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Abstract

As an important part of modern furniture, panel furniture already has a large number of consumers and good long-term prospects. Panel furniture is also an important product type in the modern forestry products supply chain. Therefore, scientific qualitative and quantitative evaluation on the design of panel furniture is of great significance. This article focuses on the research of the establishment of an evaluation system at the design stage of panel furniture, according to the concept and feature requirements of panel furniture. The article also establishes a design evaluation system combining Kansei engineering and analytic hierarchy process methods. Additionally, this article will discuss in detail the authors’ methodological approach on the procedures used, statistical methods, and evaluation model of panel furniture design.

Keywords

Panel furniture design evaluation system Kansei engineering analytic hierarchy process

Introduction

Panel furniture is a type of combined furniture made of various wood-based panels connected by hardware. It has become an important part of modern furniture with the developments and direction of the furniture industry nowadays, adjusting to the growing demand and supply of wood materials.^1,2 Key features of panel furniture include high resource utilization ratio, high automation, ease of assembly, and being dismantled, as well as high structural performance.³ These features are seeking panel furniture becoming more popular among consumers. Design evaluation refers to the comparison and assessment on the design problem-solving schemes during the product design.⁴ As design thinking modes gradually mature, design evaluation is attracting more and more attention.⁵ Currently, there is little research being conducted on the design evaluation of panel furniture. However, intense market competition and changeable consumer demands,⁶ introducing a scientific design evaluation system and alternative methods at the furniture design stage, can better direct the design and production of panel furniture.

Aim and objectives

From the perspective of the furniture life cycle, a complete furniture design evaluation includes design stage evaluation, production stage evaluation, usage stage evaluation, sale stage evaluation, and recycle stage evaluation.⁴ In this article, a furniture evaluation system and methods at the design stage are studied, according to the characteristic requirement of panel furniture with a prospective and instructive significance:

During the design stage of panel furniture, the establishment of reasonable design evaluation system, adoption of scientific evaluation method, orderly procedure, rigorous statistic, and a combination of qualitative and quantitative design evaluation will help to understand the needs of customers and thus avoids low-grade designs and improves design efficiency.

An optimal scheme would avoid wood material waste and reduce design cost⁷ and thus better direct the production and sales of panel furniture.

The study results will enrich the theoretical research on furniture design evaluation and promote the benefits of upgrading the furniture industry.

System construction

The design stage of panel furniture can be further divided into pre-design preparation stage and scheme design stage. Therefore, the design evaluation can also be divided into pre-design evaluation and design scheme evaluation, each of which covers different evaluation contents and objectives. As shown in Figure 1, the evaluation contents at the pre-design preparation stage of panel furniture mainly include the evaluation of attitude indexes of different customers on the panel furniture attributes, panel furniture market research, environmental research, and the operation and manufacturing conditions of panel furniture makers. The pre-stage design evaluation is carried out to get to know the specific conditions and correlations among customers and consumers, markets and enterprises, and provide an evaluation basis for a design scheme. The main contents of a design scheme evaluations system include the determination of indicators, mathematical statistical analysis of indicators, and scheme optimization and decision, which could provide a basis for scheme design and data for scheme decision. In addition, it enables evaluations for panel furniture design to be well prepared with qualitative and quantitative methods and conforms to the objectives of furniture design evaluations.

Figure 1.

Construction of panel furniture design evaluation system at the design stage.

Evaluation method application

Related work

Recent research in design evaluation by various researchers has seen the numbers of evaluation methods increasing and becoming more comprehensive, from their original subjective and linguistic judge modes to quantitative mathematic statistical model and even computation-based intelligent system.^8,9 Currently, the relatively widely used evaluation research methods are the fuzzy evaluation method, the specialist evaluation method, the grey system method, artificial neural networks, Kansei engineering, and analytic hierarchy process (AHP), among others as shown in Table 1.

Table 1.

Comparison on different evaluation methods.

