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PERFORMANCE ASSESSMENT BETWEEN TAIWAN AND
KOREA TFT-LCD PANEL INDUSTRY FROM A RISK AND
RETURN PERSPECTIVE BEFORE AND AFTER GLOBAL FINANCIAL CRISIS
ABSTRACT
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
Taiwan primarily rely on Original Equipment Manufacturers (OEMs) whereas Korea focuses principally on vertical integration, including development of its own
international brand architecture. Additionally the financial structures of the manufacturers in both countries are different resulting in varied business models and
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
on influences upon the different business models and different financial structures. The Data Envelopment Analysis (DEA) was used to evaluate a company’s relative
performance based on multiple parameters, to develop a matrix of risk and return and Malmquist Productivity Index to demonstrate their efficiency and technical
productivity trend before and after the global financial crisis.
The empirical results indicate: (1) Korea’s manufacturers have a higher ratio of G&A expenses v.s. Taiwan’s manufacturers having a higher fixed asset ratio and debt
ratio. The revenue of companies in both countries had been significantly affected by global financial crisis. (2) Company’s performance is consistent with the economic
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
represent lower risk and higher return compared with Taiwan manufacturers. Outstanding performance manufacturers had provided direction for motivating Taiwan
manufacturers towards vertical integration and to reduce their debt ratios. (4) There are different phenomenon that influence Taiwan and South Korea productivity
improvement, and show overall industry improvement was mainly attributable to technological phenomenon.
Keywords: TFT-LCD Panel Industry, Business Model, Financial Structure, Global
Financial Crisis, Data Envelopment Analysis (DEA), Malmquist Productivity Index
1. INTRODUCTION
*
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
between Taiwan and Korean panel industry.
The production volume from Taiwan’s TFT-LCD panel industry in 2007 represented 37.34% of global market needs which ranked Taiwan as the largest manufacturer of display
panel with production
*
valued at approx. 1.7 trillion NTD which represented approx. 13% of Taiwan’s total GDP. Employment in related industries reached as high as 0.2 million, equivalent to
approx. 3% of the families in Taiwan. Economy were influenced by the global financial crisis in 2008, a result Taiwan’s market share fell to approx. 35.20%, slightly
behind Korea’s 35.43%. The TFT-LCD panel industry is still Taiwan’s 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
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
Taiwan panel manufacturers.
Figure 1: Global market share between Taiwan and
Korea’s panel industry [19]
Currently, the global TFT-LCD panel industry supply chain rests principally in Korea, and Taiwan and is characterized by different niches of competitiveness. For
example, Korea’s major advantage lies in manufacturing and development of their own-brand strategy. Taiwan does not have strong basic research and development
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
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’s was
approx. 33%. When financial leverage is properly adopted, it can create opportunities for high profits. However if improperly adopted, it may increase the company’s
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
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
good economic conditions and in doing so, should the economy reverse the detrimental effects will be minimized.
Table 1: Taiwan, Korea panel display industry – 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
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
inputs and outputs.
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
[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
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
specific manufacturer through a risk and return matrix to identify higher return manufacturers with lower risk, and establish the benchmark for the industry.
2. LITERATURE REVIEW
2.1 Efficiency
“Efficiency” 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
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
business strategy, overall performance cannot be measured. The organization with multiple inputs and multiple outputs is unable to carry out a comprehensive
organizational performance assessment. There are researchers trying to use a weighted approach but the choice of the weights are often too subjective, thus reducing
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.
However, Regression Analysis cannot deal with situations where there are multiple outputs, and the results only represent “mean value”, not the most optimal solution.
Plus Regression Analysis ignores special circumstances of individual firms and cannot provide a clear distinction between high efficiency and low efficiency
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
create a larger bias. Hence it is questionable whether to use this method to analyze the productivity of firms.
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
economic efficiency of firms (Economic Efficiency, EE; or Overall Efficiency, OE), Farrell analyzes of the concept of productivity can be interpreted by Isoquant.
