377 HỘI THẢO KHOA HỌC QUỐC GIA VỀ LOGISTICS VÀ QUẢN LÝ CHUỖI CUNG ỨNG VIỆT NAM LẦN THỨ 4 (CLSCM-2024) A PERFORMANCE EVALUATION FOR SHIPPING LINE IN TAIWAN: AN APLLICATION OF DATA ENVELOPMENT ANALYS SỬ DỤNG PHƯƠNG PHÁP PHÂN TÍCH ĐƯỜNG BAO DỮ LIỆU ĐỂ ĐÁNH GIÁ HOẠT ĐỘNG CỦA CÁC HÃNG TÀU TẠI ĐÀI LOAN THI MY - NGOC LE* , HIEP NGOC NGUYEN Faculty of Logistics and Supply Chain Management, Dai Nam University *Email:
[email protected] Abstract This paper attempts to evaluate efficiency for shipping industry with financial indicators. Data on the industry were obtained from Taiwan Market. It analysing the performance of the shipping line firms during the period of 2020- 2023, the DEA was used to determine that achieved the highest performance in terms of the input and output variables. This study aims to quantify the shipping industry’s operational efficiency and to provide an overview of the state of operations so that managers and entrepreneurs can enhance their performance. We use data envelopment analysis (DEA) to calculate the operating efficiency 11 shipping companies in this paper. According to the result, Yang Ming, U-Ming, and Franbo Lines are the shipping lines with the highest average productivity. These three brands will continue to have the highest average productivity in the industry. This paper can be a beneficial reference to shipping line firms for the policymakers, investors, development, and shipping line management. Keywords: DEA, Taiwn Logistics. Tóm tắt Bài báo tập trung đánh giá hiệu quả của ngành vận tải biển dựa trên các chỉ tiêu tài chính. Dữ liệu về ngành vận tải biển được thu thập từ Taiwan Market. Bài báo phân tích hoạt động của các hãng tàu trong khoảng thời gian 2020-2023, sử dụng phương pháp DEA để quyết định các biến đầu vào và đầu ra giúp các hãng tàu đạt hiệu quả cao nhất. Mục tiêu nghiên cứu là định lượng hiệu quả hoạt động của ngành vận tải biển và cung cấp một cái nhìn tổng quát về thực trạng hoạt động trong ngành, qua đó giúp các nhà quản lý và doanh nhân cải thiện chất lượng hoạt động. Chúng tôi sử dụng phương pháp phân tích đường bao dữ liệu (DEA) để tính toán hiệu quả hoạt động của 11 công ty vận tải biển. Kết quả cho thấy, Yang Ming, U-Ming và Franbo Lines là những hãng tàu có năng suất trung bình cao nhất. Ba hãng này sẽ tiếp tục có năng suất trung bình cao nhất trong ngành. Bài báo này có thể là nguồn tham khảo hữu ích cho các nhà làm luật, các nhà đầu tư, phát triển và quản lý hãng tàu. Từ khóa: DEA, Taiwn Logistics. 1. Introduction Companies in Taiwan account for three of the top 20 largest container cargo shipping enterprises in the world. According to calculations by the Australian Strategic Policy Institute, a think tank, roughly a third of global shipping - and therefore almost one-quarter of the entire global trade by volume -passed through these waters. With Japan and Singapore, Taiwan connect two of the most critical destinations for the global economy. Two of Taiwan’s own ports, Kaohsiung and Taipei, are among the largest in the world. Taiwanese ship operators, notably Evergreen, Yang Ming Marine control more than 10% of global container capacity. Data envelopment analysis (DEA) is an effective method for evaluating the performance of firms in the same industry. DEA use input and output data of firms to asses the performance of each firm relative to others in the group. The DEA approach can be used to solve the above mentioned weight assignment problems. There are numerous large shipping businesses in Taiwan. Shipping makes up more than 90% of the company's revenue, and it is one of its primary operations along with shipping agency, ship and container trading, port container terminal operating, and ship and container renting. Given the unpredictability of greater variations in supply and demand for transportation services, this will be a challenging task. So this study will help managers in shipping lines make decisions for investors in the
