Quality performance indices construction of bicycle components manufacturing industry using a fuzzy analytical hierarchy process.

Chen, Jui-Kuei ; Chen, I-Shuo

INTRODUCTION

Due to intense global competition, each industry must develop self-assessment protocols to continually improve organizational performance (Crosby, 1979; Deming, 1986; Garvin, 1991; Neves & Nakhai, 1993; Mele & Colucio, 2006; Sitalakshmi, 2007), especially in unexpected situations (Sousa & Voss, 2002; Sila & Ebrahimpour, 2002, 2003). Many organizations implement total quality management (TQM) to generate a competitive advantage (Nilsson et al., 2001; Chan & Quazi, 2002) and to reduce cost (Antony et al., 2002).

Although the advantages of successful implementation of TQM can be numerous (Philips et al., 1983; Garvin, 1983; Cole, 1992; Zhang, 2000), studies have indicated that some organizations fail to implement TQM successfully (Brigham, 1993; Dooyoung et al., 1998). They have also found that the way TQM is implemented is central to its long-term success within an organization (Ghobadian & Gallear, 2001). Therefore, successful implementation of TQM is a critical issue for various organizations.

Because Taiwan recently joined the World Trade Organization (WTO), expanded economic ties with China, and has begun to face competition from foreign countries, industries in Taiwan are encountering numerous new challenges. Therefore, more and more studies in both academia and industry are focusing on improving performance. However, such studies on Taiwanese-owned industries in China are rare. Because China has the advantages of low costs, a large labor force, etc., Taiwanese-owned industries in China have begun to focus on high quality to create their market shares. Taiwanese bicycle companies such as Giant has become famous around the world. However, such companies cannot produce excellent bicycles without high quality components. Some Taiwanese bicycle component manufacturing has been moving to China because of lower labor costs. Therefore, it is important to discuss how to maintain and improve quality when labor costs are low. This study aims to provide the Taiwanese-owned bicycle component manufacturers in China with a clear way to improve quality performance.

LITERATURE OVERVIEW

Quality

Quality has historically been defined as the degree of conformance to a standard (Sitalakshmi, 2007). Quality is also considered "fitness for use" (Juran & Gryna, 1980) and "conformance to requirement" (Crosby, 1979). Deming (1986) defined quality as a predictable degree of uniformity and dependability at low cost that is suited to the market. In general, quality is a relative concept (Harvey & Green, 1993). Quality has a variety of meanings (Sitalakshmi, 2007), and its range of meanings can cause confusion (Shield, 1999). Since higher quality is associated with greater market share and return on investment (Philips et al., 1983; Cole, 1992), lower manufacturing costs, improved productivity (Garvin, 1983) and improved strategic performance (Zhang, 2000), a growing number of industries are emphasizing quality improvement.

Total Quality Management

TQM is also known as Continuous Quality Improvement (CQI) and Strategic Quality Management (SQM), but TQM is the term most frequently used (Sitalakshmi, 2007). TQM can be defined as a strategic architecture requiring evaluation and refinement of continuous improvement practices in all areas of business (Roosevelt, 1995). TQM requires long-term perspective, customer focus, top management commitment, system thinking, providing training and tools in quality, increased employee participation, development of a measurement system and continuous improvement (Neves & Nakhai, 1993). Corrigan (1995) defined TQM as a management philosophy that builds a customer-driven, learning organization dedicated to total customer satisfaction through continuous improvement in the effectiveness and efficiency of the organization and its processes. Recent literature has defined TQM as a management style based upon producing quality service as defined by the customer to achieve an organization's strategic imperative through continuous process improvement (Tseng et al., 2007).

A body of recent literature has proved that practicing TQM can help companies improve their performance (Knod, Jr. & Schonberger, 2001; Wadsworth et al., 2002; Chase et al., 2006; Han et al, 2007), reduce the costs of poor quality such as scrap, rework, late deliveries, warranty, replacement, etc. (Antony et al., 2002), and generate unique competitive advantages (Reed et al., 2000). In addition, many studies have constructed a framework for quality improvement (Johnson, 1993; Susan, 1995; Martinez-Lorente et al, 2000). Martinez-Lorente et al (1998) found that an organization's size, nationality and product value affect the application of TQM. Grandzol (1998) indicated that employee satisfaction has a positive correlation with TQM and annual employee turnover rate (Dean & Bowen, 1994; Adam et al., 1997). Studies have also discovered that positive employee perceptions of TQM lead to higher satisfaction (Boselie & van der Wiele, 2002). In addition, researchers have pointed out that an organization's willingness to change and desire to satisfy its customers also affects TQM success (Madu & Kuei, 1993; Brah et al., 2002). The major focuses of TQM are summarized in Table 1.

