{"id":33107,"date":"2018-02-20T23:50:30","date_gmt":"2018-02-20T23:50:30","guid":{"rendered":"https:\/\/writemyessayfree.com\/critique-the-article-the-dea-articles-show-how-dea-can-be-used-in-the-real-life-application-as-a-tool-to-calculate-the-efficiency-of-dmu-in-particular-dea-is-useful-since-it-can-combine-both-financi"},"modified":"2017-08-16T06:12:21","modified_gmt":"2017-08-16T06:12:21","slug":"critique-the-article-the-dea-articles-show-how-dea-can-be-used-in-the-real-life-application-as-a-tool-to-calculate-the-efficiency-of-dmu-in-particular-dea-is-useful-since-it-can-combine-both-financi","status":"publish","type":"post","link":"https:\/\/www.benedictsol.com\/blogs\/critique-the-article-the-dea-articles-show-how-dea-can-be-used-in-the-real-life-application-as-a-tool-to-calculate-the-efficiency-of-dmu-in-particular-dea-is-useful-since-it-can-combine-both-financi\/","title":{"rendered":"Critique the Article The DEA articles show how DEA can be used in the real life application as a tool to calculate the efficiency of DMU. In particular, DEA is useful since it can combine both financial and non-financial metrics while the many traditional accounting method can only consider the financial metrics."},"content":{"rendered":"
\n

Critique the Article<\/p>\n

The DEA articles show how DEA can be used in the real life application as a tool to calculate the efficiency of DMU. In particular, DEA is useful since it can combine <\/p>\n

both financial and non-financial metrics while the many traditional accounting method can only consider the financial metrics.<\/p>\n

Summarize and analyze the article based on the following guideline How to critique a journal article.pdf
Please emphasize \u2018 What is the contribution of the paper to the relevant literature \u2018 ?<\/p>\n

PERFORMANCE ASSESSMENT BETWEEN TAIWAN AND
KOREA TFT-LCD PANEL INDUSTRY FROM A RISK AND
RETURN PERSPECTIVE BEFORE AND AFTER GLOBAL FINANCIAL CRISIS <\/p>\n

ABSTRACT <\/p>\n

Taiwan and Korea are in fierce competition in the TFT-LCD panel industry and together dominate over 70% of the international market share. Business strategies in <\/p>\n

Taiwan primarily rely on Original Equipment Manufacturers (OEMs) whereas Korea focuses principally on vertical integration, including development of its own <\/p>\n

international brand architecture. Additionally the financial structures of the manufacturers in both countries are different resulting in varied business models and <\/p>\n

financial risk exposures.
The purpose of this research is to explore the performance efficiency in utilizing resources and performance variance before and after a global financial crisis based <\/p>\n

on influences upon the different business models and different financial structures. The Data Envelopment Analysis (DEA) was used to evaluate a company\u2019s relative <\/p>\n

performance based on multiple parameters, to develop a matrix of risk and return and Malmquist Productivity Index to demonstrate their efficiency and technical <\/p>\n

productivity trend before and after the global financial crisis.
The empirical results indicate: (1) Korea\u2019s manufacturers have a higher ratio of G&A expenses v.s. Taiwan\u2019s manufacturers having a higher fixed asset ratio and debt <\/p>\n

ratio. The revenue of companies in both countries had been significantly affected by global financial crisis. (2) Company\u2019s performance is consistent with the economic <\/p>\n

trends and high-risk firms have been hit harder by the phenomenon of decline in the efficiency of performance. (3) Korea manufacturers during the period of 2004-2008 <\/p>\n

represent lower risk and higher return compared with Taiwan manufacturers. Outstanding performance manufacturers had provided direction for motivating Taiwan <\/p>\n

manufacturers towards vertical integration and to reduce their debt ratios. (4) There are different phenomenon that influence Taiwan and South Korea productivity <\/p>\n

improvement, and show overall industry improvement was mainly attributable to technological phenomenon. <\/p>\n

Keywords: TFT-LCD Panel Industry, Business Model, Financial Structure, Global
Financial Crisis, Data Envelopment Analysis (DEA), Malmquist Productivity Index <\/p>\n

