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Quantification and integration of an improved Kano model into QFD based on multi-population adaptive genetic algorithm
Accepted Manuscript Quantification and Integration of an Improved Kano Model into QFD based on Multi-population Adaptive Genetic Algorithm Lina. He, Wenyan. Song, Zhenyong. Wu, Zhitao. Xu, Maokuan Zheng, Xinguo Ming PII: DOI: Reference:
S0360-8352(17)30475-8 https://doi.org/10.1016/j.cie.2017.10.009 CAIE 4946
To appear in:
Computers & Industrial Engineering
Received Date: Revised Date: Accepted Date:
25 August 2015 31 May 2017 8 October 2017
Please cite this article as: He, Lina., Song, Wenyan., Wu, Zhenyong., Xu, Zhitao., Zheng, M., Ming, X., Quantification and Integration of an Improved Kano Model into QFD based on Multi-population Adaptive Genetic Algorithm, Computers & Industrial Engineering (2017), doi: https://doi.org/10.1016/j.cie.2017.10.009
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Quantification and Integration of an Improved Kano Model into QFD based on Multi-population Adaptive Genetic Algorithm a
Lina. He
a
b
c
*, Wenyan. Songb, Zhenyong. Wuc, Zhitao. Xud, Maokuan, Zhenge, Xinguo Minge
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, R.P. China
School of Economics and Management, Beihang University, Beijing, 100191, R.P. China
College of Mechanical Engineering, Guangxi University, Nanning, R.P. China
d
College of Economics and Management, Nanjing University Of Aeronautics And Astronautics, 29
Jiangjun Avenue, Nanjing, 211106, R.P. China
e
Shanghai Research Center for Industrial Informatics, Shanghai Key Lab of Advanced Manufacturing
Environment, Institute of Computer Integrated Manufacturing, School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Minhang District, Shanghai, 200240, R.P. China Corresponding author: Lina. He, School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, R.P. China E-mail: [email protected] Telephone: 86-1521-671-1815
Acknowledgment The authors would like to thank the anonymous reviewers for their valuable comments, suggestions and corrections, which greatly improved the quality of the paper. The authors would like to acknowledge the financial support provided by School of Mechanical Engineering in Southwest Jiaotong University. 1
Abstract: In an effort to address the inherent deficiencies of traditional Kano model and quality function deployment (QFD), this paper proposes an improved Kano model named as importance-frequency Kano (IF-Kano) model and integrates IF-Kano model into QFD. Considering the interaction between frequencies and importance weights of customer requirements (CRs), the IF-Kano model adopts the logical Kano classification criteria to categorize CRs. Then, both qualitative and quantitative results derived from IF-Kano model are integrated into QFD with a non-linear programming model. The model aims to determine appropriate Kano categories of CRs and target values of engineering characteristics (ECs) with a view to achieving an optimal design solution under the best balance between enterprise satisfaction and customer satisfaction (CS). To solve the presented model, a multi-population adaptive genetic algorithm (MPAGA) is designed. Finally, an example of a home elevator design is given to demonstrate the feasibility and effectiveness of the developed approach and algorithm.
Key words: Kano model; quality function development; customer requirement; multi-population adaptive genetic algorithm
1.
Introduction
In competitive environment, manufacturing enterprises are increasingly focusing on designing product with high customer satisfaction (CS) and low price. The critical challenge for manufacturers is how to make effective analysis of customer requirements (CRs) to provide decision support for optimal product design. Various methods and tools have been developed to help companies obtain a better understanding of CRs and incorporate the CRs into product design to achieve optimal solution. Among them, Kano model and quality function deployment (QFD) have been widely adopted to enhance the competitiveness of companies by helping them focus on CRs in product development (Ji et al., 2014). QFD translates CRs into engineering characteristics (ECs) to plan and manage new product development (Mizuno & Akao, 1990), and is applied in many researches to improve product design to satisfy CRs (Jia & Bai, 2011; Y.-L. Li et al., 2011; Luo et al., 2010). By describing the interrelationships between CRs and ECs, and the correlations among ECs, the main task of product planning using QFD is to determine target values of ECs for achieving higher overall CS (Fung et al., 2005). In order to provide decision-making in QFD, the systematic and rational mathematical programming models for the targets setting of ECs have received flourishing advances in the last decade (Delice & Güngör, 2009; Luo et al., 2010; Yang & Yoo, 2016; Zhong et al., 2014). In Consideration of CRs’ heterogeneity, a novel QFD-based product planning approach for determining optimal target levels of ECs is proposed (Luo et al., 2015). These models can help a company to make key tradeoff between what the customers want and what the company can afford to build with QFD analysis. However, the significant challenge of QFD implementation is the difficulty in understanding CRs systematically (Cristiano et al., 2001; Lee et al., 2008). While, CRs analysis is the key starting 2
point for execution and if it does not accurately reflect what the customer expects from a product, the model may lead to inaccurate forecasts (Kwong et al., 2011). Kano model identifies CRs in more detail by classifying and prioritizing CRs based on how they affect CS. Thus, Kano model can be one solution to address QFD’s inadequacy of recognizing CRs (Sireli et al., 2007). In order to incorporate the nature of CRs into QFD, many researchers proposed to integrate Kano model into QFD. However, the traditional Kano (T-Kano) model used in these integrative approaches provides limited decision support for designers. In general, it is conducted with functional and dysfunctional question pair for each CR developed to a number of respondents, and every respondent’s answer pair for each CR should be aligned with the predefined evaluation table. Thus, the reduplicative survey is time-consuming, and the Kano classification obtained merely based on subjective evaluations may cause subjective bias. As a decision-making tool, the ultimate goal of Kano classification analysis is to provide decision support for producer’s capacity allocation in product design to fulfill CRs. Different classification schemes may influence producer’s resources allocation, product design strategy and CS (Xu et al., 2009). The appropriate Kano classification should reflect customer’s perception under the constraint of producer’s capacity to fulfill the CRs. While, the T-Kano inherently emphasizes the customer and market perspectives only, and fails to account for the producer’s capacity (Xu et al., 2009). Producer’s capacity constraints are usually accommodated by the product development team based on expertise, such that the product will only fulfill those CRs that are affordable to the producers (Matzler & Hinterhuber, 1998). In practice, without consideration of producer’s capacity, the classification analysis seldom holds significance. Therefore, to provide comprehensive design support, the Kano classification of CRs should emphasize the customer’s perception and producer’s capacity simultaneously. The reasonable Kano classification scheme should lead to an optimal tradeoff between CS and producer’s capacity (Xu et al., 2009). The main advantage of QFD is to make tradeoff between CS and producer’s capacity. To address the above issues of CRs analysis, this paper proposes a refinement of Kano model: importance-frequency Kano (IF-Kano) model. Based on the classification method of CRs proposed in (Stone et al., 2008) and theoretical foundation of the analytical Kano (A-Kano) model developed by Xu et al. (2009), IF-Kano model provides a set of classification criteria to categorize CRs considering the interaction between frequency and importance. Then, to determine the appropriate Kano categories of CRs and target values of ECs with consideration of an optimal tradeoff between CS and producer’s capacity, the IF-Kano model is integrated into QFD with a computational model. Finally, a multi-population adaptive genetic algorithm (MPAGA) is developed to solve the integrated IF-Kano and QFD computational model. The rest of this paper is constructed as follows. In the next section, some specific aspects of Kano model and the integrative approaches of Kano model and QFD are discussed. In Section 3, the IF-Kano model for CRs analysis is proposed. In Section 4, a systematic process model is developed to integrate IF-Kano model into QFD. In order to solve the model, MPAGA is presented in Section 5. A case study associated with the design of home elevator is presented in Section 6 to demonstrate the proposed method and algorithm. Discussions and conclusions are presented in Section 7 and 8, respectively.
3
2.
Literature review
2.1.
Kano model
Kano et al. (1984) have developed a two-dimensional model to understand CRs and their impact on CS. The Kano model divides CRs into six categories, each of which affects CS in a different way. Referring to Fig. 1, Kano categories are briefly explained as follows (Berger et al., 1993; Kwong et al., 2011; C.-H. Wang, 2013) : Fig. 1. Kano model.
Attractive (A): The functional presence of these attributes will result high level of CS while their absence will not affect CS.
One-dimensional (O): The functional presence of these attributes will generate CS while their absence will results in non-satisfaction.
Must-be (M): Customers take the presence of these attributes for granted. Insufficiency of these attributes will results in extreme non-satisfaction, but the sufficiency will not increase satisfaction level.
Indifferent (I): The attributes in this category, whether present or not, does not affect CS.
Reverse (R): The presence of these attributes will generate non-satisfaction, and vice versa.
Questionable (Q): This outcome indicates that either the responses do not make any logical sense, or the question was phrased incorrectly. Table 1. Kano evaluation table (Source by Berger et al. (1993)). Functional
Dysfunctional Like
Must-be
Neutral
Live-with
Dislike
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
As shown in Table 1, the Kano model uses functional and dysfunctional questionnaires, and 5 by 5 evaluation table as conducting instrument (Kano et al., 1984). The final Kano categories of CRs are evaluated according to response frequencies (Y. Li et al., 2009; Song et al., 2013). The highest frequency represents the dominant customer view (L.-F. Chen, 2012). Kano model is widely used in the analysis of CRs (T. Wang & Ji, 2010). However, the T-Kano model is a qualitative method that provides limited decision support for designer (Berger et al., 1993; He et al., 2015; Wu & Wang, 2012; Xu et al., 2009). Berger et al. (1993) initialized the quantitative analysis of Kano model with two quantitative CS coefficients, named as satisfaction index (SI) and dissatisfaction index (DI) to reflect the average impact of a CR on CS or dissatisfaction. Then, the CS coefficients have been modified and utilized as adjustment factor for re-prioritizing CRs to achieve maximum CS (Matzler & Hinterhuber, 1998; Tontini, 2007). T. Wang and Ji (2010) proposed a novel approach to measure and quantify the relationships between CS and the fulfillment of customer requirements as depicted in Kano model. Wu and Wang (2012) presented a continuous approach for fuzzy Kano evaluation to allow quantitative analysis of the CRs with an evaluation index which allows 4
the CRs to be prioritized. However, the Kano categories of CRs in these studies are obtained based on two-dimensional questionnaires and Kano evaluation table, and cannot avoid the inherent deficiencies described in Section 1. To address the deficiencies of T-Kano, Xu et al. (2009) developed the A-Kano model by introducing Kano classifiers and a configuration index to incorporate quantitative measure into CS, leading to an optimal trade-off between CS and producer’s capacity. However, the statistical analysis of all responses using the Kano questionnaire is still time-consuming. Moreover, there is a lack of comprehensive analysis of relationships between CRs and ECs.
