Compliance of Customer’s Needs with Producer’s Capacity: A Review and Research Direction

By

Md. Mamunur Rashid

A study report submitted to the Department of Mechanical Engineering of the requirements for the record of research student for the Month January to March, 2010.

FACULTY OF ENGINEERING

DEPARTMENT OF MECHANICAL ENGINEERING

NATIONAL UNIVERSITY CORPORATION KITAMI INSTITUTE OF TECHNOLOGY

165 Koen-cho, Kitami, Hokkaido 090-8507, JAPAN.

MARCH, 2010

2

ACKNOWLEDGEMENT

The author is highly grateful and indebted to Professor Jun’ichi Tamaki, Department of

Mechanical Engineering of National University Corporation Kitami Institute of Technology for

accepting me as a research student will effect from January-March,2010 and doctoral student

will effect from April 2010.The author is also highly grateful to Dr Sharif Ullah, Associate

Professor, Department of Mechanical Engineering of National University Corporation Kitami

Institute of Technology, Japan for his continuous inspiration & encouragement, valuable

suggestions and untiring support throughout this work.

The author is also grateful to Professor Masashi Sasaki, Mechanical Engineering Graduate

Program Director of National University Corporation Kitami Institute of Technology, for

providing assistance at different stages of this Graduate School.

The author gratefully acknowledges for the different assistances received from Dr. Mohammad

Rafiqul Islam, JSPS Fellow and Associate Professor of Rajshahi University of Engineering and

Technology, and Dr. S.M. Muyeen, JSPS Fellow and Mr. Md. Rafiqul Islam Sheikh, Doctorate

Candidate of Kitami Institute of Technology and Associate Professor of Rajshahi University of

Engineering and Technology, Bangladesh during this work.

My appreciation goes to International centre of Kitami Institute of technology for helping me in

many ways.

The author is thankful to the authority of Kitami Institute of Technology and the Government of

Japan for permitting him for this research work.

The author would like to express his sincere thanks to all others Teachers and Staffs of

Mechanical Engineering Department, who directly or indirectly have helped him in completing

this work properly.

The author is gratefully to the authority of Bangladesh Institute of Management, Dhaka and the

Government of Bangladesh for providing leave during this research work.

Finally, the author is grateful to my Mother, spouse Mimi, son Sakib and daughter Elmi for their

encouragement and understanding of this study.

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ABSTRACT

This is a literature review on product development for compliance customer’s needs with

producer’s capacity from a state of the art review 144 papers in this field. The main contributions

are perceived by this study including product development practices. This report is discussed the

probable challenges, which will be solved in the present doctoral study. A brief review of

methods and features of product development is illustrated. These are product modularity,

product family optimization, kansei engineering, axiomatic design theory, quality function

deployment and trading agent trade-off. It is also addressed for kano model and product value

chain, a method for design from kano indices for quantification, kano classifiers for

categorization of customer needs, configuration index for product configuration design,

compliance customer’s satisfaction and producer’s capacity by kano evaluator and a design

process model of analytical kano for decision making. Probabilities are derived from Kano

evaluation table for a starting work of proposed system development. An example of prospect

system for doctoral study is proposed and discussed of the proposed system. At last is concluded

a conclusion in this regard.

Key words: Product Family, Customer Needs, Kano Model, QFD, Product Modularity,

Axiomatic Design Theory, System Development, Doctoral Study, Particle Swarm Optimization

4

CONTENTS

Title Page No.

Title Page 1

Acknowledgement 2

Abstract 3

Keywords 3

Contents 4

List of Symbols 5-8

List of Figures 9

List of Tables 10

1.0 Introduction and Challenges 11

2.0 Product Development 12

2.1 Product Platform and Modularity 17

2.2 Product Family Optimization 19

2.3 Kansei Engineering 23

2.4 Product Design Methods and Tools 24

2.5 Axiomatic Design Theory 27

2.6 Quality Function Deployment 29

2.7 Trading Agent Trade-off 30

3.0 Kano Model and Product Value Chain 31

3.1 A Method for Design from Kano Indices for Quantification 34

3.2 Kano Classifiers for Categorization of Customer Needs 37

3.3 Configuration Index for Product Configuration Design 38

3.4 Compliance Customer’s Satisfaction and Producer’s Capacity by Kano Evaluator 39

3.5 A design Process Model of Analytical Kano for Decision Making 39

4.0 Probabilities Calculation from Kano Evaluation Table 4 40

5.0 An Example of Prospect System for Doctoral Research 44

6.0 Discussion 45

7.0 Conclusion 46

References 47

Appendix: A Profile of Author 57

5

LIST OF SYMBOLS

Symbol Meaning

s the market segment

F a set of functional requirement

J a total of Customers

j individual customer

fi respondent evaluation according to functional

dysfunctional forms of Kano question.

∀ all

∃ there exist

A attractive

O one-dimensional

M must be

I indifferent

R reverse

Q questionable

X average dissatisfaction score

Y average satisfaction score

r is the vector with concern angle α

ρ configuration index

ri, αi kano indices

E kano evaluator

U overall customer satisfaction

C overall cycle time index

USL upper specification limit

µ population mean

ζ ,ω regression co-efficient

ADT axiomatic design theory

DBC design for customer

QFD quality function deployment

6

PCI process capability index

r0,αL,αH kano classifiers

KPIs key performance indicators

V product variants

CDI commonality versus diversity index

PSO particle swarm optimization

MOO multi objective optimization

DSM design structure matrix

ANN artificial neural network

DFFS design for six sigma

STEP standard for the exchange of product data

UMP universal manufacturing platform

STBFs start-up technology-based firms’ theory

DM design matrix

PDCA plan-do-check-act

PDCS a product definition and customization system

CA conjoint analysis

KA kohonen association

DKH a novel design knowledge hierarchy

APF architecture for product family

MDO multilevel multidisciplinary design

PPCEM product platform concept exploration method

APDL axiomatic product development life cycle

MOGAs multi objective genetic algorithms

SIO selection-integrated optimization

TQM total quality management

VBPDM variation based platform design method

UML unified modelling language

TIPS/TRIZ theory of inventive problem solving

CAs customer attribute

FRs functional requirement

7

DPs design parameter

PVs process variables

LVs logistic variables

SPC statistical process control

PCI product line commonality index

CI commonality index

DCI degree of commonality index

CMC comprehensive metric for commonality

CNs customer needs

SCM supply chain management

PCA principal component analysis

∆ defined

COPE a design decomposition model for

complex product environment

DM design matrices

DSM design structure matrix

NPD/PD/NPDP new product development process

CMM-I capability maturity model -integrated

PPCEM product platform concept exploration method

GA genetic algorithm

AIS artificial intelligence system

ID3 inductive learning based knowledge

extraction method

DFMC design for mass customization

VOC voice of customer

DSP design strategy project

SPD strategic product design

BNPD branded new product development

IDE industrial design engineering

CT computed tomography

CCD charge coupled device

8

STL stereo lithography CAD

CAE computer aided engineering

FEM field emission microscopy

ADV agile design for variety

PCI product line commonality index

SIO selection-integrated optimization

DPLM design process life cycle management

PLM product life cycle management

DSP decision support problem

EPV empirical performance validity

ESV empirical structural validity

TPV theoretical performance validity

TSV theoretical structural validity

C-K concept-knowledge

9

LIST OF FIGURES

No. Name Page

Figure 1 Main Challenge of this study 11

Figure 2 Product Development Aspects 13

Figure 3 Delft Innovation Model for Product Development 14

Figure 4 Definition of Product Development 15

Figure 5 Design Process 15

Figure 6 Process of New Product Development 16

Figure 7: Platform Parameters and Individual Parameters 18

Figure 8 Product Family Generations 20

Figure 9 Part Family Structure from Customer Requirements 21

Figure 10 Multidisciplinary Designs and Optimization Process 21

Figure 11 Principle of Architecture of Product Family 22

Figure 12 Kansei Design Methodology Workflow 23

Figure 13 Domains of Axiomatic Design Theory with Linked Logistics Domain 28

Figure 14 Component of Quality House 30

Figure 15 Multi Agent Architecture 30

Figure 16 A Kano Model for Customer Satisfaction 32

Figure 17 Product Value Chain Adapted from Xu et al, 2009 33

Figure 18 Vector Representation of Customer Perception on a Kano Diagram 36

Figure 19 Kano Classifier and Kano Categories 37

Figure 20 An Analytical Kano Design Process 40

Figure 21 A Model for Prospect System 45

10

LIST OF TABLE

No. Name Page

Table 1 Product Design Variables and Tools 24

Table 2 List of Methods and Tools for Product Development 25-26

Table 3 Kano Questionnaire 34

Table 4 Kano Evaluation Table 35

Table 5 Scores for Functional/Dysfunctional Features 36

Table 6 Self –Stated Importance Score 37

Table 7 Evaluation of Functional Individual Features of Kano Model 41

Table 8 Probabilities in % of Functional Features of Kano Model 41

Table 9 Probabilities in % of Functional Individual Features of Kano Model 42

Table 10 Evaluation of Dysfunctional Individual Features of Kano Model 43

Table 11 Probabilities in % of Dysfunctional Features of Kano Model 43

Table 12 Probabilities in % of Dysfunctional Individual Features of Kano Model 44