1. INTRODUCTION
*<\/p>\n

The global TFT-LCD panel industry is mainly located in Taiwan and Korea, which represent over 70% of the market demand. Figure 1 is a graphical view of the competition <\/p>\n

between Taiwan and Korean panel industry.
The production volume from Taiwan\u2019s TFT-LCD panel industry in 2007 represented 37.34% of global market needs which ranked Taiwan as the largest manufacturer of display <\/p>\n

panel with production <\/p>\n

*<\/p>\n

valued at approx. 1.7 trillion NTD which represented approx. 13% of Taiwan\u2019s total GDP. Employment in related industries reached as high as 0.2 million, equivalent to <\/p>\n

approx. 3% of the families in Taiwan. Economy were influenced by the global financial crisis in 2008, a result Taiwan\u2019s market share fell to approx. 35.20%, slightly <\/p>\n

behind Korea\u2019s 35.43%. The TFT-LCD panel industry is still Taiwan\u2019s most important industry and has significant influence upon Taiwan economic development.
From Table 1, the positioning between Korea and Taiwan panel industry in the up stream are similar, however, in the middle stream and lower stream are distinctly <\/p>\n

different. Brand in the downstream market, Samsung and LG are within the top five global brands in the LCD monitor and LCD TV market, and are more competitive than the <\/p>\n

Taiwan panel manufacturers. <\/p>\n

Figure 1: Global market share between Taiwan and
Korea\u2019s panel industry [19] <\/p>\n

Currently, the global TFT-LCD panel industry supply chain rests principally in Korea, and Taiwan and is characterized by different niches of competitiveness. For <\/p>\n

example, Korea\u2019s major advantage lies in manufacturing and development of their own-brand strategy. Taiwan does not have strong basic research and development <\/p>\n

capability nor does its own brand names, but Taiwan manufacturer are recognized by excellent quality, low cost and high degree of flexibility to focus on <\/p>\n

manufacturing.
The TFT-LCD panel industry is a capital-intensive industry. In 2008 major panel manufacturers in Taiwan averaged a debt ratio of approx. 46% whereas Korea\u2019s was <\/p>\n

approx. 33%. When financial leverage is properly adopted, it can create opportunities for high profits. However if improperly adopted, it may increase the company\u2019s <\/p>\n

financial risk exposure [20].
Taiwan and Korea panel industry undertook different strategies in their supply chain arrangement, and different financial structures to enable enterprises to better <\/p>\n

cope with the risks. They need to understand their position under competition and improve their business model strategy. It is their expectation to perform well in a <\/p>\n

good economic conditions and in doing so, should the economy reverse the detrimental effects will be minimized.
Table 1: Taiwan, Korea panel display industry \u2013 The main product list of up, middle and low stream of value chain [19]
Industry Classification Item Taiwan S. Korea
Up stream Polarizer 27% 26%
Glass Substrate 29% 27%
Color Filter 39% 40%
Large-Size Backlight Module 18% 15%
Large-Size Cold Cathode 20% 31%
Middle Stream Notebook Panel 35.2% 59.9%
LCD Monitor Panel 53.7% 34.3%
LCD TV Panel 40% 40.9%
Low Stream Brand Cheimei, Hann. G, Hannspree, Tatung LG, Samsung<\/p>\n

The DEA analytic approach with linear programming was used to deal with multiple inputs and outputs at the same time without the prior knowledge of the function of <\/p>\n

inputs and outputs.<\/p>\n

As it is objective, DEA analytic approach has become the major method to evaluate performance which can also can provide recommendations to the company for improvement <\/p>\n

[4]. The main purposes of this research are as follows:
1. Utilizing CCR (DEA model created by Charnes, Cooper and Rhodes) and BCC (DEA model addressed by Banker, Charnes and Cooper) model under DEA approaches verify <\/p>\n

the technical efficiency, purely technical efficiency and scale efficiency from 2006 to 2008 for panel manufacturers in Taiwan and Korea.
2. Adopting the mean value and variance of efficiency from the sample manufacturers between 2004 and 2008 into DEA window analysis to observe the behavior of the <\/p>\n

specific manufacturer through a risk and return matrix to identify higher return manufacturers with lower risk, and establish the benchmark for the industry. <\/p>\n

