# Smart Product Design Process through the Implementation of a Fuzzy Kano-AHP-DEMATEL-QFD Approach

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## Abstract

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## 1. Introduction

## 2. Literature Review

#### 2.1. Quality Function Deployment

#### 2.2. Kano Model

#### 2.3. Analytic Hierarchy Process (AHP)

#### 2.4. Decision-Making Trial and Evaluation Laboratory (DEMATEL)

- It effectively analyzes the mutual influences (both direct and indirect effects) among different factors and understands the complicated cause and effect of relationships in the decision-making problem [14].
- It can visualize the interrelationships between factors and enable the decision-maker to clearly understand which factors have mutual influences on one another. The DEMATEL can be used not only to determine the ranking of alternatives but also to find out critical evaluation criteria and measure the weights of evaluation criteria [14].
- Although the AHP can be applied to rank alternatives and determine criteria weights, it assumes that the criteria are independent and fails to consider their interactions and dependencies. The Analytic Network Process (ANP), an advanced version of the AHP, can deal with the dependence and feedback between criteria; but the assumption of equal weight for each cluster to obtain a weighted supermatrix in the ANP is not reasonable in practical situations [14].

- It determines the ranking of alternatives based on interdependent relationships, but other criteria are not incorporated in the decision-making problem [14].
- The relative weights of experts are not considered in aggregating personal judgments of experts into group assessments [14].
- It cannot take into account the aspiration level of alternatives as in the Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method or obtain partial ranking orders of alternatives as in the Elimination et Choix Traduisant la Realité (ELECTRE) approach [14].

- Adequate relative importance (weight) of customer requirements with the implementation of AHP.
- Real impact estimation of customer requirements (fuzzy Kano)
- The dependence relationship between design actions (DEMATEL)
- The relation between design action and customer needs (QFD)

## 3. Proposed methodology

- Step 1: Identification of customer needs. These customer needs were identified through an evaluation of customer claims and recommendations, but also by the permanent contact with physicians.
- Step 2: The design department at the company analyzed the needs and proposed some design features/actions to fulfill customer expectations.
- Step 3: The fuzzy Kano survey was implemented to the physicians. This survey allowed the team to classify each need as must-be, one dimensional, and excitement. The relevance of this classification laid in identifying the influence that each need would have on customer satisfaction or dissatisfaction.
- Step 4: Implementation of AHP. With the implementation of AHP, the perceived weights of each need would be identified.
- Step 5: Implementation of DEMATEL. With the implementation of DEMATEL, the overall influence for each one of the design actions/features would be identified.
- Step 6: All the collected information was compiled in the QFD matrix. AHP and Kano influence values would be located next to its corresponding customer needs (rows). The columns would have the design actions/features and also its corresponding weight (calculated with DEMATEL).
- Step 7: The design team filled a survey to identify the relationship between each customer’s needs and each design action.
- Step 8: Calculation of satisfaction influence for each design action, through the information in the QFD matrix.
- Step 9: Prioritization of design actions/features.

#### 3.1. Kano Model

- I like it this way
- Must be this way
- Indifferent
- Live with it this way
- I dislike it this way

- Inclusion of an influence table: The inclusion of this table is directly linked with the Kano evaluation table (Table 3). The objective of this table is to determine the influence of each one of the Kano table cells on customer satisfaction. It is important to point out that though cells ((2.5—expect, dislike), (3.5—neutral, dislike), (4.5—live with, dislike)) are in the must-be category, their influence on customer satisfaction is different, considering that cell ((3.5—neutral, dislike)) is the pure state of a must-be feature. On the other hand, it can be said that cells ((2.5—expect, dislike) and (4.5—live with, dislike)) as not being the pure state of the must-be feature, their influence on their satisfaction will not be the same as the cell ((3.5—neutral, dislike)). This is the reason why though the three cells are in the same category, their influence is different.

- I enjoy it that way instead of I like it this way.
- I expect it that way instead of must be this way.
- I am neutral instead of indifferent.
- Dislike but can live with it instead of living this way.
- Dislike and cannot accept it instead of I dislike it this way.

#### 3.2. Analytic Hierarchy Process

- Structuring a decision problem and selection of criteria: The first step is to decompose a decision problem into its constituent parts. In its simplest form, this structure comprises a goal or focus at the topmost level, criteria (${C}_{i}$ ), subcriteria ($S{C}_{ij}$) at the intermediate levels, while the lowest level contains the alternatives available for the decision-maker (${A}_{k}$) (Figure 6).

