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
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.
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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.
3
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
…Voice of Customer:Customer SegmentsNeedsFeedbackSatisfaction…
Methods/Tools:Scientific AnalysisQFDTRIZAxiomatic DesignKnowledge-Based DesignSimulationBrainstormingLCADesign for XCAD/CAM/CAEPrototypingDesign of ExperimentSix-SigmaLeanMass CustomizationValidationVerification…
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
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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
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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 …,
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: