• Journal of Distribution Science
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• v.19 no.7
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• pp.29-39
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• 2021

Purpose: As China experienced a crisis due to Covid-19, the global supply chain collapsed and affected the world. Therefore, it is time for a change in port operational efficiency, increasing in importance with changes in the global supply chain. This study analyzed Shanghai Port's efficiency, the world's largest port and representative hub port in Northeast Asia, by looking at the relationship between facility factors and cargo throughput to present hub port development's timely implications. Research design, data and methodology: This study applied the Charnes, Cooper, and Rhodes (CCR) and Banker, Chames, and Cooper (BCC) models of the data development analysis (DEA) to construct an analysis from the input-oriented and output-oriented perspectives. Results: As a result, Yidong Container Terminal can be considered the most optimized in facilities and operation processes. Yidong and Shengdong Container Terminal should maintain current operating levels, while Pudong Container Terminal should review facility investments. Also, Zhendong, Huong, Mingdong, and Guandong Container Terminal should be reviewed to increase cargo throughput or to adjust current input variables in the current state. Conclusions: Therefore, the utilization of the container terminal input variables should be reviewed, and the factors of inefficiency should be improved. Moreover, the strategic focus of container terminal operations should be on increasing annual cargo throughput.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.118-120
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• 2023

Increasing port competition driven by the containerisation has motivated ports and terminals to focus on their performance to efficiently utilise the available resources and to make strategic decisions in port development and expansion. With both inter-port andintra-port competition increasing in the port of Colombo, this study aims to measure the efficiency of the container terminals in Colombo comparing to terminals in the port of Busan using the DEA window analysis to determine their operational efficiency and to provide suggestions for future port development activities. Multiple window analyses were conducted using CCR and BCC models with different orientations and window lengths to compare the efficiencies of 11 DMUs in both ports during the period from 2015-2019 to measure the efficiencies prior to the COVID-19 pandemic. Results revealed the largest terminal operator, PNC in Busan, to be the most efficient overall, while the second highest efficiency was recorded by one of the smallest terminal operators, SAGT in Colombo, among the sample. Although use of DEA in port performance measurement has been popular for many years, efficiency measurements in the port of Colombo, the main hub port in the South Asian region, has not been comprehensively studied so far.

• Journal of Distribution Science
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• v.11 no.8
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• pp.5-14
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• 2013

Purpose - We live in a world of constant change and competition. Many airports have specific competitiveness goals and strategies for achieving and maintaining them. The global economic recession, financial crises, and rising oil prices have resulted in an increasingly important role for facility investment and renewal and the implementation of appropriate policies in ensuring the competitive advantage for airports. It is thus important to analyze the factors that enhance efficiency and productivity for an airport. This study aims to determine the efficiency levels of 20 major airports in East Asia, Europe, and North America. Further, this study also suggests suitable policies and strategies for their development. Research design, data, and methodology - This paper employs the DEA-CCR, DEA-BCC, and DEA-Malmquist production index analysis models to determine airport efficiency. The study uses data on the efficiency and productivity of the world's leading airports between 2006 and 2010. The input variables include the airport size, the number of runways, the size of passenger terminals, and the size of cargo terminals. The output variables include the annual number of passengers and the annual cargo volume. The study uses basic data from the 2010 World Airport Traffic Report (ACI). The world's top 20 airports (as rated by the ACI report) are investigated. The study uses the expanded DEA Model and the Super Efficiency Model to identify the most effective airports among the top 20. The Malmquist productivity index analysis is used to measure airport effectiveness. Results - This study analyzes longitudinal and cross-sectional data on the world's top 20 airports covering 2006 to 2010. A CCR analysis shows that the most efficient airports in 2010 were Gatwick Airport (LGW), Zurich Airport (ZRH), Vienna Airport (VIE), Leonardo da Vinci Fiumicino Airport (FCO), Los Angeles International Airport (LAX), Seattle-Tacoma Airport (SEA), San Francisco Airport (SFO), HongKong Airport (HKG), Beijing Capital International Airport (PEK), and Shanghai Pudong Airport (PVG). We find that changes in airport productivity are affected more by technical factors than by airport efficiency. Conclusions - Based on the study results, we offer four airport development proposals. First, a benchmark airport needs to be identified. Second, inefficiency must be reduced and high-cost factors need to be managed. Third, airport operations should be enhanced through technical innovation. Finally, scientific demand forecasting and facility preparation must become the focus of attention. This paper has some limitations. Because the Malmquist productivity index is based on the hypothesis of the, the identified production change could be over- or under-estimated. Further, as DEA estimates the relative efficiency. It also cannot generalize to include all airport conditions because the variables are limited. To measure airport productivity more accurately, other input variables and environmental variables such as financial and policy factors should be included.