- 2.
- Construct a set of pairwise comparison matrices for criteria, sub-criteria, and alternatives (Equation (2)).$$A=\left[\begin{array}{cccc}1& {a}_{12}& \dots & {a}_{1n}\\ {a}_{21}& 1& \dots & {a}_{2n}\\ \dots & \dots & \dots & \dots \\ {a}_{n1}& {a}_{n2}& \dots & 1\end{array}\right]Where,{a}_{ij}=\frac{1}{{a}_{ji}}$$
- 3.
- Perform computations to find the importance of each criterion and alternative (Equation (3)).$${W}_{i}=\frac{{\left({{\displaystyle \prod}}_{j=1}^{n}{a}_{ij}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}}{{{\displaystyle \sum}}_{i=1}^{n}{\left({{\displaystyle \prod}}_{j=1}^{n}{a}_{ij}\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$n$}\right.}},i,j=1,2,\dots ,n$$
- 4.
- Calculate the maximum eigenvalue, consistency index, Consistency Ratio (CR), and normalized values for criteria and alternatives to ensure that the calculated weights derived from the pairwise comparison matrix are acceptable. Obtaining CR implies the previous calculation of the maximum eigenvalue (λmax) by applying Equation (4).$${\lambda}_{max}=\frac{{{\displaystyle \sum}}_{i=1}^{n}c{v}_{i}}{n}$$

#### 3.3. DEMATEL

- Calculate the average matrix: Each respondent is asked to evaluate the direct influence between any two factors by an integer score ranging from 0, 1, 2, and 3, representing ‘no influence’, ‘low influence’, ‘medium influence’, and ‘high influence’, respectively. The notation of ${x}_{ij}$ represents the degree to which the respondent believes factor i affects factor j. For i = j, the diagonal elements are set to zero. For each respondent, an n x n non-negative matrix can be established as X^k = [${x}_{ij}$^k], where k is the number of respondents, where 1 ≤ k ≤ H, and n is the number of factors. So, X^1, X^2, X^3, …, X^H are the matrices from H respondents. To summarize all opinions from H respondents, the average matrix A = [${a}_{ij}$] is constructed as follows (refer to Equation (8)):$${a}_{ij}\frac{1}{H}{{\displaystyle \sum}}_{k=1}^{H}{x}_{ij}^{k}$$
- Compute the normalized initial direct-relation matrix, where normalization of initial direct-relation matrix D is performed by D = A × S, where S is calculated by implementing Equation (9).$$S=\frac{1}{\underset{1\le i\le n}{\mathrm{max}}{{\displaystyle \sum}}_{j=1}^{n}{a}_{ij}}$$Each element in matrix D ranges between zero and one.
- Calculate the total relation matrix T, where T is defined as stated in Equation (10).$$T=D{\left(I-D\right)}^{-1}$$Here, I is the identity matrix.

- 4.
- Set up a threshold value to obtain the digraph: Since matrix T provides information on how one factor affects another, a decision-maker must set up a threshold value to filter out some negligible effects. Only the effects greater than the threshold value are chosen and depicted in a digraph. The threshold value is usually set up by computing the average of the elements in Matrix T. The digraph can be acquired by mapping the dataset described in step 3 [65].

#### 3.4. Quality Function Deployment

- The first region: Known as the “what region”, is the region where the costumers’ requirements are organized. It is divided into two columns. The customers’ requirements can be found in the first column, and the weight or importance that each requirement represents to the customer can be found in the second column. Each weight is denoted as ${w}_{i}$ [19].
- The second region: Known as the “how” region, contains design factors. This region is divided into two lines. The design factors can be found in the first line, whereas the second line indicates the net correlation or influence of each design factor. The net influence of each factor can be denoted by ${c}_{j}$ [15].
- The third region: Known as the “what vs. how” region. In this region, it is defined how much impact design factors have on customer needs. The scores in this region can go from 0 (no influence or relation) to nine (total influence relation). Each score is denoted by ${s}_{ij}$ [15].
- The fourth region: Known as the “how much” region. The net importance of each design factor can be found in this region. This net importance considers the influence between the different design factors, the weight of the customer needs, and the impact that the design factor has on customer needs. In this region, the target values and specifications tolerances can also be expressed. The net effect of each design factor in customer satisfaction can be obtained by $\sum}_{i}({c}_{j}\ast {w}_{i}\ast {s}_{ij})$. The design factor’s priorities are obtained by organizing them in a descending trend of this calculation [15].
- The fifth region: The “benchmarking” region where the product or service delivered by the company is compared with products or services of competitors in terms of how much the own product or service can perform against the competitors regarding the customer needs [15].
- The sixth region: Known as the “how vs. how” region. In this region, the type of correlation between each two design factors (DF) is defined. It can be in terms of how strong is the relation or just indicating if it is positive, negative, or neutral, depending on which way of improvement we have fixed for each DF. In this last case, it would represent the impact of improving one specification on the other one [15].

