Abstract
Although the market share of domestic children’s furniture is increasing annually, some potential problems limit its long-term and stable development, and there is still a gap in China compared with foreign countries. This study focused on the demand preferences for growable children’s beds and examined the design features that influence these preferences. This study introduces a combination of Hierarchical Analyses (AHP), Quality Function Development (QFD), and the Platts Conceptual Decision Matrix (PUGH) into the innovative design of a research model for children’s furniture (AHP-QFD-PUGH). This study screened and classified the decision-making indicators obtained from the research, ranked their importance by quantitative calculation, and finally proposed an optimal design solution. Additionally, to further study the structural characteristics, the function-behavior-structure (FBS) model served as a supplementary analysis tool to effectively circumvent subjective factors in product design. This integrated model accurately explored user needs and product characteristics, providing substantial guidance and new ideas for optimizing the design of growable children’s beds and enhancing growth of the children’s furniture industry.
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Research on the Design of Growable Children’s Beds Based on Combined Hierarchical Analyses
Nan Wang and Yin Zhao *
Although the market share of domestic children’s furniture is increasing annually, some potential problems limit its long-term and stable development, and there is still a gap in China compared with foreign countries. This study focused on the demand preferences for growable children’s beds and examined the design features that influence these preferences. This study introduces a combination of Hierarchical Analyses (AHP), Quality Function Development (QFD), and the Platts Conceptual Decision Matrix (PUGH) into the innovative design of a research model for children’s furniture (AHP-QFD-PUGH). This study screened and classified the decision-making indicators obtained from the research, ranked their importance by quantitative calculation, and finally proposed an optimal design solution. Additionally, to further study the structural characteristics, the function-behavior-structure (FBS) model served as a supplementary analysis tool to effectively circumvent subjective factors in product design. This integrated model accurately explored user needs and product characteristics, providing substantial guidance and new ideas for optimizing the design of growable children’s beds and enhancing growth of the children’s furniture industry.
DOI: 10.15376/biores.19.4.8084-8102
Keywords: Growable children’s beds; AHP; QFD; PUGH decision matrix; FBS model; Product characteristics
Contact information: College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing, Jiangsu Province, 210037, China; *Corresponding author: zhaoyin@njfu.edu.cn
INTRODUCTION
With economic development and changes in family structure, the market share of children’s products continues to rise, and the children’s furniture market faces new opportunities. According to 2021 data, the number of children under 16 years old in China exceeded 300 million (Han et al. 2021). The retail sales of children’s furniture are approximately 100 billion yuan. Approximately 40% of Chinese children have their own room and furniture (Luo et al. 2023), which shows that children’s furniture demand is experiencing explosive growth. In addition to several common children’s beds on the market, including cribs, cradle beds, single beds, bunk beds, and functional beds (Zhang and Xu 2020), children’s beds, as an important part of children’s furniture, are being constantly subdivided according to the needs of scenarios (Xia 2022), and demands for designs have also begun to diversify. Research shows that some foreign children’s furniture has attracted much attention and is rapidly developing in the direction of environmental protection and safety, differentiation, multifunctionality, and creativity. For example, IKEA considers children’s ergonomics, styling, and color schemes and introduces a variety of multipurpose reusable household products for children (Ye et al. 2021b). The size of the bedroom furniture market for children (Fan and Zhao 2011), which is now becoming the most promising market in the U.S. furniture industry, is still growing. The traditional Italian crib can be combined and expanded into other furniture for children. Constrained by costs and investments, China’s furniture industry struggles with weak original design and innovation (Li and Yao 2021), leading to single-use products, homogeneity, and a shortage of prominent independent design brands (Jiang and Chen 2023). Research has shown that children have a strong desire to explore and are rich in creativity. They enjoy interactive activities and like to explore the world around them actively. Meanwhile, they gradually have a wide range of interests and possess their own favorite objects and toys. They can arrange their own small space and have a certain degree of independence (Sun et al. 2024). However, many domestic children’s furniture enterprises currently lack a deep understanding of the market; most children’s beds are only ‘miniaturized’ adult furniture (Liu and Zhu 2023), emphasizing bright colors and cartoon patterns (Wang 2022; Ren and Zhu 2024) over structure and safety, and neglecting sustainable use, entertainment, and educational qualities (Yu 2010). In