An integrated approach for defining bicycle design

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Int. J. Industrial and Systems Engineering, Vol. 19, No. 3, 2015

An integrated approach for defining bicycle design factors with consideration of gender differences Chang-Hsien Hsu Department of Business Administration, Asia University, Wufeng, Taichung 41354, Taiwan Email: [email protected]

Liang-Yuh Ouyang and Chun-Ming Yang* Department of Management Sciences, Tamkang University, Tamsui, Taipei 25137, Taiwan Email: [email protected] Email: [email protected] *Corresponding author Abstract: Quality function deployment (QFD) is an extremely effective method of determining customer requirements (CRs) and developing new products to meet those requirements. However, most of the previous research on QFD is focused on customers in general, and this approach has limited applicability in regards to enhancing customer satisfaction or gaining market share. This study develops a methodology integrating market segmentation theory, QFD, and fuzzy theory for developing a new bicycle with consideration to different CRs. Firstly, a market segmentation questionnaire is used to collect information regarding the emphasis customers place on various customer requirements. Secondly, the QFD is used to transform these CRs into engineering characteristics (ECs). Finally, to obtain the key EC values, the relevant crisp values were solved using fuzzy theory. The proposed approach provides the means by which businesses can identify key CRs and reduce areas of uncertainty in the period between production and sales. An illustrative example is given to demonstrate the application of the proposed approach at the end of this paper. Keywords: bicycle; market segmentation; quality function deployment; QFD; fuzzy theory. Reference to this paper should be made as follows: Hsu, C-H., Ouyang, L-Y. and Yang, C-M. (2015) ‘An integrated approach for defining bicycle design factors with consideration of gender differences’, Int. J. Industrial and Systems Engineering, Vol. 19, No. 3, pp.326–347. Biographical notes: Chang-Hsien Hsu is an Assistant Professor in the Department of Business Administration at Asia University in Taiwan. He earned his PhD in Graduate Institute of Management Science from Tamkang University. His research interests are in the field of process capability index, quality management, and Six Sigma. He has published in International Journal of Production Research, International Journal of Information and Management Sciences, Proceedings of the Institution of Mechanical Engineers, Part B: Copyright © 2015 Inderscience Enterprises Ltd.

An integrated approach for defining bicycle design factors

327

Journal of Engineering Manufacture, International Journal of Advanced Manufacturing Technology, Central European Journal of Operations Research, and Quality & Quantity. Liang-Yuh Ouyang is a Professor in the Department of Management Sciences at Tamkang University in Taiwan. He earned his MS in Mathematics and PhD in Management Sciences from Tamkang University. His research interests are in the field of production/inventory control, probability and statistics. He has published in Journal of the Operational Research Society, Computers & Operations Research, European Journal of Operational Research, Computers and Industrial Engineering, International Journal of Production Economics, IEEE Transactions on Reliability, Production Planning & Control, Mathematical and Computer Modelling, Applied Mathematical Modelling, Applied Mathematical and Computation, and Journal of Global Optimization. Chun-Ming Yang is a PhD Candidate in the Department of Management Sciences at Tamkang University in Taiwan. He earned his MS in Department of Business Administration from Asia University. His research interests are in the field of process capability index, quality management, and decision analysis. He has published in International Journal of Production Research, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, International Journal of Information and Management Sciences, and Advances in Information Sciences and Service Sciences.

1

Introduction

In recent years, enterprises around the globe have shifted from being productionorientated to customer-orientated regardless of the product design, research and development (R&D), or decision-making strategy of the enterprise. Marketing by segmentation has also helped to satisfy the requirements of different customers (Tsai and Chiu, 2004). With the increased emphasis on customer orientation, achieving high-quality product development techniques that fully meet customer requirements (CRs) are crucial. Quality function deployment (QFD), a commonly used method to identify CRs, employs the house of quality (HOQ) to convert collected CRs into appropriate engineering characteristics (ECs) and to identify CRs critical to enhancing customer satisfaction (Hsiao, 2002; Bottani and Rizzi, 2006; Zhai et al., 2009). Wang and Chin (2011) pointed out that the HOQ is effectively applied to define the priority of design requirements by considering customers’ needs and expectations. By using HOQ as a product planning and improvement tool, an effective sustainable product development process can be obtained (Büyüközkan and Çifçi, 2013). In building an HOQ, customer expectations and perceptions about a product are usually obtained and systemised into a number of key CRs. Subsequently, specialists confirm which technological requirements satisfy the voice of the customer (VOC) and analyse the weighted relationship between the two. As a result, QFD inevitably involves a considerable amount of uncertain information, such as linguistic variables, which are difficult to predict and measure (Sun et al., 2000; Yang et al., 2003). This presents additional challenges in satisfying CRs. In this respect, fuzzy theory has proven to be an effective mathematical method to process linguistic variables (Kwong and Bai, 2002; Kwong et al., 2007; Zhai, et al., 2009;