2. LITERATURE REVIEW <\/p>\n

2.1 Efficiency
\u201cEfficiency\u201d is evaluated mainly by the ratio of output and input. From a result perspective, it means utilizing a given combination of inputs to achieve the maximum <\/p>\n

outputs. And from a cost perspective, it means that the output has been generated by minimal input.
Ratio analysis is the most frequently used, most convenient and is principally used for output and input comparisons. The advantage is easy-to-read, however due to the <\/p>\n

business strategy, overall performance cannot be measured. The organization with multiple inputs and multiple outputs is unable to carry out a comprehensive <\/p>\n

organizational performance assessment. There are researchers trying to use a weighted approach but the choice of the weights are often too subjective, thus reducing <\/p>\n

its applicability [14].
Econometrics usually applies Regression Analysis to identify variables by using numbers of input as independent variables and single-output as the dependent variables. <\/p>\n

However, Regression Analysis cannot deal with situations where there are multiple outputs, and the results only represent \u201cmean value\u201d, not the most optimal solution. <\/p>\n

Plus Regression Analysis ignores special circumstances of individual firms and cannot provide a clear distinction between high efficiency and low efficiency <\/p>\n

manufacturers [3]. With the least squares method for the production function, we must assume that it is linear, if the data on the distribution is non-linear, it will <\/p>\n

create a larger bias. Hence it is questionable whether to use this method to analyze the productivity of firms. <\/p>\n

2.2 Frontier Analytic Approach
The Production Frontier Analysis was proposed by Farrell [11] who addressed the concept of the border. Farrell used mathematical programming methods to acquire the <\/p>\n

economic efficiency of firms (Economic Efficiency, EE; or Overall Efficiency, OE), Farrell analyzes of the concept of productivity can be interpreted by Isoquant. <\/p>\n

Figure 2. <\/p>\n

Figure 2: Farrell production frontier theory [9] <\/p>\n

Lovell [12] pointed out that the analysis method commonly used to assess the production of border efficiency, whether in accordance with prior estimates of production <\/p>\n

function form of the boundary method can be divided into Parametric Frontier Method and Non-Parametric Frontier Method, which parameters frontier analysis of a random <\/p>\n

boundary (Stochastic Frontier Analysis, SFA) was represented by DEA (Data Envelopment Analysis, DEA). Non-parametric Frontier method in which the data envelopment <\/p>\n

analysis use linear programming to estimate the efficiency, can handle multiple inputs and multiple outputs. There is no restriction for inputs and outputs. There is <\/p>\n

no restriction on production function, and data was decided by the mathematical weights, with no subjective elements from a resource management point of view to <\/p>\n

provide recommendations on how to improve, and objectively achieve multiple inputs multiple outputs relative efficiency assessment [16].
In this research, as a result of multiple inputs and outputs, and expectations in addition to the performance efficiency of the assessment may be made available to <\/p>\n

non-efficient manufacturers to improve their efficiency. Data Envelopment Analysis was selected for this research to analysis the performance between Taiwan and Korea <\/p>\n

panel industries. <\/p>\n

2.3 Application of Data Envelopment Analysis in Panel Industry
Chi-Wen Chou [7] selected fixed assets, R&D expenses, the number of employees as the input variables, and revenue as the output variable, to analyze key factors that <\/p>\n

impact Taiwan\u2019s TFT-LCD business performance and found that most of the key factors are from manufacturing technology improvement and from the acquisition of advanced <\/p>\n

machinery and equipment. To highlight the current TFT-LCD industry Taiwan manufacturers should actively develop their next-generation production lines, and to enhance <\/p>\n

productivity in new capacity in order to maintain competitive market advantage.
Sung-Tseng Hsun [13] selected the total assets, operating costs, G&A expenses and the number of employees as the input variables, and used revenue and net profit for <\/p>\n

the output variables during the research period of the fourth quarter of 2001 to the first quarter of 2004, which found that fluctuations in the economy, price <\/p>\n

fluctuations related to performance with a certain degree of correlation.
Shih-Chi Chang, Che-Peng Lin, and
Meng-Hsin Lu [3] selected fixed assets, total assets and operating costs as the input variables, and net operating incomes and total assets return rate as the output <\/p>\n

variables to measure the dynamic operating efficiency of Taiwan TFT-LCD industry during years 2001 and 2005 by DEA window analysis and Malmquist product index method. <\/p>\n