## 4. Case Study: A Hip Replacement Surgery Aid Device for Elderly People

## 5. Implementation of the Proposed Approach

## 6. Discussion

## 7. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Total hip implant scheme [2].

**Figure 5.**Membership functions [24].

Author | Used Methods | ||||
---|---|---|---|---|---|

AHP | QFD | DEMATEL | Kano | Comments | |

Ortiz et al. [36] | X | X | Integration with TOPSIS. Inclusion of fuzziness. No relation between design actions and factors. | ||

Rodriguez et al. [16] | X | Integration with fuzziness. Prioritization of criteria. No integration with AHP or DEMATEL to interpret in a better way the relation of the different factors. | |||

Yazdani et al. [19] | X | X | A better understanding of the influence between the different factors. Poor capacity to translate linguistic requirements because of the lack of fuzziness in the model. | ||

Bolar et al. [21] | X | Integration with the Hidden Markov model. | |||

Ortiz et al. [23] | X | X | Lack of fuzziness for better translation of spoken requirements. | ||

Pakizehkar et al. [25] | X | X | X | Good interpretation of user needs and expectations. Lack of fuzziness to better translate linguistic desires. No integration with DEMATEL for better interpretation of the influence between factors. | |

Lee et al. [26] | X | Integration with fuzziness. Prioritization of healthcare actions. Lack of weighting methods and influence methods, such as AHP and DEMATEL. | |||

Wang and Wang [28] | X | X | Integration with fuzziness. Lack of integration with QFD and DEMATEL to determine the most important design actions. | ||

Lee et al. [29] | X | X | Integration of fuzziness. Good capacity to establishing a relation between design actions and customer satisfaction. QFD step can be improved using DEMATEL and AHP to tune the weights of the model. | ||

Li et al. [30] | X | X | Prediction of customer satisfaction. No relation to product design actions. | ||

Ji et al. [31] | X | X | Not strong to translate linguistic requirements. | ||

Tontini and Tontini [33] | X | X | Not strong to translate linguistic requirements. | ||

Yu and Ko [35] | X | Prioritization of customer requirements. No interaction with design actions. | |||

Oddershede et al. [40] | X | Lack of integration of fuzziness and DEMATEL to interpret customer requirements effectively. | |||

Sabu et al. [41] | X | Determination of factors in the decision process. No integration with DEMATEL to increase the ability of the model to determine the factors in a better way. No integration of fuzziness in the process. | |||

Zaidan et al. [43] | X | Integration with TOPSIS. No design application. | |||

Helingo et al. [44] | X | Method selection. No integration with design actions. | |||

Improta et al. [46] | X | Determination of weights to be used in a systems dynamics model. | |||

Büyüközkan and Çifçi [47] | X | Supplier evaluation. Integration with ANP and TOPSIS. | |||

Tzeng et al. [49] | X | Determination of the effect of e-learning programs. No relation to design actions. | |||

Dey et al. [51] | X | X | No design action analysis. | ||

Cheng et al. [53] | X | Determination of customer requirements. No relationship with improvement actions. | |||

Uygun and Sencer [57] | X | Integration of fuzziness and ANP. Used solely for the evaluation purposes of a healthcare system. | |||

Li et al. [30] | X | X | Determination of priorities of customer requirements. No relation to design actions. | ||

Kang et al. [58] | X | X | X | Integration with fuzzy logic. Determination of customer requirements. Prioritization of design actions. It can be improved with the integration of DEMATEL to consider the influence of the different actions on the others. | |

Chih-Hsuan and Jiun-Nan [59] | X | X | Determination of design actions. It can be improved if integrated with Kano to have a better representation of the impact each action has on customer satisfaction. | ||

Pakizehkar et al. [25] | X | X | X | Must be integrated with DEMATEL and fuzzy logic to understand customer requirements effectively. | |

Wu and Wang [24] | X | Integration with fuzziness. A good prediction of customer satisfaction. | |||

Proposed model | X | X | X | X | Integration of fuzziness. Great ability to determine the satisfaction index associated with each customer requirement. Relationship between design actions and customer requirements. Use of AHP and DEMATEL to better understand customer needs and design actions impact. |