11

1.0 Introduction and Challenges

Product is a yield of producer including service or goods. Product is essential for satisfying

customer needs. It is also shown for producer technical ability. Producer’s revenue is fluctuated

due to change customer trends by age, income, education, automation and technological

advancement. The most appropriate leveraging strategy is introduced with respect to the target

market segments with considering of customer trends (Weck et al., 2005). Products families are

introduced into the market to meet the different segments of customers. This will be considered

main challenge to comply among satisfaction, affordability of customer and production rate,

Customer Segment 1

Customer Segment 2

Product Family

SatisfactionAffordabilityProduction RateTechnical AbilityValue ChainCompetition

Main Challenge

Figure 1: Main Challenge of this Study

technical ability, value chain and competitor of manufacturer in various customer segments,

(Browing, 2002) which are also shown in figure 1. Now, cost saving and shorter lead times of

product development are considered for sustaining in the market. For this purpose, models are

developed to incorporate the effects of constituent costs such as production cost, holding cost,

setup cost, and shortage cost including a shelf-life constraint (Xu & Sarker, 2003) ,and, to reuse

existing product platform for introducing product family for cost saves and shorter new product

development and introducing in the market duly and leverage for cost saving. Practical

examples are eliminated or minimized human error in manufacturing process and management as

12

a result of mental and physical human imperfections for both quality and cost saving

(Burlikowska & Szewieczek, 2009). A cost modeling framework is allowed the value of

commonality to be quantified for design and manufacturing cost (Willcox & Wekayama, 2003).

Design structure of global service distribution is introduced for the service-oriented process

analysis to fulfill the customer requirements efficiently; the services should not only in a higher

quality but also within a shorter reaction time and at a low price (Meier& Kroll, 2008). So,

challenges of producer will be tackled different customer segments for facilitating cost saving by

fulfilling individual customer needs. So, manufacturer is followed regarding laws of

development of consumer needs (Petro, 2005), customer pain points (Handfield & Steininger,

2005), and attention of changing customer needs by adapting design requirements

(Hintersteiner, 2000).

The next challenge will be optimized of quantity of production, which can be trade-off among

companies’ fixed inputs including price of product, technical ability, labor, capital entrepreneurs,

physical resources and information resources with customers’ uncertain inputs including

satisfaction, demand of the product, purchasing ability and information resources. Systematic

design and evaluation of segmented production system structures are adapted of production

system’s organizational structure to be more reactive to a volatile and diversified market

behavior (Cochran et al., 2000). An Optimized production system design is segmented of the

manufacturing enterprise into small, flexible and decentralized production units including

presented segmentation procedure utilizes an axiomatic design framework and supports lean

management practices following strategic, organizational, and technological design aspects.

The last challenge of this study will be developed a generic system for product family

optimization. This system is allowed of four domains including customer attributes, functional

requirement, design parameters and process variables of Suh’s axiomatic design theory.

2.0 Product Development

Product development is the continuous process for human civilization. Summarized of existing

work for product development are following. The process of product development is conducted

very unsystematically and even resources are wasted of many companies, because of a lack of

communication and coordination between the different functions and aspects is involved in

product development. For the case of product development, time is considered a critical factor

and as time to market is become more important. Presently, electronic commerce and growth of

13

the internet is created new opportunities of product development. Many organizations are

followed the complex systems for product producing that have to be directed in order to meet

specific customer expectations (Martin, 2001). Every company’s economic growth and

sustainable value creation is targeted for long-term. Practical experiences of large structure are

suffered by efficiency losses due to increasing organizational complexity and bureaucracy (Matt,

2009). An analysis of existing design process models is introduced for modeling product

development processes, and detailed descriptions of the activities (Tate & Nordlund, 1996).

Designers and entrepreneurs and Engineers are followed many method or technique for

designing a new products. These aspects, methods and tools are shown in figure 2.

Organizational Aspects:Strategic GoalsHierarchyCoordinationTeamingSupport…

√

×

…Voice of Customer:Customer SegmentsNeedsFeedbackSatisfaction…

Methods/Tools:Scientific AnalysisQFDTRIZAxiomatic DesignKnowledge-Based DesignSimulationBrainstormingLCADesign for XCAD/CAM/CAEPrototypingDesign of ExperimentSix-SigmaLeanMass CustomizationValidationVerification…

Peripheral Aspects:CompetitorsSupply ChainRegulationsEnvironment…

Product Development

Figure 2: Product Development Aspects, Methods and Tools

Delft innovation model of product development is conceived from strength, weakness,

opportunity and threat (SWOT) analysis of the organization. It is also called a fuzzy front end of

product development. After SWOT analysis, the organization is developed a new product with

compliance their standard. Muddy back end will be studied for the market introduction.

Evaluation of product is done by the information of consumers. So, delft innovation model is

applied for product development, market introducing, and evaluation of product use (Buijs &

Abbing, 2008). Figure 3 is shown delft innovation model, one of them for product development.

14

Figure 3: Adapted Delft Innovation Model, Buijs & Abbing, 2008

Data mining techniques are identified generic routings from large amount of production

information and process data available in a firm’s legacy systems to cope with the textual and

structural types of data underlying generic routings (Jiao et al., 2007). The rapid change of

technology has been led to shorter product life cycles for many products most particularly in

consumer electronics (Minderhond & Fraser, 2005). Product development is considered

Strategic situation of the company

Generating Search Areas

External Analysis

Internal Analysis

Search Areas

Evaluation

Search Areas

Generating Product ideas

Inte

rnal

An

alys

is o

f Bo

ttle

neck

Exte

rnal

Nee

d An

alys

is

Ideas

Evaluation

Design Brief

Product Development

Tech

nolo

gy

Dev

elop

met

Mar

ket

Dev

elop

men

t

Product Design

Evaluation

New Product development

Fuzzy Front end of Innovation

Strategic Product Position

Eval

uatio

n of

Pr

oduc

t

Eval

uatio

n of

Pr

oduc

t Use

Product Use

Product in Use Product Use

Evaluation

Product Launch

Market Introduction

Man

ufac

turin

g

Dis

trib

utio

n,

Prom

otio

n an

d Sa

les

Product

Muddy Back End

15

including product cost, quality and time-to-market each to more and more important. Figure 4 is

shown of definition of product development.

Figure 4: Definition of Product Development Adapted from Mital, et al. 2007

A novel product definition and customization system (PDCS) is established for organizations to

meet this demand in today’s competitive and globalised business climate including product

definition using the laddering technique and a novel design knowledge hierarchy (DKH), and

product customization using an integrated methodology of conjoint analysis (CA) and Kohonen

association (KA) techniques for making design decisions via customer involvement, i.e. a

strategy for transferring customer preference into a specific product concept. (Chen et al., 2005).

Integrated design knowledge is applied for reuse framework, bringing together elements of best

practice reuse, design rationale capture and knowledge- based support in a single coherent

framework (Baxter et al., 2007).

Figure 5: Design Process

The STBFs is introduced for undertaking their new product development (NPD) the relationship

between corporate strategy, NPD process features .New product success factors are considered

including corporate strategy theory, new product development theory, entrepreneurial theory,

technology management theory, economic development theory and business incubator theory

(Beven, 2007). A study is applied for the effectiveness of systematic and conventional

approaches to design (Sivaloganathan et. al., 2000). A formal basis for the creation of an

automated reasoning system is supported for creative engineering design (Sushkov et al., 1995).

A stepwise procedure based on quantitative life cycle assessment is integrated of environment

aspects in product development (Nielsen & Wenzel, 2002). A model is developed for concurrent

product and process design (Roemer & Ahmed, 2010). Various design concepts are evaluated in

order to identify the ‘Best’ concept with application of fuzzy logic for design evaluation and

proposes an integrated decision-making model for design evaluation at developing a computer

tool for evaluation process to aid decision-making (Green & Mamtami, 2004). A design

structure matrix (DSM) is provided a simple, compact, and visual representation of a complex

Design Process Information about needs and constraints

Sufficient information to Generate a system that meets the needs and constraints

Transform raw Materials Quickly, easily, economically & efficiently Desired Product (Quality, Value & Utility)

16

system that supports innovative solution to decomposition and integration problems for product

development (Browing, 2002). Figure 6 is shown a process of new product development.