• Journal of Korea Technology Innovation Society
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• v.19 no.4
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• pp.711-735
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• 2016

Drug Development is very important for promoting public health and pharmaceutical industry. There has been many studies on the efficiency of drug development, but there are few studies on the drug development R&D performed by government. Since CCR model assumes unidirectional influence of input and output, it is not appropriate to analyze the efficiency of R&D due to the time-lag and spill-over effect. Also, BBC model which assumes variable returns to scale has difficulty in deriving priorities between decision making units. Recently, Range Adjusted Measure (RAM) model has been suggested in R&D efficiency analysis. RAM model measures the efficincy by eliminating inefficiencies under variable returns to scale assumption, and its strong monotonicity enables to provide clear priorities between decision making units. In this study, we analyzed the efficiency of national R&D programs for drug development using the two-step approach, including RAM model and Tobit regression analysis, and discussed major policy implications.

• The Korean Journal of Financial Management
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• v.25 no.4
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• pp.183-212
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• 2008

The purpose of this paper is to analyze the efficiency change and determinants of the korean non-life insurance companies. we use DEA (Data Envelopment Analysis) model to measure company efficiency change and use GLS, Tobit model, FIixed effect model, Random effect model, GMM to measure efficiency determinants. we utilize ten non-life insurance companies in korea and the panel data for five from 2001 to 2005. The empirical results show the following findings. First, technical efficiency shows that approximately 15.5% of inefficiency exists on the non-life insurance companies and it reveals that the cause for technical inefficiency is due to scale inefficiency. Second, Dea Window results show that the stable dissimilarity by standard deviation, LDP of CCR. Third, the results of efficiency determinants show that increase efficiency is depend on the premium income and real estates.

• Korea Journal of Hospital Management
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• v.16 no.4
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• pp.35-66
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• 2011

This study analyzed efficiency by utilizing DEA analytical technique centering on materials for 2009 of 20 major university hospitals in capital area. Input variables were utilized professor & full-time doctor, resident, nurse & number of bed hospitals. Output variables were analyzed by dividing number of annual outpatients & number of annual inpatients, and annually total outpatient profit & inpatient profit into a model of the standard for number of patients and the standard for medical profit. DEA analysis was elicited efficiency score by applying CCR, BCC, BFG, scale profit, and SE model. Through t-test after eliciting efficiency score, the implications were suggested by comparing efficiency between DMU in Seoul and DMU in capital area, by comparing between high-class general hospitals and general hospitals, and by comparing between high-class general hospitals in Seoul and 5 big hospitals. As a result of analysis, the major university hospitals in capital area showed high efficiency as a whole close to "1," but indicated low efficiency relatively in CCR field. Thus, the expansion in scale within capital area was indicated to reach the limit. Second, in a model of analyzing the standard for number of patients, the medical institutions, which are being operated efficiently, were indicated to be 10 DMUs. In the standard for medical profit, 12 DMUs were analyzed to be operated efficiently. Third, the efficiency in general hospital was higher than high-class general hospital. Thus, the efficiency of operation was indicated to be more important than scale. Also, large high-class hospitals(big 5) where are located in downtown Seoul showed the higher efficiency than other general high-class general hospitals, but were indicating very low efficiency in some DMUs. Fourth, as a result of generalizing and evaluating the number of patients and the medical profit, the efficient DMU was indicated to be more when analyzing on the basis of medical profit than the standard for number of patients. Thus, major university hospitals in capital area were indicated to make more effort for section in medical profit. Based on the analytical results of efficiency, a strategy for reinforcing efficiency in inefficient DMU was indicated to be needed a strategy of creating customers for promoting number of patients and a strategy for making operation efficient for increasing profitability.

• International Commerce and Information Review
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• v.16 no.4
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• pp.55-75
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• 2014