2.2.
Integration of Kano model into QFD
Many researchers proposed combining Kano model with QFD. Generally, the Kano model is integrated into QFD by adjusting the important weights for re-prioritizing attributes in QFD (Chaudha et al., 2011; C.-C. Chen & Chuang, 2008; Tontini, 2007). For instance, C.-C. Chen and Chuang (2008) introduced an adjustment coefficient (K) and the raw weights were adjusted by multiplying with K. The value of K varied according to Kano category, and values of ‘‘4’’, ‘‘2’’, ‘‘1’’ and ‘‘0’’ were assigned to the attractive, one-dimensional, must-be and indifference categories, respectively. The integrative approach provides basis for adjusting the relative priority of product attributes based on Kano categories. However, these approaches remain to be qualitative analysis of CRs, and provide limited decision support(Ji et al., 2014; Xu et al., 2009). Moreover, it is subjective to assign the adjustment values to Kano categories according to the experience (Ji et al., 2014; Nahm, 2013). In addition, the T-Kano in these studies has deficiencies mentioned in Section 1. Another integrative approach integrates Kano model into QFD by inferring the contribution of different CRs to CS and provides a mathematical programming model for optimal design solution (Ji et al., 2014; Lai et al., 2004). For instance, Ji et al. (2014) developed an optimization model that integrated Kano model into QFD to optimize product design to maximize CS under cost and technical constraints. Kano model was quantified by the relationship between CRs and CS, both qualitative and quantitative results of Kano model were integrated into QFD with a mixed integer nonlinear programming model. This integrative approach makes it possible to integrate quantitative analysis of Kano model into QFD for the issue of incorporating the CRs into product design. However, these studies ignore the deficiencies of T-Kano. First, the Kano survey of T-Kano is time-consuming. Second, the Kano classification method inherently emphasizes the customer and market perspectives only, and fails to account for the producer’s capacity to fulfill the CRs.
3.
IF-Kano model
Before quantitative analysis of Kano model is performed, the IF-Kano model is developed to identify Kano category of each CR on the basis of frequency of mention of the CR and average weight of the CR. Stone et al. (2008) proposed an importance-frequency CRs identification method to classify highly weighted, low frequency CRs as core CRs, and lightly weighted, high frequency CRs as differentiating CRs. With the definition of Kano categories in T-Kano model, core CRs equates to must-be CRs, and differentiating CRs can be further divided into one-dimensional and attractive CRs. One-dimensional CRs possess higher weight than attractive CRs. In addition, the lightly weighted, low frequency CRs 5
are classified as indifferent CRs in this paper. Considering the interaction between CRs frequency and weight, the IF-Kano model classifies CRs into four categories, including indifferent, must-be, attractive, and one-dimensional.
3.1.
Kano indices
Each CR can be represented as and
, where
is the normalized frequency for
.
is the normalized important weight for
and
,
can be obtained according to the Equations
(1) and (2).
Where
and
denote the raw values of important weight and frequency for
The value pair
.
should fall in 0-1, and can be plotted in a two-dimensional diagram. As
shown in Fig. 2, the characteristics of where importance as a vector
can be represented as a vector, i.e.,
,
and . The rationale of representing the frequency and is that it becomes equivalent to a polar form, i.e., the magnitude of the vector
denotes the overall evaluation value of
, and the angle
frequency and importance. Both
and
determines the relative level of are collectively called Kano indices.
Fig. 2. Kano classifiers and Kano categories.
3.2.
Kano classifiers
Based on the corresponding location of CRs in the importance-frequency diagram, the IF-Kano model classifies CRs into four Kano categories, i.e., indifferent, must-be, attractive, and one-dimensional, as shown in Fig. 2. A threshold value of vector magnitude, important and lower frequency ones. If
,
, is used to differentiate CRs from less
is considered as unimportant and infrequent, and
thus defined as an indifferent CR. The region defined by the sector OGN in Fig. 2, is considered as the indifferent region. Hence,
is named as an indifferent threshold.
Likewise, a lower threshold value of vector angle is defined as
, if
and
,
is considered as a must-be CR. Thus, CRs falling into the sector DEGH of Fig. 2 can be defined as must-be CRs, and
is called a must-be threshold.
A higher threshold value of the vector angle is defined as
, such that for
, if
and
, it is considered as an attractive CR. The region of the attractive CRs is shown as sector ABMN. Hence, If
is named as an attractive threshold. and
,
is considered as a one-dimensional CR. The region of the
one-dimensional CRs is shown as sector BCDHM. The set of thresholds
,
and
are collectively called Kano classifiers, denoted as
. Different Kano classifiers lead to different classification schemes, and the threshold values of Kano classifiers may be problem-specific and context aware for different application. With the same Kano classifiers, CRs may follow a life cycle based on the dynamic change of importance and frequency. As shown in Fig. 2, a CR can start with being indifferent turns into attractive on to 6
one-dimensional and finally ends as must-be, which agrees with the study conducted by Kano (Kano, 2001).
3.3.
Configuration index
Xu et al. (2009) presented that decision-making based on the Kano classification inevitably suffered the discontinuity problem and proposed a configuration index to alleviate the problem. Based on the IF-Kano model, this research proposes the corresponding configuration index to represent the priority of a CR is contained in the product design. The configuration index
Given a particular
, the configuration index
, is defined as follows.
is proportional to
, which agrees with the
observation that a CR with greater importance and higher frequency is more likely to be included in the product. At the same time, for a specific
,
decreases with an increase of
, which reflects the
decreasing priorities associated with the Kano categories in order of must-be, one-dimensional and attractive.
4.
Integration of IF-Kano model into QFD
As CRs in different Kano categories have different impact on CS (Kano et al., 1984), the IF-Kano model can enhance designer’s understanding of CRs and provide decision support to product design. In the IF-Kano model, different Kano classifiers lead to different classification schemes, which in turn influence the decision support to product design, producer’s capacity allocation and CS. Thus, the optimal values of Kano classifiers should be selected, leading to an optimal tradeoff between customer’s satisfaction and producer’s capacity (Xu et al., 2009). To address this issue, the IF-Kano model is integrated into QFD to develop an optimization model to determine the appropriate Kano classifiers and target values for a set of ECs. The basic concept of the model is to translate CRs into ECs to maximize shared surplus to leverage upon both customer’s and producer’s concerns. The consumer surplus is modeled as the overall customer’s satisfaction ( ) of the product, and the producer surplus is simulated as the overall cost ( ). The notations used in the model are listed as follows. Notations Number of customer requirements Number of engineering characteristics Number of competitors Level of fulfillment for
,
Level of fulfillment for
,
Normalized level of fulfillment for , Performance of by the th competitor, Normalized relationships between Configuration index for Configuration index for
and
, ,
Degree of customer satisfaction with
, 7
, ,
,
Kano category for
,
The overall customer satisfaction Cost of unit improvement for , Cost required for achieving
,
Total cost of product development Shared surplus of product
4.1.
Constructing the House of Quality
For new product planning, a house of quality (HoQ) diagram is used in the QFD processes to describe the important factors, i.e. CRs and ECs and their relationships. Hence, it should be constructed for the IF-Kano and QFD optimization model. The HoQ consists of six main parts, including CRs, ECs related to CRs, relationship matrix between CRs and ECs, correlation matrix among ECs, technical benchmarking and market benchmarking. Considering the correlations among the ECs and relationships between CRs and ECs, the method (Equation (4)) proposed by Wasserman (1993) is employed to obtain the normalized relationship values between CRs and ECs.
where and
denotes the relationship between
and
,
denotes the correlation between
.
4.2. Based on
Normalizing the target values of ECs ECs,
competitive products are benchmarked. Depending on the nature and vagueness,
ECs are measured using numerical values, and linguistic variables. As shown in Fig. 3, the linguistic variables are defined through triangular membership function , where , , represent the lowest, most likely and highest levels of the triangular membership function, respectively. As different ECs have different measurement methods, it is not reasonable to evaluate them synthetically by using their respective raw values. To solve this problem, a normalization method is developed to transfer the specific technical values into levels of fulfillment in the proposed model. ECs can be divided into two categories: positive and negative ones. For the positive ECs, the performance of EC is positively proportional to its target value, and vice versa with the negative ECs. For ECs measured with crisp data, the two categories of target values of ECs can be normalized according to the Equations (5) and (6). Equations (7) and (8) represent the normalized target values of ECs, which are measured with linguistic variables.
8
where
( ) denotes the specific technical value of
of technical value of
,
(
,
(
) is the maximum limit
) is minimum limit of technical value of
represents the distance of two fuzzy numbers (
), where
.
,
.
Many distance measurement functions have been proposed (C.-T. Chen, 2000; Zwick et al., 1987), but the vertex method developed by C.-T. Chen (2000) is an effective and simple method to calculate the distance between two triangular fuzzy numbers. Then the distance between two fuzzy triangular numbers using vertex method can be calculated as follows. .
Fig. 3. Linguistic term set.
4.3.
Formulation of the overall customer satisfaction
The degree of overall CS towards the to-be-designed product can be considered as a mathematical aggregation of the fulfillment levels of CRs, which can be represented as ， ， ， . In this paper, the summation of the degree of CS for individual CR is adopted to express as follows:
， ， ， Based on the approximate method proposed by Florez-Lopez and Ramon-Jeronimo (2012) and Kano categories of CRs defined in Section 3, the fulfillment level of
can be converted
into the degree of CS as follows (Fig. 4):
The fulfillment levels of ECs can be transformed into the fulfillment levels of CRs by multiplying the normalized relationship matrix.
Referring to the constraint of Equation (11), as
, it is necessary to normalize
to
.
Fig. 4. Relationship between customer satisfaction and CR fulfillment.
4.4.
Formulation of the design cost
Apart from the consideration of CS, other multiple resources are required for supporting the development of a new product, such as product development time, development cost, manufacturing 9
cost, human resource, technical expertise, advanced equipment, tools and other facilities (Zhong et al., 2014). These resources can be aggregated in financial terms at the level of strategic planning. The overall cost
depends on the levels of attainment of
，it can be viewed as the summation of
costs required for achieving the level of individual EC.
The configuration index of determined by Equation (3) can be transform into the configuration index of by multiplying the normalized relationship matrix.
4.5.
Formulation of the optimization model
Standing on the perspective of enterprise, the primary concern in product planning process based on IF-Kano model and QFD is to determine the appropriate Kano classifiers and the target values of ECs for the new product to maximize the shared surplus. Accordingly, the objective function is established in Equation (16), and the constraints of the model are established in Equations (17)-(21).
