Table 3.5 Integrated Evaluation Matrix

Solutions --> Customer Satisfaction Value + Integrated Evaluation Index ir-------------~R~a-n~k~V~a~lu-e-1--9~,~9~=~h~ig~h---------------,

Customer Requirement

(CR)

Relative Weight of

Importance of CR (C)

Good insulation to keep hot or cool 0.35

Durability (unbreakable) 0.15

Easy to fil! and serve 0.1

Easy to open and close 0.09

Comfortable handle 0.08

Stable base 0.08

Does not spill 0.06

Nice look (appearance) 0.05

Easy to clean 0.04

Total Customer Satisfaction Value for Different Design

Average Sale Per Year (S) (units)

Unit Cost (UC) ($/unit)

Total Cost of Product Units Per Year (Ct) /'!;/\I.,<:>r\

Sale Priee Per Unit (Priee) ($/unit)

Current Design

Glass Sottie

7

2

6

7

8

8

8

7

6

200000

$8.61

1,722,000

$11.00

79

Solution 1

Stainless Steel Sottie

8

9

8

9

9

8

8

8.14

257188

$11.00

$14.50

Solution 2

Plastic Sottie

6

6

8

9

8

8

7

9

7

7.04

222433

$7.00

,557,030

$9.00

Table 3.6 Development Matrix

r~ëst$olïïtion: U!liiC~!rt::~ëustomer$lItjSfaëïitl~?E~lïïatjtln Inde x?· ..•..

80

3.5 Development Matrix and Diagram

As the most beneficial idea, solution 1 deserves further scrutiny. The study starts

from the unit cost of the steel bottle. Given different unit cost, the planned cost for every

design function is calculated. Compared to the original design (glass bottle) and the steel

bottles with different unit cost, the customer satisfaction value for every function is

inputted and the total customer satisfaction value is calculated. Similarly, the risk

analyses for different bottles with different unit costs are performed at the level of design

functions based on the three given risk attributes (cost, technique and market). The

approaches described in Section 2.6, Section 2.4, and Section 3.3 are used and the

process will not be presented here in detail. Finally, the total risk assessment levels are

inputted and the integrated evaluation indexes are calculated as described before.

In summary, the data for unit cost, customer satisfaction value, and integrated

evaluation index are gathered in the Table 3.7, and the values are used to build Figure 3.2,

in which the unit cost to customer satisfaction relationships and the unit cost to evaluation

index relationships are shown. For the unit cost vs. customer satisfaction curve (pink), the

design with unit cost $13.00 has the highest customer satisfaction value. Whereas

according to the unit cost vs. evaluation index curve (dark blue), the design with unit cost

$11.50 maximizes the evaluation index, which means the best integrated value for

consideration of cost and risk. Likewise, if the junction point of two curves is taken, the

unit cost is about $12.30, which infers best combination of customer satisfaction,

financial consideration and chance of success.

81

Table 3.7 Unit Cost versus Customer Satisfaction Value and Evaluation Index

Unit Cost vs. Customer Satisfaction Value and Integrated Evaluation Index

9.20 1.20

Q) 9.00 1.00 x ::J

ca Q)

8.80 ""0 > c::

c:: 0.80 c: ---Customer 0 0 :;:; 8.60 :a Satisfaction Value 0

$ ::J (Mt) (/) 8.40 0.60 ca :;:; > CIl W ---+- Integrated Cf)

8.20 ""0 Evaluation Index .... Q) Q) 0.40 "ê (NV) E 0 8.00 Cl ..... Q) (/)

0.20 .....

::J c:: ü 7.80

7.60 0.00

a a a a a a a a a a N Ln 00 a M Ln t- a ...... ,.....; ,.....; ,.....; N N N N M ...... ...... ...... ...... ...... ...... ...... ...... ...... ~ ~ ~ ~ ~ ~ ~ ~ ~

Unit Cost ($)

Figure 3.2 Unit Cost versus Customer Satisfaction and Evaluation Index Diagram

82

4.1 Conclusion

CHAPTER4

CONCLUSION AND FUTURE WORK

The model developed in this thesis incorporates the methodologies of value

engineering and risk analysis within the QFD process. In addition, quantitative

parameters are defined and deployed into matrices to establish the weight of importance

of CR, DC, DF, RA, the relationships between CR and DC, cost related information, risk

assessment level, customer satisfaction value, evaluation index, etc. The merging of these

techniques makes the QFD process more robust and more quantitatively oriented.

