PERFORMANCE EFFICIENCY EVALUATION OF THE TAIWAN S SHIPPING INDUSTRY: AN APPLICATION OF DATA ENVELOPMENT ANALYSIS

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1 PERFORMANCE EFFICIENCY EVALUATION OF THE TAIWAN S SHIPPING INDUSTRY: AN APPLICATION OF DATA ENVELOPMENT ANALYSIS Wen-Cheng LIN Ph. D pogamming student Depatment of Shipping and Tanspotation Management National Taiwan Ocean Univesity 2, Pei-Ning Rd. Keelung, Taiwan, R.O.C. Fax: D @mail.ntou.edu.tw Chin-Feng LIU Pofesso Depatment of Shipping and Tanspotation Management National Taiwan Ocean Univesity 2, Pei-Ning Rd. Keelung, Taiwan, R.O.C. Fax: B0008@mail.ntou.edu.tw Ching-Wu CHU Pofesso Depatment of Shipping and Tanspotation Management National Taiwan Ocean Univesity 2, Pei-Ning Rd. Keelung, Taiwan, R.O.C. Fax: cwchu@mail.ntou.edu.tw Abstact: This pape attempts to constuct an efficient evaluation fo a shipping industy with financial indicatos, because sometimes non-financial indicatos ae difficult to obtain and ae doubtful o untustwothy. So financial indicatos ae an impotant method to evaluate pefomance. A financial statement is audited by CPA and it is easy to gathe, so we do not doubt its tustwothiness. The main pupose of this pape is to measue the opeating efficiency of a shipping industy and to highlight the status of opeation pefomance so that manages o egulatos can impove thei pefomance. We use data envelopment analysis (DEA) to calculate the opeating efficiency of 14 shipping fims in this pape. The empiical esults show that pefomance evaluation fo a shipping industy can be moe compehensive if financial atios ae consideed. Key Wods: pefomance evaluation, data envelopment analysis (DEA), financial atios, BCG matix 1. INTRODUCTION Because of the damatic change in infomation technology and the business opeation envionment, fims face seious competition. This situation has also happened in the shipping industy. A good opeating pefomance is citical fo successful business. Accodingly, pefomance evaluation is a main concen of this study. In the business management, financial atios ae usually one of the indicatos used to evaluate a 467

2 fim s pefomance. Geneally, the financial infomation of a company s business opeations will be epoted in the yealy financial statements, and a financial atio simply constitutes one item divided by anothe in the financial statement. Financial atios can be viewed as a peliminay efeence fo the analysis of the business pefomance. This pape attempts to povide an acceptable evaluation of a fim s pefomance fo manages. Taditionally, the evaluation of a fim s pefomance usually employs the financial atio method, because it povides a simple desciption about the fim s financial pefomance in compaison with pevious peiods and helps to impove its pefomance of management. Howeve, if we use the elated financial indicatos to measue technical efficiency in a shipping industy, it will lead to a poblem of the weight assignment to each indicato. The financial atio method can be an appopiate method when fims use a single input o geneate a single output. Howeve, as in many fims, they employ vaious inputs to povide vaious sevices (outputs). Which atio be selected becomes an issue of evaluatos when a geat numbe of elated financial indicatos ae involved. One of the solving methods is to aggegate the aveage among all indicatos in ode to integate a single measuement. The DEA appoach can be used to solve the above mentioned weight assignment poblem. It uses a mathematical pogamming method to geneate a set of weights fo each indicato. It consides how much efficiency could be impoved, and anks the efficiency scoes of individual fims. Chanes et al. (1978) wee the fist to descibe the DEA model (the CCR model), using a mathematical pogamming model to detemine the efficiency fontie when moe than one measue is used. Thus, the DEA model developed in this pape is able to maintain fainess in pefomance assessment, geneating objective weights. This pape is divided into five sections. The fist section is the backgound of the eseach poblem including the impotance of the evaluation pefomance fo a shipping industy. The second section eviews liteatue elevant to the evaluation pefomance. We discuss the poposed DEA model and input/output items, espectively in thid section. The fouth section compaes DEA esults to the financial atios analysis. Lastly, we conclude the empiical findings and suggest aeas fo futhe eseach. 2. LITERATURE REVIEW Having been employed in liteatue fo many decades, financial atios ae the simplest tools fo evaluating the financial pefomance of fims. We can employ financial atios to detemine a fim s solvency, capital stuctue, pofitability, liquidity and asset tunove. Beave (1966) used financial atios to develop an indicato that best diffeentiated between failed and non-failed fims using univaiate analysis techniques. Altman (1968) used financial atios to pedict copoate bankuptcy. He found that the bankuptcy model has an accuacy ate of 93%, and is vey successful in pedicting failed and non-failed fims. Ohlson (1980) employed financial atios to pedict a fim s cisis. He found that thee ae fou factos affecting a fim s vulneability. These factos ae fim s scale, financial stuctue, pefomance, and liquidity. Benstein (1988) found financial atios could divide into fou kinds: 468

