Analyzing factors influencing industrial competitiveness of Thai silver jewelry industry using analytic hierarchy process

Chia sẻ: Nguyễn Thảo | Ngày: | Loại File: PDF | Số trang:12

Thêm vào BST

Báo xấu

16
lượt xem 1
download

Download Vui lòng tải xuống để xem tài liệu đầy đủ

Promoting export-based industry is a challenging task. Industrial competitiveness is frequently used as the overall indicator. The article offers a novel result of priority weights of Thai silver jewelry industry.

Chủ đề:

Bình luận(0) Đăng nhập để gửi bình luận!

Lưu

Nội dung Text: Analyzing factors influencing industrial competitiveness of Thai silver jewelry industry using analytic hierarchy process

International Journal of Mechanical Engineering and Technology (IJMET) Volume 10, Issue 03, March 2019, pp. 1301-1312. Article ID: IJMET_10_03_132 Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=3 ISSN Print: 0976-6340 and ISSN Online: 0976-6359 © IAEME Publication Scopus Indexed ANALYZING FACTORS INFLUENCING INDUSTRIAL COMPETITIVENESS OF THAI SILVER JEWELRY INDUSTRY USING ANALYTIC HIERARCHY PROCESS Chanida Phittayanon and Vichai Rungreunganun Department of Industrial Engineering, Faculty of Engineering King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand. ABSTRACT Promoting export-based industry is a challenging task. Industrial competitiveness is frequently used as the overall indicator. The article offers a novel result of priority weights of Thai silver jewelry industry. Factors that may influence the industry were collected by literature reviews. Then, industry experts and entrepreneurs choose and classify the factors into performance drivers. There are 32 factors organized into 7 categories or drivers. Next, the analytical hierarchy process (AHP) is utilized to calculate priority weights of factors. As a result, the sales-and-marketing driver is the most important driver, and labor- intensive tasks dominate the competitiveness of the industry. On the other hand, the research community could use the priority weight of factors in their research and develop policies to promote the industry. Keywords: Silver Jewelry, AHP, Supply Chain, Competitiveness. Cite this Article: Chanida Phittayanon and Vichai Rungreunganun, Analyzing Factors Influencing Industrial Competitiveness of Thai Silver Jewelry Industry using Analytic Hierarchy Process, International Journal of Mechanical Engineering and Technology, 10(3), 2019, pp. 1301-1312. http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=3 1. INTRODUCTION The jewelry industry is a large part of Thailand economic [1] which employed several thousand high-skill workers and millions of people in related industries [2]. Thailand is one of the top exporters of silver jewelry (HS 711311) in the global market [3]. Competing in the global jewelry market is a key challenge for Thai silver jewelry industrial. The jewelry industry is very dynamic. The price of raw materials is very fluctuated, especially for precious metal. The jewelry product is a fashion product with a short life cycle. http://www.iaeme.com/IJMET/index.asp 1301 editor@iaeme.com
Chanida Phittayanon and Vichai Rungreunganun As a result, the manufacturing process is a fine process with low volume production. Therefore, it relies heavily on high-skill labor processes. To be more competitive, manufacturers, wholesalers, retailers, and traders of Thai jewelry industry have established the Thai Gem and Jewelry Traders Association (TGJTA) [4] to promote the industry. Similarly, the Thai government also establish the Gem and Jewelry Institute of Thailand (GIT) [5]. These private and public organizations have invested in several projects to overcome challenges and increase competitive advantages among its members and the industry as a whole. However, there are still debate among experts on the effectiveness of investment to promote the industry. The overall industrial competitiveness is a generic indicator that used in several industries [6]–[10]. However, there is no commonly agreed method to evaluate IC for the silver jewelry industry. This study uses the analytic hierarchy process (AHP) technique to develop a model to evaluate industrial competitiveness. In this work, we collect factors that influencing the silver jewelry competitiveness from literature and use the AHP method to determine the priority weights of the factors. This article is organized as followed. The literature review is carried out in Section 0. The AHP methodology that we used in this work is discussed in Section 0. The resulting AHP evaluation is shown in Section 0. A discussion of AHP priority weights is presented in Section 0. Then, a conclusion is presented in Section 0. 