Subject to
The objective function is the overall shared surplus towards the to-be-designed product. denotes individual CS for
, and can be obtained by applying different CS functions according to
Equation (11). Equations (17)-(19) represent the lower and upper bounds of Kano classifiers, where changes from 0.1 to 0.9, from
to
changes from
to
, and
changes
. Equation (20) concerns the constraint of minimum fulfillment
level for a given EC. The fulfillment level of each EC is set to a benchmark with the average performance of major competitors in the industry. Equation (21) defines to be from 0 to 1. The optimization method proposed in this paper can be considered as a process of finding the appropriate Kano classification of CRs and target values of ECs, which lead to the optimal product design scheme.
5.
Improved adaptive genetic algorithm
The model is formulated as a typical non-linear programming model. Genetic algorithm (GA) is one of the widely applied evolutionary algorithms to the design and optimization of non-linear programming problems. However, with the broad application and in-depth study of GA, its many defects are 10
exposing constantly, such as premature convergence, low efficiency of optimization, etc. Therefore, it is important to focus on how to overcome various defects of the standard GA (SGA). A multi-population adaptive genetic algorithm (MPAGA) is developed to solve the integrated IF-Kano and QFD optimization model. The multi-population is embraced for migration to maintain the diversity of populations as a whole and to avoid premature convergence (Gong et al., 2007). The adaptive scheme is embedded in the MPAGA to improve the efficiency of genetic search process.
5.1.
Coding strategy
In the process of optimization model solving by MPAGA, each solution is defined by a chromosome in real coding scheme with
genes. As shown in Fig. 5, each chromosome consists of two
sections: Kano classifiers and the ECs target values. The first 3 genes are used to indicate the values of Kano classifiers
,
and
, while the target values of ECs are represented by the last
For example, a chromosome is reveals that the values of
,
genes. , which
and
are 0.21, 0.32 and 0.96, while the target values of 7 ECs are
0.51, 0.63, 0.82, 0.72, 0.64, 0.66 and 0.78. Fig.5. One feasible chromosome coding scheme.
5.2.
Adaptive tournament selection
In tournament selection, sets of individuals are selected randomly from the population and their fitness is compared with one another. The individual with the highest fitness is taken into the mating pool. It has been established that the loss of diversity of the population and the selection intensity would increase with the tournament size (Blickle & Thiele, 1995). An evolutionary learning process is dynamic and requires different selection intensity and diversity at different learning stages to speed up the convergence or avoid local optima. In order to keep population diversity with lower selection intensity in the early stage and speed up the convergence with a higher selection intensity in the last stage of the evolution, the adaptive tournament size is designed with Equation (22), as shown in Fig. 6.
G is the maximal generation number, g is the current generation number, T is the current tournament size,
and
denote the maximal and minimal tournament size, respectively. Fig.6. Adaptive tournament size.
5.3.
Adaptive crossover and mutation
In SGA, the crossover and mutation probabilities are fixed values, which always lead to the premature phenomenon and local convergence. In order to avoid these defects, the novel adaptive strategy is adopted in MPAGA to adapt the crossover and mutation probabilities based on the fitness statistics of the population and the generation number. In the same generation, individuals with lower fitness values should obtain higher crossover and mutation probabilities to eliminate the bad solutions, and 11
individuals with higher fitness values should be endowed with lower crossover and mutation probabilities to protect the optimal solutions. Moreover, the crossover and mutation probabilities should decrease with the increase of the generation number to promise the global search capability in the early stage and enhance the local search capability in the last stage of the evolution. Above all, the adaptive crossover and mutation probability functions can be represented with Equations (23) and (24). As shown in Fig. 7, the proposed mechanism can avoid any dramatic changes.
and
are the crossover and mutation probabilities,
minimal crossover probabilities, probabilities,
and
and
and
are the maximal and
are the maximal and minimal mutation
are the largest and lowest fitness values of group,
higher fitness value of the two crossover individuals, is the maximal generation number,
represents the
is the fitness value of mutation individual,
is the current generation number.
and
are weighted
coefficients for the fitness and generation number in the operators, whose values can be determined by the expert evaluation method and etc., and
+
.
Fig. 7. Adaptive crossover and mutation probabilities (
).
For the real-coded GA, the mutation operators include uniform mutation, non-uniform mutation, multi-non-uniform mutation, etc. In this paper, the improved non-uniform mutation is adopted. Let us assume is a chromosome and is a gene to be mutated, the resulting gene
is created as following:
where
Here, ,
and and
are respectively the lower and upper bounds of the variable have the same meanings as they are in Equation (24),
distributed random numbers between 0 and 1,
bad solutions (when
and
,
,
are two uniformly
returns a value in the range [0, 1] such that
increase. This property allows this operator to eliminate the
is small), and protect the optimal solutions. Meanwhile, this operator tends to
search the space uniformly at early stages (when
5.4.
,
is a system parameter determining the degree of
non-uniformity (Michalewicz, 1996). The function approaches to zero as
and
.
is small), and very locally at later stages.
Procedure of the proposed algorithm
With the improvement methods mentioned above, Fig. 8 presents the procedure of MPAGA. Fig. 8. Procedure of the proposed algorithm. Step 1. Initialize the control parameters of the algorithm, such as the number of population 12
,
population size
, maximal generation G, etc. and initialize M populations according to certain rules.
Step 2. Let
Step 3. Define the objective function as the fitness function. According to the fitness, choose the first
individuals from the
populations to construct the population
, the
is
among the M populations.
the individual with the largest fitness value Step 4. Let
. Assume that
populations evolve for a population evolutionary period by
adaptive tournament selection, adaptive crossover and adaptive mutation operators separately. Step 5. According to the fitness, choose the first construct the population population
. If
Step 6. If
If
. Let , let
individuals from the
populations to
be the individual with the largest fitness value ,
, check the generation number , go to Step 4.
in
. .
is the interval between the migrations, and
is a positive
integer.
If
, mix the population
first
6.
, and choose the
individuals from the mixed populations to update the population
individuals in population Step 7. If
and population
to update the population
, present the satisfactory individual
. Use the
. And then, go to Step 4.
and the corresponding fitness value
.
Case study
In order to verify the feasibility and effectiveness of the integrated IF-Kano and QFD approach and MPAGA, the development of home elevator is illustrated as a numerical example, and the results are also presented and analyzed in this section. Firstly, initial CR candidates are collected by focus group, individual interviews, and then affinity diagram or cluster method is used to organize those candidates. Then, suppose that 7 major CRs are identified to represent the biggest concerns of customers, including ‘comfortableness of the space’ (
), ‘load capacity’ (
conservation’ (
), ‘operation stability’ (
), ‘low noise’ (
), ‘aesthetic’ (
), ‘operation safety’ (
), ‘resource
). Frequency of occurrence for each distinct
CR in the investigation is recorded and a self-stated importance questionnaire on the each CR is conducted with interviewers without showing them the frequency. Then, the normalized frequency, normalized importance weight for each CR are obtained, and can be represented as
The vectors of the Kano indices with respect to 7 CRs can be easily computed as follows:
Subsequently, the detailed contents in the HoQ in Fig. 9. are determined one after another. Based on the professional knowledge of this product, the QFD team recognizes 7 important ECs related to the CRs: ‘performance of driving system’ ( elevator car’ ( system’ (
), ‘decoration level’ (
), ‘rated speed’ ( ), ‘energy consumption’ (
), ‘rated load’ (
), ‘size of
), ‘performance of emergency
). Then, the relationships between the CRs and ECs as well as the correlations among the
ECs are evaluated, and the normalized relationship matrix between CRs and ECs can be obtained according to Equation (4), as shown in Fig. 9. Meanwhile, the limit values and cost coefficients for ECs are specified according to industry standards and shown at the base of the HoQ. Besides, three 13
competitors are taken into consideration and their corresponding target values of ECs are evaluated by the QFD team on the basis of enterprise environments and strategies. Depending on their nature and vagueness, target values of ECs are measured using linguistic variables, and numerical values. The seven linguistic terms in Fig. 3 are employed in the benchmarking information evaluation. Then, the normalized target values of the ECs of all competitors can be obtained by Equations (5)-(8) as follows:
The lower and upper bounds of the Kano classifiers are represented as . The lower and upper bounds of ECs fulfillment levels are represented as
Fig. 9. The HoQ of the home elevator. Finally, the optimization model can be formulated with Equation (16). To solve the optimization model, the MPAGA procedure is coded in Matlab R2014b in a PC with Intel Core2 i5, and with 4 GB DDR II RAM at 2.4 GHz. The number of populations is set as 3, the population size as 200, the maximum generations as 500, ,
,
, ,
,
, and
,
,
,
.
Table 2. Results obtained by MPAGA. Optimal results
Near optimal results
Serial number
4
EC
CR
EC
CR
0.4511
0.9923
A
0.9429
0.9445
0.4519
0.9941
A
0.9517
0.9529
0.5585
0.7261
O
1.0
1.0
0.5591
0.7268
O
1.0
1.0
0.7249
0.7974
O
0.8350
0.8350
0.7269
0.7996
O
0.8390
0.8390
0.6776
0.6098
M
0.8304
0.4856
0.6775
0.6097
M
0.8337
0.4862
0.5930
0.9488
A
0.9412
0.9429
0.6361
1.0177
O
0.9668
0.9668
0.6716
0.4030
A
0.9179
0.9213
0.7668
0.4601
O
0.9140
0.9140
0.5418
0.7585
A
0.9149
0.9185
0.5453
0.7634
A
0.9352
0.9373
2.2714
2.3059
S
2.2866
2.3140
U
1.0067
1.0035
The final optimal results of the optimization model are obtained with the MPAGA method, as shown in Table 2. Based on the optimal fulfillment levels of ECs, the corresponding cost allocations are derived and the total cost value is 2.2714. According to threshold values of the Kano classifiers, the Kano categories for CRs can be obtained and are listed in Table 2. Subsequently, the CR fulfillment level and individual CS can be calculated, and the total CS is 2.2866. In total, the product achieves a shared surplus value of 1.0067. Furthermore, using the invertible functions of Equations (5)-(8), the specific technical target values of an individual EC can be obtained. Then, based on the Kano categories for CRs, ECs’ technical target values and cost allocation, the producer can make detailed 14
product design plan.
7.
Discussions
In this section, the effectivenesses of the proposed IF-Kano model, integration method of QFD and IF-Kano, multi-population adaptive genetic algorithm are discussed.