Taiwan's National Quality Award (NQA) has been widely used to evaluate industries. It involves the measurement of seven dimensions: leadership and operation ideals, strategy management, the development of customers and a market, human resources and knowledge management, the application and management of information strategy, process management, and operation performance. Because of NQA's prevalence, we have included its concepts in the development our measurement indices.

FUZZY ANALYTICAL HIERARCHY PROCESS (FAHP)

Fuzzy Set Theory

Professor L.A. Zadeh first developed the fuzzy set theory in 1965 while trying to solve fuzzy phenomenon problems that exist in the real world (e.g., uncertain, incomplete, unspecific and fuzzy situations). Fuzzy set theory can describe set concepts in human language better than traditional set theory can. It represents unspecific and fuzzy characteristics in the language of evaluation, and it uses a membership function concept to represent the field in which a fuzzy set can be permitted to "incompletely belong" and "incompletely not belong."

Fuzzy Number

In our Universe of Discourse, U is a whole target, and each target is called an element. Fuzzy [??], which on U stated that a random x [right arrow]U, appoints a real number [[mu].sub.[??]](x) [right arrow] [0,1] We consider anything above that level of x under A.

The universe of real number R is a triangular fuzzy number (TFN), [??], which means x [member of] R,

appointing [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII],

The triangular fuzzy number above can be written as [??] = (L, M, U) ,where L and U represent fuzzy probability between the lower and upper boundaries of evaluation information, as shown in Figure 1. Assume two fuzzy numbers a [[??].sub.1] = ([L.sub.1],[M.sub.1],[U.sub.1]) and [[??].sub.2] = ([L.sub.2],[M.sub.2],[U.sub.2])

1. [[??].sub.1] [direct sum] [[??].sub.2] = ([L.sub.1], [M.sub.1], [U.sub.1]) [direct sum] ([L.sub.2], [M.sub.2], [U.sub.2]) = ([L.sub.1], [L.sub.2], [M.sub.1] + [M.sub.2], [U.sub.1], [U.sub.2])

2. [[??].sub.1] [cross product] [[??].sub.2] = ([L.sub.1], [M.sub.1], [U.sub.1]) [cross product] ([L.sub.2], [M.sub.2], [U.sub.2]) = ([L.sub.1], [L.sub.2], [M.sub.1] [M.sub.2], [U.sub.1], [U.sub.2]), [L.sub.t] > 0, [M.sub.t]>0,[U.sub.i]>0

3. [[??].sub.1] - [[??].sub.2] = ([L.sub.1], [M.sub.1], [U.sub.1]) - ([L.sub.2], [M.sub.2], [U.sub.2]) = ([L.sub.1]- [L.sub.2], [M.sub.1] - [M.sub.2], [U.sub.1] - [U.sub.2])

4. [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

[FIGURE 1 OMITTED]

Fuzzy Linguistic Variable

The fuzzy linguistic variable is a variable that reflects the different levels of human language. Its value represents the range from natural to artificial language. When precisely reflecting the value or meaning of a linguistic variable, there must be an appropriate way to interpret the value. Variables for a human word or sentence can be considered with numerous linguistic criteria, such as equally important, moderately important, strongly important, very strongly important, and extremely important, as shown in Figure 2. Their definitions and descriptions are shown in Table 3. For the purpose of the present study, the five criteria above (i.e., equally important, moderately important, strongly important, very strongly important and extremely important) are used.

[FIGURE 2 OMITTED]

Calculation Steps of FAHP

The four-step procedure is as follows:

Step 1: Comparing the performance score

Assuming K experts, we precede to decision-making on P alternatives with n criteria.

Step 2: Constructing the fuzzy comparison matrix:

We use a triangular fuzzy number to represent the meaning of questionnaires, and construct positive reciprocal matrixes.

Step 3: Examine consistency of fuzzy matrix [??]

Assume A = [[a.sub.ij]] is a positive reciprocal matrix, and [??] = [[[??].sub.ij] is a fuzzy positive reciprocal

matrix. If A = [[a.sub.ij]] is consistent, [??] = [[[??].sub.ij] will also be consistent.