**Table 2.**Kano evaluation table [24].

Functional | Dysfunctional | ||||
---|---|---|---|---|---|

1. Like | 2. Must-be | 3. Neutral | 4. Live With | 5. Dislike | |

1. Like | Q | A | A | A | O |

2. Must-be | R | I | I | I | M |

3. Neutral | R | I | I | I | M |

4.Live with | R | I | I | I | M |

5. Dislike | R | R | R | R | Q |

**Table 3.**Kano influence table [20].

Functional | Dysfunctional | ||||
---|---|---|---|---|---|

1. Enjoy | 2. Expect | 3. Neutral | 4. Live With | 5. Dislike | |

1. Enjoy | 0 | 0.4 | 0.5 | 0.6 | 1 |

2. Expect | -0.2 | 0 | 0.1 | 0.15 | 1.8 |

3. Neutral | -0.25 | -0.05 | 0 | 0.2 | 2 |

4.Live with | -0.3 | -0.075 | -0.1 | 0 | 1.6 |

5. Dislike | -0.5 | -0.9 | -1 | -0.8 | 0 |

${\mathit{\mu}}_{\mathit{i}\mathit{j}}$ | Expressed Kano Scale | I Enjoy It that Way | I Expect It that Way | I Am Neutral | Dislike but Can Live | Dislike and Cannot Accept It | |
---|---|---|---|---|---|---|---|

Expressed Kano Scale | J | 1 | 2 | 3 | 4 | 5 | |

i | $m\left({D}_{j}\right)$$m\left({F}_{i}\right)$ | 0 | 0 | 0 | 0.75 | 0.25 | |

I Enjoy it that Way | 1 | 0 | 0 | 0 | 0 | 0 | 0 |

I Expect it that Way | 2 | 1 | 0 | 0 | 0 | 0.75 | 0.25 |

I Am Neutral | 3 | 0 | 0 | 0 | 0 | 0 | 0 |

Dislike but Can Live | 4 | 0 | 0 | 0 | 0 | ||

Dislike and Cannot Accept it | 5 | 0 | 0 | 0 | 0 | 0 | 0 |

$\mathit{m}\left({\mathit{F}}_{\mathit{i}}\right)$ | $\mathit{m}\left({\mathit{D}}_{\mathit{j}}\right)$ | |
---|---|---|

1 | 0 | 0 |

2 | 1 | 0 |

3 | 0 | 0 |

4 | 0 | 0.75 |

5 | 0 | 0.25 |

Customer Need | Wi (Global Weight) | Global Satisfaction (Si) | Type |
---|---|---|---|

Reliability | 0.112 | 1.8 | Must-be |

User Friendly | 0.042 | 0.63125 | Indifference |

Friendly Interface | 0.049 | 0.375 | Indifference |

Low Invasion | 0.111 | 0.596875 | Indifference |

Quick Installation | 0.068 | 0.5625 | Indifference |

Software Stability | 0.073 | 0.975 | Must-be |

Collateral Issues | 0.137 | 0.975 | Must-be |

Accuracy | 0.133 | 1.6 | Must-be |

Communication | 0.064 | 1.1 | Must-be |

Trustable Data | 0.126 | 1.8 | Must-be |

Software Upgrade | 0.034 | 0.3 | Attractive |

Operational System | 0.034 | 0.3 | Attractive |

Open System | 0.019 | 0 | Indifference |

Customer Need | Global Weight (Wi) |
---|---|

Reliability | 0.112 |

User Friendly | 0.042 |

Friendly Interface | 0.049 |

Low Invasion | 0.111 |

Quick Installation | 0.068 |

Software Stability | 0.073 |

Collateral Issues | 0.137 |

Accuracy | 0.133 |

Communication | 0.064 |

Trustable Data | 0.126 |

Software Upgrade | 0.034 |

Operational System | 0.034 |

Open System | 0.019 |

BT | 3I | C | RSA | SA | DB | BT | SW | CC | IHM | D | R | D-R | D+R | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

BT | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.385 | −0.385 | 0.385 |

3I | 0.002 | 0.005 | 0.088 | 0.000 | 0.079 | 0.034 | 0.068 | 0.009 | 0.006 | 0.000 | 0.291 | 0.252 | 0.039 | 0.543 |

C | 0.017 | 0.001 | 0.031 | 0.005 | 0.083 | 0.236 | 0.009 | 0.094 | 0.069 | 0.000 | 0.545 | 0.845 | −0.299 | 1.390 |

RSA | 0.201 | 0.134 | 0.081 | 0.000 | 0.016 | 0.020 | 0.001 | 0.007 | 0.005 | 0.000 | 0.475 | 0.275 | 0.200 | 0.750 |

SA | 0.012 | 0.000 | 0.206 | 0.001 | 0.017 | 0.181 | 0.002 | 0.028 | 0.014 | 0.000 | 0.460 | 0.513 | −0.053 | 0.973 |

DB | 0.067 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.067 | 0.000 | 0.000 | 0.133 | 0.952 | −0.819 | 1.086 |