The window analysis results show that CMO has the highest mean efficiency value followed by AUO and CPT having the lowest overall fluctuation and mean efficiency <\/p>\n

value, whereas QDI had the highest overall fluctuation value. The results of Malmquist Product Index method shows that overall TFT-LCD industry productivity is <\/p>\n

decreased approximately 4.6% and that only AUO\u2019s productivity increased approximately 6.9% during the observation period.
Yen-Chih Chiu [6] refers to the theory of microeconomics and selected labor, capital and raw materials as inputs, and revenue as output, and used Data Envelopment <\/p>\n

Analysis to assess Taiwan\u2019s panel manufacturers production efficiency. The results showed that the overall panel industry continues to expand and the gap between <\/p>\n

manufacturers are growing. The efficiency of the overall industry, pure technical efficiency and scale efficiency changes are subject to the recession phenomenon. <\/p>\n

2.4 Risk-return and Performance Evaluation
The companies who bear higher risk will expose its revenue performance to greater uncertainty. The risks are always divided into economic risk, operational risk and <\/p>\n

financial risk [20]. To consider breakeven point, greater fixed costs generate greater operational risk. A company with higher debt results in higher interest costs, <\/p>\n

which will impact EPS. The greater degree of financial leverage, with a little change in EBIT, EPS will change significantly so, the company with high debt will easily <\/p>\n

became a troubled enterprises during times of recession.
Cheng-Chung Chu [8] explored whether the TFT-LCD industry can promote profit and reduce risk through vertical integration and strategic alliances. By choosing of TFT-<\/p>\n

LCD industry in different aspects, from year 2000 and 2003 years the research considered the Return on Equity (ROE), Return on Asset (ROA), Return on Sales (ROS) as <\/p>\n

performance indicators, the mean value and the standard deviation represent its return and risk.
The panel industry is affected by business cycles. Manufacturer\u2019s performance will change in accordance with the economy, while three years from 2006 to 2008, the <\/p>\n

panel industry had experienced two economic fluctuations, and the recession in 2008 was due to the consuming power from the market decline, which was different than in <\/p>\n

2006 resulting in the imbalance between supply and demand. This research observes whether companies can utilize their investments in an efficient way, whether a high-<\/p>\n

risk company will be impacted in a financial crisis, and whether the performance will be relatively decline. Data Envelopment Analysis was used to evaluate the <\/p>\n

performance instead of using the traditional performance indicators to evaluate the average and performance variation between Taiwan and Korea panel manufacturers <\/p>\n

during the 2004-2008 time frame. <\/p>\n

3. METHODOLOGIES AND DATA <\/p>\n

3.1 Data Envelopment Analysis
The Data Envelopment Analysis (DEA) methodology developed by Charnes, Cooper and Rhodes [5] proposed \u201cMeasuring the Efficiency of Decision Making Units\u201d. Their <\/p>\n

research was applied to Farrell\u2019s \u201cProduction Frontier\u201d concept, incorporating the Constant Return to Scale (CRS) and applied mathematic programming to measure the <\/p>\n

efficiency of inputs and outputs variables between 0 and 1. This model is known as the CCR model. The efficiency of its definition refers to the \u201cRealm of Plato <\/p>\n

Optimal\u201d, which is the most favorable rating on the subject.
The efficiency derived from CCR model is the overall efficiency or technical efficiency (Technical Scale Efficiency, TE). However, the production environment of each <\/p>\n

DMU (Decision Making Unit) is not always the same; they may not have the constant returns to scale, \u201cto increase one unit of inputs will also increase one unit of <\/p>\n

output\u201d, If a DMU is inefficiently indicated by the result from CCR model, it may be due to the DMU is not constant returns to scale, led to scale of inefficiency <\/p>\n

rather than technical inefficiency. Thus, to be more practical, Banker et al. [1] in 1984 developed an extension CCR model that is referred to as the BCC model. Figure <\/p>\n

3 represents the comparison between CCR and BCC models.
CCR model assumes that the production process has constant returns to scale, i.e., if increase inputs in certain ratio, then output will increase in the same ratio. <\/p>\n