For comparison, Table 3 shows the results from the T-Kano model in (Berger et al., 1993) and A-Kano model in (Xu et al., 2009). The T-Kano model is a qualitative method that cannot differentiate CRs within the same category. Compared with the T-Kano model, the configuration index in the IF-Kano model can effectively reflect the differences among CRs within the same category. For example, both
and
are classified as One-dimensional CRs in Table 2,
and the configuration indexes are 0.581 and 0.771, which can represent the different customer perceptions considering their importance weights and frequencies. Thus, the IF-Kano classifiers and configuration index can help designers recognize the important CRs that have great impact on customer perceptions and enhance decision support in product design. Although the A-Kano model can also quantitatively analyze the CRs based on the corresponding configuration index, its results are not as accurate as those from IF-Kano, because the Kano questionnaires and scoring scheme in A-Kano model are determined in subjective manner, and the work of data collection and processing is tedious. While, the IF-Kano model extends A-Kano model by considering the mentioned frequency and weight of CRs, which can be easy and quick to get with the record and self-stated importance questionnaire.
Table 3. Comparative results of the case study. Analytical Kano Traditional Kano
CRs
M
A
O
I
12
18
28
42
0.524
Kano category
Configuration index
Kano category
I
0.431
A
0
20
80
0
O
0.364
M
55
40
5
0
M
0.542
O
48
42
8
2
M
0.843
M
29
65
5
1
A
0.721
A
16
64
16
4
A
0.075
A
63
27
7
3
M
0.191
I
1.9503
2.0970
S
1.9085
2.0643
U
0.9786
0.9844
The shared surplus values resulted from the integration model with T-Kano and A-Kano are listed in Table 3. The integration of QFD with T-Kano aims to optimize ECs fulfillment levels, and the Kano categories of CRs are determined by the Kano survey without considering the tradeoff between CS and producer’s capacity. Thus, the resulted solution may not be the optimal one. Although the integration of QFD and A-Kano optimizes the ECs fulfillment levels and CRs Kano categories, it may not promise an optimal solution because of the deficiency in A-Kano mentioned in Section 7.1 While, the integration of IF-Kano and QFD not only optimizes ECs fulfillment levels, but also select the appropriate CRs Kano categories. It establishes a coherent decision 15
framework that deals with the interaction between the customer domain and the producer domain. Making a comparison between the optimal solution and the near optimal solution in Table 2, it can be seen that the different Kano classifiers lead to different classification scheme, which in turn influence the product design, cost, CS, and shared surplus. Although the CS of the near optimal solution is higher than the optimal solution, but the optimal solution is more economical. The shared surplus value of the near optimal solution is 1.0035. While, the optimal solution offers larger expected shared surplus value 1.0067, which indicates better strategies to leverage upon CS and producer’s capacity. Thus, integrating IF-Kano model into QFD can provide the logical classification criteria and optimal ECs fulfillment levels to enhance the competitiveness of companies.
With multi-population and adaptive strategy, MPAGA provides a more efficient way to solve the integrated IF-Kano and QFD model. For comparison, SGA is applied in the case. MPAGA and SGA have the same population size, maximum generations, and
. The crossover rate, mutation
rate and tournament size in SGA are 0.7, 0.05 and 15. Fig. 10 shows the fitness curves of MPAGA and SGA. It can be seen that MPAGA converges to the optimal results after 180 generations, while SGA converges to it after 425 generations. Thus, the convergence speed of MPAGA is faster than SAG. In addition, the optimal shared surplus value gained by MPAGA is 1.0067, whereas the value of 0.9965 is yielded in SGA, and MPAGA can get a better result. Consequently, MPAGA is superior to SGA when solving the optimization problem. Fig. 10. The performance between MPAGA and SGA.
8.
Conclusion
Identifying critical CRs and incorporating CRs into product design have been recognized as principle factors for product development. QFD and Kano model are two widely used customer driven approaches for CRs analysis. However, the two models have their own difficulties. Following the theoretical foundation of T-Kano model, this research proposes an improved Kano model (IF-Kano) considering the interaction between CRs importance weights and frequencies. The IF-Kano model defines a set of logical classification criteria for Kano categories and allows quantitative measure of CRs. Then, both qualitative and quantitative results of IF-Kano model are integrated into QFD with an optimization model to determine the appropriate Kano categories of CRs and target values of ECs. The optimization model aims to maximize the shared surplus, which considers not only the overall CS, but also the enterprise satisfaction with the costs committed to the product. To solve the proposed model efficiently, the improved adaptive genetic algorithm is proposed. The optimization model can be meaningful and reasonable in the following circumstances. From the engineers’ perspective, decisions on product positioning and design have become challenges because of the emergency of micro-segment markets with different needs and certification specifications. The customers with the same importance-frequency distribution of CRs may be divided into different micro-segments. In different micro-segments, different Kano classification schemes may be generated based on the traditional Kano model. In this circumstance, the method proposed in this manuscript can assist the engineers to strategically select the targeted micro-segments, which has the same Kano classification scheme with the optimization results. Then, the optimal product design scheme is positioned in the targeted micro-segments. Thus, the producer and customers in the targeted 16
micro-segments both can obtain the maximal shared surplus. From customers’ perspective, their needs may be latent and difficult for customers to describe. Furthermore, customers may not be well informed about the differentiation among the product variants and trade-offs between product cost and its features. The factors mentioned above may lead to the imprecise, ambiguous, and even incorrect and conflicting CRs, which have significant negative implications on design and manufacturing of the product in terms of quality, lead time, and cost. Thus, it is necessary to conduct intensive customer interactions, and guide customers to their optimal choices during the product design process (Blecker et al., 2004). While, the product design scheme and the Kano classification scheme obtained through the proposed method can be presented in an understandable form during the interaction process, which may guide customers to adjust their initial CRs to obtain the optimal product with maximal shared surplus. However, it is commonly believed that the relationship between CRs and CS is complex, and this paper adopts a simple approximate method. Future work should focus on further study and explanation of the CS for CRs. Moreover, the proposed model needs subjective inputs, and contains vagueness and uncertainty, which are not handled in this study. It is necessary to deal with the subjectivity and uncertainty to improve the model. In addition, the theory of customer co-creation is crucial to improve the understanding of CRs. The multi-issue negotiation mechanism adopted in the existing literature (Altun & Dereli, 2014; Altun et al., 2013; Altun et al., 2016; Dereli & Altun, 2012) provides new options for customer co-creation in the development process. Thus, the further adaptation of the multi-issue negotiation to the integrated IF-Kano and QFD methodology has great potential to enhance customer co-creation and elicit latent CRs.
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in
QFD
using
a
fuzzy
chance-constrained
modelling
approach.
Neurocomputing. Zwick, R., Carlstein, E. & Budescu, D.V. (1987). Measures of similarity among fuzzy concepts: A comparative analysis. International Journal of Approximate Reasoning, 1(2), 221-242.
20
Satisfied Attractive attribute One-dimensional attribute Indifferent attribute Functional
Dysfunctional
Must-be attribute
Reverse attribute Dissatisfied
Fig. 1. Kano model.
Fig. 2. Kano classifiers and Kano categories.
Fig. 3. Linguistic term set.
21
Fig. 4. Relationship between customer satisfaction and CR fulfillment.
Fig. 5. One feasible chromosome coding scheme.
Fig. 6. Adaptive tournament size.
22
Fig. 7. Adaptive crossover and mutation probabilities ( Chromosome representation
).
Real coding scheme
Initialization Population 1
Population 2
Population M Select the optimal individuals to form population M+1 Selection
Selection
Crossover
Crossover
Mutation
Mutation
Mutation
New population 2
New population M
New population M+1
Selection
Selection
Crossover
Crossover
Mutation New population 1
. . .
Select the optimal individuals to form population M+2
Update the populations
Y
Y N
N
Output the optimal solution
Fig. 8. Procedure of the proposed algorithm.
23
Adaptive tournament selection Adaptive crossover Adaptive and non-uniform mutation
ECs (CR CRs
configuration
Normalized relationship matrix between CRs and ECs
index) 0.064
0.1401
0.051
0.012
0.4632
0.33
0.0021
0.0016
0.581
0.1008
0.0936
0.426
0.1931
0.0011
0.0827
0.1028
0.771
0.4339
0.1562
0.1336
0.0035
0.0053
0.0923
0.1751
0.936
0.3351
0.1411
0.0735
0.0069
0.0072
0.0821
0.3541
0.394
0.1591
0.1037
0.0639
0.0711
0.0029
0.4661
0.1332
0.062
0.172
0.1853
0.1831
0.2401
0.1009
0.0065
0.1021
0.129
0.1163
0.0081
0.0092
0.2611
0.4859
0.0043
0.1152
0.804
0.364
0.458
0.227
0.103
0.381
0.6
—
ms-1
kg
m2
—
M
1.6
1000
2.24
M
9400
H
H
1.5
1500
3.4
H
8200
M
M
1.75
1350
2.85
M
9000
FH
Min
L
0.5
450
1.3
FL
7000
L
Max
VH
2.5
1600
3.56
FH
12000
VH
2.2
1.3
1.1
0.9
1.6
0.6
1.4
(EC configuration index) Unit
(Cost coefficient)
Fig. 9. The HoQ of the home elevator.
Fig. 10. The performance between MPAGA and SGA.
24
—
Table 1. Kano evaluation table (Source by Berger et al. (1993)). Functional
Dysfunctional Like
Must-be
Neutral
Live-with
Dislike
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
Table 2. Results obtained by MPAGA. Optimal results
Near optimal results
Serial number
EC
4
CR
EC
CR
0.4511
0.9923
A
0.9429
0.9445
0.4519
0.9941
A
0.9517
0.9529
0.5585
0.7261
O
1.0
1.0
0.5591
0.7268
O
1.0
1.0
0.7249
0.7974
O
0.8350
0.8350
0.7269
0.7996
O
0.8390
0.8390
0.6776
0.6098
M
0.8304
0.4856
0.6775
0.6097
M
0.8337
0.4862
0.5930
0.9488
A
0.9412
0.9429
0.6361
1.0177
O
0.9668
0.9668
0.6716
0.4030
A
0.9179
0.9213
0.7668
0.4601
O
0.9140
0.9140
0.5418
0.7585
A
0.9149
0.9185
0.5453
0.7634
A
0.9352
0.9373
2.2714
2.3059
S
2.2866
2.3140
U
1.0067
1.0035
Table 3. Comparative results of the case study. Analytical Kano Traditional Kano
CRs M
A
O
I
12
18
28
42
0
20
80
55
40
48
0.524
Kano category
Configuration index
Kano category
I
0.431
A
0
O
0.364
M
5
0
M
0.542
O
42
8
2
M
0.843
M
29
65
5
1
A
0.721
A
16
64
16
4
A
0.075
A
63
27
7
3
M
0.191
I
1.9503
2.0970
S
1.9085
2.0643
U
0.9786
0.9844
25
A Kano model based on customer requirement frequency and importance is proposed.