2. LITERATURE REVIEW 2.1. Industrial Competitiveness Companies always need to balance resources between various business objectives, i.e. problem solving for a short-term benefit or investing in resources for long-term growth. This results in a complex relationship between short-term and long-term growth. In addition, companies also need to overcome challenges outside of the company. Industrial competitiveness has become a major business indicator for entrepreneurs, economists, and industrial engineers. Evaluating industrial competitiveness is a challenging task that relies heavily on expert in the business. The Analytic Hierarchy Process (AHP) has become a popular technique to combine difference opinion from many experts [6]–[10]. The AHP technique also provides a statistical tool to check for consistency in the expert opinion. An AHP method has been used to evaluate industrial competitiveness for a generic company by considering only the marketing, company resources, and environment factors [6]. Applying AHP with the Potter diamond model has been proposed to evaluate IC [7]. Similarly, AHP was used to help in a decision-making process of investing in automation robots in the automotive industry [8], [9]. In sustainable manufacturing, the same technique has been used by emphasizing on Green technology [10]. 3. THAILAND SILVER JEWELRY INDUSTRY 3.1. Supply Chain In Thailand, there are a lot of firms in the jewelry manufacturing segment. Most of them entered this industry by the trading of gemstone or other jewelry. A study of Thailand silver jewelry industry [11] has shown that the supply chain structure of Thailand silvery jewelry industry can be divided into 4 stages as shown in Figure 1. http://www.iaeme.com/IJMET/index.asp 1302 editor@iaeme.com
Analyzing Factors Influencing Industrial Competitiveness of Thai Silver Jewelry Industry using Analytic Hierarchy Process Figure 1 Supply Chain of Thai silver jewelry industry The upstream process involves raw material acquisitions of precious metals and gemstones. In Thailand, jewelry’s raw materials are mainly imported. This is the lowest value-adding process in the supply chain because raw materials must be imported. The midstream process includes processing of raw materials. These processes include cutting, polishing, and processing of gemstones and precious metals. These are a medium value- adding process. The downstream process is the manufacturing of jewelry which can be divided into two groups. The first group is handmade jewelry manufacturers that focus on the high-end market. This is a high value-adding process. The other group is machine-based manufacturers that mass produce for the high-volume market. This is a low value-adding process. The distribution process includes domestic retailer with its brand and export to the global market. This process is a high value-added process. The Thai Ministry of Industry has announced a master development plan [12]. The master plan stated three priority factor-categories to promote the industry. The three categories are (1) manage raw material cost, (2) create a measure to improve marketing channels, and (3) improve supporting structure to promote the industry. 3.1.1. Manufacturing Segment The cost structure of Thailand jewelry manufacturers has been studied in 2003 [13]. Major parts of raw material cost are for gemstones and precious metals which are about 35% and 30%, respectively. While the total cost of raw material is about 70%. Most silver jewelry manufacturers in Thailand are small and medium businesses [1]. The composition is as followed. 58% of them are family businesses, 20% of them are small businesses, 5.3% of them are medium businesses, and only 0.8% of them are large businesses. It is also found that most jewelry businesses are inherited from their parents [2]. And, a lot of jewelry manufacturers are original equipment manufacturers (OEM) for a large global brand [2]. A study of problems in Thailand jewelry industry in 2002 [14] has suggested some measures to improve the industry. These measures included improving labor competency and jewelry design capability. http://www.iaeme.com/IJMET/index.asp 1303 editor@iaeme.com