Evaluation methods	Advantages	Disadvantages
Fuzzy evaluation method	1. Fuzzy evaluation can be used to deal with the fuzzy evaluation object by means of accurate numerical methods, and it can be used to make a scientific, reasonable, and practical quantitative evaluation on information which contains fuzzy parts.2. The evaluation result is a vector that contains rich information, which can be described accurately by the evaluation object, and can be further processed to obtain the reference information.	1. The calculation is complex, and the determination of the weight vector has strong subjectivity.2. When the index set U is large, under the constraint of the weight vector sum equals 1 condition, relative coefficient of membership degree is usually small, the weight vector does not match with the fuzzy matrix R, the result will be ultra fuzzy phenomenon, resolution is very poor.
Specialist evaluation method	1. Simple procedures and calculations. Set up evaluation levels and standards for appropriate evaluation projects.2. Intuitive. Each level is achieved by scoring.3. Evaluation items that can be calculated and cannot be calculated can been taken into consideration.	1. The rationality and authority of expert selection.2. Avoid the subjective tendency of experts.
Grey system method	1. Simple calculations, the data need not be normalized.2. No need for a large number of samples, nor the classical distribution law, as long as small number of representative samples.	1. The absolute correlation degree is treated by the initial point zero method. When the difference of the analysis factors is large, it is difficult to get the reasonable result because of the inconsistent dimension between the variables.2. The absolute value correlation degree is affected by the maximum and minimum values in the data. Once there are some extreme values in the data sequence, the correlation degree will change.
Artificial neural networks	1. Fast calculation speed and high efficiency of problem solving;2. Self-learning ability;3. Strong fault tolerances.	1. The lack of ability to explain their reasoning processes and basis.2. Cannot ask the user for necessary inquiries, and when the data are not enough, the neural network cannot work.3. Turning all the characteristics of a problem into numbers, and all the reasoning into numerical calculations, which could cause the loss of information.4. Theory and learning algorithms need to be further improved.
Kansei engineering	1. Explore the feelings and needs of customers from the perspective of human factors and psychology.2. The design characteristics are identified from the consumer’s perceptual image at the qualitative and quantitative level.	1. The matching problem between Kansei adjectives and design elements.2. The use of Kansei adjectives scale is discrete and linear; perceptual knowledge modeling in this way exits significant imprecision and uncertainty.
Analytic hierarchy process method	1. The advantages of both qualitative and quantitative analysis.2. It includes not only the subjective logic judgment and analysis but also the accurate calculation and deduction, so that the decision-making process is highly organized and scientific.3. Solve the problem in a systematic way.	1. Cannot provide new solutions for decision making.2. The qualitative component is more than the quantitative component, not convincing.3. Data statistics and the weight are difficult to determine when there are too many indicators.4. Accurate calculation of eigenvectors and eigenvalues is more complex.

The fuzzy evaluation method is a popular design scheme evaluation method. Vanegas and Labib¹⁰ studied engineering evaluation specifically and gave a summary to the fuzzy evaluation method. However, the subjective influence from the decision maker cannot be avoided during the quantization of evaluation indicators, which could weaken the objectivity of the evaluation result.

As for the specialist evaluation method, Li Zhiheng et al. studied the application process of the specialist evaluation method in the initiation of scientific and technology projects. However, the evaluators are only the scholars in relevant fields who are familiar with the indicators of relevant issues. Therefore, the evaluation result may be one-sided¹¹ and conflict with the customer-oriented furniture design principles.

In the grey system method, the grey clustering method is adopted to cluster the evaluation indicators and then carry out an evaluation through the calculation of the clustered indicators. For example, Q Li et al.¹² have evaluated the health condition of the marine ecosystem using this method and pointed out that due to the complexity of the evaluation object, a single evaluation method is not comprehensive enough during the actual application.

Artificial neural networks are an evaluation method simulating a biological intelligence system. J Zhi et al.¹³ studied the evaluation of green buildings using this method, and Sun Qiang used this method in the evaluation of the quality of car sounds.¹⁴ However, professional software programming is required for the artificial neural network method; therefore, there may be some difficulties in the practical application of this method.

Kansei engineering was established in 1986. It can reflect people’s emotional change toward and perception of things and enable a quantitative analysis of the popularity and demand of different design elements among customers. This helps the design of expected and satisfactory products¹⁵ and fully embodies the customer-oriented design philosophy. Examples are as follows: J Wang and Z Wu¹⁶ evaluated 32 chairs with high artistic quality using the Kansei engineering method and gave instructions to practical design through a theoretical analysis; M Zhou and Xiong¹⁷ used this method in the evaluation of the design effect of product material and gave advice to designers in product design; Q Zhang¹⁸ evaluated the sport utility vehicle (SUV) body shape using this method and was subsequently able to express customer demands precisely and turn them into design elements; and H Guan and Z Chen¹⁹ evaluated the furniture design scheme with the application of Kansei engineering and achieved the goal of optimizing a furniture design scheme. Therefore, it is apparent that this method is widely used in the evaluation of product design with high operability.