Step 4: Calculate fuzzy evaluation of number [[??].sub.i]

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

Step 5: Calculate fuzzy weight [[??].sub.i]

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

Step 6: Make non-fuzzy

We find the best crisp value or non-fuzzy value in accordance with the center of area (COA or Center Index, CI) approach, which was developed by Teng and Tzeng (1993), allowing us to calculate clear weights for each index. The calculation method is as follows:

[BNP.sub.i] = [([UR.sub.i] - [LR.sub.i])+([MR.sub.i]-[LR.sub.t])]/3 + [LR.sub.i], [for all]i

AN EMPIRICAL STUDY

After summarizing the relevant literature and conducting in-depth interviews, we have extracted 33 indices within eight dimensions, as shown in Table 4. To sample the opinions of senior managers and related background experts in bicycle component manufacturing companies, 50 questionnaires were sent out, and 41 were returned (3 were discarded for statistical reasons). The overall response rate was 76%.

Over half (55%) of the respondents were male; 53% of the respondents were between 41-50 years old, and 34% were 31-40 years old; 47% of the respondents had worked in the field between six and ten years, and about 29% had worked between 11 and 20 years; 76% of the respondents has master's degrees and 16% had bachelor's degrees. Over half (87%) the respondents had a background in industry, and about 13% of respondents had an academic background. Detailed demographic information is provided in Table 3. The overall ranking of factors is given in Table 4 which is above.

CONCLUSION

With increasing economic ties to China, joining the WTO and competition from foreign countries, industries in Taiwan must develop competitive advantages to survive. In addition, because of China's low labor costs, large real estate, etc., more and more Taiwanese companies have moved to China to expand their factories. Maintaining and upgrading overall product quality has become an important issue for such companies. At this time, some Taiwanese bicycle manufactures (e.g., Giant) have achieved global success; however, it's impossible for them to produce excellent bicycles without high quality components. This study aims to provide a clear way for Taiwanese bicycle component manufacturing companies in China to conduct quality improvement.

After analyzing the opinions of senior managers and related background experts, we found that the five most critical factors are Process of R&D and Innovation (0.122), Strategy for a Product (0.101), The Operation and Improvement of Strategy (0.092), Social Responsibility (0.077), Input of R&D and Innovation (0.048).

For bicycle component manufacturers, creating low cost processes to produce components will greatly affect their final prices. Thus, we suggest that Taiwanese bicycle component manufacturers in China create R&D teams to focus on the construction of processes. This study indicates that companies ought to develop product strategies to make components more durable, to make them more interchangeable, etc. These kinds of strategies will help make the bicycle component manufacturers attractive to more bicycle companies. Today's bicycle manufacturing market is changing drastically, and more bicycle companies are customer oriented. Thus, it is crucial for bicycle component manufacturers to create products that can be used in various kinds of bicycles. This study suggests that component manufacturers can allocate marketing employees to different branch companies. Since people in China generally use bicycles for their daily transportation, if branch companies can fully understand the main needs of customers, they can justify allocating large amounts of resources to create key components for their buyers. In addition, social responsibility has become both a way of marketing and an avenue for social contribution for various industries. Thus, this study suggests that bicycle component manufacturers can create more social activities to promote their products while contributing to society. Lastly, making used or discarded parts into useful resources can decrease costs and increase the quantity of products. Hence, we suggest that bicycle component manufacturers organize recycling groups.

Although many factors can contribute to the quality improvement of Taiwanese bicycle component manufactures in China, due to the limited resources of organizations and the 80/20 theory, it is critical to focus on the most profitable and helpful ways to improve quality and performance. Towards this end, this study suggests that those companies ought to first focus on the top five factors to improve quality performance. If additional resources remain, addressing other TQM factors based on the individual needs of an organization will make quality improvement more successful.

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Jui-Kuei Chen, Tamkang University

I-Shuo Chen, National Chiao Tung University

Table 1. Different aspects of TQM

Authors TQM Factors

Black & Porter, 1996 People and customer management,
 supplier partnerships, communication of
 improvemen information, customer
 satisfaction orientation, external
 interface management, teamwork
 structures for improvement, operational
 quality planning, quality improvement
 measurement systems, and corporate
 quality culture.

Terziovski & Samson, 1999 Customer-focus related positively to
 organizational performance in the areas
 of customer satisfaction, employee
 morale, delivery, productivity, cash
 flow, and sales growth.

Reed et al., 2000 Customer focus, leadership and top
 management commitment, training and
 education, team and culture.

Ugboro & Obeng, 2000 Top management leadership and
 commitment, teamwork, flow of
 information within the organization,
 employee involvement and empowerment.

Brah et al., 2000; Top management support, customer focus,
Das et al., 2000 employee involvement, employee
 training, employee empowerment,
 supplier quality management, process
 improvement, service design, quality
 improvement rewards, benchmarking and
 cleanliness and organization.