Figure 2.
Figure 2: Farrell production frontier theory [9]
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
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
boundary (Stochastic Frontier Analysis, SFA) was represented by DEA (Data Envelopment Analysis, DEA). Non-parametric Frontier method in which the data envelopment
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
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
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
non-efficient manufacturers to improve their efficiency. Data Envelopment Analysis was selected for this research to analysis the performance between Taiwan and Korea
panel industries.
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
impact Taiwan’s TFT-LCD business performance and found that most of the key factors are from manufacturing technology improvement and from the acquisition of advanced
machinery and equipment. To highlight the current TFT-LCD industry Taiwan manufacturers should actively develop their next-generation production lines, and to enhance
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
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
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
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.
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
value, whereas QDI had the highest overall fluctuation value. The results of Malmquist Product Index method shows that overall TFT-LCD industry productivity is
decreased approximately 4.6% and that only AUO’s 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
Analysis to assess Taiwan’s panel manufacturers production efficiency. The results showed that the overall panel industry continues to expand and the gap between
manufacturers are growing. The efficiency of the overall industry, pure technical efficiency and scale efficiency changes are subject to the recession phenomenon.
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
financial risk [20]. To consider breakeven point, greater fixed costs generate greater operational risk. A company with higher debt results in higher interest costs,
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
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-
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
performance indicators, the mean value and the standard deviation represent its return and risk.
The panel industry is affected by business cycles. Manufacturer’s performance will change in accordance with the economy, while three years from 2006 to 2008, the
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
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-
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
performance instead of using the traditional performance indicators to evaluate the average and performance variation between Taiwan and Korea panel manufacturers
during the 2004-2008 time frame.
3. METHODOLOGIES AND DATA
3.1 Data Envelopment Analysis
The Data Envelopment Analysis (DEA) methodology developed by Charnes, Cooper and Rhodes [5] proposed “Measuring the Efficiency of Decision Making Units”. Their
research was applied to Farrell’s “Production Frontier” concept, incorporating the Constant Return to Scale (CRS) and applied mathematic programming to measure the
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 “Realm of Plato
Optimal”, 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
DMU (Decision Making Unit) is not always the same; they may not have the constant returns to scale, “to increase one unit of inputs will also increase one unit of
output”, 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
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
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.
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
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
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
technical efficiency. Technical efficiency is a combination of both pure technical efficiency and scale efficiency, i.e.,
Technical Efficiency Pure Technical
Efficiency 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
decision-making units and the Most Productive Scale Size (MPSS).
Figure 3: Technical efficiency, pure technical efficiency and scale efficiency [14]
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.
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
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
units. It also can provide relative efficiency to decision-making units in different time periods in order to their changes.
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
timeframes. Then Shephard Distance Function was applied to decompose tfpch into technical change (techch) and technical efficiency changes (effch). Technical
efficiency changes (effch) can also be divided into pure technical efficiency change (pech) and scale efficiency change (sech). The value for each individual are
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-
term approach.
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
mandatory financial disclosure reports. The financial analysis data of Korean panel companies was obtained and derived from Nonura, JP Morgan, Macquare Capital
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
timeframe a total of five years.
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
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
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
and outputs are positively correlated. When the inputs increased, outputs are also increased complying with the DEA isotropic relation requirement meaning that the
selected inputs and outputs in this research are appropriate.
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
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
4. EMPIRICAL RESULTS
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
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
value of the efficiency between 2004-2008 to determine a risk/return matrix.
4.1 Statistical Analysis
Examination of Taiwan and Korea’s panel industries included ratio of G&A expense, fixed assets ratio, debt ratio and revenue changes during the research period. Figure
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
impacted in 2008 due to the decline in demand as result from the global financial turmoil.