However, the returns to scale of the production process may increase or decrease, the assumption that constant returns to scale will increase progressively is not <\/p>\n

appropriate. Banker, Charnes and Copper [1] derived the BCC model by the four axioms of possible production sets and the Shephard distance function that can measure <\/p>\n

the pure technical efficiency (PE) and the scale efficiency (SE).
In the above discussion, the efficiency value derived from CCR model is called technical efficiency and the efficiency value obtained by the BCC model represents pure <\/p>\n

technical efficiency. Technical efficiency is a combination of both pure technical efficiency and scale efficiency, i.e.,
Technical Efficiency \uf03d Pure Technical
Efficiency \uf0b4 Scale Efficiency.
Therefore, we can divide technical efficiency by pure technical efficiency then obtain the scale efficiency, that is,
Technical Efficiency \/ Pure Technical
Efficiency = Scale Efficiency.
The pure technical efficiency determines whether the decision-making units maximize the output from the inputs, and scale efficiency measures the ratio between <\/p>\n

decision-making units and the Most Productive Scale Size (MPSS).
Figure 3: Technical efficiency, pure technical efficiency and scale efficiency [14] <\/p>\n

3.2 DEA Window Analysis
DEA is a static measurement of operating efficiency that measures the efficiency value of a single year. DEA window analysis measure the dynamic operating efficiency. <\/p>\n

If we can compare the relative operating efficiency of each manufacturer, using static and dynamic efficiency, and identify the cause of excellence and to provide <\/p>\n

specific recommendations, the results will contribute to the development of the industry [3].
Window analysis was first proposed by Banker, Charnes and Copper [1] and supplements the traditional DEA model when we do not have enough number of decision-making <\/p>\n

units. It also can provide relative efficiency to decision-making units in different time periods in order to their changes. <\/p>\n

3.3 Malmquist Productivity Index
This research uses the Malmquist Productivity Index to evaluate the decision-making unit changes in technical, technical efficiency and productivity across multi-year <\/p>\n

timeframes. Then Shephard Distance Function was applied to decompose tfpch into technical change (techch) and technical efficiency changes (effch). Technical <\/p>\n

efficiency changes (effch) can also be divided into pure technical efficiency change (pech) and scale efficiency change (sech). The value for each individual are <\/p>\n

defined as follows:
If tfpch>1, means that DMU productivity is increased from period t to t+1; on the contrary, if tfpch<1, means that DMU productivity is decreased from period t to t + 1. if effch>1, means technological efficiency is
improved to some extent; on the contrary, if effch<1, shows technological efficiency is decreased. If techch>1, means the technology has improved.
If techch<1 means the technology has declined. When pech>1, means that the pure technical efficiency has improved;
When sech>1, means that in comparison to the period t, period t + 1 has become close to a constant scale of return, which is gradually to the optimal size of a long-<\/p>\n

term approach. <\/p>\n

3.4 Data Collection
The sample data of Taiwan LCD panel companies was obtained from the database of Taiwan Economic Journal Co., Ltd., which includes the company annual reports and <\/p>\n

mandatory financial disclosure reports. The financial analysis data of Korean panel companies was obtained and derived from Nonura, JP Morgan, Macquare Capital <\/p>\n

Advisors. The samples include the panel companies on the Taiwan Stock Exchange and two major panel manufacturers in Korea. Research period was from 2004 to 2008 <\/p>\n

timeframe a total of five years. <\/p>\n

3.5 Data Qualification
When estimate efficiency by using DEA method, selects the appropriate inputs and outputs items. These items must be able to explain the impact of the efficiency <\/p>\n

measure. Therefore, the inputs and outputs must have an isotropic relation that increases both inputs and outputs [2]. Thus, this research tests the relations between <\/p>\n

inputs and outputs of each of the years by using Pearson Correlation Test. The test results are shown in Table 2 and Table 3. As these tables show, each of the inputs <\/p>\n

and outputs are positively correlated. When the inputs increased, outputs are also increased complying with the DEA isotropic relation requirement meaning that the <\/p>\n

selected inputs and outputs in this research are appropriate. <\/p>\n

Table 2: Coefficient of Pearson Correlation Test results from annual inputs and output (revenue)
Year Output \/ Inputs G&A
Expenses Fixed Assets Debt Total Assets
2004 Revenue 0.871 0.916 0.762 0.912
2005 0.849 0.894 0.814 0.938
2006 0.912 0.861 0.735 0.857
2007 0.961 0.961 0.704 0.838
2008 0.928 0.925 0.857 0.912 <\/p>\n

