To demonstrate the application of the proposed approach, a case study associated with the design of a smartphone PSS is introduced in this section.
4.1. Preliminary Identification of Customer Requirements
We retained two categories of the Kano model requirements: category O (e.g., camera and internal memory) and category A (e.g., beautiful appearance). The collection and analysis of the customer online comment data required the assistance of data mining technology. In this part, we used the user comment block of Tmall (
https://www.tmall.com) for demonstration because this block displays real online user reviews supported by data mining technology. The content of each product comment was analyzed and filtered to generate different evaluation tags.
Figure 1 shows a comment tag for the Huawei P30 Pro.
As shown in the figure, seven tags are screened out from the evaluation of 16,891 items. The number in brackets at the end of each tag indicates the number of comments related to the tag. The customer requirements represented by red labels belong to categories O and A in the Kano model. The green label reflects the customer complaints and dissatisfaction, which can be regarded as reverse demand and are not included in the statistical scope of this study. By collecting and analyzing the comment tags of the top 20 smartphone models being sold, we classified these tags semantically and obtained the top 12 customer demand classification tags as the initial customer requirements. These original labels have the characteristics of being colloquial, incomplete, and ambiguous, and they cannot be directly used to define customer requirements. The next step is to collect more accurate customer requirements and further categorize them using a traditional questionnaire investigation. The importance of this part is to delimit a relatively vague scope for further clarification and refinement of customer requirements. It is equivalent to a preliminary and efficient market survey, which can help us better define the framework of customer requirement identification and improve the subsequent questionnaire investigation.
4.3. Weight Assessment of Customer Requirements
Based on the identification of the customer requirements in the previous section, a hierarchical structure model of customer requirements was established. The first layer is the target layer, that is, the customer perceived requirements. The second level defines the four types of requirements in chronological order and is represented by the character F. The third level is the index level, which is deepened and expanded on the basis of the second level, and is specific to 20 specific indicators. The hierarchical structure is shown in
Figure 3. We used the FAHP method to improve the demand hierarchy of the traditional Kano model and combine the opinions of the industry experts with the subjective judgments of customers to obtain the more accurate weights of the customer requirements. The importance of each index was compared by the experts. The evaluation results are expressed by nine scales (0.1–0.9). Among them, 0.5 indicates that two indexes are equally important, 0.9 indicates that one index is extremely important compared with another one, and 0.1–0.4 indicates converse comparison. If index
is compared with index
, and the judgment result
is obtained, then the result of
compared with
is
. The judgment matrix obtained by pairwise comparison is as follows [
86]:
We interviewed experienced practitioners from the mobile phone industry. The interviewees, who comprised 15 experts in the field, included salesmen from smartphone dealers and marketing staff from the mobile phone manufacturers. The forms of interviews included face-to-face conversations, WeChat (WeChat data report 2018: WeChat is the most widely used instant messaging application in China; as of September 2018, WeChat has covered more than 96% of smartphones in China, and the total number of active accounts has reached 1.082 billion (
https://support.weixin.qq.com/cgi-bin/mmsupport-bin/getopendays)) communication, and e-mail consultation. The judgment matrix corresponding to each expert was obtained. In this study, 15 expert judgment matrices were processed using the arithmetic average, and a decimal number was reserved to obtain the preliminary fuzzy judgment matrix.
The consistency of the fuzzy judgment matrix reflects the consistency of the people’s thinking and judgment. However, in practical decision analysis, the constructed fuzzy judgment matrices are usually inconsistent because of the complexity of the problem and the prejudices people may have. Therefore, transforming the obtained inconsistent matrices into consistent matrices is necessary. This study referred to Reference [
87] for the consistency judgment of matrices and for constructing the fuzzy consistent judgment matrices. The judgment matrix of the first level is F, and the judgment matrices of the second level are
, respectively. Among the five matrices we obtained, all were inconsistent matrices except for matrix
. Thus, these fuzzy inconsistent matrices needed to be adjusted to become fuzzy consistent matrices. The adjusted fuzzy consistent judgment matrices are as follows:
Assuming that there are n primary requirements, the weight ranking vector is
where the value of
is
where
is an index factor indicating the difference in importance degree, and its value range satisfies
. The greater the value is, the less attention is paid to the difference in the importance degree between the indicators. To provide a better reference for designers, this study defines
. Please refer to Reference [
88] for a more detailed introduction to the weight calculation method.