The HOQ matrix not only demonstrates hierarchical customer requirements and their

relationships with design characteristics, but also, it prioritizes the design characteristics

and presents the evaluation of the product at the level of design characteristics. Secondly,

the function tree allows product decomposition into design functions. Basic functions and

secondary functions are identified. Actual cost is given for every function. In the next

matrix, cost and risk analysis matrix 0 (CRAM 0), the target unit cost is first set for the

CUITent model. Cost and risk are analyzed at the design function level. Actual to planned

cost ratios and risk assessment levels are separately compared based on the different

criteria for design functions. Meanwhile, the total risk assessment level for aIl design

characteristics is calculated for the current design based on all given risk attributes. Then,

the areas with opportunities for improvement are determined and feasible solutions are

created.

83

Similarly, cost and risk analysis matrix 1, cost and risk analysis matrix 2, ... , cost and

risk analysis matrix n are built for solution 1, solution 2, ... , solution n, in which the

detailed cost and risk analysis are made respectively for every solution. The total risk

assessment levels for all design characteristics are calculated for different solutions based

on the same risk attributes given in CRAM O. Furthermore, in order to make comparisons

between the CUITent design and proposed solutions, necessary cost and risk related

information are organized in the integrated evaluation matrix. Customer satisfaction and

the integrated evaluation index with consideration of cost and risk are used as criteria by

which the CUITent design and proposed solutions are evaluated and the best solution is

determined. The development matrix is constructed for the best solution. Given different

unit costs for the best solution, cost and risk analysis are deployed at the level of design

functions. Then, customer satisfaction and the integrated evaluation index are determined.

Unit cost to customer satisfaction relationships and unit cost to integrated evaluation

index relationships are show in the unit cost versus customer satisfaction and evaluation

index diagram. The best solution with reasonable target unit cost is the one which can

obtain the best balance between customer and enterprise satisfaction with consideration

of financial factors and design uncertainties.

In this model, all the matrices are deployed in a hierarchical manner. The product is

decomposed into design functions, based on which detailed cost and risk analysis are

made for the current design and solutions. It makes the whole process traceable from

customer requirements with strong market focus to design functions and to the actual

solutions, which are design oriented. In addition, with the intention of achieving the best

balance between the external (customer) and the internaI (company) satisfaction,

84

customer satisfaction value (Mt) and integrated evaluation index (NV) are defined and

calculated. An example of the evaluation procedure for different designs and their (Mt,

NV) relationships with the target unit cost for the best solution demonstrates the

capability of the model.

As stated in Chapter 1, the objectives ofthis thesis are:

• to develop a quantitative QFD model integrated with the methodologies of value

engineering and risk analysis, which considers not only the customer satisfaction,

but also the enterprise satisfaction by making detailed cost and risk analysis for

the design

• to introduce the new concepts of customer satisfaction value and an integrated

evaluation index to assist in making a decision

• to propose a method for companies to determine a realistic target unit cost for a

design in order to obtain the best balance between customer and enterprise

satisfaction with consideration of financial factors and design uncertainties.

By integrating the methodologies of value engineering and risk analysis with QFD, a

quantitative QFD method is presented in this thesis. Within the developed quantitative

QFD model, detailed cost and risk analyses incorporate the evaluation of how well

customer requirements are satisfied. During this process, the parameters, i.e., an

integrated evaluation index (NV) and customer satisfaction value (Mt), are defined and

determined to direct the design in order to attain the appropriate balance between

customer and enterprise satisfaction. So, the first two objectives are fulfilled.

During the development process for the proposed design, given different unit cost

(UC) for the product, the values of Mt and NV are determined and their relations with

85

DC are discussed. By observing the unit cost versus costumer satisfaction and evaluation

index diagram and analyzing the related data, companies are able to decide the realistic

unit cost for the preferred design under the criteria of achieving weIl balanced customer

and company satisfaction with consideration of customer requirements, financial factors,

and design uncertainties. Thus, the third objective is achieved. An example was presented

in the thesis to demonstrate the effectiveness of the method.

Due to limited time and space, it is impossible to organize a large number of cases for

varied products and elaborate on the analyses in just one thesis, but the one example

demonstrated the concepts which would be the same for other examples. The hierarchical

organization of the matrices in the method malœs ca1culations easier, and the decision

process faster. With the implementation of this integrated quantitative QFD method,

more knowledge will be acquired and more experience accumulated quicker.