The AHP method was established in the 1970s. It is a design evaluation method that combines qualitative and quantitative evaluations and it features strict logic.^20,21 Using this method, a person’s subjective consciousness is divided into many layers, each of which will be subjected to a weight calculation. This method is widely used in the evaluation of green products.^22,23 Examples include the following: M Chen and Z Wu²⁴ studied the conceptual design evaluation system using the AHP and introduced the evaluation principle and operation procedures in detail, X Deng et al.²⁵ summarized the weight calculation method in the AHP and analyzed the application, and H Du²⁶ introduced fuzzy AHP that combines the fuzzy analytical method and AHP, thus introducing the concept of combining these two methods.

Evaluation method

Many uncertain factors may affect the design during the design process. Scientific and effective evaluation methods are required to analyze, process, and integrate various complex factors and transfer the fuzzy emotion and demand preference into digital language for quantization and analysis.

The use of the AHP and the determined furniture design evaluation lack scientific data support, which can cause subjectivity and irrationality. Kansei engineering has high reliability thanks to the reasonable quantification of the selected evaluation indicators, but there are still some shortcomings. The weight analysis and comparison of indicators and the parallel correlations are among key indicators that increase the difficulty in the determination of index weight to a certain extent.²⁶ In this article, a design evaluation model combining the AHP and Kansei engineering is introduced for the design evaluation of panel furniture at the design stage. The evaluation method and steps of this design evaluation system are shown in Figure 2.

Figure 2.

Evaluation method of combination of Kansei engineering and AHP.

Procedure and statistics

Figure 3 shows the basic operation procedure of panel furniture design evaluation at the design stage and the scientific method for each step.

Figure 3.

Basic procedure and statistics method of panel furniture design evaluation.

Likert scale

A questionnaire survey was carried out among consumers and experts in panel furniture design, with the answers analyzed using a Likert scale.²⁷ In this questionnaire, 12 questions were designed in consumer survey, 300 questionnaires were issued by researchers in person from June to August 2016 and a total of 289 valid questionnaires were collected. Each of the 12 questions involves an aspect on panel furniture. From these questions, people’s attitudes and needs toward panel furniture can be studied. All individuals participate from different social groups in Chengdu, Chongqing, and Guiyang of China. The relative degree is classified into five grades from 1 to 5 (5 stands for “very much like or agree,” 4 stands for “like or agree,” 3 stands for “slightly like or agree,” 2 stands for “dislike or don’t agree,” and 1 stands for “very dislike and don’t agree”) to elevate the attitude index of customers toward the attributes and features of panel furniture and to help to choose an effective evaluation indicator. Through this quantitative statistic approach, the correlation between panel furniture and the age, gender, and occupation of consumers can be directly reflected. This will help enterprises to properly conduct product positioning and develop an effective marketing strategy.

Calculating a scale and weight based on AHP

A design evaluation index system is established for panel furniture design evaluation using AHP. As shown in Figure 3, four levels are set in this system, that is, level A, level B, level C, and level D, with the horizontal values being the level indicators. The steps and methods of weight calculation at each level based on AHP are introduced hereunder:

Construct a judgment matrix. The judgment matrix shows the comparison relation between two elements at each level in the evaluation index system. The A-B judgment matrix shown as follows indicates the comparison of two elements in Level B:

A	B₁B₂	B … B_n
B₁	B₁₁	B₂₁	B₃₁	…	B_n1
B₂	B₁₂	B₂₂	B₃₃	…	B_n3
B₃	B₁₃	B₂₃	B₃₃	…	B_n3
⋮	⋮	⋮	⋮	⋮
B_n	B_1n	B_2n	B_3n	…	B_nn

Choose proper scale. Large number of psychological experiments and society surveys show that 1–9 scale can reflect the differences between the feelings of different people on different attributes; therefore, it is adopted to evaluate the two elements of each level in the survey of customers and furniture design experts (Table 2).

Calculate the product of elements at each level in the judgment matrix M_i

M_{i} = Π_{j = 1}^{n} a_{ij} (i, j = 1, 2, \dots, n)

(1)

Calculate the eigenvector of judgment matrix w_i

w_{i} = \sqrt[n]{M_{i}}

(2)

Normalize w_i and calculate the relative weight W_i of an indicator in this level

W_{i} = \frac{w_{i}}{\sum_{i = 1}^{n} w_{i}}

(3)

Calculate the synthetic weight $U_{i}$ of the indicator in this level

U_{i} = \sum_{h = 1}^{m} U_{h} W_{hi}

(4)

where n and m represents dimension of matrix in this level and the higher level, respectively, U_h is the synthetic weight of element h of higher level, W_hi is the relative weight of the element h of higher level relative to element i of this level, and the weight of level A is 1.