addition, children’s furniture on sale in China generally has high prices and a short service life, resulting in many consumers preferring to place their children in adult beds, which is not only harmful to the development of children’s bone structure but also leads to potentially fatal dangers (Nakamura et al. 1999). Moreover, although the design of domestic children’s furniture is increasingly focused on eco-friendly materials (Wan et al. 2015), and even most consumers are willing to pay a price premium (Wan et al. 2018), the enforcement of environmental standards remains insufficient (Wei and Madina 2022). Domestic children’s beds still have much room for development in terms of functionality, sustainability, safety, and fun (Mai et al. 2022). Many scholars have also proposed their own design suggestions. For example, a scholar designed a multifunctional modular children’s bed through a comparative analysis of the characteristics of children’s behavior and psychology at various stages, based on the principles of children’s furniture design and the concept of growth. The study provided a new type of design idea better adapted to the growth of Chinese children (Huang and Wang 2020). With the help of the vision in product (VIP) design principle and analogical analysis method, future intelligent children’s beds should have the interaction characteristics of comfort, exploration, and empathy with users (Tong 2023). The problems of non-standardized shape parameters and imperfect functional design of children’s beds for two-child families were analyzed, and the KANO model and TRIZ theory were integrated into designing a multifunctional children’s bunk bed (Zhu 2022). Although there is some scholarly research on children’s beds, the research remains in a single theoretical category and lacks the application of comprehensive methods. The summary of the design indices of children’s beds is also imperfect, not in-depth and has some limitations.
The Analytic Hierarchy Process (AHP) is a qualitative‒quantitative demand-weighting research method used to help decision-makers make systematic analyses and judgments in complex decision-making environments (Huang et al. 2022). The method stratifies the decision factors for qualitative and quantitative analysis and ranks the results to improve decision-making efficiency and accuracy (Han et al. 2023). Quality Function Deployment (QFD) is a model that transforms user needs into product technical characteristics by establishing the House of Quality (HOQ) (Tian et al. 2024), aiming to transform customer needs into product or service design requirements so that the product or service can meet customer expectations (Xiong et al. 2023).
The Platts Conceptual Decision Matrix (PUGH decision matrix) is a quantitative decision analysis tool proposed by the British management scientist Stuart Pugh. It is used to qualitatively rank candidate solutions by comparing their scores with those of reference solutions; the tool is suitable for the efficient and systematic evaluation of designs at the evaluation decision stage (Li et al. 2021). The advantages of the comprehensive theoretical model include that it can be more objective in obtaining reliable experimental results. Using the AHP method for quantitative research and for quantitatively evaluating the priority of user requirements at the same time helps overcome the subjectivity of the affinity diagram (also known as the KJ method), thus improving the accuracy of user requirements research (Neira-Rodado et al. 2020; Wei et al. 2023). QFD can link user requirements with product functional characteristics and technical features to improve product and service quality at the product and service manufacturing phase (Ginting et al. 2020), which can compensate for the inability of AHP to directly translate user requirements into product functions (Li et al. 2023). The use of PUGH for solution evaluation decision-making helps rationalize and logically make decisions by validating the product’s suitability through the ranking of design metric scores. Thus, in recent years, AHP and QFD have been widely applied in the field of furniture design (Jiao and Zuo 2021; Xu and Xia 2023; Wu et al. 2023) and they can help effectively capture and analyze the suggestions of customers and experts and improve the design and comprehensive planning of furniture. For example, AHP is used in the evaluation and design of dining chairs, which can shorten the time and cost of the design and greatly improve the quality of the chair as well as customer satisfaction (Liu et al. 2023). The AHP method has been used to conduct in-depth quantitative research on the design preferences of children’s furniture from the characteristic elements of children’s lockers (Zhao and Xu 2023; Zhang and Xu 2023), children’s study tables (Miao et al. 2024), and other children’s furniture (Miao et al. 2023). In addition, the QFD is utilized to determine the design preferences of users under the open office mode, providing new ideas for the design of office wooden tables (Lyu et al. 2022). Recently there have been many studies on the combination of AHP, QFD (Kwong and Bai 2002; Wu and Zhang 2022; Li et al. 2023), and PUGH applied to the field of product design, forming a complete product development process, and jointly improving the reliability and scientific nature of the design process. The three integrated models are mainly used in intelligent products (Zhu et al. 2022; Nixon et al. 2013), but not in the field of furniture design.