378 HỘI THẢO KHOA HỌC QUỐC GIA VỀ LOGISTICS VÀ QUẢN LÝ CHUỖI CUNG ỨNG VIỆT NAM LẦN THỨ 4 (CLSCM-2024) future and also help companies identify their strengths and weaknesses compared to competitors, thereby formulating appropriate strategies to improve performance. The paper includes five part. The first section is the introduction. The second part indicate some previous studies related to performance assessment. The third section introduces material and methods regarding the theory of the Malmquist and the Pearson correlation coefficient. The fourth section presents the results and discussion. The last section discusses the conclusion contribution and, shortcoming of the research. 2. Literature review Data Envelopment Analysis is a technique applying in mathematical programming posed by Charnes et al. [1]. Weng - Cheng Lin, et al.[2]. applied the DEA model to evaluate performance of shipping industry. Research findings indicate that taking financial ratios into account can lead to a more thorough performance review of the shipping business. Bing-Lian Liu, et al.[3].used DEA models and Malmquist TFP approach to measure the efficiency of container terminals in mainland China. The three-year average Malmquist TFP index score of 1.125 indicates an improvement in China's container terminal productivity. As a result, mainland China's container ports are more effective at handling the containers of foreign shipping lines than those of domestic shipping lines. Shih-Liang Chao, et al.[4]. applied dynamic network DEA to evaluate the efficiency of container shipping companies CSC' period from 2013 to 2015. Every CSC's division and company efficiency scores are calculated and analyzed. Hong-Oanh Nguyen, et al.[5]. employs bootstrapped DEA to a sample of the 43 biggest ports in Vietnam and contrasts the findings with traditional DEA and stochastic frontier analysis (SFA) findings. The findings demonstrate that although the efficiency scores derived from the three approaches offer consistent and helpful assessments of the ports' efficiency, they diverge considerably. Horng-Jinh Chang and Ling-Chu Liao.[6] applied the data envelopment analysis (DEA) model to estimated performance methods for the ocean freight forwarder. The use of DEA is readily adaptable and expandable to comparable environments for other businesses, in other maritime zones, including the deep sea and the short sea. Pei Fun Lee, et al.[7]. used the DEA model to evaluate efficiency of logistics companies. The findings indicate that all Malaysian logistics companies that use a data envelopment analysis (DEA) methodology have an operational risk component. In the suggested model, the operational risk capital need component is indicated by the basic indicator approach (BIA). Nguyen - Dai Duong, et al.[8] DEA Malmquist was utilized to conduct an output-oriented CCR and BCC DEA model analysis of the 26 container terminals located in Vietnam.To assess how the productivity of container ports has changed over time, the Malmquist Productivity Index (MP I) was also used. Chia-Nan Wang, et al.[9] used the DEA Malmquist and EMB model for the evaluation of seaport terminal operators. The DEA model assesses the total productivity growth rates of the companies and the EBM to calculate the efficiency inefficiency score of each company. Toshiyuki - Sueyoshi, et al.[10] applied the Data Envelopment Analysis (DEA) to evaluate energy and environment. The study is that technology innovation in engineering and natural science to enhance reduce problems climate change and environment pollutions. Magdiel A. Agüero-Tobar,et al.[11] used the DEA model to estimate the efficiency of the logistics performance of twelve Chilean containerized port cargo terminals. E. Krmac et al. [12] applied the DEA methodology to evaluate port performance. The result of the study is important for researchers as well as port managers and policy markers for analyzing future port performance.Rujia Chen & Yaping Zhang [13] used the SBM-DEA model to evaluate freight transport efficiency, and optimize freight structure . The findings offer new views on the development of carbon mitigation techniques in addition to important insights into freight structure optimization. 