Performance Assessment for Electronic Manufacturing Service Providers Using Two-Stage Super-Efficiency SBM Model

Applied Economics

ISSN: 0003-6846 (Print) 1466-4283 (Online) Journal homepage: http://www.tandfonline.com/loi/raec20

Performance assessment for electronic manufacturing service providers using two-stage super-efficiency SBM model

Chia-Nan Wang, Hsien-Pin Hsu, Yen-Hui Wang & Thi-Thu-Huyen Pham

To cite this article: Chia-Nan Wang, Hsien-Pin Hsu, Yen-Hui Wang & Thi-Thu-Huyen Pham (2016): Performance assessment for electronic manufacturing service providers using two- stage super-efficiency SBM model, Applied Economics, DOI: 10.1080/00036846.2016.1229446

To link to this article: http://dx.doi.org/10.1080/00036846.2016.1229446

Published online: 13 Sep 2016.

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Download by: [UCL Library Services] Date: 28 January 2017, At: 23:05 APPLIED ECONOMICS, 2016 http://dx.doi.org/10.1080/00036846.2016.1229446

Performance assessment for electronic manufacturing service providers using two-stage super-efficiency SBM model Chia-Nan Wanga,b, Hsien-Pin Hsuc, Yen-Hui Wangd and Thi-Thu-Huyen Phame aNational Kaohsiung University of Applied Science, Kaohsiung, Taiwan; bTon Duc Thang University, Ho Chi Minh City, Vietnam; cNational Kaohsiung Marine University, Kaohsiung, Taiwan; dChihlee University of Technology, New Taipei City, Taiwan; eFoxconn Electronics Inc., Honi, Vietnam

ABSTRACT KEYWORDS Manufacturing Service providers (EMSs) offer services to Original Equipment Manufacturers Data envelopment analysis; (OEMs). However, increasing challenges require an EMS to be more capable, adaptable and EMS industry; forecast; grey responsive. For survival, an EMS manager has to understand its relative efficiency in the industry. model; performance In addition, an investor also requires such information for investments. In this research, we assessment propose a novel approach, which combines GM(1,1) with a two-stage super-efficiency slacks- JEL CLASSIFICATION based measure (SBM) model, to forecast and assess the efficiencies of 18 EMSs. The GM(1,1) was C22; C53; C67; D78; M21 first used to forecast future data of EMSs, and then the two-stage super-efficiency SBM model was used to measure the marketability and profitability efficiencies for an EMU in two stages. The results build a ‘past-current-future’ view on the two efficiencies for each EMS. In addition, the profitability efficiency can help justify the reasonability of marketability efficiency. Our results showed that Hon Hai tops the rankings in both profitability and marketability efficiencies. These results also provided information about relative efficiencies of these EMSs, which helps EMS managers and investors to make better decisions.

I. Introduction keeping pace with demand; (4) keeping the business model competitive and (5) managing geopolitical risk Equipment Manufacturing Service Providers (EMSs), as their biggest challenge. To deal with these chal- also known as Contract Manufacturer (CM), provide lenges, an EMS needs to understand its relative effi- subcontract services such as design, testing, manufactur- ciency among rivals in order to survive in the industry. ing to Original Equipment Manufacturers (OEMs). For investors, they also require such information for Since 1960s, OEMs started subcontracting services to better decision-making on investments. For these pur- EMSs in order to lower labour cost, increase profit and poses, an effective approach is required to assess and avoid risks, leading to the rapid increase of market share predict the performance of an EMS. for EMSs. Due to continuous efforts to advance their For an enterprise, understanding the future per- manufacturing capability, equipment and purchasing formance is probably more important than the power, the EMS has become an important industry. pastperformanceasthiscanhelpmangersforesee After many years of prosperity, however, many future results and they can immediately do some- EMSs today are facing difficulty of increasing more thing to change unacceptable results. There are market shares due to the trends of globalization and some approaches available for forecasting, includ- integration. In addition, factors such as more demand- ing the statistical methods and Grey Model (GM). ing customers, advancing technology, increasing Between the two approaches, the GM has the labour cost and shortening product lifecycles have advantage of requiring fewer data. To assess the put more challenges on EMSs. The International past as well as future performance of an EMS, an Global Manufacturing Outlook indicated that Top 5 effective methodology is definitely required. In challenges faced by electronic manufacturers include: past studies, Data Envelopment Analysis (DEA) (1) intense competition and prices pressure; (2) effi- has been widely used to assess the efficiency of ciency in research and development; (3) IT system an enterprise, termed decision-making unit

(DMU). For example, Charnes, Cooper and Ang, and Poh 2006; Hernández-Sancho, Molinos- Rhodes (1978) proposed CCR model to evaluate Senante, and Sala-Garrido 2011), we have thus pro- the efficiency of an enterprise while Banker et al. posed using a two-stage super-efficiency SBM model to (1984) proposed BCC model to measure the effi- assess the EMS performances, in addition to using GM ciency of a DMU. These models, however, are (1,1) for forecasting. based on the proportional reduction (enlargement) Our approach, which combines GM(1,1) with a of input (output) (Chang et al. 2013). The model two-stage super-efficiency SBM model, is novel as based on radial assumption has the following this kind of combination has never been used to weaknesses: (1) it does not give information assess the performances of EMSs in terms of profit- regarding the efficiency of the specific inputs or ability efficiency and marketability efficiency. To outputs and (2) it is difficult for ranking the per- facilitate empirical study, we have additionally pro- formance of the efficient DMUs. Differing to these posed an 8-step procedure. Following this proce- traditional models, Seiford and Zhu (1999)pro- dure, after determining the input and output posed a two-stage approach that broke down effi- variables, we have first selected 18 DMUs from the ciency into two components (efficiencies) that are Top 50 EMSs in the industry and collected their assessed in two respective stages. This kind of historical data from 2011 to 2014. Then, the GM models enabled authors to better understand the (1,1) was used to predict their input and output source(s) of inefficiency. However, in that study, a values from 2015 to 2017. After this, we employed traditional DEA model was employed. Recently, the two-stage super-efficiency SBM model to assess two-stage models have been increasingly appeared the efficiency of each DMU. In the first stage of the such as in Chen (2009), Zha and Liang (2010)and model, the equity and employees were used as inputs Song, Wang and Liu (2014). Especially, in Song, and revenue and profit were used as outputs and they Wang and Liu (2014), the authors proposed a two- assessed the profitability efficiency of each DMU. In stage slacks-based measure (SBM) model to assess the second stage, the outputs of the stage 1 served as