Table 3: Coefficient of Pearson Correlation Test results from annual inputs and output (gross margin)
Year Output \/ Inputs G&A
Expenses Fixed Assets Debt Total Assets
2004 Gross Margin 0.731 0.826 0.781 0.879
2005 0.812 0.784 0.814 0.912
2006 0.742 0.721 0.723 0.935
2007 0.721 0.86 0.719 0.899
2008 0.808 0.912 0.820 0.903 <\/p>\n

4. EMPIRICAL RESULTS <\/p>\n

Based on the empirical models, variance was used as the input variable to assess the DEA performance. Impacts to panel manufacturers who bear high-risk was even higher <\/p>\n

during the financial crisis in 2008, and were analyzed to develop recommendations to improve their performance. DEA-window analysis was used to calculate the mean <\/p>\n

value of the efficiency between 2004-2008 to determine a risk\/return matrix. <\/p>\n

4.1 Statistical Analysis
Examination of Taiwan and Korea\u2019s panel industries included ratio of G&A expense, fixed assets ratio, debt ratio and revenue changes during the research period. Figure <\/p>\n

4 to Figure 7 show the annual trend for the aforementioned items.
The conclusion based on the above observation, Taiwan and Korea panel manufacturers bear various risks in business model and financial structure. Revenue was severely <\/p>\n

impacted in 2008 due to the decline in demand as result from the global financial turmoil. <\/p>\n

Figure 4: G&A expense ratio trend [10,15] Figure 5: Fixed Assets ratio trend [10,15] <\/p>\n

Figure 6: Debt Ratio Trend [10,15] <\/p>\n

Figure 7: Trend of revenue growth [10,15] <\/p>\n

4.2 Manufacturer\u2019s Efficiency Analysis this research, the first model used was CCR to
Utilizing input-oriented DEA model in this acquire technical efficiency of the sample firms, research, attention was given to performance between followed by the use <\/p>\n

of BCC model. To calculate the risk and return between Taiwan and Korea panel technical efficiency, technical efficiency was divided industries during the period from <\/p>\n

2006 to 2008. In by purely technical efficiency to derive scale <\/p>\n

efficiency. By comparing pure technical efficiency against the scale efficiency, we identified whether the main source of inefficiency is purely technical inefficiency <\/p>\n

or scale inefficiency. If it is from purely technical inefficiency, the reasons are due to management\u2019s inappropriate decision making while utilizing of resources. If <\/p>\n

inefficiency however resulted from scale inefficiency, we can evaluate causes with a return on investment analysis under such scale of operation, and make judgment to <\/p>\n

expand or reduce its scale of operation.
Referring to Norman and Stocker [18] in 1991, clustering the efficiency, based on the number of relative efficiency being apply by decision-making units, the <\/p>\n

decision-making unit will be segregated into four groups or categories to include; the strong unit, the edge of the efficiency units, the edge of non-efficient units <\/p>\n

and non-efficient units.
We conclude Manufacturer\u2019s Efficiency Analysis during the period from 2006 to 2008 and the results as showed as Table 4, 5 and 6. Observation of the economic cycle, <\/p>\n

the world\u2019s seventh generation production lines in 2006 were developed. However, the growth of demand from market was less than the supply from the production, coupled <\/p>\n

with sharply falling commodity prices, the panel industry suffered significantly in 2006. The demand from emerging markets gradually recovered the panel industry in <\/p>\n