Chanida Phittayanon and Vichai Rungreunganun 3.1.2. Surrounding Factors influencing the industry Lacking domestic raw material is the main challenge in the upstream process. Thai silver jewelry needs to import most of the precious metal and gemstone [2], [14]–[16]. However, labor competency is the main challenge in midstream and downstream processes. Considering the economy, the market ratio of Thai silver jewelry industry is approximately 80% exported and 20% domestic sales. Therefore, the domestic economic situation would not have a major impact on the industry. The Thai government has influenced the industry by issuing some measures. For example, the establishment of the jewelry industrial development section within the Ministry of Industry and the Gem and Jewelry Institute of Thailand [5]. There are some tax measures to promote the industry, i.e. conditional VAT exempt, 1%-withholding tax exempt, no import tax, and VAT exempt for rough gemstones (exclude diamond and peal). 4. DEVELOPMENT OF THE AHP MODEL In 1971, Saaty proposed the AHP technique [17] which is a multi-factor decision-making tool. Major advantages of AHP include an ability for solving unstructured problems with a high level of complexity. This technique works by divide the problem into multiple hierarchy levels. The hierarchy structure is set as followed. The top level is designated as the objective, while lower levels are for drivers and factors. The priority weight within each hierarchy level is calculated using predetermined measurement and judgment of experts using pairwise comparisons. Then, the priority ranking and weight values are generated. To determine relative priorities of factors that affect the industry, this research uses the AHP technique which consists of the following steps. Step 1: Define the objective Step 2: Determine lower-level factors Step 3: Construct a hierarchy structure of factors Step 4: Survey of expert judgments of pairwise comparisons Step 5: Determine priority weights of factors Step 6: Check for consistency of pairwise comparisons 4.1. Define the objective The objective of the AHP model is set to determine the industrial competitiveness (IC) of Thailand silver jewelry industry. This is to improve the competitiveness and prioritize resource allocation to promote the industrial. 4.2. Determine lower-level factors A list of factors that may be influencing the industry was obtained from a literature review. Then, the list is confirmed by a focus-group discussion. As a result, a total of 32 factors influencing the industrial were identified. Next, factors were divided into seven factor- categories which can be considered as competitive drivers. Seven drivers are business partnerships (BP), internal management (IM), manufacturing (MF), product design (PD), standard and certification (SC), sales and marketing (SM), and surrounding factor (SF). 4.3. Construct a hierarchy structure of factors Drivers and factors that were identified in the previous step were organized in a hierarchy structure which comprises of the objective, drivers, and factors. Therefore, a three-level hierarchy structure was developed as presented in Figure 2. The top level of the model is the http://www.iaeme.com/IJMET/index.asp 1304 editor@iaeme.com