the environmental efficiency. Their model was inputs and the return on capital (ROC) and market foundabletoprovideamoreprofoundanalysis value were used as outputs to assess the market- for decision-making. In a later study, Song and ability efficiency of each DMU. The results obtained Guan (2015) further proposed a super-efficiency from the two-stage approach gave a ‘past-current- SBM model to evaluate the e-government perfor- future’ view on the performance of each EMS in mance of environmental protection administra- terms of profitability and marketability efficiencies. tions in the 16 cities of the Anhui province in After analysis, some managerial/investment implica- China based on an analysis of their websites. tions are derived and presented in the conclusion Their results showed the capability of super-effi- section. ciency SBM model in discerning the efficiency of This rest part of this article is organized as fol- e-government web sites (DMUs). lows. Section II includes a literature review on the From our literature review, it is found that neither Grey system theory and DEA. Section III introduces two-stage approach nor super-efficiency SBM model the research methodology that includes Grey fore- has ever been used to assess the performances of an casting model GM(1,1) and the super-efficiency EMS. In addition, for an EMS, it is noted that profit- SBM model. Section IV conducts an empirical ability efficiency, which represents the capability of a study and analyses the results. Section V has a con- DMU in making profit, has never been used to justify clusion and suggestion on future research direction. the marketability efficiency that represents the market values of an EMS. Since our literature review showed that a two-stage model (approach) could provide more II. Literature review profound analysis for decision-making and a super- Grey system theory efficiency SBM model was capable in discerning the efficiency of an DMU (Song, Wang and Liu 2014), Introduction and related research meanwhile a slacks-based non-radial DEA model Grey system theory was introduced by Deng (1989). appeared to have higher discriminatory power (Zhou, In Grey system theory, a system totally unknown is APPLIED ECONOMICS 3 called ‘black’ system; a system totally known is called Forecasting accuracy ‘white’ system; a system partially known is called ‘grey’ A forecast always exists forecasting error that indi- system. Accordingly, most systems are ‘grey’ system cates the accuracy of forecasting model used. (Lin and Liu 2004). The Grey system theory has Forecasting error is defined as the difference become popular due to its simplicity of using fewer between forecasted and actual values. This research amount of historical data for forecasting (Deng 1989), employs the Mean Absolute Per cent Error (MAPE) compared with statistical methods. In Grey system to measure the forecasting error. The formula is theory, the process of generating forecasting data is defined in Equation (1). The smaller the MAPE called generation of grey sequence (Liu and Lin 1998). value the higher the forecasting accuracy. Grey system theory has attracted many research- "# Xn 1 Ai Fi ers and been widely used (Liu and Lin 1998). Basic MAPE ¼ 100 ; (1) contents of Grey system theory used in this research n i¼1 Ai are introduced as follows. where Ai: the actual value in time period i (1) Grey Generating: a technique aims to find Fi: the forecast value in time period i the concealed regulation and characteristic n : is the number of data observations from the disorderly and unsystematic data. Grey generating can reduce randomness of data and promote their regular pattern. Data envelopment analysis (2) Grey Model: a model for prediction based on historical. It generates differential equations Farrell – the measure of efficiency for establishing a ‘whitening model’. Various For performance measurement, Farrell (1957) pro- kinds of GMs have been proposed for appli- posed an input-oriented nonparametric method, cations. GM(1,1) is used for prediction; GM which measures the technical efficiency of an enterprise. His model is related to the inequality as the (1,2) is usually used for multi-dimensional prediction; GM(1,3) is used for normalized cost function is less than or equal to the N-dimensional relational analysis. input distance function. Originally, Farrell defined (3) Grey Prediction: a procedure using GM to technical efficiency in terms of input and output generate forecasting data. The selection of a quantities. However, as data are commonly furn- suitable GM is necessary. ished in terms of expenditure and revenue, rather than physical input and output, Farrell decomposed Some past studies have employed GMs for fore- efficiency into two parts: technical and allocate. casting. Kazemi et al. (2011) used a GM based on Together the two efficiencies, it measures the overall Markov Chain to predict the energy demand in Iran efficiency. However, Farrell model is unable to deal up to the year 2020. Chang and Kung (2006) applied with multiple inputs and outputs. GM(1,1) to improve the investment performance. That study selected DMUs from Taiwan 50 Index Introduction of DEA model and collected historical data from 1998 to 2005. DEA is a data-oriented approach that can be used to Their results showed the Grey Markowitz efficiency assess the performances of a set of entities, called frontier investment portfolio model could improve DMUs, which transform multiple inputs into multi- the investment performance effectively. Li (2011) ple outputs. Charnes, Cooper and Rhodes (1978) proposed an improved GM, based on GM(1,1), to described DEA as a mathematical method, a new predict Jingdezhen’s tourism revenues from 2003 to way to obtain empirical estimates of relation such 2010. Their results showed that the improved GM as the production functions and efficient production could obtain a much better predictive result. Feng possibility surfaces. DEA has become widely used to and Huang (2011) applied GM(1,1) to predict the assess the DMU performance in various application waste production of the Shanghai city. Compared domains. with traditional model, the GM(1,1) appears more The DEA procedure includes three stages. The feasible and effective. first stage is the selection of DMUs with similar 4 C.-N. WANG ET AL. operations and objective. Also, these selected DMUs that study, MPI had been employed. In the manu- have to subject to similar ‘technological’ and ‘mar- facturing and service industries, DEAs have been ket’ conditions and use same kinds of inputs to ever applied to assess business performances and produce same kinds of outputs. The second stage is strategies. For example, Chandraprakaikul and the selection of input and output variables. The third Suebpongsakorn (2012) used DEA to explore the stage is the selection of suitable DEA model to assess operational performances of 55 Thai logistics com- relative performances among DMUs based on their panies. The DEA helped to find the sources of inef- input and output values. When selecting a DEA ficiency to be tackled by corresponding plan. model, it needs to take care of two issues: the type Charles, Kumar, and Kavitha (2012) used DEA to of returns-to-scale and the orientation of the model. assess the efficiency of a PCB assembly line. Chang Usually, an additive model (such as the CCR model et al. (2013) proposed non-radial DEA model with proposed by Charnes et al. 1997) is suitable for the the SBM to analyse the environmental efficiency of situation of constant returns-to-scale (CRS). The China’s transportation section. They concluded, the CCR model measures the efficiency for each vari- proposed model was more realistic in explaining able, but pure technical efficiency and scale effi- energy consumption system. Song and Wang ciency are aggregated into one value. (2015) proposed a DEA model, which included production congestion and undesirable outputs, to Data envelopment analysis models assess the environmental efficiency. In that study, CCR model. Based on Ferrell’s framework, Charnes, the technological change was divided into productive Cooper, and Rhodes (1978) proposed a CCR model technological change and energy-savings emission to estimate the relative efficiency of a DMU. Unlike reduction technological change to establish their the traditional DEA model that can only deal pro- influences on the production congestion phenom- blems with two inputs and one output, the CCR enon. They found productive technological change model can deal problems with multiple inputs and could not relieve the degree of production conges-