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Khademi-Zare et al., 2010). The mathematical operations in fuzzy theory transform linguistic variables into crisp values. Although fuzzy theory has been widely applied in QFD-aided analysis and enables VOC to be understood in a more systematised manner, this study discovered that a number of studies involving QFD found that customer orientation was not truly achieved (Hsiao, 2002; Kwong and Bai, 2002; Yang et al., 2003; Kwong et al., 2007; Juan et al., 2009; Kuijt-Evers et al., 2009; Zhai et al., 2009; Liu, 2011; Jia and Bai, 2011). Therefore, the contributions of past studies have been limited in terms of satisfying different CRs. In view of the above, this study proposed an approach that integrates market segmentation, QFD, and fuzzy theory to identify different CRs and increase customer satisfaction. Within the proposed methodology, the concept of market segmentation is firstly incorporated into QFD. Subsequently, fuzzy theory is employed to convert the uncertainty of VOC into the specificity of crisp values. This was applied to a case study that enabled bicycle manufacturers to design and produce more sophisticated products. The objectives of this study were to: 1

establish and categorise the differences among CRs of various customer groups

2

use fuzzy theory to identify the factors of engineering that require improvement for various groups of customers

3

incorporate green concepts into QFD so that the manufacturers may take environmental factors into consideration in the design and development of products.

In accordance with the final objective, Suchard and Polonsky (1991) indicated that an increasing number of customers consider ecological compatibility when purchasing products and are willing to pay an additional 15%–20% for environmentally friendly products. Therefore, we included green concepts in our methodology to increase purchase intention and reduce the impact on the environment. This study employed an online questionnaire to determine the emphasis customers place on the various functions of products and their current satisfaction with those functions. The rest of this study is organised as follows. In Section 2, background descriptions of bicycle design, market segmentation, QFD, and fuzzy theory are presented. In Section 3, a methodology for determining the aggregated importance of ECs in QFD is presented. The research design is introduced in Section 4. In Section 5, an illustrative example about the development of a new bicycle is used to illustrate the proposed methodology. Finally, conclusions are made in Section 6.

2

Literature review

2.1 Bicycle design In the last 50 years, bicycles have become increasingly diverse in design, with the emergence of racing/road bicycles, mountain bicycles, BMX bicycles, utility bicycles, and electric bicycles (Wilson, 2004). The uses of bicycles evolve with consumer demands generated from the lifestyle changes of civilians and the economic development of a country. For example, in North America, they are most commonly used for leisurely


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activities (Pucher et al., 1999), while in the Netherlands, bicycles are mostly considered as vehicles for commuting (Rietveld and Daniel, 2004). Structurally, bicycles comprise a multitude of components and a failure in the design of any part can easily result in discomfort for the customer, reducing their satisfaction with the product with a negative impact on a company’s business performance and image. The governmental policy on bicycle design and their influence on the human body, and CRs have been the focus of numerous studies. Pucher et al. (1999) suggested that the increased public willingness to ride bicycles in North American countries is attributed to the development of bicycle lanes and parking facilities as well as policies formulated with political support. Kwong and Bai (2002) selected the design requirements for the rear wheels of mountain bikes from 19 customers using a fuzzy analytic hierarchy process (fuzzy AHP) in conjunction with QFD. Bressel and Cronin (2005) investigated the feelings of both sexes with regard to the pressure of bicycle seats. Zhai et al. (2009) proposed a QFD-based approach combined with rough sets and converted the safety CR from seven customers between the ages of 18 and 30 into ECs related to bicycle frame measurements. On the other hand, structurally, as bicycles comprise a lot of parts, a failure in the design of any part can easily cause discomfort to the customer, then reduce their satisfaction with the product, and finally affect a company’s business performance and image. Hence, selecting a suitable bicycle is essential for cyclists. Obviously as the means to design high quality bicycles that satisfy various CRs are crucial to bicycle manufacturers.