Figure 6: Process of New Product Development

An information technology (IT) framework is solved the NPD problem through automatic

generation of information. The framework is used the concept of information templates or

models and a rule based system to generate manufacturing instructions. (Dean et al., 2008).

Information cannot be summed for decoupled designs and overcome this problem by applied

joint probability density function and uniformly distributed design parameters (Frey et al.,

2000).Two important issues in configuration product design are considered including

requirement configuration and engineering configuration (Shao et al., 2005).A deliberate

business process is involved hundreds of decisions and supported by knowledge and tools for

product development (Krishnan & Ulrich, 2001). The algebraic properties of fuzzy sets under

the new operations “drastic product” and “drastic sum” is introduced by Dubois in 1979 and the

algebraic properties in the case where these new operations are combined with the well-known

operations for fuzzy sets. The properties of fuzzy relations are also shown under a new

composition of fuzzy relations which is defined by using the drastic product development

(Mizumoto, 1981). The products model is solved two essential problems redundancy of both

technical and marketing effort and lack of long term consistency and focused for an approach to

managing new products (Meyer, 1992). Reused design is applied for product development

modeling and analysis & optimization (Ong et al., 2008). Off drape and hand off fabrics are

applied for 3D material simulation for garment manufacture (Palicska, 2008). Integrated design

of products and their underlying design processes are provided for a systematic fashion,

motivating the extension of PLM. It is included the lifecycle considerations of design processes

and design process lifecycle management (DPLM) (Panchal et al., 2004). ‘Validation Square’ is

validated by testing its internal consistency based on logic in addition to testing its external

relevance based on its usefulness with respect to a purpose (Pedersen et al., 2000). C-K theory

is applied for innovative design (Hatchuel & Weil, 2003). The development of a framework is

Synthesis Detailed Plans Production Marketing and sales

Market/ Consumer Needs Company Policy Business strategy Idea Gen

17

incorporated of different models for environmental analysis, with the option of a broader scope

that also includes economic and social aspects, thus covering the three pillars of sustainability

(Heijungs et al., 2010). Identified factors is explained or significantly contributed to successful

launch of product development of an innovation (Haapaniemi & Seppanen, 2008).

2.1 Product Platform and Modularity

Product modular and platform design is an important elemental technique in life cycle design for

improving, e.g., maintainability, upgradability, reusability, and recyclability. Following works

are reviewed for design of product platform and modularity. A method for determining modular

structure is aggregated various attributes related to a product life cycle and evaluating geometric

feasibility of modules (Umeda et al., 2008). A multi-criteria optimization is applied in product

platform design (Nelsen II et al., 1999). Product families are derived from scalable product

platforms that can be exploited from both a functional and a manufacturing standpoint from both

a functional and a manufacturing stand point to increase the potential benefits of having a

common platform. Product modularization is the concept of grouping a number of components

into modules, and defining interfaces between modules (Simpson et al., 2001). Product

modularization is applied cost saving in product development through using more components of

an existing product and existing processes. Now, modularization is attracted of manufacturing

company for the role of cost minimization. Analysis and improvement of product modularization

methods are arranged with complex products and also complexity has several aspects including

many variants, mix of different technologies, and having different solutions for one and the same

function (Holmqvist & Persson, 2003).A common platform is balanced against the constraints

of individual product variants and constraints of the family as a whole (Zugasti et al., 2000). A

systematic methodology for manufacturing modular products in a reconfigurable manufacturing

system is applied to the production of a DC motor and a ball screw (Yigit et al., 2002). A basic

theory for understanding interface a strategy in modular product innovation through a literature

review is covered a number of concepts including product architecture, functional modules,

internal and external interfaces, product platforms and families (Chen& Liu, 2005). A

methodology for achieving the goal is defined as a set of modules, platform parameters and

individual parameters, firstly, product modules are identified; second, a strategy of choosing

platform parameters is investigated based on considering the influence of customizing individual

parameters upon the activities, such as design, die, machining, assembly, service and

18

management, in all product life cycle; third, an optimization model is employed to determine the

value of platform parameters whose carriers are the modules (Fei et al., 2009).

Figure 7: Platform Parameters and Individual Parameters

Developed function structure is applied for each product. After comparing function structures for

common and unique functions, rules are applied to determine possible modules. This process is

defined possible architectures. Each design is represented using a matrix of functions versus

products, with shared/unique function levels indicated (Dahmus et al., 2001). Common product

platforms and customizable modules in a strategic product development (PD) process are

developed a portfolio of derived product variants in a tactical customization process (Sellgren,

2008). A combination of axiomatic design (AD) theory, traditional traffic conflict analysis, and

TRIZ are applied to re-design urban intersections for improved efficiency. A clustering

algorithm is applied for common module identification that takes into account possible degrees

of commonality for modular product platform design. A module commonality is used

dendrograms for platform design (Otto, 2005, 2008). Product platform design is introduced to

improve commonality in custom products (Farrell & Simpson, 2003). Platform under

performance loss constraints is selected in optimal design of product families (Fellini et al.,

2002). Huang et al., 2003, are identified and development a product platform for mass

customization. The methodology of Axiomatic design is implemented the universal

manufacturing platform (Houshmand & Mokhtar, 2009). Modularity is applied for a product

life-cycle engineering (Ishii, 1995).

Platform Parameters Individual Parameters

X1 X2 … Xp Xp+1 Xp+2 … Xn

Vp+1,1 Vp+2,2 … Vn,1 1st Product

… … … … …

V1 V2 … Vp Vp+1,j Vp+2,j … Vn, j jth Product

… … … … ….

Vp+1,m Vp+2,m … Vn, m mth Product

Values of Platform Values of Individual Parameters Parameters

19

2.2 Product Family Optimization

According to the web, two definition of product family; first is considered a set of items as a

related group in forecasting, capacity planning or other functions. Second is also considered a

product family of a subset of product line which has certain attributes in common. Product

family optimization is considered the main challenge. So, few studies in this regard are reviewed.

Product variety optimization under modular architecture is developed (Fujita & Yoshida, 2002,

2001). Assembly sequence design methodology is applied for product family optimization (Guta

& Krishnana, 1998).Commonality versus diversity index (CDI) is considered to assess the

commonality and diversity within a family of products. The CDI is enabled the designers design

and improved the product family (Alizen et al., 2007 , 2009) Application of Axiomatic Design

and Design Structure Matrix to the decomposition of engineering systems is applied for complex

product environment of product development (Guenov & Barker, 2004 ) . Scale based product

families are presented a new single-stage approach for simultaneously optimizing a product

platform by PPCEM tools, and one or more dimensions to satisfy a variety of customer

requirements. These methods are improved an existing family of products (Messac et al., 2002).

A global methodology to form product families is taken advantages of fuzzy product

configuration. Fuzzy logic is considered as a way to improve the decision-making process

because of its ability to manage information more accurately than binary logic (Barajas &

Agard, 2009). The variation is raised when estimating the product line commonality index (PCI)

for a family of products are used data gathered from product dissection. Products family

optimization is considered for cost saves and profits maximization (Thevenot & Simpson,

2007). For comparing product family modeling with modeling single product is faced with

technical challenge (Tseng & Jiao, 2000). The applicability of the selection-integrated

optimization (SIO) methodology is applied for product family optimization (Khire & Messac,

2006). Exploration method is considered for conceptual design of a family of products including

product platform (Simpson et al., 1996, 1998, 2001, 2004). A solution of globally distributed

manufacturing networks is made interdependency between product design and choice of

production site (Grauer et al., 2007). Multiple products within a common application domain,

systematic use of a software product family process can be increased productivity including cost,

quality, effort and schedule (Ahmed et al., 2008). Data-mining based methodology for the

design of product families can be satisfied the needs of customers. It can be leaded to better

20

design of product and operation of processes as well as to sustainable processes or product

platforms (Agard & Kusiak, 2004). Particle swarm optimization is applied for the computation

of all global minimizers including product family optimization. The PSO strategy is solved

multi-objective problems since 1999. Particle swarm optimization (PSO) is considered for an

advancement technique for unconstrained continuous optimization problem (Parsopoulos and

Vrahatis, 2004). An example of product family generations is shown in figure 8.