Analyzing Factors Influencing Industrial Competitiveness of Thai Silver Jewelry Industry using Analytic Hierarchy Process overall objective which is the industrial competitiveness. The second level represents seven categories of competitiveness drivers. Finally, the third level contains factors influencing the industry of each category. This model presents the operational relationship between the overall objective, drivers, and factors of the competitiveness that relevant to silver jewelry industrial in Thailand. Figure 2 The AHP model for industrial competitiveness of Thai silver jewelry industry. 4.4. Survey of expert judgments of pairwise comparisons This step performs a comprehensive analysis of industrial competitiveness. By collecting of expert judgments and evaluation. Experts were carefully chosen from the industry. In this study, a group of 43 experts and entrepreneurs from Thailand jewelry firms were invited. First, they were briefed about the research and the AHP method. Then, they were interviewed to assess pairwise comparisons among seven drivers based on their potential to improve industrial competitiveness. Then, each factor in the third level is compared with other factors within the same category. As proposed by Saaty [17], the pairwise comparison uses a nine-point scale of relative preference which is described in Table 1. Table 1 Relative preference for a pairwise comparison [17] Scale Level of Relative Importance 9 Vastly more importance 7 Largely more importance 5 Much more importance 3 More importance 1 Same importance 8, 6, 4, 2 Intermediate level By utilizing the scale shown in Table 1, eight pairwise comparison matrices for the overall objective and seven drivers were constructed. Results of the comparison are put into a matrix form. An example of the matrix of sales and marketing driver is shown in Table 2 in which the cell data 𝑎𝑖,𝑗 represents the relative importance of the 𝑖 factor with respected to the 𝑗 factor and can be computed using geometric mean of all responses as: http://www.iaeme.com/IJMET/index.asp 1305 editor@iaeme.com
Chanida Phittayanon and Vichai Rungreunganun 1 𝑛𝑟 𝑛𝑟 1 𝑎𝑖,𝑗 = (∏ 𝑎𝑖,𝑗,𝑟 ) = , 𝑎𝑗,𝑖 𝑟=1 where index 𝑟 = 1,2,3, … , 𝑛𝑟 refers to each response from an expert, and 𝑛𝑟 is the number of responses. Therefore, the pairwise comparison matrix 𝑃 can be constructed as: 1 𝑎1,2 𝑎1,3 … 𝑎1,𝑛 1 𝑎1,2 1 𝑎2,3 … 𝑎2,𝑛 1 1 𝑃= 𝑎1,3 𝑎2,3 1 … 𝑎3,𝑛 , ⋮ ⋮ ⋮ ⋱ ⋮ 1 1 1 [𝑎1,𝑛 𝑎2,𝑛 𝑎3,𝑛 … 1 ] where 𝑛 is the number of drivers or factors in the same category. For instance, if an expert evaluates that the “place strategy” factor is more important than “after-sales service” the factor, the value of 3 was chosen according to the scale of relative preferences in Table 1. Therefore, reciprocally the “after-sales service” is 1/3 times less important than the “place strategy” factor, i.e. 𝑎𝑖,𝑗 = 1/𝑎𝑗,𝑖 . Thus, a pairwise comparison matrix can be created in a similar manner. The example of a pairwise comparison matrix for a sales- and-marketing driver is shown in Table 2. Table 2 Pairwise comparison matrix (P) of the sales-and-marketing driver Sales and marketing driver F1 F2 F3 F4 F5 Active marketing campaign (F1) 1 1.1149 0.7618 0.9755 1.0224 Price strategy (F2) 0.8970 1 1.1732 0.8160 1.0832 Place strategy (F3) 1.3127 0.8523 1 1.0809 1.2845 Product Brand (F4) 1.0251 1.2256 0.9252 1 1.1186 After-sales service/warranty (F5) 0.9781 0.9232 0.7785 0.8940 1 Total 5.2128 5.1160 4.6387 4.7663 5.5087 4.5. Determine priority weights of factors This step is to calculate the priority weight of all drivers and factors. First, the pairwise comparison matrix 𝑃 is normalized by dividing each cell in column 𝑖 by a summation of all cells in column 𝑖. This generates a normalized pairwise comparison matrix 𝑃norm as shown in norm Table 3, in which a summation of cells 𝑃𝑖,𝑗 in each column is 1. The 𝑃norm is given by: 𝑛 𝐴𝑗 = ∑ 𝑎𝑖,𝑗 , 𝑖=1 norm 𝑃𝑖,𝑗 𝑃𝑖,𝑗 = , 𝐴𝑖 1 𝑎1,2 𝑎1,3 𝑎1,𝑛 𝐴1 𝐴2 𝐴3 … 𝐴𝑛 1 1 𝑎2,3 𝑎2,𝑛 𝑎1,2 𝐴1 𝐴2 𝐴3 … 𝐴𝑛 norm 𝑃 = 1 1 1 … 𝑎3,𝑛 , 𝑎1,3 𝐴1 𝑎2,3 𝐴2 𝐴3 𝐴𝑛 ⋮ ⋮ ⋮ ⋱ ⋮ 1 1 1 1 [𝑎1,𝑛 𝐴1 𝑎2,𝑛 𝐴2 𝑎3,𝑛 𝐴3 … 𝐴𝑛 ] http://www.iaeme.com/IJMET/index.asp 1306 editor@iaeme.com