Figure 4: G&A expense ratio trend [10,15] Figure 5: Fixed Assets ratio trend [10,15]
Figure 6: Debt Ratio Trend [10,15]
Figure 7: Trend of revenue growth [10,15]
4.2 Manufacturer’s 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
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
2006 to 2008. In by purely technical efficiency to derive scale
efficiency. By comparing pure technical efficiency against the scale efficiency, we identified whether the main source of inefficiency is purely technical inefficiency
or scale inefficiency. If it is from purely technical inefficiency, the reasons are due to management’s inappropriate decision making while utilizing of resources. If
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
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
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
and non-efficient units.
We conclude Manufacturer’s 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,
the world’s seventh generation production lines in 2006 were developed. However, the growth of demand from market was less than the supply from the production, coupled
with sharply falling commodity prices, the panel industry suffered significantly in 2006. The demand from emerging markets gradually recovered the panel industry in
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
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
efficiency. However, high-risk panel manufacturers in Taiwan generally experienced poor performance. Inefficient manufacturers in Taiwan, accounted for 36 percent of
the sample manufacturers. The value of inefficient is averaged 0.73. However with the economic boom in 2007, the Taiwan panel manufacturers’ revenue and profit were
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.
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
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,
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.
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
expected result is idle capacity, increased burden from interest costs and a decline in operational efficiency.
Table 4: Manufacturer’s efficiency analysis-2006
Year Manufacturer Technical Efficiency Consulted Numbers Pure Technical Efficiency Consulted Numbers Scale Efficiency Returns to
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
Table 5: Manufacturer’s efficiency analysis-2007
Year Manufacturer Technical Efficiency Consulted Numbers Pure Technical Efficiency Consulted Numbers Scale Efficiency Returns to
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
Table 6: Manufacturer’s efficiency analysis-2008
Year Manufacturer Technical Efficiency Consulted Numbers Pure Technical Efficiency Consulted Numbers Scale Efficiency Returns to
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
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
guideline for improvement. Especially in the economic downturn the non-efficient manufacturers try to find ways to reallocate their resources to improve their
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
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
critical and required improvements especially during 2006 and 2007, approximately 30% were categorized as non-efficiency. The debts in 2008 are considered first
priority to be improved, approximately 34% could not efficiently contribute to output, which was significantly higher than in 2006 and 2007. Inefficiency in fixed
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’s panel makers have positioned themselves in the supply
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.
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
global financial crisis, manufacturers experienced significant reduction in revenue and profits and were burdened by interest from debt. As result their overall
efficiency declined.
Table 7: Slack variable analysis – 2006
Year Manufacturer Input variables
Fixed Assets G&A Expenses Debt Total Assets
2006
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%)
Table 8: Slack variable analysis – 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 (-41.9%) (-52.2%) (-22.3%) (-15.6%)
URT (-15.3%) (-41.5%) (-9.1%) (-21.4%)
Hann Star (-39.1%) (-37.3%) (-17.7%) (-17.8%)
LGD (-23.5%) (-13.1%) (-15.6%) (-18.2%)
Table 9: Slack variable analysis – 2008
Year Manufacturer Input variables
Fixed Assets G&A Expenses Debt Total Assets
2008
CPT (-21.5%) (-15.1%) (-23.2%) (-18.5%)
TPO (-33.3%) (-23.7%) (-37.6%) (-21.1%)
GP (-35.6%) (-42.5%) (-32.3%) (-7.2%)
PT (-37.7%) (-33.3%) (-45.8%) (-11.3%)
PVI (-21.5%) (-17.2%) (-37.4%) (-15.5%)
Wintex (-21.2%) (-18.8%) (-31.2%) (-21.7%)
EMT (-45.1%) (-43.2%) (-33.8%) (-23.1%)
URT (-32.8%) (-37.7%) (-32.2%) (-11.2%)
Hann Star (-43.5%) (-28.1%) (-29.1%) (-9.8%)
LGD (-42.6%) (-45.3%) (-36.5%) (-13.5%)
CPT (-25.7%) (-17.5%) (-41.3%) (-22.2%)
4.4 DEA – Window Analytic Approach
It is difficult to distinguish performance from a risk and return perspective simply from manufacturer’s yearly output. This study adopted DEA Window Analysis from
years 2004 to 2008 and provided the mean and variance of their efficiency to determine business performance in terms of return and stability in terms of risk involved
in accordance to the theory of investment.