2.2 Market segmentation In today’s highly competitive market, mass marketing is no longer able to satisfy the needs of different customers. More consumers are pursuing personalised products and services to display their uniqueness and identity. This poses added difficulties for modern enterprises in measuring and predicting the purchase patterns of customers, consequently widening the gap between production and marketing. To establish closer relationships with customers, companies must utilise market – segmentation and deploy appropriate products, services, and resources (Tsai and Chiu, 2004). Market segmentation is an important method of strategic marketing that can help managers devise marketing programs to satisfy the specific needs of different segment customers (Boejgaard and Ellegaard, 2010). Tsai et al. (2011) also indicated that the examination of market segmentation in managerial practice is a useful technique to help various industries understand customer defection and provide strategic insights for targeting segments and positioning service strategies. The concept of market segmentation was first presented by Smith (1956), and posits that certain characteristics exist among the customers in the market, which can be divided or segmented. Some simple and basic ideas on the market segmentation have been found in several bicycle-related studies. For example, Bergstrom and Magnusson (2003) classified the bicycle travellers into winter cyclist, summer-only cyclist, infrequent cyclist, and never cyclist. Heinen et al. (2010) clustered the bicycle users into 10 km group. Nkurunziza et al. (2012) examined the effect of personal, social and physical environmental factors on bicycle commuting. Li et al. (2013) used the approach to attitudinal market segmentation for identifying the potential markets of bicycle commuting.

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C-H. Hsu et al.

In addition to practical applications of market segmentation, the method of segmentation has also become essential in the theory. Green and Krieger (1995) presented two approaches to market segmentation, with one being an a priori approach (socio-demographic characteristics or simple behavioural characteristics). Verbeke et al. (2007) indicated that using demographic variables (such as gender or age) to segment target customer groups contributes to better communication between buyers and sellers. Geraghty and Torres (2009) contend that demographic variables provide sellers with marketing focus and promote quicker access to the target market. For the reasons above, this study conducted market segmentation using demographic variables to assist companies in gaining access to target markets more quickly and understand the CRs of different customer groups.

2.3 Quality function deployment QFD originated in Japan in 1960; Akao and Mizuno combined the function deployment of value engineering (VE) with quality tables to create a quality matrix, which is now known as QFD (Akao, 1990). Hauser and Clausing (1988) and Hauser (1993) indicated that the efficacious use of QFD can reduce the development period by 40% and the costs of initiating the process and engineering by 30%. Pramod et al. (2007) proved QFD to be an effective technique for product development, in particular for converting a customer’s vague language into technical languages. Zarei et al. (2011) illustrated that QFD can be a widely used customer-driven and manufacturing management tool and is commonly used in new product development processes to translate CRs into appropriate product ECs. Mukaddes et al. (2012) presented QFD as a powerful tool to be used in identifying CRs whilst also paying heed to quality consciousness. Hsu et al. (2013) indicated that QFD is an effective approach to grasping CRs and planning products, and provides advantages regarding both enhancement of customer satisfaction and accelerating product design and development. QFD is a basic concept that uses a series of HOQ to transform qualitative requirements into quantitative specifications (Yang et al., 2011). In general, an HOQ can be created using single or multiple relationship matrices. The process can be divided into the following five steps (see Figure 1; Zhai et al., 2009). Step 1

Surveys or interviews are conducted to identify and establish CRs (WHATs).

Step 2

Weights and relative importance of WHATs are computed.

Step 3

ECs to address CRs are developed based on literature or expert opinions (HOWs).

Step 4

The weighted relationship between WHATs-HOWs are measured by experts, and the measurements are placed in relation matrices.

Step 5

Priorities and targets for the design are set.