The refined Kano model and QFD are integrated with an optimization model.
A multi-population adaptive genetic algorithm is designed.
Appropriate Kano categories and engineering characteristic’s target value are selected.
26
S0360-8352(17)30475-8 https://doi.org/10.1016/j.cie.2017.10.009 CAIE 4946
To appear in:
Computers & Industrial Engineering
Received Date: Revised Date: Accepted Date:
25 August 2015 31 May 2017 8 October 2017
Please cite this article as: He, Lina., Song, Wenyan., Wu, Zhenyong., Xu, Zhitao., Zheng, M., Ming, X., Quantification and Integration of an Improved Kano Model into QFD based on Multi-population Adaptive Genetic Algorithm, Computers & Industrial Engineering (2017), doi: https://doi.org/10.1016/j.cie.2017.10.009
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Quantification and Integration of an Improved Kano Model into QFD based on Multi-population Adaptive Genetic Algorithm a
Lina. He
a
b
c
*, Wenyan. Songb, Zhenyong. Wuc, Zhitao. Xud, Maokuan, Zhenge, Xinguo Minge
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, R.P. China
School of Economics and Management, Beihang University, Beijing, 100191, R.P. China
College of Mechanical Engineering, Guangxi University, Nanning, R.P. China
d
College of Economics and Management, Nanjing University Of Aeronautics And Astronautics, 29
Jiangjun Avenue, Nanjing, 211106, R.P. China
e
Shanghai Research Center for Industrial Informatics, Shanghai Key Lab of Advanced Manufacturing
Environment, Institute of Computer Integrated Manufacturing, School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Minhang District, Shanghai, 200240, R.P. China Corresponding author: Lina. He, School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031, R.P. China E-mail: [email protected] Telephone: 86-1521-671-1815
Acknowledgment The authors would like to thank the anonymous reviewers for their valuable comments, suggestions and corrections, which greatly improved the quality of the paper. The authors would like to acknowledge the financial support provided by School of Mechanical Engineering in Southwest Jiaotong University. 1
Abstract: In an effort to address the inherent deficiencies of traditional Kano model and quality function deployment (QFD), this paper proposes an improved Kano model named as importance-frequency Kano (IF-Kano) model and integrates IF-Kano model into QFD. Considering the interaction between frequencies and importance weights of customer requirements (CRs), the IF-Kano model adopts the logical Kano classification criteria to categorize CRs. Then, both qualitative and quantitative results derived from IF-Kano model are integrated into QFD with a non-linear programming model. The model aims to determine appropriate Kano categories of CRs and target values of engineering characteristics (ECs) with a view to achieving an optimal design solution under the best balance between enterprise satisfaction and customer satisfaction (CS). To solve the presented model, a multi-population adaptive genetic algorithm (MPAGA) is designed. Finally, an example of a home elevator design is given to demonstrate the feasibility and effectiveness of the developed approach and algorithm.
Key words: Kano model; quality function development; customer requirement; multi-population adaptive genetic algorithm
1.
Introduction
In competitive environment, manufacturing enterprises are increasingly focusing on designing product with high customer satisfaction (CS) and low price. The critical challenge for manufacturers is how to make effective analysis of customer requirements (CRs) to provide decision support for optimal product design. Various methods and tools have been developed to help companies obtain a better understanding of CRs and incorporate the CRs into product design to achieve optimal solution. Among them, Kano model and quality function deployment (QFD) have been widely adopted to enhance the competitiveness of companies by helping them focus on CRs in product development (Ji et al., 2014). QFD translates CRs into engineering characteristics (ECs) to plan and manage new product development (Mizuno & Akao, 1990), and is applied in many researches to improve product design to satisfy CRs (Jia & Bai, 2011; Y.-L. Li et al., 2011; Luo et al., 2010). By describing the interrelationships between CRs and ECs, and the correlations among ECs, the main task of product planning using QFD is to determine target values of ECs for achieving higher overall CS (Fung et al., 2005). In order to provide decision-making in QFD, the systematic and rational mathematical programming models for the targets setting of ECs have received flourishing advances in the last decade (Delice & Güngör, 2009; Luo et al., 2010; Yang & Yoo, 2016; Zhong et al., 2014). In Consideration of CRs’ heterogeneity, a novel QFD-based product planning approach for determining optimal target levels of ECs is proposed (Luo et al., 2015). These models can help a company to make key tradeoff between what the customers want and what the company can afford to build with QFD analysis. However, the significant challenge of QFD implementation is the difficulty in understanding CRs systematically (Cristiano et al., 2001; Lee et al., 2008). While, CRs analysis is the key starting 2
point for execution and if it does not accurately reflect what the customer expects from a product, the model may lead to inaccurate forecasts (Kwong et al., 2011). Kano model identifies CRs in more detail by classifying and prioritizing CRs based on how they affect CS. Thus, Kano model can be one solution to address QFD’s inadequacy of recognizing CRs (Sireli et al., 2007). In order to incorporate the nature of CRs into QFD, many researchers proposed to integrate Kano model into QFD. However, the traditional Kano (T-Kano) model used in these integrative approaches provides limited decision support for designers. In general, it is conducted with functional and dysfunctional question pair for each CR developed to a number of respondents, and every respondent’s answer pair for each CR should be aligned with the predefined evaluation table. Thus, the reduplicative survey is time-consuming, and the Kano classification obtained merely based on subjective evaluations may cause subjective bias. As a decision-making tool, the ultimate goal of Kano classification analysis is to provide decision support for producer’s capacity allocation in product design to fulfill CRs. Different classification schemes may influence producer’s resources allocation, product design strategy and CS (Xu et al., 2009). The appropriate Kano classification should reflect customer’s perception under the constraint of producer’s capacity to fulfill the CRs. While, the T-Kano inherently emphasizes the customer and market perspectives only, and fails to account for the producer’s capacity (Xu et al., 2009). Producer’s capacity constraints are usually accommodated by the product development team based on expertise, such that the product will only fulfill those CRs that are affordable to the producers (Matzler & Hinterhuber, 1998). In practice, without consideration of producer’s capacity, the classification analysis seldom holds significance. Therefore, to provide comprehensive design support, the Kano classification of CRs should emphasize the customer’s perception and producer’s capacity simultaneously. The reasonable Kano classification scheme should lead to an optimal tradeoff between CS and producer’s capacity (Xu et al., 2009). The main advantage of QFD is to make tradeoff between CS and producer’s capacity. To address the above issues of CRs analysis, this paper proposes a refinement of Kano model: importance-frequency Kano (IF-Kano) model. Based on the classification method of CRs proposed in (Stone et al., 2008) and theoretical foundation of the analytical Kano (A-Kano) model developed by Xu et al. (2009), IF-Kano model provides a set of classification criteria to categorize CRs considering the interaction between frequency and importance. Then, to determine the appropriate Kano categories of CRs and target values of ECs with consideration of an optimal tradeoff between CS and producer’s capacity, the IF-Kano model is integrated into QFD with a computational model. Finally, a multi-population adaptive genetic algorithm (MPAGA) is developed to solve the integrated IF-Kano and QFD computational model. The rest of this paper is constructed as follows. In the next section, some specific aspects of Kano model and the integrative approaches of Kano model and QFD are discussed. In Section 3, the IF-Kano model for CRs analysis is proposed. In Section 4, a systematic process model is developed to integrate IF-Kano model into QFD. In order to solve the model, MPAGA is presented in Section 5. A case study associated with the design of home elevator is presented in Section 6 to demonstrate the proposed method and algorithm. Discussions and conclusions are presented in Section 7 and 8, respectively.
3
2.
Literature review
2.1.
Kano model
Kano et al. (1984) have developed a two-dimensional model to understand CRs and their impact on CS. The Kano model divides CRs into six categories, each of which affects CS in a different way. Referring to Fig. 1, Kano categories are briefly explained as follows (Berger et al., 1993; Kwong et al., 2011; C.-H. Wang, 2013) : Fig. 1. Kano model.
Attractive (A): The functional presence of these attributes will result high level of CS while their absence will not affect CS.
One-dimensional (O): The functional presence of these attributes will generate CS while their absence will results in non-satisfaction.
Must-be (M): Customers take the presence of these attributes for granted. Insufficiency of these attributes will results in extreme non-satisfaction, but the sufficiency will not increase satisfaction level.
Indifferent (I): The attributes in this category, whether present or not, does not affect CS.
Reverse (R): The presence of these attributes will generate non-satisfaction, and vice versa.
Questionable (Q): This outcome indicates that either the responses do not make any logical sense, or the question was phrased incorrectly. Table 1. Kano evaluation table (Source by Berger et al. (1993)). Functional
Dysfunctional Like
Must-be
Neutral
Live-with
Dislike
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
As shown in Table 1, the Kano model uses functional and dysfunctional questionnaires, and 5 by 5 evaluation table as conducting instrument (Kano et al., 1984). The final Kano categories of CRs are evaluated according to response frequencies (Y. Li et al., 2009; Song et al., 2013). The highest frequency represents the dominant customer view (L.-F. Chen, 2012). Kano model is widely used in the analysis of CRs (T. Wang & Ji, 2010). However, the T-Kano model is a qualitative method that provides limited decision support for designer (Berger et al., 1993; He et al., 2015; Wu & Wang, 2012; Xu et al., 2009). Berger et al. (1993) initialized the quantitative analysis of Kano model with two quantitative CS coefficients, named as satisfaction index (SI) and dissatisfaction index (DI) to reflect the average impact of a CR on CS or dissatisfaction. Then, the CS coefficients have been modified and utilized as adjustment factor for re-prioritizing CRs to achieve maximum CS (Matzler & Hinterhuber, 1998; Tontini, 2007). T. Wang and Ji (2010) proposed a novel approach to measure and quantify the relationships between CS and the fulfillment of customer requirements as depicted in Kano model. Wu and Wang (2012) presented a continuous approach for fuzzy Kano evaluation to allow quantitative analysis of the CRs with an evaluation index which allows 4
the CRs to be prioritized. However, the Kano categories of CRs in these studies are obtained based on two-dimensional questionnaires and Kano evaluation table, and cannot avoid the inherent deficiencies described in Section 1. To address the deficiencies of T-Kano, Xu et al. (2009) developed the A-Kano model by introducing Kano classifiers and a configuration index to incorporate quantitative measure into CS, leading to an optimal trade-off between CS and producer’s capacity. However, the statistical analysis of all responses using the Kano questionnaire is still time-consuming. Moreover, there is a lack of comprehensive analysis of relationships between CRs and ECs.