Non-parametric method such as technology efficiency, DEA/Window model and Malmquist Productivity Index (MPI) are used to measure efficiency and productivity of ICT (Information and Communication Technology) manufacturing industry and service industry over the period 2007-2011. The results of this paper indicate following: (1) Technology efficiency of the ICT manufacturing industry were found as the range of 0.34 and 0.39 over the sample period. Technology efficiency of the ICT service industry were found as the range of 0.16 and 0.20 over the sample period. (2) The geometric average of the Malmquist TFP indexes on ICT manufacturing industry indicated the productivity improvement an average of 8.3 percent. The geometric average of the Malmquist TFP indexes on ICT service industry indicated the productivity improvement an average of 1.6 percent. (3) TIER analysis result on ICT manufacturing industry showed that optimal bench marking made by storage devices${\rightarrow}$wireless communication equipment${\rightarrow}$broadcasting equipment${\rightarrow}$radio, recording and playback devices${\rightarrow}$computers, printers, video and audio-visual equipment path. TIER analysis result on ICT service industry indicated that optimal bench marking made by computers and packaged software${\rightarrow}$wired communication${\rightarrow}$communication, information, detection equipment${\rightarrow}$consulting and construction for computer systems integration${\rightarrow}$industrial machinery and equipment rental${\rightarrow}$telecommunications reseller${\rightarrow}$system software development and delivery${\rightarrow}$hosting path.

• Journal of Distribution Science
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• v.16 no.5
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• pp.91-101
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• 2018

Purpose - This study compares the management efficiency of retailers in China, Korea and other global countries. China's retail industry is experiencing a recession. In order to strengthen the competitiveness of retailers, it is necessary to manage the efficiency. Therefore, we analyzed the management efficiency of Chinese retailers as well as Korea and global retailers who are competing with Chinese retailers. Research design, data, and methodology - The DEA(Data Envelopment Analysis) carried out for evaluating the relative efficiency of multiple DMUs (decision making units) with homogeneity. Data were collected from the American Retail Trade Association (2017). In those distributors' data, 5 of China and 5 of Korea and 10 of other global countries' analyzed. CCR and BCC analysis were performed to determine the cause of the inefficiency of DMUs by measuring the technical efficiency, pure technology efficiency and scale efficiency. Result - Among the 20 retail distributors, Costco, Kroger (Global), Eland World, BGF(Korea) are operating efficiently. Chinese retailers are operating inefficiently. Retailers' CRS status means the growth rate of input is equal to the growth rate of output. In the case of DRS status, the ratio of output to input variable is much smaller. In order to improve inefficiency, reducing input variables can be a solution. For the firms in IRS status, the rate of increase in output is relative greater than the input. That means efficiency is good condition. The analysis result shows that most retailers are showing DRS status especially Chinese retailers. Scale efficiency is a major cause of inefficiency rather than pure technology efficiency. It is recommended for ineffective retailers to reduce inputs to become efficient retailers. Otherwise, retrain existing employees or introducing advanced technologies to increase the output. Conclusions - Most of Chinese retailers are operating inefficiently which caused by the excessive investment in the inputs. On the other hand, Other global retailers are analyzed to be efficient by DEA. In this study, benchmarking targets of some retailers' suggested to improve the management efficiency especially in inputs.

• Journal of Korea Port Economic Association
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• v.26 no.1
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• pp.1-19
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• 2010

This paper focuses measuring the efficiency of container yards on container terminals in Busan (Gasungdae, Shinsundae, Gamman, New Gamman, Uam, Gamchon, PNC) and Gwangyang(GICT, KEC, Dongbu, KIT) using Data Envelopment Analysis(DEA) approach. Container terminals in Busan and Gwangyang play an important role in the region's economic development. The results show that Shinsundae was an efficient DMU during the period of 2007 to 2009, while Gamman, New Gamman and PNC were efficient terminals in 2009. The very inefficient terminals were shown to be GICT, KEC, Dongbu and KIT. GICT(2009), KEC(2009), Dongbu(2008-2009), KIT(2009) on Gwangyang Port were found to be relatively the inefficient terminals in terms of the returns to scale. This study also finds that the efficiency of Shinsundae terminal was so high as to be abel to keep its efficiency in spite of the additional increase of the inputs from 2007 to 2009. Gamman terminal was in the decreasing returns to scale in 2009, while the other terminals were in the increasing returns to scale. It means that we are able to improve the efficiency of the Gamman terminal with increasing returns to scale through enlarging the scale.

• Journal of Navigation and Port Research
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• v.36 no.6
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• pp.527-534
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• 2012

In order to achieve sustainable growth and gain competitive advantages business performance should be monitored regularly by a company. In the port industry container terminal operators are facing growing competition. A large scale of new container terminals are constructed and the number of new container terminal operators are increasing. Container shipping lines are gaining bargaining power against terminal operators in terms of negotiating terminal usage. The competitive environments result in reduced cargo handling charges and poor financial performance of container terminal operators. It becomes very important to examine how efficiently container terminal operators are operating their terminals and how to improve their performance. This paper investigates the measurement efficiency for container terminal operators in Korea using Data envelopment analysis(DEA) of DEA-CCR and DEA-BCC Model. This paper finds out which container terminal operators are inefficient and how to improve their management efficiency.