Figure 8: Product Family Generations

Fuzzy genetic is applied for prioritization in multi-criteria decision problems. A particle swarm

optimization-based multi-objective algorithm is also applied for flow shop scheduling. The

evolutionary algorithm is searched of the pareto optimal solution for objectives by considering

the make span, mean flow time, and machine idle time. The algorithm was tested on benchmark

problems to evaluate its performance. The results is shown that the modified particle swarm

optimization algorithm performed better in terms of searching quality and efficiency than other

traditional heuristics (Sha & Hsing-Hung, 2009). A multi level multidisciplinary design

optimization (MDO) is applied family optimization (Ferguson et al., 2009). A variation-based

method is applied for product family design (Nayak et al., 2002). An information modeling

framework is applied for product families to support mass customization manufacturing on a

combination of elements of semantic relationships with the object-oriented data model (Jia &

Tseng, 2000). A modeling framework is applied for relationship between product functionality

and manufacturing resources. A design method for investigation of product family structure is

realized for required product functional variety with efficient utilization of manufacturing

resources (Kimura & Nielsen, 2005). In the circumstances of product family optimization, PSO

(Eberhart & Kennedy, 1995) can be applied to solve multi objective optimization (MOO) with

multi level focus on product develop capacity of producer compliance with customer needs by

Time

Product Product Generations

Product Family

Product Family Generations

Expa

nsio

n

21

using Suh’s axiomatic design theory for sustaining business in global market. Part family

structure is shown in figure 9.

Step: 1

Functional requirement

analysis

Step: 2

Functional structure design

Step: 3

Technical Structure design

Customer Requirements

Figure 9: Part Family Structure from Customer Requirements

A PFA from three perspectives, mainly the functional, behavioral and structural views can be

provided a generic architecture to capture and utilize commonality, within each NPD and extends

to a common product line structure (Tseng & Jiao, 2000). Multidisciplinary design and

optimization process is shown in figure 10.

Figure 10: Multidisciplinary Design and Optimization Process

A method using design of experiments to help screen unimportant factors is identified factors of

interest to the product family, and a multi-objective genetic algorithm, the non-dominated sorting

genetic algorithm, to optimize the performance of the products in the resulting family (D’souza

and Simpson, 2003). Where a homogeneous the portfolio is begun by analyzing the current

22

product offerings to determine customer needs and functions. A heterogeneous portfolio is one

that has no common components, or shares a minimum number of components. A granulation

process is removed redundancies, and identified physical function carriers to deliver the required

functions (Salhieh, 2007). Principle of Architecture of Product family is shown in figure 11.

Universal support is influenced for product family and customer segments.

Figure 11: Principle of Architecture of Product Family

A specific product within the product family including the variation mechanisms are used to

derive a specific product from the generic platform. The coverage of the platform and the

variation mechanisms used are not totally unrelated (Wijnstra, 2005). A genetic algorithm based

method can be done to help find an acceptable balance between commonality in the product

family and desired performance of the individual products in the family (D’souza and Simpson,

2003). A resource capability model is supported for product family analysis (Nielsen & Kimura,

2006).A model of product family information is applied for the purpose of supporting various

applications, and for achieving an efficient utilization of information (Sivard, 2000). A new

family of products is developed by designers and manufacturers with considering its supply

Universal Support

C

Customer Segment

Customer Segment 3

Customer Segment i

Customer Segment m

Customer Segment 1 V1

V2

V3

Vi

Vm

E1

E2

E3

En

Demarcation Enabler

Pattern Mechanism

Market Segments Product Family Architecture of Product

23

chain. First step of the design process, designers are proposed various solutions for the set of

variants of a product family and their bill-of-materials. Second step is selected some of these

variants while choosing the architecture of the supply chain and a mixed integer linear

programming model is investigated that optimizes the operating cost of the resulting supply

chain while choosing the product variants (Lamothe et al. 2006).

2.3 Kansei Engineering

Kansei engineering is well-known last 30 years including as an ergonomics and consumer

oriented technology for developing a new product, when a consumer wants to buy something,

he/she will have a kind of feeling and images ( Kansei in Japanese) in his/her mind. Figure 12 is

shown an overview of workflow for a typical study using kansei engineering:

Figure 12: Kansei Design Methodology Workflow

If the sensing of the customers is done properly by kansei engineering, then the product will be

successful. Otherwise, the new product will be very complex to fit to the market, even if kansei

engineering is used. Kansei engineering as a powerful consumer-oriented technology is applied

for product development (Nagamachi, 2002).Implicit shape parameterization is used for kansei

design methodology. Two major problems are shown kansei engineering to shape design. First is

given an implicit or unspoken parameterization of shape design features that could confuse

results; second is provided a low dimensional basis for compound shapes at the same time as

encoding small design features and respecting constraints (Nordgren & Aoyama, 2007). A

dramatic impact of affective (kansei) and mass customization can be delivered new products that

Collect Kansei Word

Select and Parameterize Product Design Feature

Create Design Concept

Reduce Dataset Dimensionality by Principal Component Analysis (PCA)

Measure Concepts Kansei by Survey

Construct New Shapes Based on ANN Solution

Build Artificial Network (ANN) Model

24

meet both the physical and psychological needs of users by interacting among designers,

engineers and manufacturer (Childs et al., 2006).

2.4 Product Design Methods and Tools The design objective is minimized the production cost by the proper applications of design

variables and tools and techniques, which is shown in Table 1.

Table 1: Products Design Variables and Tools

Design Objectives and Sub Objectives Minimize Production Costs

Key Performance Indicators (KPIs) Production Cost=Material Cost + Labor Cost

Design Variables Complexity of parts

Organization of the Production Process,

Materials , Technology needed

Shops facilities ,Technical Capabilities

Level of attention,Number and size of parts

Tools Production Simulation Tools ,Concept design

Structural design Tools

The summarized list of methods and tools is shown in Table 2, case study is used of these

reviewed papers as like, ship design, cameras and power tool, high vacuum pump, web site

development, Volkswagagen and vehicle. Six sigma is used for calculating a given process

capability can be interfaced with the axiomatic design schematization of the product for

designers .The best way to address customers’ needs and module-interface specifications are

represented by the DSM, a heuristic method & QFD-based method (Archidiaconal et al., 2002).

A mathematical programming model is applied for revenue Management under customer choice

(Chen & Homen-de-Mello, 2010).The construction of process models is applied for the product

development process of the construction of managerially useful decision aids (Smith &

Morrow, 1999).

25

Table 2: List of Methods and Tools of Product Development

Study Methods/ Tools Endo, 2005; Karr et al,2009; MCDM, CAD/CAE, Simulation, Multi

Agent, Agard & Kusiak, 2004; Data Mining Algorithm Arcidiacono et al.,2002; Brown, 2005; Cochran et al., 2000; Dickinson & Brown, 2009; Ghemraoui et al., 2009; Houshmand & Mokhtar, 2009; Hintersteiner and Tate, 1998; Hintersteiner, 2000 ; Harutunian et al.,1996; Kurniawan, et al.,2004; Lee, 2003; Martin, 2001; Nordlund, 1996; Suh, 1998; Suh, E.S. 2005;

Axiomatic Design (AD) Theory, DFFS (Six Sigma), FMEA and Fishbone Diagram, Lean, Lean Enterprise Model, BPR , Risk Analysis, Standard for the Exchange of Product data( STEP), Universal Manufacturing Platform (UMP)

Alizon et al., 2007, 2009; commonality versus diversity index Buijs & Abbing, 2008; Delft innovation Model Baxter et al., 2007; Knowledge based design Barajas & Agard, 2009; Green & Mamtami, 2004; Dran et al., 1999; Lee, Y.C & Huang, S.Y (2009), Matzler, K. & Hinterhuber, H.H. (1998), Sauerwein, 1999,Valtasaari,2000; Wood & Antonsson, 1989 ;

Fuzzy Logic, Kano Model, QFD

Beven, 2007; Start-up Technology-Based Firms theory (STBFs)

Browing T.R.(2001), Dong, Q & Whitney, D.E (2001)

Design Structure Matrix (DSM), Design Matrix (DM)

Burlikowska, M. D, & Szewieczek ,D (2009) Poka-Yoke, PDCA cycle

Childs et al. 2006; Nagamachi,2002; Nordgren & Aoyama, 2007;

Kansei Engineering

Chen& Homen-de-Mello,2010;Fujita, 2002; Fujita & Yoshida, 2001;

Mathematical Programming Models, Modular Architecture , Design Simulation

Chen & Liu, 2005; Dahmus et al. 2001; Modular Product Architecture

Chen et al. 2005; PDCS, CA,KA,DKH

Nayak et al. , 2002; Variation-Based Platform Design Method (VBPDM)

Chen & Liu, 2005; Dahmus et al. ,2001; Modular Product Architecture

D’souza and Simpson, 2003; Multi Objective Optimization, Genetic Algorithm,

Dean, 2008; Bill of Materials Durling and Niedderer, 2007; design practice

26

Table 2: List of Methods and Tools of Product Development (continued)

An analytically sound tool is facilitated for rapid assessments of security system nonperformance

in terms of probability of adversary success at the facility or asset level using concepts from

fuzzy logic (McGill & Ayyub, 2007). A focus on the interaction between the market, product

and supply system is worked in a concurrent engineering environment (Sahlin, 2000).