Two problems are evident in the creation of HOQ within steps 1 and 3. For one, CRs are formulated from an entire collection of requirements. Note that the development of a single product is stipulated by multiple CRs, each of which can be converted into multiple ECs. In comparison, a single EC can influence many CRs (Kwong et al., 2007). However, it is apparent from many previous studies that the CRs in QFD are generally regarded as an overall demand. Consequently, methods of evaluation often neglect the


331

importance of customisation. The second problem involves fuzziness and uncertainty from the linguistic variables established by customers and experts. As a result, an effective method must be applied in a careful manner to process the complex relationships among CRs, ECs, and CRs-ECs. Figure 1

House of quality

2.4 Fuzzy theory Human thought processes often include emotions, vagueness, and intuition. Hence, any human intervention, be it with other people, matters, or objects, can lead to uncertainty and fuzziness, which conventional bivalent logic is incapable of fully describing. Hatami-Marbini et al. (2012) proposed that the observed values of the input and output data in real-world problems are often ambiguous or vague. These ambiguities can be represented by linguistic terms characterised by fuzzy numbers in QFD to reflect the decision-makers’ intuition and subjective judgements. Nosratabadi et al. (2013) stated that there are many decision-making situations in the real-world in which the information cannot be assessed precisely in a quantitative form but may be in a qualitative one. For this reason, Zadeh (1965) developed fuzzy sets and the idea of membership, enabling two-logic and descriptions of real phenomenon to be transformed from a qualitative to quantitative form. This quantitative value lies within the range [0, 1], where 0 and 1, respectively, indicate the minimum and maximum degree of membership, while all the intermediate values indicate degrees of ‘partial’ membership (Chen and Ko, 2010). To obtain crisp values from fuzzy results, defuzzification must be conducted following a process of fuzzy inference. Defuzzification quantifies the results of fuzzy inference into the membership function value of output variables, which is then converted into a specific value, facilitating subsequent calculation and comparison (Liu, 2011).

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Fuzzy theory solves the issue of uncertainty in human thinking and enhances the precision of decision-making for companies. A number of scholars have studied the fuzzy theory to QFD and developed various fuzzy QFD approaches. For example, Liu (2011) employed fuzzy QFD and fuzzy multi-criteria decision-making (Fuzzy MCDM) to develop a product design and selection system capable of overcoming issues associated with fuzziness in the linguistic variables used by developers (ECs) and consumers (CRs). Bevilacqua et al. (2012) developed a new fuzzy-QFD approach to test and characterise customers’ rating of extra virgin olive oil. Dursun and Karsak (2013) presented a QFD-based fuzzy MCDM approach for a supplier selection problem.

3

Methodology

In this section, a methodology for determining the aggregated importance of ECs in QFD is introduced. First, the HOQ is constructed by using the market segmentation questionnaire. Then, the fuzzy customer normalised weight (CNW) and fuzzy CRs-ECs relationship matrix are determined based on a fuzzy expert system; that is, MATLAB software. Using this step, the importance of ECs can be determined. Finally, the aggregated importance of ECs can be obtained using the values of the fuzzy CNW and fuzzy CRs-ECs relationship matrix. HOQ can convert CRs into executable ECs. Nonetheless, in many real-life situations, more than one CR is involved, and the CRs are not equal in importance and thus cannot be treated in the same manner. For the calculation of weight, Lyman (1990) compared conventional gap theory, which only measures expectation (Ei), to using the difference between expectation and satisfaction (Ei – Si). The study found that the latter was more efficient in indicating the importance of a given quality requirement and prioritising improvements for the quality requirements. The formula of their improved method is represented by equation (1) below: WiVOC = Ei ( Ei − Si ) , i = 1, 2,...., m

(1)

where WiVOC represents the weight of ith VOC. Determining the correlation factors of CRs and CRs-ECs in HOQ requires linguistic variables formulated by customers or experts in the relevant domains. However, the process of human thinking and decision-making often involves personal emotions, uncertainty, and fuzziness. Zadeh (1965) first proposed fuzzy theory to effectively deal with the issue of uncertainty. In Zadeh’s theory of fuzzy sets, Ux is nominated as a collection of objects, called the universe, whose elements are denoted by x. A fuzzy subset x in Ux is characterised by a membership function μ x , where μ x : U x → [0,1]. The membership function associates with each member x in Ux a real number μ x ( x) in the interval [0, 1], which represents the grade of membership of x in x. The fuzzy numbers more commonly used in QFD are triangular fuzzy numbers (Yang et al., 2003). Kwong et al. (2007) proposed using fuzzy theory to convert CRs into ECs as well as compare the related data between the two. CRs and CRs-ECs are first regarded as basic information input into the process of fuzzy inference, and a fuzzy rule Ri (‘if-then’) is formulated as equation (2):