This study uses AHP, QFD, and PUGH decision-making (AHP-QFD-PUGH integrated model) to transfer user needs to product function and styling design and, ultimately, proposes an optimal design solution. Moreover, the function-behavior-structure (FBS) model can translate key design elements into specific structural design requirements (Cui 2024). Thus, to ensure the rationality of the design results, this study innovatively adds the FBS model based on AHP-QFD (Hu and Wang 2021) to achieve more accurate mapping between the final presentation of the product and key user needs (Zhang et al. 2023). The combination of these methods can provide scientific guidance for the innovative design of children’s beds, as well as a new way of thinking about furniture design.
In summary, the purpose of this study is to provide new methods and ideas for the design and research of children’s beds and similar furniture to promote children’s bed design and development. The design preference characteristics of relevant users for children’s beds are explored in detail, and substantial guiding suggestions are provided for optimizing the design of children’s beds and enhancing children’s growth-oriented furniture.
EXPERIMENTAL
Research Methodology
This study applied the AHP-QFD-PUGH integrated model to the design process of growable children’s beds, which was divided into five steps: User requirement extraction, requirement hierarchy, requirement function transformation, function-behavior-structure mapping transformation, and design practice and evaluation. The design process of growable children’s beds based on AHP-QFD-PUGH modeling is shown in Fig. 1. First, the KJ method was used to construct a list of user requirements for growable children’s beds to accurately identify user requirements and extract functions. Second, the AHP method was used to assign weights to the evaluation indicators to improve the scientific nature of the program evaluation process. Third, the QFD method was used to rank the importance of the design features that match the corresponding requirements. Fourth, the FBS model was used to achieve hierarchical mapping of function-behavior structures, thus transforming the design features derived from the QFD model into structural features to improve the feasibility and reliability of user behavior and structural design (Xuan et al. 2021). Finally, design practice was carried out based on the above analyses. The PUGH decision matrix was constructed to validate and evaluate the multiple schemes, and the optimal scheme was eventually selected.
Fig. 1. Flow chart of growable children’s beds based on AHP-QFD-PUGH modeling
Research Process
Acquisition of growing child beds design requirements
First, a research questionnaire was designed using a large amount of data and performing literature searches on children’s furniture and beds. After that, the basic needs were researched via interviews and questionnaires, and the third-level demand indicators among the indicators of growable children’s beds were filtered. The survey included children aged 6 to 12, parents ranging from 25 to 45 years, and relevant designers. A total of 73 questionnaires were distributed in this survey. There were 4 invalid questionnaires that were removed, leaving a total of 69 valid questionnaires, with an effective acceptance of 94.5%. On this basis, the scattered and disorderly demand indicators were categorized and upwardly refined and summarized into four types of second-level demand indicators: appearance, system, function, and economy. The appearance demands included a beautiful shape, warm colors, and no corners to prevent bumping. The systemic demands included a stable structure with a robust load-bearing capacity, an adjustable size, a scientific structure, and easy assembly. The functional demands included storage, parent-child interaction, growth ability, fun design, ease of cleaning and safety protection. The economic demands included cost-effectiveness, environmental sustainability, and durability. Eventually, the first-level demand indicator for the product design of growable children’s beds was derived. These demands were more in line with the results of existing research on the demand for children’s beds (Yan 2023; Liu and Wang 2024).
AHP method to determine the weight of each indicator
After obtaining the ordered and comprehensive user demand indicators, the hierarchical analysis method was used to conduct quantitative research on the indicators collected by collation. Hierarchical analysis, as a decision-making method that combines qualitative and quantitative analysis, can compensate for the possible problems of strong subjectivity and difficult operation, thereby improving the objectivity and effectiveness of subsequent decision-making.