3 solvency, capital stuctue, pofitability, and tunove. Accoding to this infomation, financial atios actually have the function to evaluate fim pefomance. Feng (2000) tied to constuct a pefomance evaluation pocess fo ailines taking financial atios into consideation. He used gey elation analysis and the TOPSIS method to ovecome the poblem of small sample and outanking of ailines. Since its intoduction by Chanes et al. (1978), thee have been many applications of DEA. Some applications have involved efficiency evaluation of oganizations with chaacteistics simila to pots, such as hospitals (Banke et al., 1986), schools (Ray, 1991), couts (Lewin et al., 1982), and ai foce maintenance units (Chanes et al., 1985). DEA has also been applied in the tanspotation secto to ailines (Banke and Johnston, 1994; Chanes et al., 1996), and ailways (Oum and Yu, 1994). Yeh (1996) was one of the fist eseaches to combine DEA with financial atio analysis. She utilized DEA to evaluate bank pefomance. He study empiically demonstated that DEA, in conjunction with financial atio analysis, can effectively aggegate and eclassify the peplexing atios into meaningful financial dimensions, which enable analyze to gain insight into the financial opeating stategies of banks. Data envelopment analysis (DEA) is an opeations eseach-based method fo measuing the pefomance efficiency of decision units that ae chaacteized by multiple inputs and outputs. DEA convets multiple inputs and outputs of a decision unit into a single measue of pefomance, geneally efeed to as elative efficiency. (Donthu and Yoo, 1998, p. 91) Donthu and Yoo (1998) assessed etail poductivity by DEA. So DEA appoach can be used to solve the weight assignment poblem and its elative efficiency to measue fim pefomance. Chen (2002) combined DEA measuements and manage pio opinion measuements to geneate a posteio measuement fo an oveall measuement of the technical efficiency of banks in Taiwan. Emel et al. (2003) applied DEA to 82 industial fims compising the cedit potfolio of one of Tukey s lagest commecial banks. He used financial atios to measue oveall pefomance in a single financial efficiency scoe the cedibility scoe. These studies attest that the DEA appoach is a good method to evaluate fim pefomance. Because DEA can solve weight poblems, we use it to measue the opeating efficiencies of 14 shipping fims in this pape. 3. METHODOLOGY Chanes, Coope, and Rhodes (1978) wee the fist to popose the DEA methodology as an evaluation tool fo decision units. DEA has been applied successfully as a pefomance evaluation tool in many fields including manufactuing, school, banks, phamacies, small business development centes, and nusing home chains. Seifod (1990) povides an excellent bibliogaphy of DEA applications. We employed a mathematical planning model (CCR model) to measue the efficiency fontie based on the concept of Paeto optimum. The basic idea of DEA is to identify the most efficient decision-making unit (DMU) among all DMUs. The most efficient DMU is called a Paeto-optimal unit and is consideed the standad fo compaison fo all othe DMUs. That is to say, a single fim is consideed DEA Paeto efficient if it cannot incease any output o educe 469