Automotive process for new product development is applied to the improvement of interface of

the modules and the component. Commonality-diversity specifications are including of degree of

Du et al., 2001; Jia & Tseng ,2000; Architecture of Product Family (APF) and Generic Product Structure (GPS)

Eberhart & Kennedy, 1995; Particle Swarm Frey et al., 2000; AD, Information Theory, Probabilistic Design and

Tolerance Design Fergason et al., 2009; Multilevel Multidisciplinary Design(MDO) Fei et al. 2009; Module based Platform Farrell & Simpson, 2003; Simpson, 1998; Timothy et al., 2001;

Product Platform Concept Exploration Method (PPCEM)

Fellini et al. ,2002; Pareto sets. Gumus, 2005; Axiomatic Product Development Life Cycle

(APDL) model, QFD and Design for X. Grauer et al. ,2007; Globally Distributed Manufacturing Networks

assessment Guta & Krishnana, 1998; Sequence Design Methodology Huang et al., 2003;Hatchuel & Weil, 2003;

Design Theory, C-K Theory.

Heijungs et al., 2010; LCA, Industrial Ecology, design for environment Helander & Jiao, 2002; E –Product Development Ishii, 1995; Product Life Cycle Engineering Khajavirad et al., 2007; Multi-Objective Genetic Algorithms (MOGAs) Khire & Messac, 2006; Selection-Integrated Optimization (SIO) Kondoh et al.,2007; Redesign Method, Production System, QFD, Quality

Value and Production Method Module Lamothe et al., 2006; Linear programming model Lilja, J. ,2005; Total Quality Management (TQM) Lossack & Grabowski, 2000; Universal Design Theory NIELSEN & KIMURA, 2006; UML- Unified Modeling Language Nelson II et al., 1999; Pareto Set Otto et al., 2008; Dendrograms Shinno et al., 2002; Usability Analysis of Man-Machine Interface Sivard, 2000; Generic Information Platform Shao et al. 2005; Data Mining and Rough Set Sushkov et al., 1995; The theory of inventive problem solving

(TIPS/TRIZ) White,1992; Six Sigma

27

commonality index (DCI), commonality index (CI), product line commonality index (PCI),

comprehensive metric for commonality (CMC) & commonality versus diversity Index (CDI).

These tools have been introduced to help designers maximize commonality within a family of

products (Sanongpong, 2009). The important points in using reverse engineering are acquired

data using non-contact 3D measuring instruments and generate CAD and CAE models based on

the derived data with automatic surfacing tool, clay galaxy for use in reverse engineering (Endo,

2005). A consumers’ perception of the factor rising health and their risk-reducing behavior is

studied for produce development (Kovacs, 2009). A technique is performed for design

calculations on imprecise representations of parameters. The Fuzzy Weighted Average technique

is used to perform these calculations. Theγ -level is measured to determine the relative coupling

between imprecise inputs and outputs (Wood & Antonsson, 1989). Two design strategies are

applied for product development including delayed selection of components, and expanding and

shrinking platform (Umeda et al., 2005).

2.5 Axiomatic Design Theory

Axiomatic design is consisted of domain in the design world, mapping between these domains,

and characterization of a design by a vector in each domain, decomposition of the characteristic

vectors into hierarchies a process of zigzagging between the domains and design axioms, viz.,

independence & information axioms. Statistical process control (SPC) and methodologies are

improved quality for valid only when they are consistent with independence & information

axioms (Suh, 1995). ADT is applied by three steps, first is attempted by observing that design is

fundamental to all engineering. Second is developed the concept that there are two simple

axioms, independence and information, that govern design, just as Newton’s laws govern

mechanics. Third is observed that in order to apply the axioms designs must be decomposed into

a hierarchical structure. This is leaded to stating that there are three essential elements to

engineering design: the axioms, the structure, and the process for creating that structure (Brown,

2005). ADT is established for studying design and derived to represent the syntactic structure of

hierarchical evolving design objects and the dynamic design process (Zeng, 2002). A version of

information axiom is applied for f-granular information including maximize the coherency that is

overall definiteness of design information. Examples of decision trees, qualitative models, and

linguistic variables, are examined the logical interactions of these formatted knowledge with the

mapping process of FRs from a set of given DPs, and vice versa. A method is determined for

28

optimal design embodiments under the following assumptions: the design approach involves the

axiomatic design theory and the design-relevant information refers to a designer’s intuition,

expressible as f-granular information (Ullah, 2002, 2004, 2005). From different fields, and

different numbers of designers of the decomposition activities of ADT is generated sub-FRs,

identifying relevant customer needs, integrating sub-DPs, directing progress of the

decomposition, dimensioning DPs, layout of DPs, carrying down and refining constraints, and

ensuring consistency between levels (Tate, 1999). Figure 13 is demonstrated domains for

product family design by axiomatic design theory.

Figure 13: Domains of Axiomatic Design theory (ADT) with linked Logistics Domain

Functional Requirements (FRs) are provided an effective design environment (Dickinson &

Brown, 2009). A systematic approach is connected customers in the product design and

development process based on Axiomatic Design (Kurniawan et al., 2004). The complexity

concept in axiomatic design theory is defined as a measure of uncertainty in achieving a desired

set of functional requirements to be revisited to refine its definition (Lee, 2003). The Universal

Design Theory (UDT) is applied to aim of integrating a broad variety of engineering domains,

such as mechanical engineering, material science, information science, chemistry, chemical

engineering or pharmaceutics to describe theoretical fundamentals and practical requirements of

UDT’s axiomatic approach to create something new in the world, a machine in mechanical

Front-End Issues

Product Family Design Back-End Issues

CAs- Customer Attributes, FRs- Functional Requirements, DPs- Design Parameters PVs- Process Variables and LVs- Logistic Variables.

Customer satisfaction

Functionality Technical Feasibility

Manufacturing Cost

Resource Allocation Supply Contracts

Product Definition Product Design Process design Supply Chain Design

Mapping I Translation

Mapping II Assignment

Mapping III Allocation

DPs

Mapping IV Output

LVs PVs FRs

CAs

29

engineering or a specific drug in pharmaceutics (Lossack & Grabowski, 2000). Axiomatic

design theory is applied for decision making and software tools for product development

(Nordlund, 1996). An axiomatic design principle is applied for lean manufacturing (Reynal &

Cochran, 1996). Axiomatic design is applied to find contradiction in an integrated approach for

product design (Rizzuti et al., 2009). Axiomatic design principles are used for a systematic

human-safety analysis (Ghemraoui et al., 2009). The axiomatic product development Lifecycle

(APDL) model is used with a robust structure to develop and capture the development lifecycle

knowledge (Gumus, 2005). A multi product manufacturing problem is consisted of the total cost

minimization through increasing of production rate and reduction of cycle time (Sharma, 2009).

A design process for flexible product platforms is contributed for the uncertainty management of

engineering system, a way to implement flexible platform strategy to act in response to prospect

uncertainties (Suh, E.S., 2005).

2.6 Quality Function Deployment

Quality Function Deployment (QFD) is focused an organizational framework tool on translating

the needs of internal and external customers into product features and later product

specifications. As a result, new product development time and cost can be decreased

significantly. Design for customer needs is used for product development with quality function

deployment. Product quality is judged individual customers. The market is critical of a business.

The easiest way is now considered through improve quality, which will be easily filled with

customers’ needs. A redesign method of production system based is applied on quality function

deployment (QFD). QFD can be used successfully in decreasing product development times,

decreasing the needs for product design changes, increase the returns of investment in product

development and of course in improving the potential for improving customer satisfaction. A

product must meet the needs of it customer chain, legal chain and social chain in some cases in

more than a hundred locations at one time (Kondoh et al., 2007). Figure 14 is shown component

of quality house. The failure mode and effect analysis (FMEA) and quality function deployment

are applied for the dynamism and competitiveness of actual markets have imposed on effective

methodologies in order to improve the quality and reliability of systems and processes including

no significant investment (Oliosi et al., 2008). For this purpose, QFD is used to inspire,

organize, and then communicate information within a company, effectively bonding the different

skills and mindsets with a company collectively. QFD is experienced for use in improving the

30

level of products, product marketing, and production and their respective subsections

(Valtasaari, 2000).