First, based on the list of user requirements obtained from the KJ method above, the elements were divided into three layers: the target, criterion, and solution. The target layer was the design of growable children’s bed products, which was indicated by the letter Y. The four secondary demand indicators were designated as the criterion layer and were denoted by the letters N1, N2, N3, and N4. The criterion layer was used to expand the detailed division of 16 solution layers, denoted by the number Nij (i = 1,2,3,4; j = 1,2…, m), to construct the hierarchical structure model, as shown in Fig. 2. Second, the study designed the AHP scoring table and invited 5 parents with extensive experience in purchasing children’s furniture, 3 scholars in related fields, 4 furniture designers, and 3 furniture salespeople, totaling 15 people, to form a decision-making team. A nine-point scale was used for comparing importance, to assign values to the needs of each of the criterion layers and the solution layer. The judgment matrix scale is defined in Table 1. Third, the AHP scoring table data were analyzed to calculate the eigenvectors and weights of each requirement. A consistency test was also performed on the judgment matrix to avoid arbitrary scoring by experts, resulting in contradictory ratings. Finally, the weight of each program layer was multiplied by the weight of the corresponding guideline layer to calculate the comprehensive index value of each level of elements overall. Furthermore, combined with the above expert team’s opinion, each specific demand was sorted to obtain user demand priorities to provide decision support for the subsequent development and design of the growable children’s bed. The consistency index (CI) and consistency ratio (CR) are defined by Eqs. 1 and 2, respectively,
(1)
where λmax is the largest eigenvalue of the judgment matrix, and n is the order of the judgment matrix. Equation 2 is as follows,
(2)
where the inconsistency of the data in the judgment matrix is within the permissible range and its consistency is considered acceptable when CR < 0.10, otherwise the judgment matrix should be appropriately corrected.
Fig. 2. Hierarchical structure model of growable children’s beds
Table 1. AHP Scoring Table
Analysis of the AHP model results
The mathematical method of geometric mean was used to process the statistical results of the 15 AHP research questionnaires completed by the decision-making team to form a comprehensive assignment result (Qiu and Zu 2018), and then the formula was used to calculate the single-level weight value of each indicator. The λmax values of the judgment matrix of the criterion and solution layers were 4.013, 3.006, 4.011, 6.072, and 3.011, respectively, and the calculated CR values were 0.005, 0.006, 0.004, 0.011, and 0.011. Because the CR values were all less than 0.1, the results were judged to have satisfactory consistency.
The single-layer weight value of the solution layer was multiplied by the single-layer weight value of the corresponding guideline layer to obtain the comprehensive weight value of the program layer. Then, the comprehensive weight ranking of the product design requirements of the growable children’s bed was carried out according to the comprehensive weight value, as shown in Table 2.
Table 2. Ranking Scale for Growable Children’s Beds
According to the comprehensive weighting results, functional demands and systemic demands were the two aspects that users were most concerned about in the design of growable children’s beds. Safety protection, a stable structure with a robust load-bearing capacity, no corners to prevent bumping, environmental sustainability and other safety considerations were at the forefront. Moreover, a scientific structure, growth ability, parent-child interactions, and cost effectiveness also needed to receive special attention. Therefore, growable children’s beds should ensure the stability of the structure, environmental sustainability, anti-bump safety, and scientific design. They should also be able to be dismantled and size-adjustable to meet growability needs as well as provide parent‒child interactions and have fun designs.
QFD method to transform design features
Based on the AHP hierarchical analysis model, the user requirements for the product design of the growable children’s bed were refined and analyzed into specific design features, which were denoted as Uk (k=1,2,3…, m), as shown in Table 3. The functions and requirements of each scenario level were accompanied by the corresponding specific design features to satisfy their requirements (Fucheng et al. 2022). Seven experts, including four teachers in the furniture manufacturing program and three furniture designers, were invited to score the correlation between user needs and design features of the growable children’s bed product design. High correlation is scored as 5 and is counted as “●” in the matrix; moderate correlation is scored as 3 and is counted as “■”; weak correlation is scored as 1 and is counted as “▲”; no correlation is scored as 0, which is indicated by no sign.
Table 3. Correspondence Table between User Requirements and Design Features
The “left wall” and the “ceiling” of the quality house conveyed the needs of users and the design requirements, respectively. The “room” was filled with the correlation scores between the needs of users and the design requirements, and the calculated weights were filled into the “basement”. The quality house of growable children’s beds is shown in Table 4. The weights of each design requirement are calculated by Eqs. 3 and 4, respectively,
(3)
where Wi represents the weight of the i-th indicator, and Eij is the relationship degree value between the i-th user need and the j-th design requirement. Equation 4 is as follows,
(4)
where Fj represents the absolute importance weight of the design requirements, and Mi represents the relative importance weight of the design requirements.