4 any input without educing othe output o inceasing othe input. An efficient fim can enjoy efficiency scoes of unity, while an inefficiency fim eceives DEA scoes of less than unity. Efficiency is the atio of the weighted sum of a fim to the weighted sum of inputs. The efficiency of any fim is computed as the maximum of a atio of weighted fims to weighted inputs, subject to the condition that simila atios, using the same weights, fo all othe fims unde consideation, ae less than o equal to one. Hee, we denote the maximum efficiency as E k, Y kj as the jth output of the kth DMU and X ki as the ith input of the kth DMU. If a DMU employs p input to poduce q output, the scoe of kth DMU, E k, is a solution fom the factional linea pogamming poblem): Max E k = q U jy j= 1 p V i= 1 q j= 1 i X U jy s.t. 1 p V i= 1 U i V X j i kj i i=1,2,,p, ε > 0 i, j j i =1,2,.,k,,R j=1,2,,q Whee U j and V i ae the vaiable weights in the jth output and the ith input, espectively. The fome model can be efomulated by adding λ = 1 to the poblem, which povides valuable infomation about the cost benefits: + S ki S kj Min TE = θ ε ( + ) λ X p q i= 1 j = 1 s.t. θ + = 0 = 1 λ Y = 1 λ = 1 i S X + = j kj = 1 ki Y j S ki = 1 + λ 0, S 0, 0, ki S i, j, k, kj Whee θ is the efficiency scoe and ε is a non-achimedean quantity which is vey minute. We can calculate the elative efficiency scoe fom the above model and futhe estimate the tageted value fo each output/input of each shipping companies. 470

5 Feng (2000) studied ailines pefomance evaluation consideing the financial factos. He pointed out that the input of an ailine is chaacteized by sunk cost, while its output by intangible poducts, and its consumption by not-stoed sevices. In view of chaacteistics of sunk cost, in addition to the fundamental items of the classified financial statements, flight equipment and inteest expense ae included in the financial factos to evaluate pefomance. Inventoy is not included among the financial factos because of its intangible poducts and not-stoed sevice chaacteistics. The chaacteistics of the shipping industy ae simila with the chaacteistics of the ai industy. Ross and Doge (2002) exploed the pefomance of distibution cente. They found that the capital and vehicles ae impotant input vaiables. With the goal of maxmiuming shaeholdes wealth, we choose stockholdes equity as a poxy fo shaeholdes wealth. Based on the above liteatue and consideing the financial vaiables, we selected two input factos: total assets and stockholdes equity. In ode to conside the diect elationship between input factos and output factos, we choose opeating evenue and net income as output items. 4. EMPRICAL RESULTS Unlike egession, DEA does not impose any paticula functional fom on the data, ceating a moe flexible piecewise linea function. So DEA is a good tool to evaluate entepises pefomance. In this study, thee will be a DMU in one company. The empiical esults seve as a valuable diagnostic tool as it can be obseved fist, with efeence to the efficiency scoe of each individual unit, and second, the slack analysis povides diection fo manageial auditing. We choose 14 open maket shipping companies as DMUs and collected the data of two input vaiables and two output vaiables fom TEJ (Taiwan Economics Jounal) database in This pape applies DEA to fouteen Taiwanese open- maket shipping companies: Fist Steamship, EVEGREEN, Sincee Navigation, U-Ming, Evegeen Intenation, TaJung, YML, TZE SHIN, Chinese Maitime Tanspot (CMT), China Containe Teminal (CCT), ETITC, WAN HAI, Shanloong Tanspotation, and Taiwan Line fo evaluation. Table 1 shows that 14 companies and input/output vaiables. In the fist analysis, fou vaiables (2 outputs and 2 inputs) wee used in DEA. The estimated efficiencies fo the 14 shipping companies in Taiwan, along with thei ank odes, ae shown in Table 2. As explained befoe, these efficiencies wee computed fo each fim afte taking into consideation the inputs and outputs of all 14 fims in the set. Hence these efficiencies ae elative efficiencies. Moeove, the efficient fims (whose efficiency =1) wee used as the benchmak. Theefoe these efficiencies epesent elative-to-best efficiencies. U-Ming, YML, WAN HAI and Shanloong lay on the efficient fontie and hence had efficiency of 1. All othe fims lay inside the fontie and hence wee inefficient (had efficiency less than 1). Given that we used 2 outputs and 2 inputs, a pictoial epesentation of the efficient fontie (which equies moe than 3 dimensions) was not possible. In ode to appeciate these concepts a hypothetical 2-dimensional efficient fontie is shown in Figue 1. This analysis could be fom DEA application using just 1 input and 1 output. The efficient fims ae on the fontie, while othe fims lie inside the fontie. The fontie basically connects the best pefomance unde diffeent input levels. 471