Analyzing Factors Influencing Industrial Competitiveness of Thai Silver Jewelry Industry using Analytic Hierarchy Process where 𝑛 is the number of drivers or factors in the same category, 𝐴𝑖 is a summation of cells in a column of pairwise comparison matrix 𝑃. Finally, a priority weights vector 𝑊 were produced (shown in the last column in Table 3) by averaging of cells in row 𝑖 of 𝑃norm as: 𝑛 1 norm 𝑊𝑖 = ∑ 𝑃𝑖,𝑗 𝑛 𝑗=1 where 𝑛 is the number of drivers or factors in the same category. Table 3 Normalized pairwise comparison matrix (𝑃norm ) of the sales-and-marketing driver, and the corresponding weight vector 𝑊 Sales and marketing driver F1 F2 F3 F4 F5 Weight Active marketing campaign (F1) 0.1918 0.2179 0.1642 0.2047 0.1856 0.1929 Price strategy (F2) 0.1721 0.1955 0.2529 0.1712 0.1966 0.1977 Place strategy (F3) 0.2518 0.1666 0.2156 0.2268 0.2332 0.2188 Product Brand (F4) 0.1966 0.2396 0.1995 0.2098 0.2031 0.2097 After-sales service/warranty (F5) 0.1876 0.1805 0.1678 0.1876 0.1815 0.1810 Priority weight is the relative importance of influence on the objective of a factor in relation to other factors. From Table 3, priority weights are ranked with the highest priority given to “place strategy” factor with weight value of 0.2188, followed by “product brand” factor with the weight value of 0.2097, then “price strategy” factor with the weight value of 0.1977, then “active marketing campaign” factor with the weight value of 0.1929, and then “after-sales service/warranty” factor with the weight value of 0.1810. 4.6. Check for consistency of pairwise comparisons This step is to validate whether the pairs of factors are evaluated consistently or not. This is important because it is possible that some evaluators may provide inconsistence judgments. The AHP technique uses the consistency ratio (𝐶𝑅) to check whether a factor can be used for the decision-making process. The 𝐶𝑅 is defined as the ratio of the consistency of the results being tested (called Consistency Index (𝐶𝐼)) over the consistency of random numbers (called Random Index (𝑅𝐼)). Following the AHP method [17], an appropriate value of 𝑅𝐼 can be selected from Table 4. The formula to calculate 𝐶𝑅 and 𝐶𝐼 are given by: 𝐶𝐼 𝐶𝑅 = , 𝑅𝐼 𝜆max − 𝑛 𝐶𝐼 = , 𝑛−1 where 𝑛 is the number of drivers or factors in the same category and 𝜆max is the maximum value of the Eigenvector which can be calculated by: 𝜆max = max(𝜆𝑖 ) , 𝛿𝑖 𝜆𝑖 = , 𝑊𝑖 𝛿 = 𝑃𝑊 where 𝛿 is a matrix multiplication result between the pairwise comparison matrix 𝑃 and the priority weight vector 𝑊, 𝜆 is an Eigenvector, and 𝑖 = 1,2,3, … , 𝑛. http://www.iaeme.com/IJMET/index.asp 1307 editor@iaeme.com
Chanida Phittayanon and Vichai Rungreunganun Table 4 Consistency ratio of random numbers [17] Size of matrix (𝒏) Random index (𝑹𝑰) 3 0.58 4 0.90 5 1.12 6 1.24 7 1.32 Table 5 shows an example calculation of the sales-and-marketing driver. Values of 𝛿, Eigenvector 𝜆 are shown in the column corresponding to their factors. The 𝜆max is the largest value of the Eigenvector 𝜆 are used to calculate 𝐶𝐼. The 𝑅𝐼 of 1.12 was selected from Table 4. Finally, the 𝐶𝑅 value of 0.007017 was obtained. If expert judgments are consistent enough to provide a meaningful estimation of priority weights, the 𝐶𝑅 value will be small. In the AHP technique, a typical threshold value is 0.1. Hence, if the 𝐶𝑅 value is less than 0.1, the degree of consistency is satisfactory. Otherwise, there might be serious inconsistencies, and priority weights might not provide meaningful results, and the evaluation should be reviewed. For example, in the sales-and-marketing driver, the 𝐶𝑅 value is 0.007017. Therefore, the degree of consistency can be considered acceptable (𝐶𝑅 ≪ 0.1). Table 5 Example of calculation of consistency ratio Sales and marketing driver 𝜹 𝝀 Active marketing campaign (F1) 0.9695 5.0273 Price strategy (F2) 0.9945 5.0314 Place strategy (F3) 1.0996 5.0257 Product Brand (F4) 1.0545 5.0286 After-sales service/warranty (F5) 0.9099 5.0270 𝜆max = 5.031437 Notes: 𝐶𝐼=0.007859, 𝑅𝐼=1.12 for 𝑛=5, 𝐶𝑅=0.007017 Similarly, pairwise comparisons for the objective and other drivers were performed. Their priority weights are calculated. And, their degrees of consistency are checked. Priority weight results of pairwise comparisons of the objective and seven drivers are shown in Section 0. From expert judgments, we check the consistency ratio to validate if evaluators make consistent assessments. The consistency results of indicators and drivers are shown in Table 6. As shown in the table, 𝐶𝑅 values of all indicators and drivers are less than 0.1, which mean that the survey is consistent. Table 6 Consistency validation of the objective and drivers Objective/Driver 𝒏 𝑪𝑰 𝑹𝑰 𝑪𝑹 Competitiveness 7 0.02283 1.32 0.01729 Business Partnership 3 0.02999 0.58 0.05172 Internal Management 5 0.05588 1.12 0.04989 Manufacturing 5 0.04677 1.12 0.04176 Product Design 5 0.04111 1.12 0.03670 Standard and Certificate 4 0.01777 0.90 0.01974 Sales and Marketing 5 0.00786 1.12 0.00702 Surrounding Factors 5 0.00447 1.12 0.00399 http://www.iaeme.com/IJMET/index.asp 1308 editor@iaeme.com