multiple outputs. Also, it can find the efficiency tion while green technology change, which stimu- frontier of a DMU and measure its relative efficiency lates environmental efficiency improvement, could among DMUs. As a result, the CCR model is the alleviate production congestion greatly. In a recent most widely used DEA model. However, it assumes study, Song and Guan (2015) proposed a super- CRS for the production function. efficiency SBM model to evaluate the e-government performance of environmental protection adminis- BCC model. Banker et al. (1984) further developed a trations in the 16 cities of the Anhui province in new model, termed BCC, featuring variable returns- China based on an analysis of their websites. The to-scale and convexity constraint. It ensures that three super-efficiency SBM model included three comparable unit is of a scale size similar to that first-level indicators: the degree of public participa- being measured. The efficiency score obtained from tion, website service quality and public satisfaction. the BCC model is equal or greater than that derived The rankings of these indicators indicate that the from CCR. The BCC model improves the CCR public participation and service quality of these model and expands its application scopes. It is a e-government websites were highly consistent; that kind of input-oriented model. is, the greater the public participation, the better the appraisals of service quality and vice versa. Their Related researches results showed that the proposed super-efficiency Some researchers have conducted DEA studies. SBM model was capable in discerning efficient and Leachman, Pegels, and Kyoon Shin (2005) used inefficient e-government websites. DEA to assess the performances of DMUs in the automobile industry. Saranga (2009) used DEA to Two-stage DEA approach identify the inefficiencies of DMUs in the Indian auto component industry. Zhao, Li and Zhang Understanding the sources of inefficiency is impor- (2011) employed DEA to evaluate the performances tant for an enterprise. One approach to achieve this of DMUs in the Chinese coal mining industry. In aim is to breakdown overall efficiency into APPLIED ECONOMICS 5 components. To do this, Seiford and Zhu (1999) in an accurate prediction (Deng 1989). The proce- proposed a two-stage DEA to assess the perfor- dure of using GM(1,1) for forecasting is detailed mances of the U.S. commercial banks. In that below: study, assets and labour were used as input variables profit and revenue were used as output variables in (1) Generate the primitive series: the first stage. Then, a traditional DEA model was used to measure the profitability efficiencies of The primitive series Xð0Þ is denoted as DMUs. In the second stage, the outputs from the Equation (2): first stage were used as inputs to generate the out- ð Þ ð Þ ð Þ ð Þ puts including returns and earnings per share. Then, X 0 ¼ðX 0 ð1Þ; X 0 ð2Þ; ...; X 0 ðnÞÞ; n 4; the traditional DEA model was used to assess the (2) marketability efficiency of DMUs. Recently, the two- ð0Þ stage DEA approaches have increasingly appeared. where X : a non-negative sequence. For example, Chen (2009) applied a two-stage DEA n: the number of data observed. to evaluate the impact of IT on firm performance. Transform ðÞ0 into a series of accumulated Zha and Liang (2010) also employed a two-stage (2) X ðÞ1 process for a game theory framework. The product data X . of the two efficiencies in two stages was used to measure the overall efficiency of a DMU. Song, In this step, the Accumulated Generation Wang and Liu (2014) employed a two-stage slacks- Operator (AGO) is used to generate the series of ðÞ1 based SBM model to measure environmental effi- accumulated data, X , which is defined in ciency as they found a traditional SBM model cannot Equation (3). ð Þ ð Þ ð Þ ð Þ deal with the scenario in which desirable outputs are X 1 ¼ðX 1 ð1Þ; X 1 ð2Þ; ...; X 1 ðnÞÞ; n 4; constant. The SBM model, based on the CRS, has (3) achieved some good results in addressing the undesirable outputs such as waste water and water gas. where The new model was found able to assess efficiency ðÞ0 ðÞ X ðÞ1 ; k ¼ 1 based on undesirable outputs, but also it can calcu- X 1 ðÞ¼k P : (4) k XðÞ0 ðÞi ; k ¼ 2; ...; n late desirable and undesirable outputs separately. As i¼1 a result, it can resolve the ‘dependence’ problem of outputs, that is, we can not increase the desirable ðÞ outputs without producing any undesirable outputs. (3) Generate the series of mean values Z 1 . Their results showed the efficiency values obtained ð Þ ð Þ by two-stage approach are smaller than those The generated series of mean values Z 1 of X 1 is obtained by the traditional SBM model, which can defined in Equation (5). provide a more profound analysis for decision-mak- ðÞ1 ¼ ðÞ1 ðÞ; ðÞ1 ðÞ; ...; ðÞ1 ðÞ; ing. However, it is found that two-stage approach Z Z 2 Z 3 Z n (5) has never been applied to the EMS industry. where Z(1) (k) is the mean value of adjacent data, which is defined in Equation (6). ð Þ 1 ð Þ ð Þ III. Research methodology Z 1 ðkÞ ¼ X 1 ðkÞþX 1 ðk 1Þ ; k 2 Grey forecasting model ¼ 2; 3; ...; n: (6) In this research, G(1,1) is used for forecasting due to its computational efficiency and popularity. One feature of G(1,1) is that it does not require full set (4) Establish the data matrix by least square of original data. However, at least four consecutive method and solve the parameter values a data collected at equal intervals are required to result and b. 6 C.-N. WANG ET AL. 2 3 2 3 ð1Þð Þ Super efficiency model (super-efficiency SBM ð0Þ z 2 1 x ð2Þ 6 ð1Þ 7 6 ð Þ 7 6 z ð3Þ 1 7 model) 6 x 0 ð3Þ 7 6 . 7 6 7 6 ::::::::::::: . 7 Let Y ¼ 6 : 7 and B ¼ 6 . 7: In the present study, a super-efficiency SBM model 4 : 5 6 . 7 ‘ 4 ::::::::::::: . 5 is used. This model is based on slacks-based mea- ð0Þ x ðnÞ ð Þ sure of efficiency’ (SBM) developed by Tone (2001). z 1 ðnÞ 1 Assumen is the number of DMUs, and X and Y, X ¼ x 2 Rmn and Y ¼ Y 2 Rsn, are input The Y is called data series and B is called data ij ij T and output matrices, X > 0 and Y > 0. And, λ is a matrix, and ½a; b is called parameter series. Then, non-negative vector in Rnand the vectors S 2 Rm we can solve the parameter values a and b using and Sþ 2 Rs indicate the input excess and output Equation (7). shortfall, respectively. Then, the model formulation, ½a; bT ¼ðBTBÞ 1BTY: (7) which provides a CRS, of the SBM model is defined in Equation (12) (Tone 2001): P 1 1 m s=x ρ¼ mP i¼1 i i0 : min þ 1 s = (12) (5) Generate the predicted accumulating ser- 1 s i¼1 si yi0 ^ð1Þ ies X . þ s:t x0 ¼ Xλ þ s ; y0 ¼ Yλ s ; λ 0; þ (13) s 0; s 0: Generate the predicted accumulating series X^ð1Þ + − defined in Equation (8). The variables S and S measure the distance of inputs Xλ and output Yλ of a virtual unit from those ð Þ ð Þ ð Þ ð Þ X^ 1 ¼ðX^ 1 ð1Þ; X^ 1 ð2Þ; ...; X^ 1 ðnÞÞ; (8) of the unit evaluated. The numerator and the denominator of the objective function of the model where measures the average distance of inputs and outputs, respectively, from the efficiency threshold. ð Þ ðÞ0 ðÞ; ¼ ^ 1 ðÞ¼ X 1 k 1 : Let an optimal solution for SBM model be X k ðÞ0 ðÞb ak þ b ; ¼ ; ...; ð ; λ; ; þÞ ð ; Þ X 1 a e a k 2 n p s s . A DMU x0 y0 is SBM-efficient, if ¼ ¼ (9) p 1. This condition is equivalent to S 0and Sþ ¼ 0, no input excesses and no output shortfalls in any optimal solution. SBM model is non-radial and deals with input/output slacks directly. The SBM (6) Apply inverse accumulated generation model returns an efficiency value between 0 and 1. operation (IAGO) to obtain forecasting The best performers have the full efficient status data. denoted by unity. The super-efficiency SBM model is based on the SBM model. Tone (2002) discriminated Applying IAGO, we can derive the forecasting and ranked these efficient DMUs by super-efficiency ð ; Þ data as shown in Equation (10). SBM model. Assuming that the DMU x0 y0 is SBM-efficient, p ¼ 1, the super-efficiency SBM ð Þ ð Þ ð Þ ð Þ X^ 0 ¼ðX^ 0 ð1Þ; X^ 0 ð2Þ; ...; X^ 0 ðnÞÞ; (10) model is defined in Equation (14). P 1 m x =x where δ¼ mP i¼1 i i0 : min 1 s (14) ¼ y =y ( s r 1 r r0 ðÞ0 ðÞ; ¼ Xn Xn ^ð0ÞðÞ¼þ X 1 k 0 : X k 1 ð1Þ ð1Þ s:t x λ x ; y λ x ; X^ ðk þ 1ÞX^ ; k ¼ 1; ...; n j j j j j¼1;Þ0 j¼1;Þ0 (15) (11) y x0 and y y0; yy y0; λ 0: APPLIED ECONOMICS 7

The super-efficiency SBM model returns a value Two-stage production model of the objective function, which is greater or equal to The performances of 18 selected EMSs are assessed by 1. The higher the value, the more efficient the unit is. a two-stage model like the one proposed in Seiford As in many DEA models, it is crucial and neces- and Zhu (1999). However, their model used a tradi- sary for a super-efficiency SBM model to consider tional DEA model. Figure 1 shows the two-stage how to deal with negative outputs. However, nega- production model. Each stage is clarified as follows: tive data should have their duly role in measuring efficiency, hence a new scheme as shown below is (1) Stage 1: in this stage, the employees, assets used in DEA-Solver pro 4.1 Manual: and equity are the three inputs variables and Let us suppose y 0: Then yþ and yþ are ro r r the revenue and profit are output variables. defined as follows. Then, a DEA model is used to assess the þ profitability efficiency of a DMU. The higher y ¼ max yrj yrj > 0 ; (16) r j¼1;...;n the output values, the higher the profitability efficiency for a DMU. þ y ¼ min y y > 0 : (17) (2) Stage 2: in this stage, the outputs (revenue r ¼ ;...; rj rj j 1 n and profit) from the stage 1 are inputs vari- If the output r has no positive elements, then it is ables and the ROC and market value are out- þ ¼ þ ¼ put variables. Then, a DEA model is used to defined that yr yr 1. The term is replaced by þ assess the marketability efficiency of a DMU. s =yr0 in the objective function in the following way. r The higher the output values, the higher the The value yr0 is never changed in the constraints. marketability efficiency for a DMU. þ þ ðÞ1 y ¼ y ¼ 1; r r The decomposition of the production process can þ þ þ þ= yr yr yr ; help identify the source(s) of inefficiency. the term is replaced by sr þ yr yr0 (18) Research procedure þ 2 þ y An eight-step procedure is used to facilitate empiri- ðÞ2 s = r ; (19) r þ cal study. Each of the steps is detailed below: B yr yr0 where B is a large positive number, in DEA-Solver Step 1: Choose DMUs: this step is about the B = 100. selection of DMUs in industry. According to In any case, the denominator is positive and Golany and Roll (1989), DEA requires the þ strictly less than yr. Furthermore, it is inversely number of DMUs to be at least twice the total þ proportion to the distance yr yr0. This scheme, number of input and output variables. We can therefore, concerns the magnitude of the non-posi- select DMUs from public sources due to trans- tive output positively. The score obtained is units parent information. The DMUs to be selected invariant, that is, it is independent of the units of should have similar operations and objective as measurement used. well as same kinds of inputs and outputs.