An integrated approach for defining bicycle design factors Ri : If

( X i1 is

xi1 , X i 2 is xi 2 ,....., X ik is xik ) , then Yi is yi ,

333 (2)

where xi1, xi2, …, xik are the fuzzy sets corresponding to the individual input linguistic variables Xi1, Xi2, …, Xik, respectively, while yi is the fuzzy set applicable to the output linguistic variable Yi. The procedures of the fuzzy rule evaluation is given as: •

the degree of membership of ‘Xi1 is xi1’, is oi1

•

the degree of membership of ‘Xi2 is xi2’, is oi2

•

the degree of membership of ‘Xik is xik’, is oik, respectively.

According to Sun et al. (2000), if a fuzzy rule employs intersections to combine each fuzzy subset, the membership function can be defined as equation (3): u x ( x) = min {u xi 1 ( x), u xi 2 ( x),...., u xik ( x )} .

(3)

If a fuzzy set rule employs union, the membership function can be defined as equation (4): u x ( x) = max {u xi 1 ( x), u xi 2 ( x ),...., u xik ( x )} .

(4)

As a result, the overall degree of membership of the premise takes the minimum among the individual degree of membership of the predicates. Therefore, the overall degree of membership, oi, in the premise of Ri can be denoted as oi = min {oi1 , oi1 ,...., oik } .

(5)

Subsequently, the output linguistic function (Yi), fuzzy set (yin), and overall degree of membership (oim) of the fuzzy inference aggregation rules can be expressed as equation (6): Yi is yi1 : oi1 ; Yi is yi 2 : oi 2 ;....; Yi is yim : oim .

(6)

All of the output values can be combined to derive an integral total value, ‘Yi is yî ‘, which also equals oi. Once the rules are defined, relevant fuzzy relations and fuzziness evaluations are integrated into a single output fuzzy region, each of which is defuzzified into a crisp value, yî , using the centroid method (Cox, 1999) as provided in equation (7): b

∫

yî = xu ( x)dx a

b

∫ xu( x)dx,

(7)

a

where a and b are the lower and upper boundaries of the Yith linguistic output, respectively; u(x) is the output membership function of the defuzzified linguistic variable Yi, ranging between a and b. The procedure of fuzzy inference and defuzzification in this study is exhibited in Figure 2.

C-H. Hsu et al.

334 Figure 2

Procedure of fuzzy inference (see online version for colours)

Kwong et al. (2007) indicated that in order to identify critical ECs, the obtained crisp values must be further computed using equation (8) as: m

Ij=

∑ CNW × FRO , j = 1, 2,...., n, i

i

(8)

i =1

where CNW is the customer normalised weight value, and FRO is the crisp value of the fuzzy relation output.

4

Research design

In the design of the items in the online questionnaire, this study referred to the studies by Kwong and Bai (2002), Lai et al. (2008) and Zhai et al. (2009) to obtain the requirements of bicycle customers. Notably, Suchard and Polonsky (1991) indicated that it is highly important to preserve the natural environment; an increasing number of customers consider ecological compatibility when purchasing products and are willing to pay an additional 15%–20% for environmentally friendly products. Fullerton and Wu (1998), Giudice et al. (1999) and Karlsson and Luttropp (2006) also indicated that with international environmental protection regulations and emerging green consumerism, companies should take environmental factors into consideration and integrate green concepts into product design. In this way, the demands of both the market and customers can be met, thereby enabling companies to maintain their competitiveness.

Steadiness

Assembly/disassembly Suitability Storage

CR5

CR6

CR7

CR3

CR4

Noise

Product lifespan

CR2

CRs Weight

CR1

No.

CR14

CR13

CR12

CR11

CR10

CR9

CR8

CRs

Recyclability

Component compatibility

Maintenance

Disposal

Colour

Design

Additional accessories

No.

EC7

EC6

EC5

EC4

EC3

EC2

EC1

ECs

Recyclability

Packaging

Assembly

Overall design

Overall colour

Safety

Function

Table 1

No.

An integrated approach for defining bicycle design factors List of CRs and ECs

335

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C-H. Hsu et al.