2007. However, triggered by global financial crisis of the United States in 2008, the panel industry was once again hit by the economy turned down.
From the sample manufacturers this research discovered that AUO and Samsung Electronics maintained efficient performance during the past 3 years. The performance <\/p>\n

efficiency of other manufacturers were affected by economic fluctuations. Even with a poor economy in 2006, the Korean panel manufacturers were able to maintain their <\/p>\n

efficiency. However, high-risk panel manufacturers in Taiwan generally experienced poor performance. Inefficient manufacturers in Taiwan, accounted for 36 percent of <\/p>\n

the sample manufacturers. The value of inefficient is averaged 0.73. However with the economic boom in 2007, the Taiwan panel manufacturers\u2019 revenue and profit were <\/p>\n

better than that of Korean companies moving LGD down to the edge of non-efficient. The inefficient manufacturers are all in Taiwan, but in 2006 the average was 0.74. <\/p>\n

The economy once again reversed in 2008. The impacts of high-risk Taiwan panel manufacturers were even greater. CMO which was in the efficient group, became an <\/p>\n

inefficient manufacturer and joint the edge of non-efficient group. The remaining 57% of manufacturers also in the group of inefficiency had an average value of 0.607, <\/p>\n

the lowest for three years.
As seen from the above analysis, other than Samsung Electronics and AUO, the companies with high-risk experienced impacts to their performance by economic down turns. <\/p>\n

During a good economy, the economies of scale will reduce manufacturing costs, resulting in higher revenue and profit. Conversely when the economy turns downward, the <\/p>\n

expected result is idle capacity, increased burden from interest costs and a decline in operational efficiency. <\/p>\n

Table 4: Manufacturer\u2019s efficiency analysis-2006
Year Manufacturer Technical Efficiency Consulted Numbers Pure Technical Efficiency Consulted Numbers Scale Efficiency Returns to <\/p>\n

Scale
2006 AUO 1 5 1 5 1 CRS
CPT 0.9356 0 0.9469 0 0.9880 IRS
CMO 1 2 1 4 1 CRS
Innolux 1 1 1 2 1 CRS
Hann Star 0.9615 0 0.9745 0 0.9867 IRS
PVI 0.8145 0 0.9717 0 0.8382 IRS
TPO 0.9117 0 0.9263 0 0.9842 IRS
Wintex 0.7383 0 0.8325 0 0.8868 DRS
EMT 0.6155 0 0.7259 0 0.8479 DRS
PT 0.9233 0 1 2 0.9233 IRS
GP 0.8621 0 0.9322 0 0.9248 IRS
URT 0.6533 0 0.9599 0 0.6806 DRS
LGD 1 7 1 6 1 CRS
SEC 1 8 1 5 1 CRS <\/p>\n

Table 5: Manufacturer\u2019s efficiency analysis-2007
Year Manufacturer Technical Efficiency Consulted Numbers Pure Technical Efficiency Consulted Numbers Scale Efficiency Returns to <\/p>\n

Scale
2007 AUO 1 3 1 6 1 CRS
CPT 0.9263 0 0.9591 0 0.9658 IRS
CMO 1 2 1 3 1 CRS
Innolux 1 1 1 0 1 CRS
Hann Star 0.6814 0 0.6892 0 0.9887 IRS
PVI 0.8148 0 0.9295 0 0.8766 IRS
TPO 0.9471 0 0.9751 0 0.9713 DRS
Wintex 0.7708 0 1 1 0.7708 IRS
EMT 0.7526 0 0.9051 0 0.8315 DRS
PT 0.9237 0 1 1 0.9237 IRS
GP 0.9653 0 0.9762 0 0.9888 IRS
URT 0.6906 0 0.8694 0 0.7943 DRS
LGD 0.971 0 1 3 0.971 DRS
SEC 1 7 1 5 1 CRS <\/p>\n

Table 6: Manufacturer\u2019s efficiency analysis-2008
Year Manufacturer Technical Efficiency Consulted Numbers Pure Technical Efficiency Consulted Numbers Scale Efficiency Returns to <\/p>\n

Scale
2008 AUO 1 3 1 6 1 CRS
CPT 0.5743 0 0.8107 0 0.7084 IRS
CMO 0.9613 0 1 1 0.9613 DRS
Innolux 0.9172 0 0.9331 0 0.9830 IRS
Hann Star 0.5521 0 0.6892 0 0.8011 IRS
PVI 0.5947 0 0.7375 0 0.8064 IRS
TPO 0.6117 0 0.7541 0 0.8112 IRS
Wintex 0.7621 0 1 1 0.7621 IRS
EMT 0.6526 0 0.8213 0 0.7946 DRS
PT 0.4877 0 0.7335 0 0.6649 IRS
GP 0.9253 0 0.9762 0 0.9479 IRS
URT 0.6213 0 0.7954 0 0.7811 DRS
LGD 1 3 1 4 1 CRS
SEC 1 5 1 7 1 CRS <\/p>\n