2.2.
Integration of Kano model into QFD
Many researchers proposed combining Kano model with QFD. Generally, the Kano model is integrated into QFD by adjusting the important weights for re-prioritizing attributes in QFD (Chaudha et al., 2011; C.-C. Chen & Chuang, 2008; Tontini, 2007). For instance, C.-C. Chen and Chuang (2008) introduced an adjustment coefficient (K) and the raw weights were adjusted by multiplying with K. The value of K varied according to Kano category, and values of ‘‘4’’, ‘‘2’’, ‘‘1’’ and ‘‘0’’ were assigned to the attractive, one-dimensional, must-be and indifference categories, respectively. The integrative approach provides basis for adjusting the relative priority of product attributes based on Kano categories. However, these approaches remain to be qualitative analysis of CRs, and provide limited decision support(Ji et al., 2014; Xu et al., 2009). Moreover, it is subjective to assign the adjustment values to Kano categories according to the experience (Ji et al., 2014; Nahm, 2013). In addition, the T-Kano in these studies has deficiencies mentioned in Section 1. Another integrative approach integrates Kano model into QFD by inferring the contribution of different CRs to CS and provides a mathematical programming model for optimal design solution (Ji et al., 2014; Lai et al., 2004). For instance, Ji et al. (2014) developed an optimization model that integrated Kano model into QFD to optimize product design to maximize CS under cost and technical constraints. Kano model was quantified by the relationship between CRs and CS, both qualitative and quantitative results of Kano model were integrated into QFD with a mixed integer nonlinear programming model. This integrative approach makes it possible to integrate quantitative analysis of Kano model into QFD for the issue of incorporating the CRs into product design. However, these studies ignore the deficiencies of T-Kano. First, the Kano survey of T-Kano is time-consuming. Second, the Kano classification method inherently emphasizes the customer and market perspectives only, and fails to account for the producer’s capacity to fulfill the CRs.
3.
IF-Kano model
Before quantitative analysis of Kano model is performed, the IF-Kano model is developed to identify Kano category of each CR on the basis of frequency of mention of the CR and average weight of the CR. Stone et al. (2008) proposed an importance-frequency CRs identification method to classify highly weighted, low frequency CRs as core CRs, and lightly weighted, high frequency CRs as differentiating CRs. With the definition of Kano categories in T-Kano model, core CRs equates to must-be CRs, and differentiating CRs can be further divided into one-dimensional and attractive CRs. One-dimensional CRs possess higher weight than attractive CRs. In addition, the lightly weighted, low frequency CRs 5
are classified as indifferent CRs in this paper. Considering the interaction between CRs frequency and weight, the IF-Kano model classifies CRs into four categories, including indifferent, must-be, attractive, and one-dimensional.
3.1.
Kano indices
Each CR can be represented as and
, where
is the normalized frequency for
.
is the normalized important weight for
and
,
can be obtained according to the Equations
(1) and (2).
Where
and
denote the raw values of important weight and frequency for
The value pair
.
should fall in 0-1, and can be plotted in a two-dimensional diagram. As
shown in Fig. 2, the characteristics of where importance as a vector
can be represented as a vector, i.e.,
,
and . The rationale of representing the frequency and is that it becomes equivalent to a polar form, i.e., the magnitude of the vector
denotes the overall evaluation value of
, and the angle
frequency and importance. Both
and
determines the relative level of are collectively called Kano indices.
Fig. 2. Kano classifiers and Kano categories.
3.2.
Kano classifiers
Based on the corresponding location of CRs in the importance-frequency diagram, the IF-Kano model classifies CRs into four Kano categories, i.e., indifferent, must-be, attractive, and one-dimensional, as shown in Fig. 2. A threshold value of vector magnitude, important and lower frequency ones. If
,
, is used to differentiate CRs from less
is considered as unimportant and infrequent, and
thus defined as an indifferent CR. The region defined by the sector OGN in Fig. 2, is considered as the indifferent region. Hence,
is named as an indifferent threshold.
Likewise, a lower threshold value of vector angle is defined as
, if
and
,
is considered as a must-be CR. Thus, CRs falling into the sector DEGH of Fig. 2 can be defined as must-be CRs, and
is called a must-be threshold.
A higher threshold value of the vector angle is defined as
, such that for
, if
and
, it is considered as an attractive CR. The region of the attractive CRs is shown as sector ABMN. Hence, If
is named as an attractive threshold. and
,
is considered as a one-dimensional CR. The region of the
one-dimensional CRs is shown as sector BCDHM. The set of thresholds
,
and
are collectively called Kano classifiers, denoted as
. Different Kano classifiers lead to different classification schemes, and the threshold values of Kano classifiers may be problem-specific and context aware for different application. With the same Kano classifiers, CRs may follow a life cycle based on the dynamic change of importance and frequency. As shown in Fig. 2, a CR can start with being indifferent turns into attractive on to 6
one-dimensional and finally ends as must-be, which agrees with the study conducted by Kano (Kano, 2001).
3.3.
Configuration index
Xu et al. (2009) presented that decision-making based on the Kano classification inevitably suffered the discontinuity problem and proposed a configuration index to alleviate the problem. Based on the IF-Kano model, this research proposes the corresponding configuration index to represent the priority of a CR is contained in the product design. The configuration index
Given a particular
, the configuration index
, is defined as follows.
is proportional to
, which agrees with the
observation that a CR with greater importance and higher frequency is more likely to be included in the product. At the same time, for a specific
,
decreases with an increase of
, which reflects the
decreasing priorities associated with the Kano categories in order of must-be, one-dimensional and attractive.
4.
Integration of IF-Kano model into QFD
As CRs in different Kano categories have different impact on CS (Kano et al., 1984), the IF-Kano model can enhance designer’s understanding of CRs and provide decision support to product design. In the IF-Kano model, different Kano classifiers lead to different classification schemes, which in turn influence the decision support to product design, producer’s capacity allocation and CS. Thus, the optimal values of Kano classifiers should be selected, leading to an optimal tradeoff between customer’s satisfaction and producer’s capacity (Xu et al., 2009). To address this issue, the IF-Kano model is integrated into QFD to develop an optimization model to determine the appropriate Kano classifiers and target values for a set of ECs. The basic concept of the model is to translate CRs into ECs to maximize shared surplus to leverage upon both customer’s and producer’s concerns. The consumer surplus is modeled as the overall customer’s satisfaction ( ) of the product, and the producer surplus is simulated as the overall cost ( ). The notations used in the model are listed as follows. Notations Number of customer requirements Number of engineering characteristics Number of competitors Level of fulfillment for
,
Level of fulfillment for
,
Normalized level of fulfillment for , Performance of by the th competitor, Normalized relationships between Configuration index for Configuration index for
and
, ,
Degree of customer satisfaction with
, 7
, ,
,
Kano category for
,
The overall customer satisfaction Cost of unit improvement for , Cost required for achieving
,
Total cost of product development Shared surplus of product
4.1.
Constructing the House of Quality
For new product planning, a house of quality (HoQ) diagram is used in the QFD processes to describe the important factors, i.e. CRs and ECs and their relationships. Hence, it should be constructed for the IF-Kano and QFD optimization model. The HoQ consists of six main parts, including CRs, ECs related to CRs, relationship matrix between CRs and ECs, correlation matrix among ECs, technical benchmarking and market benchmarking. Considering the correlations among the ECs and relationships between CRs and ECs, the method (Equation (4)) proposed by Wasserman (1993) is employed to obtain the normalized relationship values between CRs and ECs.
where and
denotes the relationship between
and
,
denotes the correlation between
.
4.2. Based on
Normalizing the target values of ECs ECs,
competitive products are benchmarked. Depending on the nature and vagueness,
ECs are measured using numerical values, and linguistic variables. As shown in Fig. 3, the linguistic variables are defined through triangular membership function , where , , represent the lowest, most likely and highest levels of the triangular membership function, respectively. As different ECs have different measurement methods, it is not reasonable to evaluate them synthetically by using their respective raw values. To solve this problem, a normalization method is developed to transfer the specific technical values into levels of fulfillment in the proposed model. ECs can be divided into two categories: positive and negative ones. For the positive ECs, the performance of EC is positively proportional to its target value, and vice versa with the negative ECs. For ECs measured with crisp data, the two categories of target values of ECs can be normalized according to the Equations (5) and (6). Equations (7) and (8) represent the normalized target values of ECs, which are measured with linguistic variables.
8
where
( ) denotes the specific technical value of
of technical value of
,
(
,
(
) is the maximum limit
) is minimum limit of technical value of
represents the distance of two fuzzy numbers (
), where
.
,
.
Many distance measurement functions have been proposed (C.-T. Chen, 2000; Zwick et al., 1987), but the vertex method developed by C.-T. Chen (2000) is an effective and simple method to calculate the distance between two triangular fuzzy numbers. Then the distance between two fuzzy triangular numbers using vertex method can be calculated as follows. .
Fig. 3. Linguistic term set.
4.3.
Formulation of the overall customer satisfaction
The degree of overall CS towards the to-be-designed product can be considered as a mathematical aggregation of the fulfillment levels of CRs, which can be represented as ， ， ， . In this paper, the summation of the degree of CS for individual CR is adopted to express as follows:
， ， ， Based on the approximate method proposed by Florez-Lopez and Ramon-Jeronimo (2012) and Kano categories of CRs defined in Section 3, the fulfillment level of
can be converted
into the degree of CS as follows (Fig. 4):
The fulfillment levels of ECs can be transformed into the fulfillment levels of CRs by multiplying the normalized relationship matrix.
Referring to the constraint of Equation (11), as
, it is necessary to normalize
to
.
Fig. 4. Relationship between customer satisfaction and CR fulfillment.
4.4.
Formulation of the design cost
Apart from the consideration of CS, other multiple resources are required for supporting the development of a new product, such as product development time, development cost, manufacturing 9
cost, human resource, technical expertise, advanced equipment, tools and other facilities (Zhong et al., 2014). These resources can be aggregated in financial terms at the level of strategic planning. The overall cost
depends on the levels of attainment of
，it can be viewed as the summation of
costs required for achieving the level of individual EC.
The configuration index of determined by Equation (3) can be transform into the configuration index of by multiplying the normalized relationship matrix.
4.5.
Formulation of the optimization model
Standing on the perspective of enterprise, the primary concern in product planning process based on IF-Kano model and QFD is to determine the appropriate Kano classifiers and the target values of ECs for the new product to maximize the shared surplus. Accordingly, the objective function is established in Equation (16), and the constraints of the model are established in Equations (17)-(21).