4.2 Constraints and Future Work

In the model developed in this thesis, both the correlation between design

characteristics and the interactions of the design functions are ignored. This is reasonable

for a simple product, but for a complex product composed of a large number of various

components, one item might relate to several different items, and there may be sorne

correlation. So, new methods or matrices should be developed to take this type of

correlation into consideration. Moreover, with increasing product complexity, many more

variables are involved and the matrices become bigger. Calculations could become

burdensome. Thus, simplification of the process should be do ne by combining variables,

developing sub-matrices, and so on.

86

In the quantitative QFD model, function analysis and classification are made for the

group of design functions that is related to the same design characteristic, i.e., every

design characteristic can de li ver at least the basis function demanded by users. In other

words, every design characteristic is independent of each other, and all related parts are

needed for the existence of the product desired by customers. Of course, this model .

allows the combination and/or elimination of the secondary functions in order to lower

cost. However, for the case where companies would like to combine parts in order to

reduce the overall cost, the method needs to be improved so that the function

classification can be made for the design functions related to the different design

characteristics (or parts) for the same product.

Lastly, standardization of risk analysis for similar products might help decision

makers monitor the pro cess more efficiently, perform benchmarking more easily, and

make product improvement faster. Effort should be addressed to the definitions of risk

attributes and risk assessment parameters, proper scales for comparison, and so on.

87

(BIUC) j:

Dt:

APPENDIX: PARAME TER DEFINITION

the actual cost required to achieve DFjk.

total actual cost required to achieve aIl the DFs.

benefits obtained from making one unit of the product by the company

when using current design or /h solution.

the ratio of benefit per unit to cost per unit for current design or /h solution.

·ili . 1 customer reqmrement.

/11

relative weight of importance of the ith CR, ICi = 1. i=1

total cost of product units per year for current design or the jth solution.

/h design characteristic.

weight of importance of /h DC.

the sum of the total weight of importance of aIl design characteristics.

Il

relative weight of importance of /h DC, and Id. = 1 . .1

j=1

kth design function which is related with lh design characteristic (DCj ), and

it is from the deepest level design functions on the function Tree, which is

then reordered and arranged in sequence in cost and risk analysis matrices.

DPj : the risk assessment level for /h design characteristic DCj to satisfy aIl the

risk attributes, which is based on the average probability of success for

DCj to fulfiIl aIl the risk attributes.

DPt: the total risk assessment level for aIl design characteristics, which is aiso

the average risk assessment level for aIl design functions.

88

x

relative weight of importance ofDF jk, If"k = 1,i = 1,2, ... ,n k=1 .1

the risk assessment level for jkth design function DFjk to satisfy aIl the risk

attributes, which is based on the average probability of success for DF jk to

fulfiIl aIl the risk attributes.

customer satisfaction rank value 1-9, (9=High), which indicates how weIl

the product made by using current design or the lh solution satisfies the ith

customer requirement CRi, the higher the value of Mij is, the better the

customer requirements are satisfied.

total customer satisfaction value for current design or each solution, which

means totaIly how weIl the product made by using current design or the jth

solution satisfies aIl the customer requirements.

NVj : integrated evaluation index with consideration of cost and risk for current

design or lh solution. The solution with higher value of NV is a more

beneficial one which can meet the integrated criteria of cost and risk better.

PCjk: the planned cost required to achieve DFjk.

Pjki : the risk assessment level for jkth design function DF jk to satisfy the ith risk

attribute RAi, which is based on the probability of success for DF jk to

fulfiIl RAi. The higher the Pjki is, the more probably the jkth design

function will fulfiIl the ith risk attribute successfuIly, which means the

better the result is for the chance of accomplishment. 0::; Pjki ::; 100% .

actual sale price per unit for CUITent design or target sale priee per unit for

lh solution.

89

relationship value, indicating the strength of relationship between ith

customer requirement and the /h design characteristic, and the scale

1,2,3,4,6,7,8,9,10 is used to denote relationships respectively. The bigger

the value, the stronger the relationship is.

RAi: ith risk attribute.

y

Rai: relative weight of importance ofith risk attribute, IRa = 1 ;;\

average sale units per year for CUITent design or the /h solution, which is

assumed to be liner ratio with the total customer satisfaction value for each

solution (Mtj). The more satisfied the customers are, the more likely they

want to buy the products.

TCO: target unit cost for the product in current design.

uq: unit cost for /h solution, which is actual unit cost for CUITent design or

target unit cost for /h solution.

ratio of the actual cost required to achieve DF jk to the planned cost

required to achieve DF jk

YBj: yearly benefits for current design or /h solution.

90

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91

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