Assets Revenue Return on Capital Equity Profitability Marketability Employees Profit Market value

Stage 1 Stage 2

Figure 1. The two-stage production model for assessing EMSs. 8 C.-N. WANG ET AL.

Step 2: Choose input/output variables: As input Table 1. Pearson correlation coefficient. and output variables are sensitive to the analy- Correlation coefficient Degree of correlation >0.8 Very high tical results, the selection of input and output 0.6–0.8 High variables for DEA is very important. To evalu- 0.4–0.6 Medium 0.2–0.4 Low ate the operational performances of EMSs, rele- <0.2 Very low vant output variables are required. For input and output variables selection, variables used in past studies can be referred. In addition, IV. Empirical results Pearson correlation test is used to check the relationship between input and output vari- Following the research procedure proposed in ables. If significant level of the two-tailed test Section 3, we conduct experiments in this section. is larger than 0.05, they satisfy the isotonicity requirement. Otherwise, we back to step 1 and choose new DMU until the criteria is met. Choose DMUs Step 3. Grey forecasting: in this step the GM(1, 1) is employed to predict the input and output After surveying the EMS industry, we selected 18 values for the selected DMUs. According to EMSs out of the top 50 world largest EMS providers Deng (1989), at least data of four consecutive in 2014, based on their sizes and positions in the periods are required for GM(1,1) to forecast. EMS industry, with the information availability of Step 4. Check forecasting accuracy: in this step these DMUs from the public source, Bloomberg the MAPE is used to measure the forecasting Business Week News, also taking into consideration. error. If the forecasting error is too high, then it According to Golany and Roll (1989), DEA requires needs to reselect the input and output variables the minimum number of DMUs to be at least twice for the forecasting model. the total number of input and output variables. The

Step 5. Choose DEA model: in this step a soft- number of EMSs selected in this research satisfies ware, the DEA-Solver, is employed and the this requirement. Table 2 shows the list of selected super-efficiency SBM model (proposed by DMUs. Tone 2002) is used to calculate profitability efficiency, marketability efficiency and MPI. Table 2. The list of EMS companies (DMUs). Step 6. Pearson correlation test:DEArequiresthe Headquarter prerequisite that input and output factors have an No. DMUs Company name address isotonicity relationship so that the increase of an 1 DMU1 Hon Hai Precision Industry (Foxconn New Taipei, Technology Co.) Taiwan input value will not decrease another output value 2 DMU2 Pegatron Taipei, Taiwan under the same condition. In this step, Pearson 3 DMU3 Flextronics Intel Ltd. Singapore 4 DMU4 Jabil Circuit Inc. St. Petersburg, correlation test is used to check the isotonicity FL relationship. The closer the correlation is to ±1, 5 DMU5 Sanmina Corporation San Jose, CA 6 DMU6 Celestica Toronto, Canada the closer to a perfect linear relationship. If the 7 DMU7 Benchmark Electronics Inc. Angleton, TX input and output variables with negative coeffi- 8 DMU8 Shenzhen Kaifa Technology Co., Ltd. Shenzhen, China 9 DMU9 Universal Scientific Industrial (Shanghai) Shanghai, China cient, then it needs to remove these input and Co., Ltd. output variables and go back to step 2 until the 10 DMU10 Plexus Neenah, WI 11 DMU11 Venture Corporation Limited Singapore prerequisite is satisfied. Table 1 shows the meaning 12 DMU12 SIIX Corporation Osaka, Japan 13 DMU13 Integrated Micro-Electronics, Inc. Laguna, of results obtained from Pearson correlation test. Philippines Step 7. Performance analysis: in this step, the 14 DMU14 Fabrinet Pathumthani, Thailand derived efficiencies of DMUs are evaluated 15 DMU15 PKC Group Reeahe, Finland and ranked in order to find useful information. 16 DMU16 VS Industry Berhad Senai, Malaysia 17 DMU17 TT Electronics Rogerstone, Step 8. Conclusions: this step gives a conclusion Wales, UK ’ and suggestion. 18 DMU18 Wong s International Holdings Limited Hong Kong APPLIED ECONOMICS 9

Choose input/output variables derived in the same way. The procedure is explained step by step as follows. This step focuses on selecting input and output variables for DMUs. Based on the operational char- Step 1: Generate the primitive sequence X(0) as acteristics of the EMS industry, the assets, employees follows using Equation (2): and shareholders’ equity are selected as input vari- X(0) = {55,664.1, 65,976.7, 74,415.0, 79,250.2} ables for the stage 1 as they are key financial indica- Step 2: Transform the X(0) to a accumulated series tors directly affecting the performance of an EMS. In X(1) using Equation (4). addition, the revenues and profits are selected as X(1) ={x(1) (1), x(1) (2), x(1) (3), x(1) (4)} output variables for the first stage and return on = {55,664.1, 121,640.8, 196,055.8, 275,306} invested capital and market value are selected as where: output variables for the second stage as they are x(1) (1) = x(0) (1) = 55,664.1 important factors able to reflect the performance of x(1) (2) = x(0) (1) + x(0) (2) = 121,640.8 a DMU in the stock market. x(1) (3) = x(0) (1) + x(0) (2) + x(0) (3) = 196,055.8 Due to limited space for presentation only the x(1) (4) = x(0) (1) + x(0) (2) + x(0) (3) + x(0) historical data of the 18 DMUs in 2012 are presented (4) = 275306 in Table 3. It is found that the values of these EMSs Step 3: Generate the mean sequence Z(1) of X(1) differ greatly due to different company sizes. using Equation (5). ðÞ ðÞ ðÞ ðÞ Z 1 ¼ Z 1 ðÞ2 ; Z 1 ðÞ3 ; ...; Z 1 ðÞn Grey forecasting ¼ð88652:45; 158848:3; 235680:9Þ Table 4 shows the input and output values of DMU1 from 2011 to 2014. We use the second column where (Assets) in Table 4 to explain how GM(1,1) is used ðÞ1 ðÞ¼1 ð ðÞ1 ðÞþ ðÞ1 ðÞÞ¼ : Z 2 x 1 x 2 88652 45 to generate forecasting data. Other data can be 2

Table 3. The historical inputs and outputs values for18 DMUs in 2012. Company (I) Assets (M) (I) Equity (M) (I) Employee (person) (I) Revenues (M) (I) Profits (M) (O) Return on capital (%) (O) Market value (M)

DMU1 65,976.70 21,891.40 1,300,000 125,519.40 8043.00 14.70 36,228.03 DMU2 12,734.40 4111.20 131,792 28,383.40 1192.80 11.03 2961.06 DMU3 11,033.80 2284.00 159,000 29,343.00 1518.00 13.72 4937.00 DMU4 7803.10 2107.30 140,000 16,140.70 1160.50 16.47 4693.00 DMU5 3167.80 963.80 44,879 6093.30 435.80 15.14 695.00 DMU6 2658.80 1322.70 29,000 6507.20 438.40 14.81 1490.00 DMU7 1501.50 1139.50 9949 2468.20 176.70 9.05 917.00 DMU8 1639.60 669.30 15,400 2641.00 65.30 3.20 997.43 DMU9 1310.30 554.50 12,842 2147.50 290.80 20.16 1922.79 DMU10 1411.50 1411.50 9600 2306.70 219.90 14.56 1063.00 DMU11 1739.80 1319.70 9868 1751.10 388.30 18.26 2211.00 DMU12 605.10 206.20 9029 1544.60 91.70 12.62 273.36 DMU13 453.40 187.90 4520 661.80 56.40 10.71 152.43 DMU14 461.40 250.70 5220 564.70 61.90 12.29 432.60 DMU15 546.80 185.50 20,590 1046.50 40.18 8.32 439.13 DMU16 203.60 98.30 2595 287.60 35.20 9.50 94.86 DMU17 584.60 299.90 5000 748.50 144.50 12.77 365.70 DMU18 350.40 195.20 3900 436.00 100.70 12.38 158.06 M: Millions of USD.