As a result, this study incorporated the concept of environmental protection into the questionnaire items. A total of 14 design factors were established for the questionnaire, including weight, noise, product lifespan, steadiness, assembly/disassembly, suitability, storage, possibility for additional accessories, design, colour, maintenance, disposal, component compatibility, and recyclability (see Table 1). This study utilised an online questionnaire for the initial phase of the investigation. The items of the questionnaire were measured using a five-point Likert scale. Respondents were required to consider the degree of emphasis on each item when purchasing bicycles as well as their satisfaction towards the bicycle they currently owned. In order to check the validity of the questionnaire and the HOQ, this study invited two experts with over five years of experience in production and development to conduct assessment and selection for the degree of correlation between ECs and CRs-ECs. This approach not only enhanced the validity of the questionnaire content, but also increased the applicability of the results derived in the study (Cooper and Schindler, 2003). For testing the reliability of the questionnaire, this study was sent along with 30 questionnaires to lead uses respondents in central Taiwan as a pre-test, all of which were retrieved and valid (Cronbach’s α = 0.898 ≥ 0.7). Therefore, the reliability of the pre-test questionnaires was deemed to be good (Nunnally, 1978).

5

An illustrative example

Owing to the fact that many professional and elite cycling teams have been formed through online platforms or forums to exchange ideas and assembly techniques, this study collected data from 100 lead uses respondents [male (68%) and female (32%)] regarding 14 CRs from the HOQ using an online questionnaire during 2009–2010. As such, the questionnaires were sent to well-known cycling forums in Taiwan. All 100 of the retrieved questionnaires were valid with a Cronbach’s α of 0.889 ≥ 0.7. Hence, the reliability of the post-test questionnaires was deemed to be good. The respondents were asked to express the emphasis they place (Ei) on each CR when selecting bicycles to purchase as well as their satisfaction (Si) with the bike they currently owned. The data was then input into equation (1). The 14 CRs within the HOQ were converted into seven ECs, namely, function, safety, overall colour, overall design, assembly, packaging, and recyclability by two experts (see Table 1). Then, the two experts evaluated the degree of correlation among the CRs, ECs, and CRs-ECs, with ▲ as low correlation, as moderate correlation, and ● as high correlation. The results are shown in Table 2. Once the correlation of CRs, ECs, and CRs-ECs was assessed, the weights of emphasis and satisfaction as expressed by male and female respondents were normalised, substituted into HOQ (see Tables 3 and 4), and then incorporated into fuzzy theory to eliminate the fuzziness of the linguistic variables.

= 50, ● = 90.

2.30

CR6

1.72 2.20 –0.20

CR11

CR12

CR13

CR14

Notes: ▲ = 10,

0.45 –1.11

CR10

0.67

0.60

CR5

CR9

2.75

CR4

–0.53

2.22

CR3

–1.01

0.58

CR2

CR8

1.39

CR1

CR7

Males

ECs

CRs weight

2.29

2.67

2.65

0.70

0.34

0.63

0.32

0.75

3.06

2.02

3.24

2.65

1.76

2.90

Females

▲

▲

●

EC1

●

●

●

▲

EC2

●

EC3

▲

●

●

EC4

▲

▲

●

●

●

EC5

●

●

▲

EC6

●

▲

▲

●

EC7

Table 2

CRs

An integrated approach for defining bicycle design factors 337

The HOQ of bicycle

18 –2

CR13

CR14

Key values

14

–8

CR8

–9

–4

CR7

CR12

19

CR6

CR11

5

CR5

6

23

CR4

4

18

CR3

CR10

5

CR9

12

CR2

CNW (%)

CR1

ECs

24.26

1.38

34.0

31.4

1.38

1.38

1.38

27.5

27.5

9.54

50.0

13.0

34.0

1.38

29.7

EC1

33.46

1.38

8.67

5.15

1.38

1.38

1.38

27.5

1.38

56.9

50.0

59.3

8.67

27.5

29.7

EC2

12.51

1.38

8.67

5.15

1.38

50.0

27.5

1.38

1.38

9.54

1.38

13.0

8.67

1.38

3.30

EC3

16.86

1.38

8.67

5.15

1.38

27.5

50.0

27.5

50.0

34.6

1.38

13.0

8.67

1.38

29.7

EC4

29.07

1.38

8.67

5.15

1.38

1.38

1.38

1.38

1.38

9.54

50.0

59.3

34.0

50.0

3.30

EC5

2.61

50.0

8.67

5.15

50.0

1.38

1.38

1.38

27.5

9.54

1.38

13.0

8.67

1.38

3.30

EC6

8.21

50.0

8.67

5.15

50.0

1.38

1.38

1.38

1.38

9.54

1.38

13.0

34.0

1.38

3.30

EC7

Table 3

CRs

338 C-H. Hsu et al.

The key values of ECs (males)