Subject to
The objective function is the overall shared surplus towards the to-be-designed product. denotes individual CS for
, and can be obtained by applying different CS functions according to
Equation (11). Equations (17)-(19) represent the lower and upper bounds of Kano classifiers, where changes from 0.1 to 0.9, from
to
changes from
to
, and
changes
. Equation (20) concerns the constraint of minimum fulfillment
level for a given EC. The fulfillment level of each EC is set to a benchmark with the average performance of major competitors in the industry. Equation (21) defines to be from 0 to 1. The optimization method proposed in this paper can be considered as a process of finding the appropriate Kano classification of CRs and target values of ECs, which lead to the optimal product design scheme.
5.
Improved adaptive genetic algorithm
The model is formulated as a typical non-linear programming model. Genetic algorithm (GA) is one of the widely applied evolutionary algorithms to the design and optimization of non-linear programming problems. However, with the broad application and in-depth study of GA, its many defects are 10
exposing constantly, such as premature convergence, low efficiency of optimization, etc. Therefore, it is important to focus on how to overcome various defects of the standard GA (SGA). A multi-population adaptive genetic algorithm (MPAGA) is developed to solve the integrated IF-Kano and QFD optimization model. The multi-population is embraced for migration to maintain the diversity of populations as a whole and to avoid premature convergence (Gong et al., 2007). The adaptive scheme is embedded in the MPAGA to improve the efficiency of genetic search process.
5.1.
Coding strategy
In the process of optimization model solving by MPAGA, each solution is defined by a chromosome in real coding scheme with
genes. As shown in Fig. 5, each chromosome consists of two
sections: Kano classifiers and the ECs target values. The first 3 genes are used to indicate the values of Kano classifiers
,
and
, while the target values of ECs are represented by the last
For example, a chromosome is reveals that the values of
,
genes. , which
and
are 0.21, 0.32 and 0.96, while the target values of 7 ECs are
0.51, 0.63, 0.82, 0.72, 0.64, 0.66 and 0.78. Fig.5. One feasible chromosome coding scheme.
5.2.
Adaptive tournament selection
In tournament selection, sets of individuals are selected randomly from the population and their fitness is compared with one another. The individual with the highest fitness is taken into the mating pool. It has been established that the loss of diversity of the population and the selection intensity would increase with the tournament size (Blickle & Thiele, 1995). An evolutionary learning process is dynamic and requires different selection intensity and diversity at different learning stages to speed up the convergence or avoid local optima. In order to keep population diversity with lower selection intensity in the early stage and speed up the convergence with a higher selection intensity in the last stage of the evolution, the adaptive tournament size is designed with Equation (22), as shown in Fig. 6.
G is the maximal generation number, g is the current generation number, T is the current tournament size,
and
denote the maximal and minimal tournament size, respectively. Fig.6. Adaptive tournament size.
5.3.
Adaptive crossover and mutation
In SGA, the crossover and mutation probabilities are fixed values, which always lead to the premature phenomenon and local convergence. In order to avoid these defects, the novel adaptive strategy is adopted in MPAGA to adapt the crossover and mutation probabilities based on the fitness statistics of the population and the generation number. In the same generation, individuals with lower fitness values should obtain higher crossover and mutation probabilities to eliminate the bad solutions, and 11
individuals with higher fitness values should be endowed with lower crossover and mutation probabilities to protect the optimal solutions. Moreover, the crossover and mutation probabilities should decrease with the increase of the generation number to promise the global search capability in the early stage and enhance the local search capability in the last stage of the evolution. Above all, the adaptive crossover and mutation probability functions can be represented with Equations (23) and (24). As shown in Fig. 7, the proposed mechanism can avoid any dramatic changes.
and
are the crossover and mutation probabilities,
minimal crossover probabilities, probabilities,
and
and
and
are the maximal and
are the maximal and minimal mutation
are the largest and lowest fitness values of group,
higher fitness value of the two crossover individuals, is the maximal generation number,
represents the
is the fitness value of mutation individual,
is the current generation number.
and
are weighted
coefficients for the fitness and generation number in the operators, whose values can be determined by the expert evaluation method and etc., and
+
.
Fig. 7. Adaptive crossover and mutation probabilities (
).
For the real-coded GA, the mutation operators include uniform mutation, non-uniform mutation, multi-non-uniform mutation, etc. In this paper, the improved non-uniform mutation is adopted. Let us assume is a chromosome and is a gene to be mutated, the resulting gene
is created as following:
where
Here, ,
and and
are respectively the lower and upper bounds of the variable have the same meanings as they are in Equation (24),
distributed random numbers between 0 and 1,
bad solutions (when
and
,
,
are two uniformly
returns a value in the range [0, 1] such that
increase. This property allows this operator to eliminate the
is small), and protect the optimal solutions. Meanwhile, this operator tends to
search the space uniformly at early stages (when
5.4.
,
is a system parameter determining the degree of
non-uniformity (Michalewicz, 1996). The function approaches to zero as
and
.
is small), and very locally at later stages.
Procedure of the proposed algorithm
With the improvement methods mentioned above, Fig. 8 presents the procedure of MPAGA. Fig. 8. Procedure of the proposed algorithm. Step 1. Initialize the control parameters of the algorithm, such as the number of population 12
,
population size
, maximal generation G, etc. and initialize M populations according to certain rules.
Step 2. Let
Step 3. Define the objective function as the fitness function. According to the fitness, choose the first
individuals from the
populations to construct the population
, the
is
among the M populations.
the individual with the largest fitness value Step 4. Let
. Assume that
populations evolve for a population evolutionary period by
adaptive tournament selection, adaptive crossover and adaptive mutation operators separately. Step 5. According to the fitness, choose the first construct the population population
. If
Step 6. If
If
. Let , let
individuals from the
populations to
be the individual with the largest fitness value ,
, check the generation number , go to Step 4.
in
. .
is the interval between the migrations, and
is a positive
integer.
If
, mix the population
first
6.
, and choose the
individuals from the mixed populations to update the population
individuals in population Step 7. If
and population
to update the population
, present the satisfactory individual
. Use the
. And then, go to Step 4.
and the corresponding fitness value
.
Case study
In order to verify the feasibility and effectiveness of the integrated IF-Kano and QFD approach and MPAGA, the development of home elevator is illustrated as a numerical example, and the results are also presented and analyzed in this section. Firstly, initial CR candidates are collected by focus group, individual interviews, and then affinity diagram or cluster method is used to organize those candidates. Then, suppose that 7 major CRs are identified to represent the biggest concerns of customers, including ‘comfortableness of the space’ (
), ‘load capacity’ (
conservation’ (
), ‘operation stability’ (
), ‘low noise’ (
), ‘aesthetic’ (
), ‘operation safety’ (
), ‘resource
). Frequency of occurrence for each distinct
CR in the investigation is recorded and a self-stated importance questionnaire on the each CR is conducted with interviewers without showing them the frequency. Then, the normalized frequency, normalized importance weight for each CR are obtained, and can be represented as
The vectors of the Kano indices with respect to 7 CRs can be easily computed as follows:
Subsequently, the detailed contents in the HoQ in Fig. 9. are determined one after another. Based on the professional knowledge of this product, the QFD team recognizes 7 important ECs related to the CRs: ‘performance of driving system’ ( elevator car’ ( system’ (
), ‘decoration level’ (
), ‘rated speed’ ( ), ‘energy consumption’ (
), ‘rated load’ (
), ‘size of
), ‘performance of emergency
). Then, the relationships between the CRs and ECs as well as the correlations among the
ECs are evaluated, and the normalized relationship matrix between CRs and ECs can be obtained according to Equation (4), as shown in Fig. 9. Meanwhile, the limit values and cost coefficients for ECs are specified according to industry standards and shown at the base of the HoQ. Besides, three 13
competitors are taken into consideration and their corresponding target values of ECs are evaluated by the QFD team on the basis of enterprise environments and strategies. Depending on their nature and vagueness, target values of ECs are measured using linguistic variables, and numerical values. The seven linguistic terms in Fig. 3 are employed in the benchmarking information evaluation. Then, the normalized target values of the ECs of all competitors can be obtained by Equations (5)-(8) as follows:
The lower and upper bounds of the Kano classifiers are represented as . The lower and upper bounds of ECs fulfillment levels are represented as
Fig. 9. The HoQ of the home elevator. Finally, the optimization model can be formulated with Equation (16). To solve the optimization model, the MPAGA procedure is coded in Matlab R2014b in a PC with Intel Core2 i5, and with 4 GB DDR II RAM at 2.4 GHz. The number of populations is set as 3, the population size as 200, the maximum generations as 500, ,
,
, ,
,
, and
,
,
,
.
Table 2. Results obtained by MPAGA. Optimal results
Near optimal results
Serial number
4
EC
CR
EC
CR
0.4511
0.9923
A
0.9429
0.9445
0.4519
0.9941
A
0.9517
0.9529
0.5585
0.7261
O
1.0
1.0
0.5591
0.7268
O
1.0
1.0
0.7249
0.7974
O
0.8350
0.8350
0.7269
0.7996
O
0.8390
0.8390
0.6776
0.6098
M
0.8304
0.4856
0.6775
0.6097
M
0.8337
0.4862
0.5930
0.9488
A
0.9412
0.9429
0.6361
1.0177
O
0.9668
0.9668
0.6716
0.4030
A
0.9179
0.9213
0.7668
0.4601
O
0.9140
0.9140
0.5418
0.7585
A
0.9149
0.9185
0.5453
0.7634
A
0.9352
0.9373
2.2714
2.3059
S
2.2866
2.3140
U
1.0067
1.0035
The final optimal results of the optimization model are obtained with the MPAGA method, as shown in Table 2. Based on the optimal fulfillment levels of ECs, the corresponding cost allocations are derived and the total cost value is 2.2714. According to threshold values of the Kano classifiers, the Kano categories for CRs can be obtained and are listed in Table 2. Subsequently, the CR fulfillment level and individual CS can be calculated, and the total CS is 2.2866. In total, the product achieves a shared surplus value of 1.0067. Furthermore, using the invertible functions of Equations (5)-(8), the specific technical target values of an individual EC can be obtained. Then, based on the Kano categories for CRs, ECs’ technical target values and cost allocation, the producer can make detailed 14
product design plan.
7.
Discussions
In this section, the effectivenesses of the proposed IF-Kano model, integration method of QFD and IF-Kano, multi-population adaptive genetic algorithm are discussed.