Table 4. Input and output values of DMU1 in 2011–2014. Hon Hai (I) Assets (M) (I) Equity (M) (I) Employee (person) (O) Revenues (M) (O) Profits (M) (O) Return on capital (%) (O) Market value (M) 2011 55,664.1 19,772.9 1,001,000 110,969.2 8561.5 14.1 29,271.2 2012 65,976.7 21,891.4 1,300,000 125,519.4 8043.0 14.7 36,228.0 2013 74,415.0 25,910.5 1,097,000 127,027.5 8185.9 15.5 35,271.2 2014 79,250.2 31,657.4 1,061,000 135,411.4 9383.1 16.88 41,098.01 M: Millions of USD. 10 C.-N. WANG ET AL.

ð Þ ðÞ 1 ðÞ ðÞ ¼ ^ 1 ðÞ¼ : Z 1 ðÞ¼3 ðx 1 ðÞþ2 x 1 ðÞÞ¼3 158848:3 k 0 x 1 55664 1 2 ðÞ1 1 ðÞ1 ðÞ1 Z ðÞ¼4 ðx ðÞþ3 x ðÞÞ¼4 235680:9 ð Þ 2 k ¼ 1 ^x 1 ðÞ¼2 122359:73 Step 4: Solve equations to find the values for ð Þ k ¼ 2 ^x 1 ðÞ¼3 195325:33 parameters a and b ð Þ 2 3 k ¼ 3 ^x 1 ðÞ¼4 275150:33 88652:45 1 6 7 ^ a ¼ ^ð1Þ ðÞ¼ : Let B ¼ 4 158848:3 1 5; θ ¼ ; k 4 x 5 362479 59 b : ¼ ^ð1Þ ðÞ¼ : 2 23568039 1 k 5 x 6 458018 55 65976:7 ð Þ 6 7 k ¼ 6 ^x 1 ðÞ¼7 562539:02 Y ¼ 4 74415 5: Step 6: Apply the accumulated reduction genera- 79250:2 tion to derive the forecast data using And then use the least square method to find a and b: Equation (11). Generate the predicted value of the original series a ^ 1 0:09 ¼ θðBTBÞ BTY ¼ according to the inverse accumulated generating b 58742:89 operation and we can obtain: Step 5: Substitute a and b to Equation (9) to ð Þ ð Þ derive the prediction equation and values: ^x 0 ð1Þ¼x 1 ð1Þ¼55; 664:1 ^ð0Þð Þ¼^ð1Þð Þ^ð1Þð Þ¼ ; : ðÞ ðÞ b b x 2 x 2 x 1 66 695 63 X 1 ðÞ¼k þ 1 X 0 ðÞ1 e ak þ ð Þ ð Þ ð Þ a a ^x 0 ð3Þ¼^x 1 ð3Þ^x 1 ð2Þ¼72; 965:60 : ð1Þ 58742 89 0:090k x ðk þ 1Þ¼ 55664:10 e ð0Þ ð1Þ ð1Þ ^ ð Þ¼^ ð Þ^ ð Þ¼ ; : 0:09 x 4 x 4 x 3 79 825 00 : ð Þ ð Þ ð Þ þ 58742 89 ^x 0 ð5Þ¼^x 1 ð5Þ^x 1 ð4Þ¼87; 329:25 0:09 : ð Þ ð Þ ð Þ ¼ 709462:00e0 090 k 653797:90: ^x 0 ð6Þ¼^x 1 ð6Þ^x 1 ð5Þ¼95; 538:97 ð Þ ð Þ ð Þ (20) ^x 0 ð7Þ¼^x 1 ð7Þ^x 1 ð6Þ¼104; 520:46 Substitute different values of k (k =0,…, 6) into Tables 5–7 show the predicted input and output above Equation (20), we can derive the accumu- values of 18 DMUs from 2015 to 2017 obtained from lated predicting values: the GM(1, 1).

Table 5. Predicted inputs and outputs values of 18 DMUs in 2015. DMUs (I) Assets (M) (I) Equity (M) (I) Employees (P) (O) Revenues (M) (O) Profits (M) (O) Return on capital (%) (O) Market value (M) DMU1 87,329.253 37,823.01 927,871 139,604.667 9991.985 18.010 42,816.031 DMU2 15,739.944 6538.420 145,962 35,330.062 2401.142 19.570 7296.305 DMU3 12,972.804 2163.105 143,771 23,098.952 1446.776 13.860 5492.915 DMU4 9149.540 2394.360 154,202 16,018.620 1008.430 11.950 4030.810 DMU5 3310.787 1414.206 41,172 6199.555 506.734 15.550 2554.677 DMU6 2552.916 1446.132 23,232 5140.311 378.304 15.090 2463.292 DMU7 1795.174 1377.649 11,646 2941.958 242.406 4.150 1648.159 DMU8 2806.866 911.144 14,255 2577.821 84.231 7.710 2197.335 DMU9 2399.209 1357.121 17,501 2774.771 341.892 20.560 8193.439 DMU10 1700.356 431.057 13,038 2377.902 225.410 13.990 1370.724 DMU11 1911.840 1391.199 10,131 1814.395 432.601 21.770 2111.864 DMU12 984.457 425.753 9612 1798.510 141.216 13.220 671.897 DMU13 606.420 272.745 14,566 951.687 111.413 13.570 350.157 DMU14 612.230 550.450 6819 748.050 81.590 15.740 921.69 DMU15 492.983 187.544 18,866 886.147 340.185 7.360 647.355 DMU16 497.982 193.112 10,411 477.948 52.026 3.670 102.576 DMU17 603.120 299.520 6423 877.790 125.340 12.000 296.670 DMU18 700.093 323.804 5694 589.217 138.286 21.950 148.920 M: Millions of USD; P: person. APPLIED ECONOMICS 11

Table 6. Predicted inputs and outputs values of 18 DMUs in 2016. DMUs (I) Assets (M) (I) Equity (M) (I) Employees (person) (O) Revenues (M) (O) Profits (M) (O) Return on capital (%) (O) Market value (M) DMU1 95,538.966 45,547.916 834,446 145,096.269 10,825.091 19.31 45,776.222 DMU2 16,941.762 7679.845 153,924 38,001.243 3053.492 23.4 10,494.809 DMU3 13,867.750 2123.827 139,545 21,650.990 1437.109 14.39 5816.039 DMU4 9507.260 2475.430 155,146 15,839.350 952.090 10.71 3814.670 DMU5 3389.861 1608.779 40,308 6262.838 537.976 15.88 3675.066 DMU6 2516.719 1484.245 21,574 4771.978 363.032 15.27 2882.966 DMU7 1895.223 1465.640 12,191 3137.970 271.486 4.62 1969.720 DMU8 3294.595 999.084 13,998 2581.540 95.942 12.97 2873.930 DMU9 3020.737 1911.587 19,863 3030.525 366.742 20.97 13,019.823 DMU10 1818.574 296.818 14,745 2415.709 228.399 13.84 1475.864 DMU11 1973.594 1416.143 10,282 1844.493 449.826 22.99 2087.775 DMU12 1156.142 541.114 9896 1884.189 163.175 13.44 957.778 DMU13 670.595 314.243 23,705 1075.195 142.074 14.8 540.930 DMU14 681.620 717.770 7573 817.770 89.200 17 1220.09 DMU15 477.842 184.265 18,412 837.778 321.544 7.01 682.524 DMU16 646.093 235.887 25,809 585.159 63.253 15.58 109.693 DMU17 607.140 297.040 6924 918.570 117.690 11.69 262.860 DMU18 870.564 391.565 6424 651.811 154.514 26.28 145.926 M: Millions of USD; P: person.