1 3 10 10 9

CR10

CR11

CR12

CR13

CR14

Key values

2

CR9

CR6 3

12

CR5

1

8

CR4

CR8

12

CR3

CR7

7 10

CR2

11

CNW (%)

CR1

ECs

17.59

1.38

27.5

27.5

1.38

1.38

1.38

27.5

27.5

3.30

50.0

3.30

27.5

1.38

28.6

EC1

22.49

1.38

1.38

1.38

1.38

1.38

1.38

27.5

1.38

52.0

50.0

52.0

1.38

27.5

28.6

EC2

2.94

1.38

1.38

1.38

1.38

50.0

27.5

1.38

1.38

3.30

1.38

3.30

1.38

1.38

2.36

EC3

10.94

1.38

1.38

1.38

1.38

27.5

50.0

27.5

50.0

29.7

1.38

3.30

1.38

1.38

28.6

EC4

17.68

1.38

1.38

1.38

1.38

1.38

1.38

1.38

1.38

3.30

50.0

52.0

27.5

50.0

2.36

EC5

8.55

50.0

1.38

1.38

50.0

1.38

1.38

1.38

27.5

3.30

1.38

3.30

1.38

1.38

2.36

EC6

10.38

50.0

1.38

1.38

50.0

1.38

1.38

1.38

1.38

3.30

1.38

3.30

27.5

1.38

2.36

EC7

Table 4

CRs

An integrated approach for defining bicycle design factors 339

The key values of ECs (females)

Males

0.45

–1.11

1.72

2.20

–0.20

CR10

CR11

CR12

CR13

CR14

0.67

2.30

CR6

CR9

0.60

CR5

–0.53

2.75

CR4

–1.01

2.22

CR3

CR8

0.58

CR2

CR7

1.39

CR1

Weight

11

4

5

14

10

7

13

12

2

8

1

3

9

6

Rank

Females

2.29

2.67

2.65

0.70

0.34

0.63

0.32

0.75

3.06

2.02

3.24

2.65

1.76

2.90

Weight

7

4

5

11

13

12

14

10

2

8

1

5

9

3

Rank

EC7

EC6

EC5

EC4

EC3

EC2

EC1

ECs

Males

8.210

2.610

29.07

16.86

12.51

33.46

24.26

Weight

6

7

2

4

5

1

3

Rank

Females

10.38

8.550

17.68

10.94

2.940

22.49

17.59

Weight

5

6

2

4

7

1

3

Rank

Table 5

CRs

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The rank of CRs and ECs among males and females

An integrated approach for defining bicycle design factors Figure 3

Membership functions of fuzzy inference, (a) CNW, (b) CRs-ECs, and (c) FRO (see online version for colours)

(a)

(b)

341

342 Figure 3

C-H. Hsu et al. Membership functions of fuzzy inference, (a) CNW, (b) CRs-ECs, and (c) FRO (continued) (see online version for colours)

(c)