Technical Interrelationship

Voice of the Company

VOC Weight Voice of Customer/Company Customer Perception

Market Analysis

Cost and Feasibility Engineering Measure

Figure 14: Component of Quality House

2.7 Trading Agent Tradeoff

Different trading agents are comprised on in the supply chain. Their management is very challenging for

sustaining business. So, supply chain could be made to absorb the vibration among the trading agent for

business success.

Figure 15: Multi Agent Architecture Adapted from Sardinha et al., 2005

A Multi agent architecture for a dynamic supply chain management is presented a flexible architecture

for dealing with the next generation of supply chain management (SCM) problem based on a distributed

multi-agent architecture of a dynamic supply chain (Sardinha et al. 2005 ). Five algorithms including AIS,

Customer Agent

Corporate Knowledge Base

Sales Representative Agent

Marketing Manager Agent

Delivery Scheduler Agent Production Scheduler Agent

Procurement buyer

Procurement Manager Agent

Supply Agent

Manufacturer Organization

31

GA, Endosymbiotic optimization, PSO and Psychological algorithm are solved a supply chain

optimization problem. Especially producer capability is aligned to meet the consumer needs

(Yadav et al., 2009). A conceptual design of mechatronic systems is applied on multi-agent

technology (Rzevski, 2003). A model is done to drive the complete multi-agent architecture,

and, at the same time, to simulate the dynamic environment (Renna et al., 2008).

3.0 Kano Model and Product Value Chain

Kano (Kano, 1984) is distinguished between three types of product requirements which

influence customer satisfaction in different ways when meet. Firstly, must-be requirements are

not fulfilled; the customer will be extremely dissatisfied. Then One-dimensional requirements

are usually explicitly demanded by the customer. Attractive requirements are neither explicitly

expressed nor expected by the customer. Reliability and validity of the kano-model have not yet

been tested thoroughly. The reliability of test-retest, alternate forms and stability of interpretation

of Kano model is done including concurrent, predictive convergent validity and methods of

classification. A kano model can be effectively incorporated customer liking in product design,

which leads to an optimization (Sauerwein, 1999). A methodology is determined the influence

of the components of products and services have on customer satisfaction and influence the

components of products and services have on customer satisfaction, the results of a customer

survey can be interpreted and how conclusions can be drawn and used for the management of

customer satisfaction is demonstrated (Sauerwein, 1996) . An analytical Kano model for

customer needs (CNs) analysis is provided decision support to product design. As a decision

making tool is applied by engineers, producers, designers for product development. The Kano

model is considered for producer concern in terms of the capacity to fulfill the CNs. The CNs is

translated into explicit and objective statements, namely the Functional Requirements (FRs). The

producer could be mapped the FRs to various product attributes, which represent the physical

form of a product. Kano indices in accordance with the Kano principles are being incorporated

quantitative measures into customer satisfaction. Two alternative mechanisms are applied to

product design. These are including the Kano classifiers are used as tangible criteria for

classifying customer needs, and the configuration index is introduced as a decision factor of

product configuration design. The merit of product configurations is justified using a Kano

evaluator, which leverages upon the both the customers’ requirements for satisfaction and the

producers’ facility (Xu et al., 2009). A new four steps methodology to manage innovation

32

project during front-end phases of Kano model is used for requirement assessment and

classification within four categories by a systemic approach dedicated to the need identification

tasks. Moreover a mathematical classification mode is suggested in order to achieve innovative

concepts comparison (Rejeb et al., 2008).

Figure 16: A Kano Model of Customer Satisfaction

Granular/imprecise probability is simulated the uncertain customer answer by using kano model.

As a result, the method is developed to measure the information content of the customer answers

integrating both simulated and real ones so that everyone can minimize the information content

of the design of a product (Ullah & Tamaki, 2009).Two-dimensional quality model of Dr. Kano

is applied an approach of fuzzy questionnaires to modify kano’s two-dimensional questionnaires

which considered as subjective and developed a mathematical calculation performance

according to the quality classification of kano’s two-dimensional fuzzy mode. It is needed of

analyzing the requirement of customer (Lee & Huang, 2009). Figure 16 is shown Kano Model

for customer satisfaction. The ideal linkage between quality practice and customer value is

applied in order to increase its strength. In accordance to the idea of continuous improvement,

the aim is to improve the reflection of the value ‘focus on the customers’ in quality practice

(Lilja, 2005). A kano’s model of customer satisfaction is explored customers’ stated needs and

1-Must be 2- One-dimensional 3-Attractive 4-Indifferent 5-Reverse

3

Performance Fully Absent (Dysfunctional)

Performance Fully Present (Functional)

High Satisfaction (Delighted)

Low Satisfaction (Disgusted)

1

2

5

4

33

unstated desires and to resolve them into different categories which have different impacts on

customer satisfaction.A customer-producer interaction along the product value chain is shown in

figure no. 17

Figure 17: Product Value Chain Adapted from Xu et al., 2009

It is shown how this categorization can be used as a basis for product development, especially for

quality function deployment with a brief discussion of the strategic importance of customer

satisfaction, and then Kano’s model and its combination with quality function deployment is

demonstrated (Matzler & Hinterhuber, 1998). Kano’s Model of Quality is developed a

conceptual framework for investigating features in the web environment that satisfy basic,

performance, and excitement needs of potential customers including differentiation of web

design features that customers take for granted from those that add value in the performance of

web specific tasks and those that generate delight, motivation, and loyalty of website users

(Dran et al., 1999).

Overall Customer Satisfaction ¥

Legacy Producer Capacity

FR

Product Attribute

Producers

Visibility Index

Customer Perceptions

Customer CNs

? ? ?

? ? ?

34

3.1 A Method for Design from Kano Indices for Quantification

Kano survey is done within specific customer segments that consist of consumers with similar demographic information. Let s denote the market segment which contains a total of J customers

(respondent), i.e. { },...,J,jtS j 21| =≡ ; a set of FRs is identified as { }IifF i ,...,2,1| =≡ ;

Surveys are carried out to collect respondents’ evaluation of fi ),,...,2,1( Ii =∀ according to the

functional and dysfunctional forms of kano questions, which are shown in table 3. For each respondent tj ),,...,2,1( Js j =∀∈ the evaluation fi ),,...,2,1( Ii =∀ is represented as

),,( ijijijij wyxe = ; Where, xij is the score given to an FR for the dysfunctional form question,

yij is the score given to an FR for the functional form question and

wij is the self-stated importance, which is the respondent’s perception of the importance of an FR

Table 3: Kano Questionnaire

For each FR (fi), the average level of satisfaction for the dysfunctional from question within

market segment s is define as iX−

, and the average level of satisfaction for the functional form

Kano question Answer

Functional Form of the question

(e.g., if the car has air bags, how do you feel?)

I like it that way

It must be that way

I am neutral

I can live with it that way

I dislike it that way

Dysfunctional form of the question

(e.g., if the car does not have air bags, how do you feel?)

I like it that way

It must be that way

I am neutral

I can live with it that way

I dislike it that way

35

question within the same market segment is defined asiY

−

, i.e.

,1

1ij

J

jij xw

JX ∑=

−

= ij

J

jijiyw

JY ∑=

=1

_ 1 (1)

A scoring system that defines consumer’s satisfaction and dissatisfaction is shown in table 5. The

scale is considered to be asymmetric because positive answers are measured to be stronger

responses than negative ones. The preliminary category of the FR is determined using following

table 4.

Table 4: Kano Evaluation Table

Dysfunctional form of the question

Like Must-be Neutral Live with Dislike

Functional form

of the question

Like Q A A A O

Must-be R I I I M

Neutral R I I I M

Live with R I I I M

Dislike R R R R Q

A, Attractive; O, One dimensional, M, Must be: I, indifferent; R, Reverse; Q, Questionable.

The value pair ( iX−

,iY

−

) can be plotted in a two-dimensional diagram, where the horizontal axis

indicates the dissatisfaction score and vertical axis stands for the satisfaction score. Most

( iX−

,iY

−

) should fall in the range of 0-1 because the negative values are results either

questionable or reverse categories. A questionable category will not be included in the averages,

and a Reverse category can be transformed out of the category by reversing the sense of

functional and dysfunctional of questions. From the customer’s perspective, the characteristic of

an FR (fi) can be represented as a vector, i.e., fi ir ≡ (ri, αi), where ri=/ ir /= 22

ii YX + is the

magnitude of ir , and αi= )/(tan 1ii XY− is the angle between ir and the horizontal axis. The

rationale of representing the satisfaction and dissatisfaction as a vector ir is that it becomes

36

equivalent to a polar form, i.e., the magnitude of the vector denotes the overall importance of fi

to the customers belonging to segment s, and the angle αi determines the relative level of

satisfaction and dissatisfaction.

Table 5: Scores for Functional/Dysfunctional Features Adapted from Xu et al., 2009

Answer to the Kano question Functional form of the question

Dysfunctional form of the question

I like it that way (like)

It must be that way (must-be)

I am neutral (neutral)

I can live with it that way (live with)

I dislike it that way (dislike)

1

0.5

0

-0.25

-0.5

-0.5

-0.25

0

0.5

1

According, the classification of an FR can be defined based on the corresponding location of the

value pair in the diagram, as shown in figure 18.

0.5 iX 1

Dysfunctional (dissatisfaction)

Figure 18: Vector Representation of Customer Perception on a Kano Diagram

Therefore, the magnitude of the vector ( ir ) is called the importance index; and the angle (αi) is

Attractive One Dimensional ir

r

αi

Indifferent Must Be

Func

tiona

l (Sa

tisfa

ctio

n)

0

0.5

i

Y

1

37

called the satisfaction index. Both 0< ri < 2 and 0< αi < 2π

are collectively called the Kano

indices. In the extreme situation, αi=0 means that dysfunction of fi causes dissatisfaction, while

functioning of fi does not enhance satisfaction, hence it is an ideal must-be element. Conversely,

αi=2π

means fi is an ideal attractive element. The self-stated importance score is normalized such

that it falls within a range of 0-1, as shown in Table 6.

Table 6: Self –Stated Importance Score

Not Important Somewhat Important

Important Very Important Extremely Important

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

3.2 Kano Classifiers for Categorization of Customer Needs

Based on the above mathematical formulation, the FRs can be classified into four categories, i.e.

indifferent, must-be, attractive and one-dimensional as shown in figure 19.

1

Attractive

O EE E

0 1

Dysfunctional (Dissatisfaction)

Figure 19: Kano Classifier and Kano Categories

3.2.1 An Indifferent Functional Requirement (FR): A threshold value of the importance

index, ro, is used to differentiate important FRs from less important ones. If ri < ro, fi is considered

A B C

Attractive One-dimensional

D

I

H Must be r0 G

Indifferent

αL αH

F

Func

tiona

l (Sa

tisfa

ctio

n)

38

as unimportant, and thus called an indifferent FR. The region defined by the sector OFI in figure

19. Where the radius is smaller than ro, is considered as the indifferent region. Hence ro is called

an indifference threshold.

3.2.2 A must be Functional Requirement (FR): Likewise, a lower threshold value of the

satisfaction index is defined as αL, such that for fi if ri > ro and αi < αL, it is considered as a must-

be FR. The region of the must-be FRs. The region of the must-be FRs corresponds to the sector

DEFG.

3.2.3 An Attractive Functional Requirement (FR): A higher threshold value of the satisfaction

index is defined as αH, such that for fi, if ri > ro and αi > αH, it is considered as an attractive FR.

The region of the attractive FRs is shown as sector ABHI.

3.2.4 An One Dimensional: If ri > ro and αL <αi < αH, fi is considered as a one-dimensional FR.

The region of the one-dimensional FRs is shown as sector BCDGH. The set of thresholds ro, αL

and αH are collectively called Kano classifiers, denoted as k= (ro, αL, αH). According to the Kano

principles, the classification of FRs provides a decision criterion for selecting the FRs that

constitutes a product configuration.

3.2.5 Observation: Determining appropriate values of Kano classifiers is challenging in that

these threshold values may be problem-specific and context-aware for different applications. In

practice, it is difficult to define universal thresholds for different products.

3.3 Configuration Index for Product Configuration Design

The configuration index iρ is defined as a function of Kano indices (ri, αi) to indicate the

probability that an FR is contained in a Production configuration. For a particular αi, the configuration index iρ is proportional to the importance index ri, which agrees with the

observation that an FR with greater influence on customer’s satisfaction/dissatisfaction is more likely to be included in the product configuration.

ii

i r

−=πα

ρ 13

22 (2)

At the same time, for a specific value of ri, iρ decreases with an increase of the satisfaction index

αi, which reflects the decreasing priorities associated with kano categories in the order of must

be, one-dimensional and attractive. Decision-making upon on the kano classification suffers the

discontinuity problem, i.e., data points located near the boundaries of two adjacent regions may

be classified as dissimilar categories, while their distinction is minor. To improve such a

39

problem, this research process is needed a configuration index to specify the priority of an FR in

fulfillment of consumer prospects. The reason of this strategy is provided better decision support

to product configuration plan.

3.4 Compliance Customer’s Satisfaction and Producer’s Capacity by Kano Evaluator

The kano classifiers and configuration indices are provided two option mechanisms to decide the

FRs to be included in a product. When product configurations is reflected the consumers’

perceptions, the producers’ have to insist to design products at efficiently and effectively.

Therefore, product development is interlinked with customer’s satisfaction and producer’s

capacity. For this purpose, a kano model is explicitly defined by a kano evaluator to estimate the

value of planned products.

The kano evaluator (E) is defined by, CU

E = (3)

Where: i) Overall customer satisfaction i

I

ii zU ∑

=

=1

ρ ; (4)

ii) the overall cycle time index,

−=

=

µσλλ TT

T

USLPC CI

3exp

1exp ; (5)

iii) zi=10

iv) Process Capability Index, T

TTCI USL

P σµ

3

−= (6)

v) USLT, Tµ and Tσ are the upper specification limit, the mean, and the

standard deviation limit of the estimated cycle time, respectively.

vi) )1

ωζµ µ +=∑=

i

T

i

I

ii

T z ; vii) ( )∑=

=I

Ii

T

iT z

1σσ (7) and (8)

3.5 A Design Process Model of Analytical Kano for Decision Making

The product planning stage is featured a series of processes including elicitation. All kinds of

needs in customer language are translated for structured engineering by kansei and QFD. Utility

analysis, conjoint analysis and statistical analysis tools are applied for the finally selection of

If fi is contained in product p,

Otherwise

40

structured needs of a design by customer (DBC). Different tools are used for product

development for elicitation of customer needs through voice of customer, kano map and web

based elicitation methods.

Figure 20: An Analytical Kano Design Process

4.0 Probabilities calculation from Kano Evaluation Table 4

Probabilities are calculated from original kano evaluation table 4. It is a starting work for a

system development. Kano evaluation table 4 is applied for specific probabilities calculations

including it is considered functional form of the Kano evaluation table, and then it is considered

dysfunctional form of the kano evaluation table. Probabilities analysis of functional form of the

kano question of Table 4: Table 7, 8 and 9, are calculated the original situation of kano

questionnaire in the view of functional form of the product. A kano model has been captured

capability of the non-linear relationship between product performance and customer satisfaction.

The kano model is constructed through customer surveys, where customer questionnaire contains

a set of questions pair of each and every product attribute.

1. Identification of Functional Requirement

{ }IifF i ,...,2,1| =≡

2. Division of Market Segments

=≡ Jjjts ,...,2,1|

3. Kano Survey

• Kano questionnaire

• Kano Scale

• Kano statistics

4. Computation of Kano Indices

ri=/ ir /= 22

ii YX +

αi= )/(tan 1ii XY−

CN FR

Kano Indices

Kano Classifiers, k= (ro, αL, αH)

iri

i

−=π

αρ 1

322

Kano Evaluator

CU

E = Configuration index,

41

A) Probabilities Analysis of Functional Form of the Kano Question of Table 4:

Table 7: Evaluation of Functional Individual Features of Kano Model

Dislike Dislike Q Dislike Like R Dislike Live with R Dislike Must-be R Dislike Neutral R Like Dislike O Like Like Q Like Live with A Like Must-be A Like Neutral A

Live with Dislike M Live with Like R Live with Live with I Live with Must-be I Live with Neutral I Must-be Dislike M Must-be Like R Must-be Live with I Must-be Must-be I Must-be Neutral I Neutral Dislike M Neutral Like R Neutral Live with I Neutral Must-be I Neutral Neutral I

Table 8: Probabilities in % of Functional Features of Kano Model

No Probabilities in % Attractive 12% Indifferent 36% Must-be 12%

One-dimensional 4% Questionable 8%

Reverse 28% Total= 100%

Table 8 is shown probabilities in % of attractive, indifferent, must-be, one-dimensional,

questionable and reverse. Table 9 is shown specific outcome of probabilities after questionnaire

evaluation from functional point of the product.