Table 10 indicates the mean value of performance through years 2004 to 2008 which represents return; and variance by which represent risk from DEA window analysis to
compare Taiwan and Korea panel manufacturers, and which replaces the traditional single financial performance indicator.
Table 10 was converted into the matrix contained in Figure 8 which represents the distribution of the manufacturers and determines the efficiency and stability of the
merits and demerits. Korean panel manufacturers are categorized as low risk high return. The panel manufacturers in Taiwan are distinguished in terms of performance
and stability, however are not as good as their competitors in Korea. The decision to pursue high risk and high returns or low risk and low returns depends upon the
decision by individual manufacturers. However for the manufacturer falling into low-risk and high-return domain, we can consider the manufacturer as a benchmark for
the industry and which can identify improvements for the others. And based on the results from this study, 4.3 Slack analysis, Taiwan panel manufacturers must consider
adjusting their resources to achieve further improvements and enhance their competitiveness.
Chun-Chou Tseng [19] concluded that the world’s leading panel manufacturers have reduced capacity utilization during global financial crisis. Taiwan panel
manufacturers have significantly reduced production to 60% and below in the fourth quarter of 2008 while Korean firms were 80%. The own brand strategy by Korea
manufacturers drives their performance better than Taiwan especially during economic downturn.
Regarding the fix assets utilization in-efficiency, it is recommended that Taiwan’s panel manufacturers could expand production lines to a higher generation, build up
their own brand strategy and adjust the financial structure in order to better insulate themselves during economic down turns. The debt ratio of Taiwan manufacturer
could also be improved to reduce financial risk [17].
Table 10: Window analysis results for Taiwan and
Figure 8: Risk and return matrix
Korean panel manufacturers
Manufacturer Mean of
Efficiency Value (Return) Variance of
Efficiency Value (Risk)
AUO 0.9559 0.0013
CMO 0.9431 0.001
TPO 0.6779 0.01
Innolux 0.8004 0.0044
URT 0.5761 0.0313
PVI 0.6746 0.0113
CPT 0.6492 0.0184
Wintex 0.6658 0.0095
Hann Star 0.7638 0.0125
GP 0.7858 0.0075
PT 0.58 0.0208
EMT 0.5822 0.0205
LGD 0.9789 0.0002
SEC 0.9934 0.0001
4.5 Malmquist Productivity Index Analysis
The panel industry is technology-intensive and characterized by having a short product life cycle. Taiwan and Korea are under intense competition which can be
sustained provided efficiency and technological improvements are continuously implemented. This research uses the Malmquist Productivity Index to analyze the average
productivity data from years 2004 through 2008 from these two countries to assess efficiency of technology and performance changes during crossed-periods.
Table 11 shows that Taiwan and Korea use different business models and financial structures, hence their reasons for productivity improvements are different. Korea
panel manufacturers are focused on efficiency improvements whereas Taiwan panel manufacturers are focused mainly on continuous technical and productivity improvements
that have been driven by continuously purchasing of new equipment, introduction of advanced technology and new product innovation.