For comparison, Table 3 shows the results from the T-Kano model in (Berger et al., 1993) and A-Kano model in (Xu et al., 2009). The T-Kano model is a qualitative method that cannot differentiate CRs within the same category. Compared with the T-Kano model, the configuration index in the IF-Kano model can effectively reflect the differences among CRs within the same category. For example, both
and
are classified as One-dimensional CRs in Table 2,
and the configuration indexes are 0.581 and 0.771, which can represent the different customer perceptions considering their importance weights and frequencies. Thus, the IF-Kano classifiers and configuration index can help designers recognize the important CRs that have great impact on customer perceptions and enhance decision support in product design. Although the A-Kano model can also quantitatively analyze the CRs based on the corresponding configuration index, its results are not as accurate as those from IF-Kano, because the Kano questionnaires and scoring scheme in A-Kano model are determined in subjective manner, and the work of data collection and processing is tedious. While, the IF-Kano model extends A-Kano model by considering the mentioned frequency and weight of CRs, which can be easy and quick to get with the record and self-stated importance questionnaire.
Table 3. Comparative results of the case study. Analytical Kano Traditional Kano
CRs
M
A
O
I
12
18
28
42
0.524
Kano category
Configuration index
Kano category
I
0.431
A
0
20
80
0
O
0.364
M
55
40
5
0
M
0.542
O
48
42
8
2
M
0.843
M
29
65
5
1
A
0.721
A
16
64
16
4
A
0.075
A
63
27
7
3
M
0.191
I
1.9503
2.0970
S
1.9085
2.0643
U
0.9786
0.9844
The shared surplus values resulted from the integration model with T-Kano and A-Kano are listed in Table 3. The integration of QFD with T-Kano aims to optimize ECs fulfillment levels, and the Kano categories of CRs are determined by the Kano survey without considering the tradeoff between CS and producer’s capacity. Thus, the resulted solution may not be the optimal one. Although the integration of QFD and A-Kano optimizes the ECs fulfillment levels and CRs Kano categories, it may not promise an optimal solution because of the deficiency in A-Kano mentioned in Section 7.1 While, the integration of IF-Kano and QFD not only optimizes ECs fulfillment levels, but also select the appropriate CRs Kano categories. It establishes a coherent decision 15
framework that deals with the interaction between the customer domain and the producer domain. Making a comparison between the optimal solution and the near optimal solution in Table 2, it can be seen that the different Kano classifiers lead to different classification scheme, which in turn influence the product design, cost, CS, and shared surplus. Although the CS of the near optimal solution is higher than the optimal solution, but the optimal solution is more economical. The shared surplus value of the near optimal solution is 1.0035. While, the optimal solution offers larger expected shared surplus value 1.0067, which indicates better strategies to leverage upon CS and producer’s capacity. Thus, integrating IF-Kano model into QFD can provide the logical classification criteria and optimal ECs fulfillment levels to enhance the competitiveness of companies.
With multi-population and adaptive strategy, MPAGA provides a more efficient way to solve the integrated IF-Kano and QFD model. For comparison, SGA is applied in the case. MPAGA and SGA have the same population size, maximum generations, and
. The crossover rate, mutation
rate and tournament size in SGA are 0.7, 0.05 and 15. Fig. 10 shows the fitness curves of MPAGA and SGA. It can be seen that MPAGA converges to the optimal results after 180 generations, while SGA converges to it after 425 generations. Thus, the convergence speed of MPAGA is faster than SAG. In addition, the optimal shared surplus value gained by MPAGA is 1.0067, whereas the value of 0.9965 is yielded in SGA, and MPAGA can get a better result. Consequently, MPAGA is superior to SGA when solving the optimization problem. Fig. 10. The performance between MPAGA and SGA.
8.
Conclusion
Identifying critical CRs and incorporating CRs into product design have been recognized as principle factors for product development. QFD and Kano model are two widely used customer driven approaches for CRs analysis. However, the two models have their own difficulties. Following the theoretical foundation of T-Kano model, this research proposes an improved Kano model (IF-Kano) considering the interaction between CRs importance weights and frequencies. The IF-Kano model defines a set of logical classification criteria for Kano categories and allows quantitative measure of CRs. Then, both qualitative and quantitative results of IF-Kano model are integrated into QFD with an optimization model to determine the appropriate Kano categories of CRs and target values of ECs. The optimization model aims to maximize the shared surplus, which considers not only the overall CS, but also the enterprise satisfaction with the costs committed to the product. To solve the proposed model efficiently, the improved adaptive genetic algorithm is proposed. The optimization model can be meaningful and reasonable in the following circumstances. From the engineers’ perspective, decisions on product positioning and design have become challenges because of the emergency of micro-segment markets with different needs and certification specifications. The customers with the same importance-frequency distribution of CRs may be divided into different micro-segments. In different micro-segments, different Kano classification schemes may be generated based on the traditional Kano model. In this circumstance, the method proposed in this manuscript can assist the engineers to strategically select the targeted micro-segments, which has the same Kano classification scheme with the optimization results. Then, the optimal product design scheme is positioned in the targeted micro-segments. Thus, the producer and customers in the targeted 16
micro-segments both can obtain the maximal shared surplus. From customers’ perspective, their needs may be latent and difficult for customers to describe. Furthermore, customers may not be well informed about the differentiation among the product variants and trade-offs between product cost and its features. The factors mentioned above may lead to the imprecise, ambiguous, and even incorrect and conflicting CRs, which have significant negative implications on design and manufacturing of the product in terms of quality, lead time, and cost. Thus, it is necessary to conduct intensive customer interactions, and guide customers to their optimal choices during the product design process (Blecker et al., 2004). While, the product design scheme and the Kano classification scheme obtained through the proposed method can be presented in an understandable form during the interaction process, which may guide customers to adjust their initial CRs to obtain the optimal product with maximal shared surplus. However, it is commonly believed that the relationship between CRs and CS is complex, and this paper adopts a simple approximate method. Future work should focus on further study and explanation of the CS for CRs. Moreover, the proposed model needs subjective inputs, and contains vagueness and uncertainty, which are not handled in this study. It is necessary to deal with the subjectivity and uncertainty to improve the model. In addition, the theory of customer co-creation is crucial to improve the understanding of CRs. The multi-issue negotiation mechanism adopted in the existing literature (Altun & Dereli, 2014; Altun et al., 2013; Altun et al., 2016; Dereli & Altun, 2012) provides new options for customer co-creation in the development process. Thus, the further adaptation of the multi-issue negotiation to the integrated IF-Kano and QFD methodology has great potential to enhance customer co-creation and elicit latent CRs.
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in
QFD
using
a
fuzzy
chance-constrained
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20
Satisfied Attractive attribute One-dimensional attribute Indifferent attribute Functional
Dysfunctional
Must-be attribute
Reverse attribute Dissatisfied
Fig. 1. Kano model.
Fig. 2. Kano classifiers and Kano categories.
Fig. 3. Linguistic term set.
21
Fig. 4. Relationship between customer satisfaction and CR fulfillment.
Fig. 5. One feasible chromosome coding scheme.
Fig. 6. Adaptive tournament size.
22
Fig. 7. Adaptive crossover and mutation probabilities ( Chromosome representation
).
Real coding scheme
Initialization Population 1
Population 2
Population M Select the optimal individuals to form population M+1 Selection
Selection
Crossover
Crossover
Mutation
Mutation
Mutation
New population 2
New population M
New population M+1
Selection
Selection
Crossover
Crossover
Mutation New population 1
. . .
Select the optimal individuals to form population M+2
Update the populations
Y
Y N
N
Output the optimal solution
Fig. 8. Procedure of the proposed algorithm.
23
Adaptive tournament selection Adaptive crossover Adaptive and non-uniform mutation
ECs (CR CRs
configuration
Normalized relationship matrix between CRs and ECs
index) 0.064
0.1401
0.051
0.012
0.4632
0.33
0.0021
0.0016
0.581
0.1008
0.0936
0.426
0.1931
0.0011
0.0827
0.1028
0.771
0.4339
0.1562
0.1336
0.0035
0.0053
0.0923
0.1751
0.936
0.3351
0.1411
0.0735
0.0069
0.0072
0.0821
0.3541
0.394
0.1591
0.1037
0.0639
0.0711
0.0029
0.4661
0.1332
0.062
0.172
0.1853
0.1831
0.2401
0.1009
0.0065
0.1021
0.129
0.1163
0.0081
0.0092
0.2611
0.4859
0.0043
0.1152
0.804
0.364
0.458
0.227
0.103
0.381
0.6
—
ms-1
kg
m2
—
M
1.6
1000
2.24
M
9400
H
H
1.5
1500
3.4
H
8200
M
M
1.75
1350
2.85
M
9000
FH
Min
L
0.5
450
1.3
FL
7000
L
Max
VH
2.5
1600
3.56
FH
12000
VH
2.2
1.3
1.1
0.9
1.6
0.6
1.4
(EC configuration index) Unit
(Cost coefficient)
Fig. 9. The HoQ of the home elevator.
Fig. 10. The performance between MPAGA and SGA.
24
—
Table 1. Kano evaluation table (Source by Berger et al. (1993)). Functional
Dysfunctional Like
Must-be
Neutral
Live-with
Dislike
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
Table 2. Results obtained by MPAGA. Optimal results
Near optimal results
Serial number
EC
4
CR
EC
CR
0.4511
0.9923
A
0.9429
0.9445
0.4519
0.9941
A
0.9517
0.9529
0.5585
0.7261
O
1.0
1.0
0.5591
0.7268
O
1.0
1.0
0.7249
0.7974
O
0.8350
0.8350
0.7269
0.7996
O
0.8390
0.8390
0.6776
0.6098
M
0.8304
0.4856
0.6775
0.6097
M
0.8337
0.4862
0.5930
0.9488
A
0.9412
0.9429
0.6361
1.0177
O
0.9668
0.9668
0.6716
0.4030
A
0.9179
0.9213
0.7668
0.4601
O
0.9140
0.9140
0.5418
0.7585
A
0.9149
0.9185
0.5453
0.7634
A
0.9352
0.9373
2.2714
2.3059
S
2.2866
2.3140
U
1.0067
1.0035
Table 3. Comparative results of the case study. Analytical Kano Traditional Kano
CRs M
A
O
I
12
18
28
42
0
20
80
55
40
48
0.524
Kano category
Configuration index
Kano category
I
0.431
A
0
O
0.364
M
5
0
M
0.542
O
42
8
2
M
0.843
M
29
65
5
1
A
0.721
A
16
64
16
4
A
0.075
A
63
27
7
3
M
0.191
I
1.9503
2.0970
S
1.9085
2.0643
U
0.9786
0.9844
25
A Kano model based on customer requirement frequency and importance is proposed.
The refined Kano model and QFD are integrated with an optimization model.
A multi-population adaptive genetic algorithm is designed.
Appropriate Kano categories and engineering characteristic’s target value are selected.
26