A consistency check shall be carried out randomly on the judgment matrix to guarantee the consistency of the matrix.

Table 2.

Linguistic definition for importance ratios of two selected items.

a_ij	Linguistic definition for comparison of the ith and the jth items
1	The ith item is equally important as the jth item
3	The ith item is slightly more important than the jth item
5	The ith item is more important than the jth item
7	The ith item is strongly more important than the jth item
9	The ith item is extremely more important than the jth item
2, 4, 6, 8	The intermediate values between two adjacent judgments
1/a_ij = a_ji	The inverse comparison between the ith and the jth items

A questionnaire was created to obtain users’ perceptual evaluation of panel furniture using paired Kansei adjectives (PKA) for indexes. A total of 21 PKA were selected by 20 experts with affinity diagram method (or KJ method, after its author, Kawakita Jiro). KJ method is usually used in design improvement. Using this method, a person’s verbal description of complex matters and various problems can be concluded and classified, and the inherent meanings and correlations among different verbal expressions can be analyzed and finally summarized in a combined graph.²⁸Figure 4 shows the operation procedure of the KJ method. Based on the results of expert interviews and customer surveys, Kansei adjectives describing panel furniture on the Internet, books and magazines, and product brochures were collected and summarized. In addition, an affinity diagram was used to screen, collect, and summarize the large number of words (which are unorganized with repetitions) to form Kansei adjectives.

Figure 4.

Emotional vocabulary selection based on KJ method.

A 7-point scale using the semantic differential method²⁹ is adopted for score judgment of the Kansei vocabulary, that is, all the adjectives describing the furniture design scheme are classified into seven different sensory images as shown in Table 3. Specifically, respondents were asked to use a 7-point semantic differential scale to rate their self-selected brand from 1 (unimportant to respondent) to 7 (important to respondent). Each group of Kansei words is scored by evaluators and then an average value Z_n is obtained.

Table 3.

A 7-point scale in semantic differential method.

Bad						Good
Extremely	Very	Quite	Neither/nor	Quite	Very	Extremely
−3	−2	−1	0	1	2	3

Finally, a design evaluation model is built: $A = \sum U_{n} Z_{n}$ , where A is final evaluation score, U_n is the comprehensive weight of element in level D, and Z_n is the average of perceptual adjective evaluation.

Conclusion and future work

In this article, panel furniture is the research subject. This furniture has become the furniture of choice and is very popular among customers and consumers. Based on the attributes and characteristics of panel furniture and the relevant research results of furniture product design and evaluation, the evaluation system of panel furniture at the design stage was established. From various common design evaluation methods, the design evaluation method combining the Kansei engineering and the AHP is adopted. The basic process of the evaluation of the panel furniture design and statistical methods are introduced, and finally a design evaluation model for panel furniture at the design stage is established. The system and the application of the methods and model will enrich the theoretical achievements in furniture design evaluation and have practical significance for the design and evaluation of panel furniture. However, the evaluation of panel furniture design shall be carried out throughout the furniture life cycle as a comprehensive design evaluation method. This article only focused on the evaluation at the design stage of panel furniture without considering the production, sale, and recycling of panel furniture. Therefore, a complete evaluation model has not been established for the design of panel furniture. Although the Likert scale and the semantic difference method are adopted to quantify the human emotional factors, the design process may still be affected by subjective factors. Unfortunately, for the time being, the ambiguity and uncertainty in design still cannot be avoided. Therefore, more in-depth empirical studies using scientific means and methods into panel furniture product design and evaluation are needed.

Footnotes

Academic Editor: Teen-Hang Meen

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the Open Research Subject of Key Laboratory (Research Base) of Industrial Design Industry Research Center (Grant No. GY-15ZD-01) and Modern Design and Culture Research Center of Sichuan Provincial Key Research Base of Philosophy and Social Sciences (Grant No. MD15E017). This research was carried out at the Sichuan Provincial Colleges and Universities Wood Industry and Furniture Engineering Key Laboratory, Sichuan Agricultural University.

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Construction and implementation of a panel furniture design evaluation system at the design stage

Abstract

Keywords

Introduction

Aim and objectives

System construction

Evaluation method application

Related work

Evaluation method

Procedure and statistics

Likert scale

Calculating a scale and weight based on AHP

Conclusion and future work

Footnotes

Declaration of conflicting interests

Funding

References