After obtaining the fuzzy judgment matrix, the weight ranking vector was calculated using Equation (2).
was obtained by calculating the matrix
F.
,
,
, and
were calculated in turn:
The weights of the secondary requirements needed to be adjusted according to the weights of the primary requirements. The calculation formula is as follows:
All the weights of the requirements were obtained based on expert scoring. Combining the data collected by the questionnaire research and the results of the experts’ scoring, the requirement weights were processed through a fuzzy comprehensive evaluation.
For the secondary requirements, the final weight ranking was obtained using Equation (3):
By multiplying the weight of each evaluation index by the relative membership value of the corresponding evaluation index of each scheme, the comprehensive index value (
) of the fuzzy evaluation was obtained. Then,
was standardized so that
Then, the final weight vector of each requirement was obtained:
The questionnaire on the customer requirements was normalized. The results of the membership degree are as follows (
Table 1). C represents the customer requirements.
According to , the final weight ranking vector was calculated as follows: W = (0.0173, 0.0375, 0.0314, 0.0171, 0.0242, 0.0526, 0.0826, 0.0543, 0.0797, 0.0850, 0.0570, 0.0435, 0.0249, 0.0221, 0.0540, 0.0270, 0.0668, 0.0846, 0.1001, 0.0383).
4.4. Output of the Engineering Characteristics
In the process of transforming customer requirements into engineering characteristics, a hierarchical structure was established. Each layer was connected by the input and output of the HoQ model to obtain a hierarchical decomposition structure. Based on the results of the identification of the customer requirements through the improved Kano model in the previous section combined with the literature review, annual reports of enterprises, and interviews with industry practitioners, we established the following corresponding relationships between the customer requirements and the engineering characteristics (
Table 2).
In this study, the relationship matrix was determined by the experts’ scores. The experts estimated the correlation between the customer requirements and the engineering characteristics based on experience. The experts’ semantic representation was classified into four levels: strong correlation, general correlation, weak correlation, and irrelevance. We invited 12 practitioners with professional knowledge who have been in the mobile phone industry for a long time to interview them and record the information we needed. According to the actual situation of expert scoring, we used the mode to obtain the final judgment matrix and used triangular fuzzy numbers to deal with the inaccurate semantic expression of the experts. We set the score of the semantic fuzzy set to U = {SP, P, WP, M}. SP denotes a strong correlation, and its triangular fuzzy numbers are (0.6, 0.8, 1); P denotes a general correlation, and its triangular fuzzy numbers are (0.4, 0.6, 0.8); WP denotes a weak correlation, and its triangular fuzzy numbers are (0.2, 0.4, 0.6); M denotes irrelevance, and its triangular fuzzy numbers are (0, 0, 0). The correlation matrix of the customer requirements and product specifications is presented in
Table 3.
The weights of the customer requirements were confirmed by the Kano model. According to QFD theory, the weight of the engineering characteristic WT is calculated by the customer requirement vector and the correlation matrix of the customer requirements and engineering characteristics. The calculation formula is as follows:
The correlation matrix is given in its abbreviated form. The improved weights of the product-related engineering characteristics are as follows:
=[(0.0733,0.1292,0.1852),(0.1097,0.1510,0.1923),(0.1455,0.2108,0.2760),(0.1793,0.2495,0.3198),(0.1720,0.2693,0.3666),(0.1643,0.2437,0.3232),(0.1238,0.1782,0.2326),(0.1527,0.2241,0.2955),(0.1641,0.2326,0.3171),(0.0916,0.1448,0.1980),(0.0531,0.0889,0.1246),(0.0645,0.1117,0.1588),(0.2100,0.3378,0.4656),(0.1420,0.2367,0.3314)]
Through the above process, the corresponding weights of the product-related engineering characteristics were obtained (
Table 4). The results can be used as a reference for better consideration of customer requirements in product design.
According to the weight of the customer requirements confirmed by the Kano model and the correlation matrix of the customer requirements and product specifications given by the experts, the HoQ model of the product-related engineering characteristics is illustrated in
Figure 3.