Motwani, 2001 Top management commitment, employee
 training and empowerment, quality
 measurement and benchmarking, process
 management and customer involvement and
 satisfaction.

Antony et al, 2002 Management commitment, role of the
 quality department, training and
 education, employee involvement,
 continuous improvement, supplier
 partnership, product/service design,
 quality policies, quality data and
 reporting, communication to improve
 quality and customer satisfaction
 orientation.

Sila & Ebrahimpour, 2002 Top management commitment, employee
 involvement, employee empowerment,
 education and training, teamwork,
 customer focus, process management,
 information and analysis systems,
 strategic planning, open organization,
 a service culture and process
 management.

Shieh & Wu, 2002 Leadership, human resource management,
 process management, supply chain
 management and information management.

Sureshchandar et al., 2002 Top management commitment and visionary
 leadership, human resource management,
 technical systems, information and
 analysis systems, benchmarking,
 continuous improvement, customer focus,
 employee satisfaction, union
 intervention, social responsibility and
 service culture.

Besterfield, 2003 Quality culture, the quality chain,
 quality assurance, commitment to
 continuous improvement and the support
 of top management.

Table 2: Definition and membership function of fuzzy number

Fuzzy Number Linguistic Variable Triangular
 fuzzy number

9 Extremely important/preferred (7,9,9)
7 Very strongly important/preferred (5,7,9)
5 Strongly important/preferred (3,5,7)
3 Moderately important/preferred (1,3,5)
1 Equally important/preferred (1,1,3)

Table 3: Demographic Information

Variable Item Distribution Percentage

1. Gender (1) Male 21 55%
 (2) Female 17 45%

2. Age (1) Under 30 5 13%
 (2) 31 ~40 13 34%
 (3) 41 ~50 20 53%
 (4) Above 51 0 0%

3. Served Years (1) Under 5 6 16%
 (2) 6 ~10 18 47%
 (3) 11 ~20 11 29%
 (4) Above 21 3 8%

4. Educational Degree (1) Bachelor's 6 16%
 (2) Master's 29 76%
 (3) Doctoral 3 8%

5. Background (1) Academia 5 13%
 (2) Industrial 33 87%
 (3) Gov Unit 0 0%

Table 4: Overall factors and their rankings.

Goal Evaluation Global Ranking
 Dimensions Weight

 Leadership and 0.204 2
 Operation
 Ideals

 Strategy 0.158 3
 Management

 R&D and 0.210 1
 Innovation

 The Development 0.146 4
 of Customers
 and a Market

 Human Resources 0.083 6
 and Knowledge
 Management

 The 0.046 8
 Applications
 and Management
 of Information
 Strategy

 Process 0.056 7
 Management

 Operation 0.097 5
 Performance
Evaluation Factors Local Global Ranking
 Weight Weight

Operational Ideals and Values 0.076 0.0155 20
Organizational Mission and Vision 0.128 0.026 15
Leadership Abilities of Top Managers 0.192 0.0392 7
TQM Culture 0.224 0.046 6
Social Responsibility 0.380 0.077 4

Organization Strategy Planning 0.179 0.0284 13
Operation Model 0.239 0.038 9
The Operation & Improvement of 0.581 0.092 3
 Strategy

Process of R&D and Innovation 0.584 0.122 1
Input of R&D and Innovation 0.231 0.048 5
Evaluations of R&D and Innovation 0.185 0.0387 8
 Results

Strategy for a Product 0.691 0.101 2
Customer and Business Management 0.196 0.0287 12
CRM 0.113 0.0165 18

HRP 0.335 0.0278 14
HRD 0.273 0.023 17
Human Resources Utilization 0.188 0.0157 19
Employee Relationship Management 0.125 0.0104 26
KM 0.079 0.007 31

Information Strategy Planning 0.622 0.0289 11
Internet Applications 0.253 0.012 24
Information Applications 0.125 0.0058 33

Product Process Management 0.604 0.034 10
Supportive Activity Management 0.224 0.013 23
Cross-Organization Management 0.173 0.0096 27

Customer Satisfaction 0.245 0.024 16
Market Development Performance 0.158 0.0152 21
Financial Performance 0.150 0.014 22
HRD Performance 0.094 0.00912 28
Information Management Performance 0.110 0.011 25
Process Management Performance 0.085 0.008 30
Innovation and Core Competitive 0.094 0.00911 29
 Ability Performance
Social Measurement 0.064 0.0062 32