Analyzing Factors Influencing Industrial Competitiveness of Thai Silver Jewelry Industry using Analytic Hierarchy Process 5. AHP MODEL FOR COMPETITIVENESS ANALYSIS The AHP hierarchy structure is shown in Figure 2. Priority weights of the competitive indicator of silvery jewelry industry are shown in Table 7. Similarly, the weights of factors on each driver are shown in Table 8 to Table 14. As shown in Table 7, the sales-and-marketing is the most important driver at 24.5%, followed by, the product-design is at 17.2%. Then, the manufacturing driver is at 15.1%. Table 7 Weights of the Silver Jewelry Competitiveness Driver Weight Business Partnership (BP) 0.068925 Internal Management (IM) 0.146062 Manufacturing (MF) 0.151112 Product Design (PD) 0.172213 Standard and Certificate (SC) 0.147543 Sales and Marketing (SM) 0.245540 Table 8 Weights of Business Partnership Factors BP Driver Weight Direct business partnership between 0.309486 raw material supplier and manufacturer Marketing strategies in managing manufacturer 0.354798 and foreign partner Branding development with foreign partner 0.335715 Table 9 Weights of Internal Management Factors IM Driver Weight Ability in internal management 0.209013 Skill and ability development of employee 0.301368 Inventory Management 0.138480 Distribution and Transportation 0.109858 Liquidity of Equity 0.241281 Table 10 Weights of Manufacturing Factors MF Driver Weight Improvement of production technology 0.200045 Mold and die manufacturing technology 0.112691 Labor proficiency in production 0.260173 Variety of raw material 0.146506 Cost of raw material 0.280585 Table 11 Weights of Product Design Factors PD Driver Weight R&D in jewelry design 0.236098 Packaging design 0.116260 Design technology 0.153457 Design ability 0.268515 Product variety 0.225669 http://www.iaeme.com/IJMET/index.asp 1309 editor@iaeme.com
Chanida Phittayanon and Vichai Rungreunganun Table 12 Weights of Standard and Certificate Factors SC Driver Weight Improvement to standard level 0.152091 Obtained certification/standard 0.146603 High-quality product 0.357133 Quality of raw material 0.344173 Table 13 Weights of Sales and Marketing Factors SM Driver Weight Active marketing campaign 0.192850 Price strategy 0.197654 Place strategy 0.215791 Product brand 0.209703 After-sales service/warranty 0.181001 Table 14 Weights of Surrounding Factors SF Driver Weight Government policies 0.127892 Domestic economic situation 0.123164 Foreign economic situation 0.250157 Consumer purchasing power 0.318467 Currency exchange risk 0.180320 6. RESULT DISCUSSION As shown in Table 7, it is found that the top three factor-categories (drivers) are (1) sales and marketing, (2) product design, and (3) manufacturing. These top-three drivers contribute over 56% of the competitive indicator. These top drivers are all labor-intensive processes in the Thai silver jewelry industry. Therefore, it is very important to promote labor competency. Although this is a piece of common knowledge in the industry, this insight is confirmed by the result of this work. By multiplying priority weights of 32 factors with the corresponding weight of its driver, impacts of each factor on the industrial competitiveness can be derived. Table 15 shows the top 10 factors with the highest impact values. Table 15 Impact of top 10 factors Factor Impact Place strategy 5.37% High-quality product 5.27% Product Brand 5.15% Quality of raw material 5.08% Price strategy 4.85% Active marketing campaign 4.74% Design ability 4.62% After-sales service/warranty 4.44% Skill and ability development of employee 4.40% http://www.iaeme.com/IJMET/index.asp 1310 editor@iaeme.com