Similarly, this study established the corresponding relationship between the customer requirements and the service-related engineering characteristics. According to the expert scores and triangular fuzzy numbers, we obtained the correlation matrix of the customer requirements and service-related engineering characteristics. The HoQ model of the service process was established. See
Figure 4 for details.
According to QFD theory, the weight of the service-related engineering characteristics was calculated by the following formula: .
The weights of the service-related engineering characteristics are as follows:
=[(0.4435,0.6218,0.8000),(0.2828,0.4302,0.5777),(0.1126,0.1574,0.2022),(0.2188,0.3216,0.4244),(0.0851,0.1341,0.1832),(0.1208,0.1862,0.2516)]
The corresponding weights of the service-related engineering characteristics were obtained, as shown in
Table 5. S represents the service-related engineering characteristics.
4.5. Importance Ranking of the Engineering Characteristics
We used the concept of possibility degree of the triangular fuzzy numbers to optimize the fuzzy output of the QFD model. Specifically, the ranking vectors of the triangular fuzzy number vectors of the product- and service-related engineering characteristics were calculated, respectively, to obtain the rankings of all indicators.
Assume the triangular fuzzy number
, and thus
is called the possibility degree of
.
The value of depends on the decision-maker’s risk attitude. When > 0.5, the decision-maker has a high-risk preference; when = 0.5, the decision-maker is risk-neutral; and when < 0.5, the decision-maker has a low-risk preference. In this study, we took = 0.5. By calculating the possibility degrees of the triangular fuzzy numbers, the possibility degree matrix of the triangular fuzzy numbers can be established. The possibility degree matrix P is a complementary matrix, and element Pij denotes the possibility degree of index i relative to index j.
The calculation formula of the ranking vector
based on the fuzzy complementary judgment matrix is as follows [
89]:
where
.
The weights of the product-related engineering characteristics obtained in the previous section were substituted into Equation (5) to calculate the possibility degree matrix of all indicators. For layout reasons, only the abbreviated form of the matrix is given here.
The ranking vector of the product-related engineering characteristics is as follows: (0.0712,0.1102,0.1488,0.1751,0.1701,0.1623,0.1211,0.1533,0.1627,0.0897,0.0521,0.0638,0.218,0.1405)T.
Similarly, the ranking vectors of the service-related engineering characteristics can be calculated, and the result is as follows:
From the ranking vector of the product-related engineering characteristics, the order of importance of the technical indicators for smartphone products from high to low is as follows based on the perspective of customer requirements: software development, hardware quality, related product development, aesthetic design, screen material and design, CPU processor, power consumption level, size and material, battery capacity, operating system, packaging and appearance, capture function, camera pixels, and product packaging. In the design of the service process, the order of importance of the engineering characteristics according to the customers is as follows: technological service, information service, consulting and business service, human resource service, logistical services, and financial insurance services. The specific rankings and weights are presented in
Table 6 and
Table 7.
Since the life cycle of smartphones is becoming shorter, users are demanding new and improved smartphone features for updated smartphones, but it is not easy to know which features are the most important to users. By quantifying the indexes indicated by customers, it is possible to smell something different or find some new discoveries about what really matters to customers. For example, people attach great importance to the software and hardware quality of smartphone products, while the product packaging is actually the least valued by customers. This is consistent with our previous knowledge. However, when it comes to services, some characteristics (logistical service and financial and insurance service) may be not as important as product managers and system designers thought. The importance weights and rankings of customer requirements are evaluated so they can be selected by designers. Since the importance weights of engineering characteristics are also provided, this helps guide designers and product managers to allocate resources reasonably and develop their products and services for maximum customer satisfaction.
Moreover, the shift from traditional product design to PSS design can be difficult for inexperienced managers and practitioners, leading to the risk that they may unintentionally opt for solutions that might reduce customers’ satisfaction. In other words, the methodology can be utilized in the development and design of PSSs. This facilitates continuous feedback, which can enable engineers to better manage the PSS development activities by verifying the inputs and outputs of each step. For enterprise, it can reduce the effort used for unimportant engineering characteristics, reduce mistakes and neglect in decision-making, mitigate the need for additional resources at later stages of the PSS development process (redesigning or reassessing the solution’s characteristics), and save the costs in product and service development. In addition, the developed product can help to achieve the desired quality and better meet customer requirements.