Table 7. Predicted inputs and outputs values of 18 DMUs in 2017. DMUs (I) Assets (M) (I) Equity (M) (I) Employees (person) (O) Revenues (M) (O) Profits (M) (O) Return on capital (%) (O) Market value (M) DMU1 104,520.464 54,850.544 750,428 150,803.892 11,727.659 20.710 48,941.073 DMU2 18,235.345 9020.530 162,320 40,874.382 3883.076 27.980 15,095.452 DMU3 14,824.436 2085.262 135,444 20,293.793 1427.507 14.930 6158.171 DMU4 9878.970 2559.250 156,095 15,662.080 898.890 9.590 3610.120 DMU5 3470.823 1830.123 39,463 6326.768 571.145 16.210 5286.815 DMU6 2481.036 1523.363 20,034 4430.038 348.377 15.440 3374.140 DMU7 2000.849 1559.251 12,762 3347.041 304.053 5.130 2354.019 DMU8 3867.072 1095.511 13,746 2585.265 109.281 21.810 3758.861 DMU9 3803.276 2692.586 22,544 3309.853 393.398 21.390 20,689.211 DMU10 1945.011 204.383 16,676 2454.117 231.427 13.680 1589.069 DMU11 2037.343 1441.534 10,436 1875.091 467.736 24.270 2063.960 DMU12 1357.768 687.732 10,189 1973.950 188.548 13.670 1365.297 DMU13 741.562 362.055 38,578 1214.730 181.174 16.130 835.640 DMU14 758.870 935.970 8410 893.980 97.520 18.360 1615.10 DMU15 463.166 181.044 17,969 792.049 303.924 6.680 719.604 DMU16 838.256 288.137 63,977 716.418 76.903 1.820 117.303 DMU17 611.190 294.570 7464 961.260 110.490 11.390 232.900 DMU18 1082.546 473.507 7249 721.055 172.646 31.470 142.992 M: Millions of USD; P: person.

Check forecasting accuracy Choose DEA model The MAPE is used to measure the forecasting accu- Some DEA models are available for efficiency mea- racy of GM(1,1). Table 8 lists the derived average surement. However, the CCR and BCC models are MAPEs for all DMUs. As almost all of them are not suitable for this research due to the following smaller than 10% and the average MAPE reaches reasons: (1) they are kind of subjective due to the lack 4.03%, it strongly confirms that the GM(1,1) model of fairly representing the true input/output for each has a high forecasting accuracy. DMU. In contrast, the two-stage model appears more objective in representing the inputs/outputs for DMUs because non-radial measure can better deal with the Table 8. Average MAPEs for all DMUs. issues of excess input and output shortfall, (2) when Average Average Average using slacks-based DEA model, it needs to handle the DMUs MAPE DMUs MAPE DMUs MAPE negative output/input values, requiring advanced DMU1 1.67% DMU7 2.20% DMU13 7.59% models such as super-efficiency SBM model, (3) DMU2 3.15% DMU8 6.40% DMU14 2.35% DMU3 4.25% DMU9 4.58% DMU15 4.30% when the total number of DMUs is less than the total DMU4 3.22% DMU10 3.90% DMU16 14.32% DMU5 3.35% DMU11 0.93% DMU17 5.24% number of evaluation criteria, a traditional DEA model DMU6 1.33% DMU12 2.04% DMU18 1.76% may have the difficulty in determining the best perfor- Average MAPE 4.03% mer(s) due to the likely appearance of efficiency values 12 C.-N. WANG ET AL.

1 for multiple DMUs. Thus, advanced models are Table 10. Summary results in 2012–2014 obtained from the required to discern the best performer(s). super-efficiency SBM model. Thus, this research employs the super-efficiency 2012 2013 2014 Stage Stage Stage Stage Stage Stage SBM model to measure the performances of DMUs 1 2 1 2 1 2 in terms of profitability efficiency and marketability 1. DMUs 18 18 18 18 18 18 2. DMUs with 000000 efficiency. When the number of DMUs is relatively inappropriate data small comparing with evaluation criteria, the super- 3. DMUs 18 18 18 18 18 18 4. Average of scores 1.222 0.867 1.266 0.839 1.252 0.798 efficiency SBM model is very capable of differentiat- 5. Efficient DMUs 13 7 12 7 11 7 ing the efficiencies of DMUs. The super-efficiency 6. Inefficient DMUs 5 11 6 11 7 11 SBM model can support the cross-period efficiency analysis that decomposes the inter-temporal effi- Performance analysis ciency change into ‘catch-up’ and ‘frontier-shift’ in accordance with the Malmquist index. Table 10 shows the summary results from 2012 to 2014 by using the super-efficiency SBM model. It indicates that the numbers of efficient and inefficient DMUs as well as the efficiency scores of Pearson correlation test DMUs change yearly. Some DMUs were efficient Table 9 shows the correlation coefficients between in stage 1 while others are efficient in stage 2. Still input and output variables based on data from 2012 others are efficient in both stages. Noticeably, most to 2013. In the first stage, as all coefficients are DMUs had higher profitability efficiency than mar- greater than 0.9 it indicates strong degree of correla- ketability efficiency. However, the number of tion. However, in the second stage some coefficients DMUs with good profitability efficiency in the are found small, especially some of them between the first stage decreased from 72% in 2012 to 61% in ROC and other input variables are less than 0.2. 2014. In the second stage, the number of DMUs with marketability efficiency greater than 1 in each

Nevertheless, this does not violate the prerequisite of DEA model requires that there is no negative year accounted for 38%. The ranking of DMUs correlations. Thus, it needs not to change the input changed yearly. Only four DMUs, Hon Hai and output variables. Technology Co Ltd, Plexus, V.S. Industry and

Table 9. Correlation of input and output data in 2012–2014. Assets Equity Employees (person) Revenues Profits ROC (%) Market value 2012 Assets 1 0.99530 0.99539 0.99712 0.99695 0.15157 0.99102 Equity 0.99530 1 0.99308 0.98680 0.99123 0.15541 0.99099 Employees (person) 0.99539 0.99308 1 0.98762 0.99663 0.14936 0.99696 Revenues 0.99712 0.98680 0.98762 1 0.99369 0.15148 0.98205 Profits (M of USD) 0.99695 0.99123 0.99663 0.99369 1 0.18294 0.99483 ROC (%) 0.15157 0.15541 0.14936 0.15148 0.18294 1 0.18857 Market value 0.99102 0.99099 0.99696 0.98205 0.99483 0.18857 1 2013 Assets 1 0.99673 0.99620 0.99709 0.99816 0.17309 0.99002 Equity 0.99673 1 0.99104 0.99051 0.99343 0.19256 0.99000 Employees (person) 0.99620 0.99104 1 0.98981 0.99650 0.17006 0.99325 Revenues 0.99709 0.99051 0.98981 1 0.99607 0.17446 0.97932 Profits (M of USD) 0.99816 0.99343 0.99650 0.99607 1 0.19659 0.99021 ROC (%) 0.17309 0.19256 0.17006 0.17446 0.19659 1 0.20964 Market value 0.99002 0.99000 0.99325 0.97932 0.99021 0.20964 1 2014 Assets 1 0.99458 0.99788 0.99762 0.99892 0.19871 0.99176 Equity 0.99458 1 0.99230 0.98828 0.99424 0.21082 0.99166 Employees (person) 0.99788 0.99230 1 0.99295 0.99661 0.18486 0.99132 Revenues 0.99762 0.98828 0.99295 1 0.99734 0.20384 0.98381 Profits (M of USD) 0.99892 0.99424 0.99661 0.99734 1 0.21684 0.99071 ROC (%) 0.19871 0.21082 0.18486 0.20384 0.21684 1 0.23984 Market value 0.99176 0.99166 0.99132 0.98381 0.99071 0.23984 1 ROC: Return on capital; M: Millions of USD APPLIED ECONOMICS 13

Table 11. Detailed results from 2012 to 2014 obtained from the super-efficiency SBM model. 2012 2013 2014 Productivity Marketability Productivity Marketability Productivity Marketability DMUs Score Rank Score Rank Score Rank Score Rank Score Rank Score Rank

DMU1 4.734 1 1.764 1 4.763 1 1.772 1 4.478 1 1.767 1 DMU2 1.072 8 0.531 16 1.429 3 0.498 15 1.306 5 0.594 9 DMU3 1.441 3 0.691 10 1.340 4 0.560 14 1.588 3 0.623 8 DMU4 0.874 14 0.828 8 0.908 14 0.728 8 0.874 14 0.575 10 DMU5 0.770 16 0.399 18 0.790 17 0.485 16 0.810 15 0.420 16 DMU6 1.085 7 0.649 13 0.990 13 0.591 13 1.049 9 0.493 13 DMU7 1.039 11 0.649 12 0.814 16 0.618 10 1.141 6 0.511 11 DMU8 0.334 18 1.374 2 0.263 18 1.504 2 0.417 18 1.342 3 DMU9 1.063 9 1.183 4 1.032 10 1.432 3 0.875 13 1.609 2 DMU10 1.039 12 0.772 9 1.090 9 0.598 11 0.955 12 0.498 12 DMU11 1.249 4 1.179 5 1.274 5 1.065 6 1.320 4 1.013 6 DMU12 1.062 10 0.581 14 1.123 7 0.377 17 1.108 7 0.439 14 DMU13 0.808 15 0.545 15 1.027 11 0.258 18 0.723 16 0.404 17 DMU14 0.616 17 1.280 3 0.875 15 1.137 5 0.704 17 1.140 5 DMU15 1.620 2 0.454 17 1.841 2 0.597 12 2.116 2 0.420 15 DMU16 1.014 13 1.002 7 1.021 12 1.012 7 1.091 10 1.002 7 DMU17 1.100 5 0.666 11 1.131 6 0.706 9 1.081 8 0.368 18 DMU18 1.092 6 1.059 6 1.093 8 1.178 4 1.014 11 1.154 4

Wong’s international Holdings Limited Company electronics Inc. are found inefficient in both kinds of had consistent ranking throughout 2012 to 2014. efficiencies. These analysis showed the two-stage Hon Hai topped the ranking in both profitability super-efficiency SBM model was able to discern the and marketability efficiencies. source(s) of inefficiency of a DMU. Figure 2 gives a Table 11 shows the details of profitability and more clear view on the ranking of these DMUs. marketability efficiencies for all 18 DMUs from In 2013, 12 and 7 DMUs had improved their effi- 2012 to 2014. The figures in boldface are higher ciencies in the first stage and second stage, respectively.

than 1, indicating efficient performance. In 2012, Hon Hai, PKC Group, Pegatron Corp, Flextronics and the average scores of profitability efficiency and Venture stood at top positions in the first stage with marketability efficiency obtained for the DMUs scores 4.763, 1.841, 1.429, 1.340 and 1.274, respectively. were 1.222 and 0.867, respectively. The results Also in this stage, Shenzhen Kaifa Technology had the showed that 13 EMSs had a profitability efficiency least score at 0.263. Seven DMUs that had a score of score greater than 1. The ranking was Hon Hai, PKC higher than 1 in the second stage were Hon Hai, group, Flextronics, Venture, TT electronics, Wong’s Shenzhen Kafa Technology, Universal Scientific international company, Celestica, Pegatron Corp, Industrial Co., Wong’s international Holdings limited, Universal Scientific Industrial, SIIX, Benchmark Fabrinet, Venture and V.S industry. Note that Jabil Electronics, Plexus and V.S industry. Hon Hai got Circuit Inc, Sanmina, Plexus and Integrated Micro- the score 4.734, which is about four times of the Electronics, Inc. did not perform well both in generat- average score (1.222) in the industry. There were 7 ing profit and in attracting stock market. Integrated DMUs had a marketability efficiency scores greater Micro – electronics, Inc. stands at 18th position in the than 1. The ranking was Hon Hai, Shenzhen Kaifa ranking on the marketability performance with score Technology, Fabrinet, Universal Scientific Industrial, of 0.258. Figure 3 shows a more clear view on the Venture, Wong’s international and V.S Industry. ranking of these DMUs. Table 11 shows the numbers of inefficient DMUs In 2014, it is obvious that the number of efficient are 5 and 11 in the first and second stages, respectively. EMSs in both stages 1 and 2 decreased in compar- It is interesting to note that Shenzhen Kaifa ison with 2012 and 2013. There were only 11 and 7 Technology and Fabrinet have higher rankings (rank efficient EMSs in the profitability and the market- 2 and 3, respectively) in the marketability efficiency but ability models, respectively. In 2014, though Hon lower ranking (rank 18 and rank 17) in the profitability Hai had lower score compared with those obtained efficiency, thus both companies appear to have been in the past 2 years, it still topped the ranking list. overvalued in the stock market. Companies including The ranking in descending order was Hon Hai, Jabil Circuit Inc, Sanmina and Integrated Micro- PKC Group, Flextronics, Venture, Pegatron Corp, 14 C.-N. WANG ET AL.

Profitability Marketability

Shenzhen Kaifa Technology Sanmina Fabrinet PKC Group Sanmina Pegatron Corp Integrated Micro-Electronics, Inc. Integrated Micro-Electronics, Inc. Jabil Circuit Inc SIIX V.S. Industry Celestica Plexus Benchmark Electronics Benchmark Electronics TT Electronics SIIX Flextronics Intl Ltd DMU

DMU Universal Scientific Industrial Co., Ltd. Plexus Pegatron Corp Jabil Circuit Inc Celestica V.S. Industry Wong’s International Holdings Limited Wong’s International Holdings Limited TT Electronics Venture Venture Universal Scientific Industrial Co., Ltd. Flextronics Intl Ltd Fabrinet PKC Group Shenzhen Kaifa Technology Foxconn Technology Co Ltd Foxconn Technology Co Ltd

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Efficiency Efficiency Profitability model Marketability model

Figure 2. The results of 2012 obtained from the super-efficiency SBM model.

Profitability Marketability

Shenzhen Kaifa Technology Integrated Micro-Electronics, Inc. Sanmina SIIX Benchmark Electronics Sanmina Fabrinet Pegatron Corp Jabil Circuit Inc Flextronics Intl Ltd Celestica Celestica V.S. Industry PKC Group Integrated Micro-Electronics, Inc. Plexus Universal Scientific Industrial Co., Ltd. Benchmark Electronics

DMU TT Electronics DMU Plexus Wong’s International Holdings Limited Jabil Circuit Inc SIIX V.S. Industry TT Electronics Venture Venture Fabrinet Flextronics Intl Ltd Wong’s International Holdings Limited… Universal Scientific Industrial Co., Ltd. Pegatron Corp Shenzhen Kaifa Technology PKC Group Foxconn Technology Co Ltd Foxconn Technology Co Ltd

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 0 0.20.40.60.8 1 1.21.41.6 Efficiency Efficiency

Figure 3. Results obtained from the super-efficiency SBM model for 2013.

Benchmark Electronics, SIIX, TT Electronics, also reported herein. The ranking was Hon Hai, Celestica and Wong’s International Holdings Universal Scientific Industrial Co., Ltd., Shenzhen Limited., etc., The efficiencies obtained from the Kaifa Technology, Wong’s International Holdings super-efficiency SBM model for the seven efficient Limited, Fabrinet, Venture and V.S. Industry. DMUs in the marketability performance model was Shenzhen Kaifa Technology had the lowest score APPLIED ECONOMICS 15

Profitability Marketability Shenzhen Kaifa Technology TT Electronics Fabrinet Integrated Micro-Electronics, Inc. Integrated Micro-Electronics, Inc. Sanmina Sanmina PKC Group Jabil Circuit Inc SIIX Universal Scientific Industrial Co., Ltd. Celestica Plexus Plexus V.S. Industry Benchmark Electronics Wong’s International Holdings Limited Jabil Circuit Inc DMU DMU Celestica Pegatron Corp TT Electronics Flextronics Intl Ltd SIIX V.S. Industry Benchmark Electronics Venture Pegatron Corp Fabrinet Venture Wong’s International Holdings Limited Flextronics Intl Ltd Shenzhen Kaifa Technology PKC Group Universal Scientific Industrial Co., Ltd. Foxconn Technology Co Ltd Foxconn Technology Co Ltd

0 0.5 1 1.5 2 2.5 3 3.5 4 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Efficiency Efficiency

Figure 4. Results obtained from the super-efficiency SBM model for 2014. in the first and second stages, the lowest one Table 12. Summarized results from 2015 to 2017 obtained belongs to TT Electronics with a score of 0.368. from the super-efficiency SBM model. 2015 2016 2017 Figure 4 shows a more clear view on the ranking Stage Stage Stage Stage Stage Stage of these DMUs. 1 2 1 2 1 2 After evaluating the data from 2012 to 2014 by 1. DMUs 18 18 18 18 18 18 2. DMUs with 000000 GM(1,1), we continue to assess the efficiencies of the inappropriate data EMSs from 2015 to 2017. Tables 12 and 13 show the 3. DMUs’ 18 18 18 18 18 18 4. Average of scores 1.189 0.793 1.178 0.639 1.244 0.698 results. The figures in boldface indicate an efficient 5. Efficient DMUs 12 7 11 5 11 6 score greater than 1. 6. Inefficient DMUs 6 11 7 13 7 12

Table 13. Detailed results from 2015 to 2017 obtained from the super-efficiency SBM model. 2015 2016 2017 Productivity Marketability Productivity Marketability Productivity Marketability DMUs Score Rank Score Rank Score Rank Score Rank Score Rank Score Rank DMU1 4.054 1 1.679 1 3.677 1 1.557 2 3.321 2 1.406 3 DMU2 1.437 3 0.633 8 1.477 4 0.841 6 1.636 3 1.006 5 DMU3 1.591 2 0.544 9 1.649 3 0.469 8 1.622 4 0.384 8 DMU4 0.882 15 0.462 11 0.835 14 0.342 12 0.779 14 1.001 6 DMU5 0.915 14 0.420 13 0.863 13 0.399 9 0.814 13 0.376 9 DMU6 0.955 13 0.422 12 0.879 12 0.343 11 0.816 12 0.295 12 DMU7 1.128 6 0.260 18 1.127 6 0.229 17 1.126 7 0.187 16 DMU8 0.388 18 1.462 4 0.405 18 1.314 3 0.430 18 1.098 4 DMU9 0.756 16 1.653 2 0.644 17 1.711 1 0.591 16 1.675 2 DMU10 1.146 5 0.409 15 1.844 2 0.314 15 3.571 1 0.214 15 DMU11 1.334 4 1.212 6 1.318 5 0.477 7 1.306 5 0.355 10 DMU12 1.043 8 0.404 16 1.034 8 0.323 14 1.015 8 0.220 14 DMU13 1.045 7 0.468 10 1.077 7 0.326 13 1.133 6 0.270 13 DMU14 0.726 17 1.226 5 0.715 15 0.124 18 0.707 15 0.410 18 DMU15 1.054 12 0.410 14 1.019 9 0.372 10 1.014 11 0.354 11 DMU16 1.142 10 1.142 7 0.645 16 1.213 5 0.543 17 0.999 7 DMU17 1.072 11 0.349 17 1.231 9 0.235 16 1.321 9 0.113 17 DMU18 1.210 9 1.476 3 1.132 10 1.244 4 1.124 10 1.870 1 16 C.-N. WANG ET AL.

The efficiencies (profitability and marketability) expected to be Hon Hai (DMU1), Pegatron of DMUs appear to decrease slightly from 2105 to (DMU14) and Flextronics (DMU3). Note that 2017. In terms of profitability efficiency, the average the Flextronics will be out of the Top 3, score stood at 1.189 in 2015, moved down to 1.178 replaced by Pegatron. Thus, the Pegatron in 2016 and is expected to move up to 1.244 in 2017, can be focused by investors. a value closing to that in 2012. In terms of market- (2) In the past period (from 2012 to 2014), the ability efficiency, the average score stood at 0.867 in top 3 most efficient EMSs in terms of market-

2012, moved down to 0.798 in 2014 and is expected ability were Hon Hai (DMU1), Shenzhen to dive to 0.698 in 2017. The efficiency ranking of Kaifa Technology a (DMU8) and Fabrinet DMUs is expected to change from 2015 to 2017. The (DMU14). But in the future period (from DMUs expected to perform well in generating profit 2015 to 2017), the top 3 most efficient EMSs are Hon Hai, Pegatron, Flextronics, Plexu and will change to Hon Hai, Shenzhen Kaifa tech-

Venture Corporation. In terms of the marketability nology (DMU8) and Universal Scientific efficiency, the expected top 5 DMUs are Hon Hai, industrial Company (DMU9). Shenzhen Kaifa technology, Universal Scientific (3) From (1) and (2), we know Hon Hai (DMU1) industrial Company, Wong’s international holding tops the ranking lists in both of the profit- and Pegatron. ability and marketability efficiencies. Though

Hon Hai (DMU1) is a good target for investment, however, its market value has been V. Conclusion sufficiently reflected.

In this research, we have first employed GM(1,1) to (4) As Shenzhen Kaifa Technology (DMU8), predict the values of the input and output variables, Universal Scientific Industrial (DMU9) and and then used a two-sage super-efficiency SBM Fabrinet (DMU14) are found with higher model to assess the efficiencies of 18 EMSs. The ranks in the marketability efficiency but

first stage measures the profitability efficiency and lower ranks in the profitability efficiency in the second stage measures the marketability effi- the past or the forecast period (from 2015 to ciency of a DMU. The profitability efficiency repre- 2017), they appear to have been overestimated sents the ability of a DMU in making profit, while in the stock market. the marketability reflects the capability of gaining (5) Pegatron (DMU2), Flextronics (DMU3)and market values. A reasonable sense suggests that a Plexus (DMU10) appear to have been underva- DMU with a higher profitability efficiency should lued in the stock market due to a higher profit- have a higher marketability efficiency. Otherwise, ability rank and a lower marketability rank. the DMU might have been undervalued. This pro- (6) It is found that the efficiency of an EMS is vides a clue for investors to target such targets for dependent from its size. Thus, a smaller EMS investments. On the contrary, investors should avoid remains with the chance to outperform a big- the investments on overvalued DMUs due to poten- ger one. tial risks. Our results also revealed inefficient DMUs (7) Our summary report enables EMSs to under- with lower ranking in profitability efficiency, which stand its relative performance among rivals so highlighted the needs for the managers of these that the managements can better understand DMUs to improve their profitability efficiency. their relative competitiveness in the industry. Some specific managerial/investing implications obtained from our results for decision-making are The main contributions of this research are listed listed as follows. as follows:

(1) In the past (from 2012 to 2014), the top 3 (1) This research provides a report for EMSs on most efficient EMSs in terms of profitability their relative efficiencies in the industry from ‘ ’ were Hon Hai (DMU1), PKC group (DMU15), a past-current-future view. For investors, it Flextronics (DMU3). But, during the period provides a reference for the selection of best (from 2015 to 2017), the top 3 EMSs are performers or potentials. APPLIED ECONOMICS 17

(2) This research has proposed a DEA approach Charnes, A., W. W. Cooper, A. Lewin, and L. M. Seiford. with two-stage super-efficiency SBM model to 1997. Data Envelopment Analysis: Theory, Methodology investigate the profitability efficiency and mar- and Applications. Norwell, MA: Kluwer Academic Publisher. ketability efficiency for EMSs. The breakdown Charnes, A., W. W. Cooper, and E. Rhodes. 1978. of efficiency into two components provides “Measuring the Efficiency of Decision Making Units.” more information for decision makers to better European Journal of Operational Research 2 (6): 429–444. understand and respond to situations. doi:10.1016/0377-2217(78)90138-8. (3) The proposed approach can also be applied to Chen, C.-M. 2009. “A Network-Dea Model with New Efficiency other application areas. Measures to Incorporate the Dynamic Effect in Production Networks.” European Journal of Operational Research 194 (3): 687– 699. doi:10.1016/j.ejor.2007.12.025. As this research only takes into account 18 DMUs Deng, J. L. 1989. “Introduction to Grey System Theory.” The from the Top 50 EMSs, including more DMUs for Journal of Grey System 1 (1): 1–24. comparison can be further investigated in future Farrell, M. J. 1957. “The Measurement of Productive research. In addition, including other input and out- Efficiency.” Journal of the Royal Statistical Society 120 – put variables for assessments can be also considered. (3): 253 290. doi:10.2307/2343100. Feng,C.Y.,andX.Q.Huang.2011. “Research on City Further, more periods of data can be further included Waste Production Forecast Based on Background Value in future study. For more advanced research, we can Optimizing GM (1,1) Model.” In Grey Systems and consider using other kinds of DEA models. Finally, Intelligent Services (GSIS), 2011 IEEE International applying the proposed approach to other industries is Conference,369–372. Emerald Group Publishing another research direction. Limited. Golany, B., and Y. Roll. 1989. “An Application Procedure for DEA.” Omega 17 (3): 237–250. doi:10.1016/0305-0483(89) Disclosure statement 90029-7. Hernández-Sancho, F., M. Molinos-Senante, and R. Sala- No potential conflict of interest was reported by the authors. Garrido. 2011. “Energy Efficiency in Spanish Wastewater ”

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