42

Table 9: Probabilities in % of Functional Individual Features of Kano Model

Pr (Dis|A) 0%

Pr (L|A) 100%

Pr (Li|A) 0%

Pr (M|A) 0%

Pr (N|A) 0%

Pr (Dis|I) 0%

Pr (L|I) 0%

Pr (Li|I) 33%

Pr (M|I) 33%

Pr (N|I) 33%

Pr (Dis|M) 0%

Pr (L|M) 0%

Pr (Li|M) 33%

Pr (M|M) 33%

Pr (N|M) 33%

Pr (Dis|O) 0%

Pr (L|O) 100%

Pr (Li|O) 0%

Pr (M|O) 0%

Pr (N|O) 0%

Pr (Dis|Q) 50%

Pr (L|Q) 50%

Pr (Li|Q) 0%

Pr (M|Q) 0%

Pr (N|Q) 0%

Pr (Dis|R) 57%

Pr (L|R) 0%

Pr (Li|R) 14%

Pr (M|R) 14%

Pr (N|R) 14%

This data will be compared with original kano model. Sample of the questionnaire is not covered

usually whole population. Data collection from population is almost impossible worked for a

researcher. So, a question is raised for above difficulty. How to sample data will be compared

with population for design decision making? It will be study to develop a relation among sample

data, unanswered people with population in the doctoral study and a hypothesis of sample data

will be compared with to population. Table 10, 11 and 12 are calculated the original situation of

kano questionnaire in the view of dysfunctional form of the product. Table 11 is shown

probabilities in % of attractive, indifferent, must-be, one-dimensional, questionable and reverse

of dysfunctional requirements of customers. Table 12 is shown specific outcome of probabilities

after questionnaire evaluation from dysfunctional point of the product.

43

B) Probabilities Analysis of Dysfunctional Form of the Kano Question of Table 4:

Table 10 Evaluation of Dysfunctional Individual Features of Kano Model

Dislike Dislike Q Dislike Like O Dislike Live with M Dislike Must-be M Dislike Neutral M Like Dislike R Like Like Q Like Live with R Like Must-be R Like Neutral R

Live with Dislike R Live with Like A Live with Live with I Live with Must-be I Live with Neutral I Must-be Dislike R Must-be Like A Must-be Live with I Must-be Must-be I Must-be Neutral I Neutral Dislike R Neutral Like A Neutral Live with I Neutral Must-be I Neutral Neutral I

Table 11 Probabilities in % of Dysfunctional Features of Kano Model

No Probabilities in % Attractive 12% Indifferent 36% Must-be 12%

One-dimensional 4% Questionable 8%

Reverse 28% Total= 100%

Table 7,8,9,10,11 and 12 are developed besides of the literature review. All tables are developed

of the starting work for system development. A system will be developed and implementation in

the doctoral Study. All tables will be applied for system design of doctoral study.

44

Table 12 Probabilities in % of Dysfunctional Individual Features of Kano Model

Pr (Dis|A) 0%

Pr (L|A) 0

Pr (Li|A) 33%

Pr (M|A) 33%

Pr (N|A) 33%

Pr (Dis|I) 0%

Pr (L|I) 0%

Pr (Li|I) 33%

Pr (M|I) 33%

Pr (N|I) 33%

Pr (Dis|M) 100%

Pr (L|M) 0%

Pr (Li|M) 0%

Pr (M|M) 0%

Pr (N|M) 0%

Pr (Dis|O) 100%

Pr (L|O) 0%

Pr (Li|O) 0%

Pr (M|O) 0%

Pr (N|O) 0%

Pr (Dis|Q) 50%

Pr (L|Q) 50%

Pr (Li|Q) 0%

Pr (M|Q) 0%

Pr (N|Q) 0%

Pr (Dis|R) 0%

Pr (L|R) 57%

Pr (Li|R) 14%

Pr (M|R) 14%

Pr (N|R) 14%

5.0 An Example of Prospect System for Doctoral Research

The design tool is applied for production. For this purpose, the following design process: to

make simulation more accessible, to standardization of databases systems to avoid interfaces, to

integrate optimization inside production simulation loop and to include outfitting in the

simulation loop are simultaneously applied for design for production (Karr et al., 2009). All of

above theory and concept will be considered for system development. A prospect system for

doctoral study is shown in figure 21 including goal, target, constraint, methods, critical factor

analysis of product development. The constraints of product development are considered

45

including hierarchy, technology, producer’s capacity, competitors, supply chain, regulations and

environments.

Targets Constraints Methods/Tools

Goal

Figure 21: A model for Prospects System

These constraints are complied with customer needs using following tools or methods, including

kano Model, QFD, kansei engineering, axiomatic design theory (ADT), fuzzy logic and particle

swarm optimization. This study will be selected some key critical success factors including cost,

lead time, flexible design, rapid delivery, decoupled components and minimum information

content of product, which are considered main contributor for product development and factors

of compliance between customer’s needs and producer’s capacity.

6. Discussion

Product development is not so easy job to fulfill the customer needs and compliance with

producer capacity. If the company can be converged with customer needs, then he could be

survived in the market. Otherwise, the company will be arisen difficulty to live in market. In

Japan, some methods are developed for product development, including kano model, quality

function development, and kansei engineering etc. All are considered customer needs as a top

priority, and then customer’s needs are translated to explicit figures and parameters of product

design. This literature is reviewed a lot of methods, tools and techniques for product

development, that are converged with customers. All of methods are considered good for product

development. Kano model is really visualized a simple method for product design. Section 2 is

discussed different methods and tools technique for cost saving and lead time minimization

purpose. Cost and lead time is now considered for the critical factor of product development to

success in the market. So, a generic method of product development is needed. It will be

Value Adding

1. Customer satisfaction of level of customer segments 2. Business satisfaction through Volume, Reliability, Time table, Cost and Skills.

Kano Model QFD, Kansei Engineering Axiomatic Design Theory (ADT), Fuzzy Logic Particle swarm Optimization …,

Hierarchy Technology Producer’s Capacity Competitors Supply chain Regulations Environments …,

Information Flow

Criti

cal F

acto

r Ana

lysi

s

46

developed and implemented in the next study. Section 3 and 4, is discussed for product

development with how to maximize the value adding for both producer and customer using the

kano model. Axiomatic design theory is holistic approach of product development since 1990.

Particle swarm optimization is an artificial neural tool for optimization. It is considered best

optimization for local and global market segments optimization. PSO also can be considered

time or dynamic factors. It is automatically given the best objectives with time. So, a kano

model, axiomatic design theory and particle swarm optimization will be applied in proposed

system in section 5, to be developed a generic method of product development for doctoral

study. The challenges are briefly discussed in section 1, which will be solved in the doctoral

study.

7. Conclusion This paper is reviewed from existence product development papers for the compliance between

customer’s needs with producer’s capacity. A proposed system model for doctoral research is

briefed here in section -5and doctoral research challenges are also addressed in section-1. A

family of product is considered to sustain in the market providing cost saving and reducing lead

time through platform-based product development. In this study is focused a kano method and

illustrated an example of prospect system for doctoral research for interactions between

consumers and the manufacturers. A kano evaluator is considered the good tool for measuring

standard of customer needs with producer capacity. This study is acted as doctoral research

directions for developing of a generic system for product family optimization using axiomatic

design theory.

47

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Appendix: A Profile of Author

Rashid, MD. Mamunur is a Management Counselor (Faculty), Production and Operations

Management Division at Bangladesh Institute of Management, Dhaka since February 16, 2004.

Prior this job he was an Assistant Engineer of Mechanical Engineering Department of Jamuna

Fertilizer Company, Bangladesh for seven years. He holds a Master of Science in Mechanical

Engineering and a Master of Business Administration. Besides of his jobs, he also did a Diploma

in Computer Science and Applications, a Post Graduate Diploma in Human Resource

Management and a Post Graduate Diploma in Marketing Management. Management

Accounting, Project Management, Safety and Maintenance Management, Information

Technology in Business and Artificial Neural System are completed by him in Graduate level

study at Industrial and Production Engineering Department of Bangladesh University of

Engineering and Technology, Bangladesh. He is published 10 papers. He is trained from

Singapore for 3 weeks on Mechatronics System Technology and 8 weeks on TQM and ISO from

Hyderabad, India. He is also taken some training at Bangladesh likes Project Management,

Vibration Monitoring and Maintenance Management. Now, he pursues for his doctoral degree at

Kitami Institute of Technology, Hokkaido, Kitami, Japan under the supervision of Professor

Jun’ichi Tamaki and Dr. Sharif Ullah will effect from April, 2010. He can be reached by e-mail:

mamun87245@gmail.com