Table 11: Taiwan and Korea panel manufacturers average productivity
Rates of Change in Technical
Efficiency
(effch) Rates of
Change in
Technical
(techch) Rates of Change in Pure Technical
Efficiency (pech) Rates of Change in Scale
Efficiency
(sech) Rates of Change in
Technical, Technical
Efficiency and
Productivity (tfpch)
Taiwan 0.961 1.085 0.985 0.973 1.045
Korea 1.106 1.097 0.987 1.124 1.213
5. CONCLUSIONS & RECOMMENDATIONS
Taiwan is actively working towards vertical integration and recently attempting its own brand strategy. However, compared with the manufacturers in Korea, Taiwan still
remains as an OEM in the value chain. Analysis of fixed assets utilization and debt ratio indicates Taiwan’s panel manufacturers actually bear higher risks than their
Korean competitors. This research investigated whether a display panel manufacturer with greater risk will have higher profits and whether their performance would be
more seriously impacted during an economic downturn. In the long-term perspective, Taiwan and Korea panel manufacturers should better understand the various types of
risks and returns they will have as result of undertaking different positioning strategies.
This research utilizes “Data Envelopment Analysis” to explore the efficiency between Taiwan and Korea TFT-LCD panel manufacturers, and utilizes the “Slack Variable
Analysis” to provide the benchmark for inefficient manufacturers to improve. Then implementing DEA-window analysis defined the risks/returns matrix and identified the
companies characterized by low-risk and high return with empirical results as follows:
1. From “Statistical Analysis”, with different business models and financial structures, Korea’s manufacturers have higher G&A expenses while Taiwan manufacturers
have higher fixed assets and debt ratios. This drives different business approaches and strategies. The business strategies in Taiwan primarily rely on manufacturers
entrenching themselves as Original Equipment Manufacturers (OEMs) whereas Koreans focuses principally on vertical integration, including development of their own
international brand architecture. As result the financial crisis in 2008 caused by weak market demand impacted manufacturers in both countries more seriously than in
previous years. However, Taiwan manufacturers experienced relatively higher operational and financial risks. From revenue growth perspective the performance of
Taiwan’s manufacturers was significantly affected.
2. By using “Data Envelopment Analysis”. AUO and Samsung Electronics are among the efficient corporation category. In 2007 on the other hand, LGD was on the brink
of being labeled a non-efficient corporation and CMO in 2008 suffered considerably more than other Korean manufacturers and was categorized as a non-efficient company
as the economy worsened and continued its downward trend. In 2007 the performance of panel manufacturers in Taiwan was better than the average in 2006 and 2008. The up
and down trends are consistent with economic trends, and manufacturers with high-risks were severely impacted. However manufacturer with higher-risks will usually
achieve better performance during expanding economic periods because of fixed assets and liabilities are in full utilization reduced their manufacturing costs,
creating higher revenue and profit performance to improve their performance efficiency. Conversely during an economic down turn there was idle capacity and interest
costs resulting in a major burden, which had resulted in a decline in operating efficiency.
3. By using “Slack Variable Analysis”, we can see the difference between Taiwan and Korea manufacturers in utilizing of resources and the area for improvement. It
is recommended to manufacturers categorized as inefficient group to reduce their resources input in certain areas to a target volume, especially in the G&A expenses,
Debt and fixed Assets, in order to approach the level of benchmark in competition.
4. Through “DEA-window analysis”, the research defined the risk/return matrix, during the period 2004-2008 which depicted variation in efficiency indicating
Korean panel manufacturers performed better than their Taiwan competitors. Own brand name strategies coupled with vertical integration gives Korean panel manufacturers
distinct advantage over their Taiwan competitors. Taiwan manufacturers can compete with their Korean competitors with improvements to their core competencies. Cost for
large production lines for higher generation display panels will be extremely high, however, because of the economic scale, efficiency should be improved for cost
reduction. Taiwan panel manufacturers are encouraged to invest in higher generation production lines, and
to develop their own brand in the future to ensure competitiveness.
5. In order to understand the reasons for change in productivity over the past five years, this research utilized the Malmquist Productivity Index to analyze the
efficiency of cross-periods. Taiwan and Korea use different business models with different financial structures resulted in different findings. Korean panel
manufacturers results are mainly due to improvements in efficiency whereas the panel manufacturers in Taiwan are mainly due to technical upgrading and the acquisition
of new equipment. The introduction of advanced technology or product innovation will increase overall productivity.