3. Methodology 3.1. Malmquist Productivity Index (MPI) The variation in total factor productivity of DMUs in two periods are expressed by the MPI values, being defined as the product of technical efficiency change (catch-up index) and technological change (frontier-shift index). Technical efficiency change is correlated with the power of the DMUs to get any efficiency improvements or deteriorations, while technological change indicates any progress in technology development and innovation of DMUs between periods 1 to 2. (by Chia-Nan Wang, 2020) The authors named that the DMUi at the period 1 is (ai 1 , bi 1 ) and at the period 2 is (ai 2 , bi 2 ) . The efficiency score of the DMUi(ai 1 , bi 1 ) t1 is measured
379 HỘI THẢO KHOA HỌC QUỐC GIA VỀ LOGISTICS VÀ QUẢN LÝ CHUỖI CUNG ỨNG VIỆT NAM LẦN THỨ 4 (CLSCM-2024) by the technological frontier t2 : d t2((ai , bi ) t1) (t1 = 1, 2 and t2 = 1, 2). To compute for the catch-up index (C), frontier- shift index (F), and Malmquist Index (MI), the following formulas can be applied: C = d 2 ((ai , bi) 2 ) d 1((ai , bi) 1) F = [ d 1 ((ai , bi) 1 ) d 2((ai , bi) 1) × d 1 ((ai , bi) 2 ) d 2((ai , bi) 2) ] 1 2 MI = C x F = d 2 ((ai , bi) 2 ) d 1((ai , bi) 1) × [ d 1 ((ai , bi) 1 ) d 2((ai , bi) 1) × d 1 ((ai , bi) 2 ) d 2((ai , bi) 2) ] 1 2 MI = [ d 1 ((ai , bi) 2 ) d 1((ai , bi) 1) × d 2 ((ai , bi) 2 ) d 2((ai , bi) 1) ] 1 2 It can be seen from the above equations that the DMU’s total factor productivity (TFP) shows the increases or decreases of the DMUs in technical efficiency and technological innovation efficiency, respectively. Technical efficiency change, technological change, and the total factor productivity of DMUi from period 1 to 2 achieved progress, stable, or regress when the value of C, F, and MI are > 1, = 1, or < 1, respectively. (by Chia-Nan Wang, 2020) 3.2. Person Correlation Coefficient The Pearson correlation is generally used in many previous studies. It has a value between −1 and +1, describing the linear dependence of two variables or sets of data, where +1 is a total positive linear correlation, 0 is no linear correlation, and −1 is a total negative linear correlation. The correlation coefficient equation of Pearson’s (r) of two variables (x) and (y) is computed below: rxy = ∑ (xi −x)(yi − y) n i=1 √∑ (xi − x) n 2 i=1 ∑ (yi − y) n 2 i=1 where n is the size of the sample; x_i, y_irepresents the individual sample points indicated with i; Moreover, x ̅=1/n ∑_1^n▒x_i is the mean of the sample, which is similar fory ̅. Because the homogeneity and isotonicity are two critical DEA data assumptions, these do the correlation test an imperative step before using DEA. This is a certainty that there is an isotonic condition between input and output variables. The input and output data must have a positive correlation (i.e. the closer the value to +1, the better positive linear correlation). (by Chia-Nan Wang, 2020). Table 1. List of shipping line company in Taiwan DMU Shipping line Company DMU1 Evergreen DMU2 Yang Ming DMU3 DMU4 DMU5 DMU6 DMU7 DMU8 DMU9 DMU10 DMU11 Tze Shin First Steamship Sincere Navigation U-Ming Taiwan Line Taiwan Navigation Franbo Lines Chien Shing China Container [Yahoo.finance.vn] Table 2. Input and Output factors and definitions Input Description Total Asset (TA) the total amount of assets of value a business owns (as cash, equipment, tools, property, etc). Liabilities (LI) Stockholders' Equity (SE) Output Gross profit (GP) Revenue Liabilities are debts that a company has to pay, and when the payment is due Stockholders' equity refers to the assets remaining in a business once all liabilities have been settled. Description Gross profit is the measure of a company’s profits directly stemming from its sales after accounting for the Cost of Goods Sold or COGS. Revenue represents the total income generated by a company from its primary operations, typically from sales of goods or services.