6 Table 1. Input/Output Vaiables of Taiwan s Fouteen Open-maket Shipping Companies (unit one thousand NT dollas) Input Output Cop. Stockholdes Opeating Net Assets equity Revenue Income Fist Steamship 3,323,320 2,575, ,754 40,490 EVEGREEN 74,377,629 38,864,522 35,207,238 3,604,776 Sincee Navigation 7,334,351 6,307, ,248 1,160,165 U-Ming 13,549,538 9,720,290 3,058,830 2,299,232 Evegeen Intenation 24,630,312 18,180,236 4,614, ,696 TaJung 10,539,678 5,836,749 4,506,664 55,177 YML 53,643,651 28,823,464 62,913,555 6,649,097 TZE SHIN 2,973,602 2,179,529 2,076, ,925 CMT 2,947,123 2,428,391 1,187, ,195 CCT 3,882, ,134 1,248,514 33,294 ETITC 14,919,000 5,580,172 10,120, ,361 WAN HAI 32,570,631 20,319,773 37,660,493 4,430,006 Shanloong 2,184,338 1,118,764 4,920, ,908 Taiwan Line 5,991,346 4,525,048 2,357, ,641 Table 2 Efficiency of Fims (DEA Vesus Regession) Cop. DEA Efficiency DEA Ranking Regession 1 Regession 2 Ranking a Ranking b Fist Steamship EVEGREEN Sincee Navigation U-Ming Evegeen Intenation TaJung YML TZE SHIN CMT CCT ETITC WAN HAI Shanloong Taiwan Line Note: a. using Retun on Assets (ROA) as the dependent vaiable. b. using Retun on Equity (ROE) as the dependent vaiable. If we had elied on taditional financial statement analysis, fo example, net income divided by aveage assets (Retun on Assets; ROA), then U-Ming would have the highest poductivity. The 472

7 situation is the same with the DEA analysis. If the fim focused on net income divided by aveage equity (Retun on Equity; ROE), then Taiwan Line would be anked numbe 1. Howeve, in the DEA analysis its efficiency was only Regession has often been used to evaluate fim poductivity. The main dawback of such analysis is that we can only use one output vaiable at a time as the dependent vaiable. Table 2 also exhibits the fim ankings by linea egession analysis using the 2 output vaiables as the dependent vaiables in 2 sepaate egession analysis. The last 2 columns of this table epesent fim ankings using the 2 input vaiables as the independent vaiables and opeating evenue and net income as the dependent vaiables, espectively. Fom this table it is clea that each of these egession-based ankings is diffeent fom the DEA-based ankings. Moeove, DEA analysis uses the best pefomance as the bases fo efficiency computation, wheeas egession uses the aveage pefomance as the bases fo computations. Also, egession optimizes acoss all 14 fims and computes one model and the same coefficients fo all fims. DEA optimizes ove each fim sepaately, and computes unique weights fo each fim. output Efficient fontie F H Regession line C A B E D G input Figue 1. DEA Vesus Regession (Hypothetical Illustation) A close look at each of the inefficient fims can be taken by sensitivity analysis at each fim level. Fo example, Table 3 displays the sensitivity analysis esults fo Taiwan line. This table shows the amount of slack in each of the contollable input and output obsevations fo this fim. This slack is computed by compaing the input and output of Taiwan line with inputs and outputs of its efficient efeence fims. Taiwan line can become efficient (incease efficiency fom to 1.00) by deceasing an inputs by coesponding slack. The Table 3 shows Taiwan line can decease stockholde s equity $310,074 (incease liability elatively). Taiwan could utilize financial leveage to attain moe opeating effect. Moeove, inteest expense could geneate tax deduction. It is an advantage of financing. 473