4.3 Slack Variable Analysis
In this study, we try to identify the area for improper resource allocation through Slack Variable Analysis of the non-efficient manufacturers. This can provide the <\/p>\n

guideline for improvement. Especially in the economic downturn the non-efficient manufacturers try to find ways to reallocate their resources to improve their <\/p>\n

efficiency and while waiting for the next economic boom. If the Slack Variable is negative, it represents the area to reduce resources and whereas the percentage <\/p>\n

represents the volume for reduction. The results from this study from 2006 to 2008 are as shown on Table 7, 8 and 9.
From the above analysis, we can see the results of Taiwan and Korea manufacturers in utilizing of resources and the area for improvement. The G&A expenses are most <\/p>\n

critical and required improvements especially during 2006 and 2007, approximately 30% were categorized as non-efficiency. The debts in 2008 are considered first <\/p>\n

priority to be improved, approximately 34% could not efficiently contribute to output, which was significantly higher than in 2006 and 2007. Inefficiency in fixed <\/p>\n

assets utilization within the three years is considered the second priority needing to be improved to approximately 28%.
The panel industry is a highly capital-intensive industry, requiring investment in plants and equipment. Taiwan\u2019s panel makers have positioned themselves in the supply <\/p>\n

chain as OEMs which differs significantly from Korea manufacturers. If the ratio of fixed assets is high, the revenue and profit will be good in a boom economy. <\/p>\n

However, when the economy experiences a decline, the larger scale of production will result in idle capacity and increase liquidity risks. In 2008 impacted by the <\/p>\n

global financial crisis, manufacturers experienced significant reduction in revenue and profits and were burdened by interest from debt. As result their overall <\/p>\n

efficiency declined.
Table 7: Slack variable analysis \u2013 2006
Year Manufacturer Input variables
Fixed Assets G&A Expenses Debt Total Assets
2006 <\/p>\n

CPT (-18.2%) (-24.4%) (-12.4%) (-21.1%)
TPO (-27.1%) (-13.9%) (-13.9%) (-13.9%)
Hann Star (-23.7%) (-23.5%) (-25.5%) (-11.5%)
PT (-45.3%) (-45.5%) (-32.4%) (-9.8%)
PVI (-13.8%) (-14.1%) (-13.2%) (-17.1%)
GP (-25.3%) (-15.6%) (-23.2%) (-12.1%)
Wintex (-37.6%) (-61.4%) (-15.7%) (-19.7%)
EMT (-22.4%) (-37.1%) (-22.1%) (-11.5%)
URT (-42.3%) (-35.8%) (-19.5%) (-21.7%) <\/p>\n

Table 8: Slack variable analysis \u2013 2007
Year Manufacturer Input variables
Fixed Assets G&A Expenses Debt Total Assets
2007
CPT (-15.8%) (-26.7%) (-19.7%) (-23.1%)
TPO (-30.7%) (-15.3%) (-21.2%) (-10.9%)
GP (-11.3%) (-37.1%) (-37.1%) (-5.3%)
PT (-43.3%) (-48.8%) (-25.3%) (-7.2%)
PVI (-19.5%) (-31.6%) (-17.5%) (-11.3%)
Wintex (-27.7%) (-25.7%) (-11.8%) (-23.8%)
EMT<\/p>\n","protected":false},"excerpt":{"rendered":"

Critique the Article The DEA articles show how DEA can be used in the real life application as a tool to calculate the efficiency of DMU. In particular, DEA is useful since it can combine both financial and non-financial metrics Read More …<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-33107","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts\/33107","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/comments?post=33107"}],"version-history":[{"count":0,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/posts\/33107\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/media?parent=33107"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/categories?post=33107"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.benedictsol.com\/blogs\/wp-json\/wp\/v2\/tags?post=33107"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}