380 HỘI THẢO KHOA HỌC QUỐC GIA VỀ LOGISTICS VÀ QUẢN LÝ CHUỖI CUNG ỨNG VIỆT NAM LẦN THỨ 4 (CLSCM-2024) 4. Results and Discussion 4.1. Select of Decision - Marking Unit (DMUs) After considering about 33 firms on the stock market, the authors have selected 11 major companies to conduct research. In this paper, Table 1 of the data of 11 of Taiwan’s top shipping line companies. The DEA is a complex method in which the input and output variables have a significant on the outcome. Selecting inputs and outputs is an essential duty in applying DEA to estimate the performance of eleven shipping line companies. In this study, the authors considered three input and two output factors, as below. 4.2. Choosing Input and Output Factors This research aim to estimate the performance the business of DMUs strategies. Selecting inputs and outputs is not only important but also must be consistent with the DEA resolution program. In this study, the author considered three inputs and two outputs, which declared as follows Table 2. 4.3. Table of Results 4.4. List of Figure 4.4. Catch-Up Index (Technical Efficiency) The catchup-up index is a measure of the technical effective modification of the shipping line, as shown in Table 3 and Fig 1. In 2020-2023, all shipping lines will grow efficiently. Among the 11 shipping lines, 9 achieved efficiency between 2020 and 2023, with an average catch-up index of better than 1. The DMU with the highest technical efficiency was First Steamship, with a value of 2.0334894. Conversely, Sincere Navigation scored 0.9507411, the lowest average. The technically efficient producer for each of the Table 3. Result of catch up index Table 4. Result of frontier - shift index Catch-up 2020=>2021 2021=>2022 2022=>2023 Average Evergreen 0.77702511 0.937608855 1.1519527 0.9555289 Yang Ming 1.04083376 1.080453153 0.8842733 1.0018534 Tze Shin 1.68868638 1.103161879 1.4848501 1.4255661 First Steamship 0.320518 1.213584104 4.56636609 2.0334894 Sincere Navigation 0.55182653 0.987568884 1.3128279 0.9507411 U-Ming 1.18461794 0.983918566 1.98379949 1.384112 Taiwan Line 0.926792 1.141115209 1.25317957 1.1070289 Taiwan Navigation 0.71654288 0.960021753 2.91646563 1.5310101 Franbo Lines 0.93474495 0.79674016 4.54525411 2.0922464 Chien Shing 0.54677161 0.959320575 1.94291628 1.1496695 China Container 0.74023163 1.026132893 1.63633917 1.1342346 Average 0.85714462 1.01723873 2.15256585 1.3423164 Max 1.68868638 1.213584104 4.56636609 2.0922464 Min 0.320518 0.79674016 0.8842733 0.9507411 SD 0.36887532 0.1135492 1.30550944 0.4048481 Frontier 2020=>2021 2021=>2022 2022=>2023 Average Evergreen 2.7785751 0.9171479 0.3198329 1.3385187 Yang Ming 2.4029517 0.9318411 0.3556537 1.2301488 Tze Shin 1.4735546 0.9973582 0.4816376 0.9841835 First Steamship 4.0277252 0.9097351 0.1861292 1.7078631 Sincere Navigation 1.8855328 0.9869902 0.5927205 1.1550811 U-Ming 1.7147738 0.9400527 0.4029441 1.0192569 Taiwan Line 0.9778519 1.0586781 0.6753844 0.9039715 Taiwan Navigation 2.4748833 1.0158213 0.3959294 1.2955447 Franbo Lines 2.4064911 0.9825119 0.2687438 1.2192489 Chien Shing 2.1933685 0.9797133 0.4070437 1.1933752 China Container 1.5233854 0.9845076 0.5421528 1.0166819 Average 2.1690085 0.9731234 0.4207429 1.1876249 Max 4.0277252 1.0586781 0.6753844 1.7078631 Min 0.9778519 0.9097351 0.1861292 0.9039715 SD 0.8147212 0.0448695 0.1437199 0.2209934 Figure 1. Each DMU’s technical efficiency changes Figure 2. Total factor productivity changes Figure 3. Each DMU’s Technological changes 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 Catch-up Evergreen Yang Ming Tze Shin First Steamship Sincere Navigation U-Ming Taiwan Line Taiwan Navigation Franbo Lines Chien Shing China Container 0 0.5 1 1.5 2 2.5 2020=>2021 2021=>2022 2022=>2023 Malmquist Evergreen Yang Ming Tze Shin First Steamship Sincere Navigation U-Ming Taiwan Line Taiwan Navigation Franbo Lines Chien Shing China Container 0 0.5 1 1.5 2 2.5 3 3.5 4 2020=>2021 2021=>2022 2022=>2023 Frontier-shift Evergreen Yang Ming Tze Shin First Steamship Sincere Navigation U-Ming Taiwan Line Taiwan Navigation Franbo Lines Chien Shing China Container
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