8 Table 3 Sensitivity Analysis of Taiwan line Vaiable Name Estimated Value Value if Weight Measued Efficient Slack Opeating Revenue ,357,181 2,357,181 0 Net income , ,641 0 Assets ,991,346 5,991,346 0 Equity ,525,048 4,214, ,074 Taiwan Line s estimated weights fo the fou vaiables ae also shown in Table 3. DEA estimates these weights such that the estimated efficiency of fo Taiwan Line is the maximum attainable. No othe combination of weights would have poduced a highe efficiency estimate fo Taiwan Line and yet satisfy all of the constaints. No one vaiable dominates the poductivity estimation of this fim. In the case of egession these weights ae the same fo all fims. In DEA these weights ae uniquely estimated fo each fim, hence, allowing fo each fim to detemine how they will become efficient. In othe wods, each fim is given its best shot because the unique weights ae estimated fo each fim so that the highest possible efficiency is estimated fo that fim. Examining the elationship between efficiency and pofitability, we use the coelation coefficient between DEA efficiency scoes and fim pofit to show thei coelation extent. Two measues of the fim pofit, i.e. Retun on Assets (ROA) and Retuns on Equity (ROE), ae selected and two coesponding coelation coefficients, (ROA) and 0.93(ROE), ae computed, espectively. We choose the highe coelation coefficient in the measues of etun on equity to poceed with the following analysis. The business stategy matix deived by Boston Consulting Goup (BCG matix) employed to illustate the individual evidence in the elationship between shipping fims opeating efficiency and pofitability (see Table 4). It has been obseved that 3 fims ae in the supe stas goup which is chaacteized by high efficiency and high pofitability. Convesely, 8 of 14 fims ae chaacteized by low efficiency and low pofit and ae placed in the poblem child goup. We can ague that the 8 poblem child fims should eaange input to impove thei pefomance. Efficiency Table 4. The Efficiency-Pofit Matix of 14 Shipping Fims Shanloong (0.4909) U-Ming (1) YML (0.9752) E k =1 (Question Maks) WAN HAI (0.9217) (Supe Stas) E k 1 ETITC (0.6389), CMT (0.4878); Evegeen (0.3921), TZE SHIN (0.2074), CCT (0.1592), Evegeen Intenation (0.1304), Fist Steamship (0.0665), TaJung (0) Sincee Navigation (0.7776) Taiwan line (0.7209) (Cash Cow) (Poblem Child) Pofit (Retun on Equity)* * The etun on equity has been standadized. 474

9 5. CONCLUSIONS This pape employs data envelopment analysis to evaluate the elative efficiency of 14 shipping companies in Taiwan. The estimated esults show that 4 fims ae elatively efficient, and that thee is a athe high level of oveall efficiency. It is U-Ming, YML, WAN HAI and Shanloong, espectively. The inefficient fims can effectively pomote esouce utilization efficiency by bette handling labo and capital opeating efficiency. We also compae the data envelopment analysis esults to the financial atios of taditional financial statement analysis and show that thee is an inconsistent esult. Thus we can not conclude which fim has a highe pefomance based on financial atios only. We encounteed some key limitations in ou eseach. Fist, output vaiables do not include quality-type indicatos, e.g. sevice quality and equipment quality, because the data wee unavailable. Second, foeign shipping companies ae not included in ou study due to limited data. It may lead to be unable to fully descibe the whole pictue in Taiwan s competitive situation. Finally, DEA does not guaantee the cause o emedy fo the identified inefficiency. Intenal audits o pee eview ae needed to define the types of opeating changes that can affect efficiency impovements. It would be a meaningful task to finish them in the futue eseach. REFERENCE Altman, E.I. (1968) Financial atios, disciminant analysis and the pediction of copoate bankuptcy, The Jounal of Finance, Vol.4, Banke, R.D., Conad, R.F., Stauss, R.P. (1986) A compaative application of DEA and tanslog methods: an illustative study of hospital poduction, Management Science, Vol. 32 No. 1, Banke, R. and Johnston, H. (1994) Evaluating the impacts of opeating stategies on efficiency in the US Ailine Industy. In: Data Envelopment Analysis: Theoy, Methodology, and Application. Kluwe Academic Publishes, Boston. Beave, W. (1966) Financial atios as pedictos of failue, Jounal of Accounting Reseach, Vol. 4, Benstein, L.A. (1988) Financial statement analysis, theoy, application, and intepetation, Jounal of Accounting Reseach, Vol. 3, Chanes, A., Coope, W.W. and Rhodes, E. (1978) Measuing the efficiency of decision making units, Euopean Jounal of Opeational Reseach, Vol. 2, Chanes, A., Coope, W.W., Clak, T. and Golany, B. (1985) A developmental study of data envelopment analysis in measuing the efficiency of maintenance units in the US Ai Foces, Annals of Opeations Reseach,

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