Due to the immense amount of data from the questionnaire and the complexity of fuzzy theory calculations, this study employed MATLAB to aid in the process of fuzzy inference (two fuzzy inputs and one output) to shorten calculation time. In addition, the membership functions of CNW, the CRs-ECs relationship matrix, and FRO were defined (Figure 3). The CNW has five different fuzzy sets, i.e., very unimportant (VU), somewhat unimportant (SU), neutral (N), somewhat important (SI) and very important (VI). The CRs-EC relationship matrix has four different fuzzy sets, i.e., low correlation (LC), rather low correlation (RLC), neutral (N) and strong correlation (SC). The FRO has seven different fuzzy sets, i.e., very low (VL), low (L), rather low (RL), neutral (N), rather high (RH), high (H) and very high (VH). Based on the results presented in Figure 3, the ‘if-then’ fuzzy rules need to be formulated according to the definition of membership functions. A total of 20 fuzzy rules were set. Once completed, the CNW and CRs-ECs values were employed. Defuzzification was conducted using min-min-max fuzzy inference and the centroid approach. In the following example, we used the emphasis and satisfaction of male respondents in CR2 (5) of the CNW and EC2 (50) of the EC. The crisp value of the fuzzy relation was derived to be 27.5 (see Figure 4). In addition, to obtain the key EC values, the crisp values were solved using equation (8). All FRO values between the CRs and EC2 can be computed in a similar way and the results summarised. The key value of the CRs and EC2 was derived to be 33.46 (see Table 3). Similarly, all FRO values from male and female respondents between the CRs and CRs-ECs are calculated and summarised in Tables 3 and 4. Finally, the CR and EC from male and female respondents were sequenced and compared, the results of which are shown in Table 5.

An integrated approach for defining bicycle design factors Figure 4

6

343

Procedure of fuzzy inference for noise and safety factors (see online version for colours)

Conclusions

In the past, QFD has been applied to collect CRs, which were then converted into ECs to aid in design and manufacturing. However, most studies, such as Kwong and Bai (2002), Benner et al. (2003), Yang et al. (2003) and Juan et al. (2009) focused only on overall CRs instead of specific CRs. Moreover, humans often provide ambiguous answers when stipulating requirements, and the ECs and CRs-ECs in HOQs are generally formulated by experts. All of these observations create difficulty for companies trying to grasp CRs and develop products capable of meeting various expectations of customers. Consequently, this study integrated QFD, market segmentation, and fuzzy theory to identify the emphasis that male and female customers put on specific functions when they shop for bicycles and determine their satisfaction with their current bicycles. The CRs were converted into ECs to facilitate analysis and comparison. Furthermore, this study included concepts of environmental protection into QFD so that bicycle manufacturers could include green concepts into their development and design. With the addition of market segmentation in QFD, the results in Table 5 show the following: 1

among 14 different CRs, the main requirement for both male and female respondents was steadiness in the ride of the bicycle, without wobbling

2

female respondents placed greater emphasis on weight, which makes sense, as women are generally smaller than men in physique and would therefore require lighter bicycles

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3

male respondents placed greater emphasis on product lifespan, design, and colour

4

female respondents placed greater emphasis on recyclability and the problem of discarded bicycles.

This result conforms to that obtained by Myburgh-Louw and O’Shaughnessy (1994) in which females were willing to pay an additional 40% to purchase green products. To summarise the results above, bicycle manufacturers should expand their product range to include more diverse appearance, colour, accessories, and component materials to maintain the hardness and toughness of the frame when their target market comprises male customers. In contrast, when the target market comprises female customers, more consideration should be placed on the weight and environmental friendliness of the bicycle. Furthermore, this study converted CRs and QFD relation matrices into specific crisp values using fuzzy theory as shown in Table 5. The results indicated the following: 1

the most critical factor emphasised by both male and female respondents was safety

2

female respondents placed greater emphasis on recyclability and packaging than on overall colour

3

among the ECs, both male and female respondents felt that recyclability was more important than other factors related to packaging.

To summarise the results above, bicycle manufacturers should assign priority to factors of safety when designing and developing bicycles in the future. Manufacturers should also place more importance on recyclability than on packaging. Finally, improvements should be made in recyclability and packaging when the target market comprises female customers and on overall colour when the target market comprises male customers. The results of this study could strengthen product development within manufacturing industries and increase overall product competitiveness and enterprise market share. Bicycle manufacturers conducting market segmentation in the future could employ the results of this study for design and improvements to meet the requirements of customisation. They could thereby enhance product quality and service standards as well as increase the level of acceptance in the market. The research structure of this study could also be applied in other industries (i.e., motorcycle or automobile manufacturing), as it provides a systematic approach to decision-making and analysis. Finally, this study included green concepts into QFD, enabling sustainable development considerations from product design to the end of the product life cycle.

Acknowledgements The authors are thankful to anonymous referees and the editor for their valuable comments and suggestions. This work was supported by the National Science Council of Taiwan under Grant No. NSC 102-2221-E-468-008.


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