According to the weight of the importance of quality characteristics in the basement part of the quality house, in the design process of growable children’s bed products, the top five design features were modular design (15.8%), robustness and wear resistance (12.3%), protective facilities (10.89%), nontoxic and harmless materials (10.6%), and rounded modeling (9.6%). The weight of these five design features was 59.1%, accounting for a large proportion of the overall design requirements; thus, these design features should be the top design priorities. The design features of conforming to children’s ergonomics (8.9%), changing appearance (8.4%), and educational and play needs (6.3%) should also be considered. The results of the present research were in line with the widespread concern among parents regarding durability, wear resistance, and harmless materials, which were consistently highlighted in similar studies (Xu 2023). However, the present experiments also revealed a notable emphasis on modular design, which had emerged as a significant factor in contemporary product design. This discovery would be highlighted in the analysis.
Table 4. Quality House of Growable Children’s Beds
Construction of scenario FBS model
Through Table 4, the design features with higher scores were extracted and summarized as regulating, protective, and entertainment functions, which guided the FBS mapping process as the functional elements of the growable children’s beds. The FBS Hierarchy Mapping on growable children’s beds is shown in Fig. 3.
Function-Behavior Transformation (F-B): the extracted sub-functional modules are transformed into specific behavioral modules respectively (Ma and Peng 2021). For example, protective facilities are designed to ensure the safety of children when they flip their body, play, and jump. This step is essential because it bridges the gap between the abstract functions and the tangible behaviors.
Behavior-Structure Transformation (B-S): based on the above functional-behavioral transformation, the contents of the behavioral modules are subdivided into corresponding structural modules. This step is inherently linked to the F-B Transformation, as it translates the desired behaviors into physical structures. Considering that different behaviors may map the same structure, the associated structures should be reasonably combined, which contributes to the compactness of the product’s overall structure. For example, the components’ detachable structure design is provided to rebuild and change shape.
Fig. 3. FBS Hierarchy Mapping on growable children’s beds
Practice and Evaluation
Based on the above analysis, the design of a growable children’s bed was focused on the three functional modules of adjustability, protection, and entertainment, covering five design features: U5, U7, U11, U12, and U1. Full consideration was given to seven structural modules: a telescopic structure, detachable parts, guard rails, a reinforced bed structure, rounded corners, multifunctional activity areas, and storage space. Considering market demand and the concept of minimalist design, three design schemes for growable children’s beds were proposed for children aged 0 to 12 years, namely, F1, F2, and F3. These designs were modular and adaptable, which allowed for size customization. The three schemes are centered on simplicity and practicality. In each case the aim was to meet consumers’ needs for basic furniture, while considering space saving and ease of maintenance. The design of Scheme F1 focused on adjusting the height and length of the bed to meet the needs of the growing child. The dimensions could be adjusted from 140 x 90 cm to 200 x 90 cm. It was made of sturdy materials and had an interesting design with softly rounded corners. The design highlights of Scheme F2 were the tight protection of the perimeter and the ability to change the shape of the modules by dismantling the parts to meet a variety of growth needs. Scheme F3 had a semi-open design that considered the growth function as well as the storage function. The telescopic structure under the bed achieved a storage function and could be transformed into a double bed, with the size changing from 200 x 85 cm to 200 x 160 cm. Starting from the simplest bed type, these schemes gradually incorporated adaptability and versatility to suit different family lifestyles and spatial conditions. Figure 4 shows an effect diagram of the specific scheme.
Fig. 4. Effect diagram of the specific scheme
The PUGH decision matrix was used to verify and comprehensively evaluate the design solutions of the growable children’s bed and, finally, determined the best design solutions and optimization direction according to the comprehensive score (Li et al. 2022), avoiding subjectivity in the design process and improving the scientific nature and satisfaction of the design solutions (Fang et al. 2023). First, Scheme F3 was selected as the benchmark scheme, and 12 users with experience purchasing children’s beds were invited to conduct a detailed comparative analysis between the remaining schemes and the benchmark scheme. The users, aged between 25 and 45 years, were equally divided between men and women, which provided a balanced perspective on the product evaluation. The combined net score was calculated based on the PUGH decision matrix. The “+,” “−,” and “S” symbols were used to rate the options, where “+” represents that the scheme is better than the benchmark scheme, scoring “+1”; “−” indicates that it is slightly worse than the benchmark scheme, scoring “−1”; and “S” represents the same and the score remains unchanged. The PUGH decision matrix of growable children’s beds is shown in Table 5.
Table 5. PUGH Decision Matrix of Growable Children’s Beds
The results of the evaluation showed that Scheme F2 had the highest net score, followed by Schemes F3 and F1. Therefore, through this evaluation method, Scheme F2 was finally adopted as the optimal design solution. The program refinement is shown in Fig. 5.