BT | 0.007 | 0.067 | 0.088 | 0.000 | 0.079 | 0.097 | 0.005 | 0.013 | 0.006 | 0.000 | 0.362 | 0.233 | 0.129 | 0.595 |

SW | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.008 | −0.008 | 0.008 |

CC | 0.038 | 0.018 | 0.265 | 0.068 | 0.232 | 0.364 | 0.137 | 0.178 | 0.018 | 0.000 | 1.317 | 0.123 | 1.194 | 1.440 |

IHM | 0.041 | 0.027 | 0.085 | 0.200 | 0.009 | 0.020 | 0.003 | 0.008 | 0.006 | 0.000 | 0.398 | 0.000 | 0.398 | 0.398 |

D + R | |||||||||||||

0.3851 | 0.5433 | 1.3897 | 0.7500 | 0.9734 | 1.0857 | 0.5952 | 0.0077 | 1.4395 | 0.3980 | ||||

Design actions | |||||||||||||

Wi | Si | BT | 3I | C | RSA | SA | DB | BT | SW | CC | IHM | ||

Customer needs | R | 0.112 | 1.8 | 5 | 7 | 5 | 3 | 9 | 0 | 7 | 7 | 7 | 0 |

UF | 0.042 | 0.63125 | 0 | 3 | 5 | 0 | 3 | 0 | 3 | 7 | 7 | 3 | |

FI | 0.049 | 0.375 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 7 | 5 | 0 | |

LI | 0.111 | 0.59687 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 7 | |

QI | 0.068 | 0.5625 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | |

SS | 0.073 | 0.975 | 0 | 3 | 0 | 0 | 5 | 0 | 0 | 3 | 5 | 0 | |

CI | 0.137 | 0.975 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 7 | |

A | 0.133 | 1.6 | 0 | 3 | 3 | 5 | 9 | 0 | 5 | 3 | 7 | 3 | |

C | 0.064 | 1.1 | 0 | 5 | 3 | 0 | 5 | 0 | 9 | 0 | 5 | 0 | |

TD | 0.126 | 1.8 | 0 | 5 | 3 | 0 | 7 | 0 | 5 | 5 | 7 | 0 | |

SU | 0.034 | 0.3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 0 | |

OU | 0.034 | 0.3 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 5 | 0 | |

OS | 0.019 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | |

Design actions total influence | 0.3882 | 2.0803 | 3.7113 | 1.9016 | 5.9959 | 0.0332 | 2.5726 | 0.0287 | 8.0851 | 0.95491 | |||

Importance | 8 | 5 | 3 | 6 | 2 | 9 | 4 | 10 | 1 | 7 |

Design Action | RANK |
---|---|

CC-Code change | 1 |

SA-Software Accuracy | 2 |

C-Consistency | 3 |

BT-Bluetooth Technology | 4 |

3I-Triple Integration | 5 |

RSA-Regulation and Standards Accomplishments | 6 |

IHM-Installation Hardware Materials | 7 |

BT-Battery Type | 8 |

DB-Database | 9 |

SW-Software Warnings | 10 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Neira-Rodado, D.; Ortíz-Barrios, M.; De la Hoz-Escorcia, S.; Paggetti, C.; Noffrini, L.; Fratea, N.
Smart Product Design Process through the Implementation of a Fuzzy Kano-AHP-DEMATEL-QFD Approach. *Appl. Sci.* **2020**, *10*, 1792.
https://doi.org/10.3390/app10051792

**AMA Style**

Neira-Rodado D, Ortíz-Barrios M, De la Hoz-Escorcia S, Paggetti C, Noffrini L, Fratea N.
Smart Product Design Process through the Implementation of a Fuzzy Kano-AHP-DEMATEL-QFD Approach. *Applied Sciences*. 2020; 10(5):1792.
https://doi.org/10.3390/app10051792

**Chicago/Turabian Style**

Neira-Rodado, Dionicio, Miguel Ortíz-Barrios, Sandra De la Hoz-Escorcia, Cristiano Paggetti, Laura Noffrini, and Nicola Fratea.
2020. "Smart Product Design Process through the Implementation of a Fuzzy Kano-AHP-DEMATEL-QFD Approach" *Applied Sciences* 10, no. 5: 1792.
https://doi.org/10.3390/app10051792