Analyzing Factors Influencing Industrial Competitiveness of Thai Silver Jewelry Industry using Analytic Hierarchy Process 7. CONCLUSION Evaluating industry competitiveness is a complex task. In this work, an AHP model has been proposed to evaluate silver jewelry industrial competitiveness. Factors influencing the industry were collected from literature and focus group interview. Then, an expert judgment survey has been conducted using the AHP technique to determine the quantitative priority of factors. In this AHP analysis, all of drivers and factors pass the consistency validation. In addition, industry experts have reviewed and agreed with priority weights. Individual firms could use the proposed method to evaluate their competitiveness and validate the proposed model by comparing the competitive indicator with revenue or profit over a period of time. In future works, this AHP model could be used to develop a system dynamic (SD) model and use it to simulate impacts of policy and measure to the industrial. ACKNOWLEDGMENT Authors would like to acknowledge the Thai Gem and Jewelry Traders Association (TGJTA) for suggesting experts in the industry. Similarly, authors greatly appreciate the help of 43 experts and entrepreneurs of Thai silver jewelry industry for their time and effort completing a lengthy questionnaire for pairwise comparisons. Authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Authors received no financial support for the research, authorship, and/or publication of this article. REFERENCES [1] S. Thammaruaksa, W. Saneha, and S. Apirajkamol, “Thai Gems and Jewelry Industries Census Project,” Univ. Thai Chamb. Commer. J., vol. 30, no. 1, 2010. [2] W. Rungreungpol, C. Kongjutirat, Y. Dumrongsri, S. Prechasud, and A. Supanitpon, “Study of Thailand Silver Jewelry Industry: Comparison with Major Competitors for Increase Competitive Stategy,” The Gen and Jewelry Institute of Thailand, 2010. [3] UN Comtrade, “UN Comtrade: International Trade Statistics,” 2018. [Online]. Available: http://comtrade.un.org/data/. [4] Thai Gem Jewelry Traders Association, “Thai Gem & Jewelry Traders Association,” 1976. [Online]. Available: www.thaigemjewelry.or.th. [5] GIT, “The Gem and Jewelry Institute of Thailand (Public Organization),” 2018. [Online]. Available: https://www.git.or.th/. [6] R. Korsakienë, “Determining Competitive Advantage: The Analytic Hierarchy Process,” J. Bus. Econ. Manag., vol. 5, no. 4, pp. 205–215, 2004. [7] W. Chang, “Application Research of AHP in Competitiveness Evaluation of Regional Sports Industry,” in 2016 International Conference on Smart City and Systems Engineering (ICSCSE), 2016, pp. 490–493. [8] S. Sirikrai, “Competitiveness Analysis: An Ahp Approach for The Automotive Components Industry in Thailand,” Thammasat Rev., vol. 12, no. 1, pp. 85–114, 2007. [9] S. B. Sirikrai and J. C. S. Tang, “Industrial competitiveness analysis: Using the analytic hierarchy process,” J. High Technol. Manag. Res., vol. 17, no. 1, pp. 71–83, Jan. 2006. [10] S. Gupta, G. S. Dangayach, A. K. Singh, and P. N. Rao, “Analytic Hierarchy Process (AHP) Model for Evaluating Sustainable Manufacturing Practices in Indian Electrical Panel Industries,” Procedia - Soc. Behav. Sci., vol. 189, pp. 208–216, May 2015. [11] T. Somboonwiwat, “Model logistics and supply chain management in gemstone,” 2008. [12] Thai Ministry of Industry, National Industrial Development Master Plan 2002-2024. 2001. http://www.iaeme.com/IJMET/index.asp 1311 editor@iaeme.com
Chanida Phittayanon and Vichai Rungreunganun [13] S. Supacharasai, “Chapter 4 Gems and Jewelry Industries,” in Industrial Competitiveness Enhancement Project under International Industrial cooperative Framework, Thai APEC Study Centre, Thammasat University, 2003, p. 87. [14] W. Lilakawiwong, “Study of Problem and Status for Competency Evaluation of SME in Silver-Gold Jewelry Industry,” Thailand Research Fund, Research Report RDG4450009, 2002. [15] J. Duangpatra, “Increase Export Capability of Thai Gem and Jewelry to SEA Market,” FAP Newsl., vol. 32, pp. 8–9, Aug. 2015. [16] S. Sutthichan and M. Suteeraroj, “Factors Affected of Competitive Advantage of Gems and Jewelry Export Industries in Thailand,” Nakhon Phanom Univ. J., vol. 2, no. 2, pp. 39–45, Aug. 2012. [17] T. Saaty, The Analytic Hierachy Process. McGraw-Hill, 1980. http://www.iaeme.com/IJMET/index.asp 1312 editor@iaeme.com