• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.5 no.2
• /
• pp.191-196
• /
• 2004

Allocative efficiency measures the extents to which the technically efficient units falls shorts of achieving minimal cost. By using this measure manager can make decision about how to redistribute organizational resources to improve price efficiency. Allocative and overall efficiency are derived on the basis of budget line and cost minimization concept. The purpose of this study is to introduce the concept of allocative efficiency and propose two modified DEA models. Examples are provided to illustrate the similarities and the application procedure of the two model. By providing example and tracing the data application procedure, we found the same results but some cautions are needed to interpret the valuation.

• Proceedings of KOSOMES biannual meeting
• /
• 2006.05a
• /
• pp.15-20
• /
• 2006

In this study, we had investigated several input factors and output factors, to maintain safety management, of domestic shipping companies, and then had analyzed the efficiency of performance of performance about each shipping companies' safety management system from 1998 year to 2004 year using DEA method As the result of analysis, the annual mean efficiency of total companies tended downward every year. Analysis was that the cause was increase of the cost of repairing ship, the cost of ship's stores and idle day of ship.

• Journal of Korea Port Economic Association
• /
• v.38 no.2
• /
• pp.123-138
• /
• 2022

Korea has been huge investments in its port system, annually upgrading its infrastructure to turn the ports into Asian hub port. However, while Busan port is ranked fifth globally for container throughput, Other Korean ports are ranked much lower. This article applies Data Envelopment Analysis (DEA) and Malmquist Productivity Index (MPI) to evaluate selected major Korean seaports' operational efficiency and productivity from 2010 to 2018. It further integrates Principal Component Analysis (PCA) into DEA, with the PCA-DEA combined model strengthening the basic DEA results, as the discriminatory power weakens when the variable number exceeds the number of Decision Making Units(DMU). Meanwhile, MPI is applied to measure the seaports' productivity over the years. The analyses generate efficiency and productivity rankings for Korean seaports. The results show that except for Gwangyang and Ulsan port, none of the selected seaports is currently efficient enough in their operations. The study also indicates that technological progress has led to impactful changes in the productivity of Korean seaports.

• The Journal of the Korea Contents Association
• /
• v.19 no.7
• /
• pp.99-109
• /
• 2019

Data Envelopment Analysis (DEA), a non-parametric productivity analysis tool, has become an accepted approach for assessing efficiency in a wide range of fields. Despite of its extensive applications, some features of DEA remain bothersome. For example DEA is good at estimating "relative" efficiency of a DMU(Decision Making Unit), it only tells us how well we are doing compared with our peers but not compared with a "theoretical maximum." Thus, in order to measure efficiency of a new DMU, we have to develop entirely new DEA with the data of previously used DMUs. Also we cannot predict the efficiency level of the new DMU without another DEA analysis. We aim to show that DEA can be used to evaluate the efficiency of ports and suggest the methodology which overcomes the limitation of DEA through hybrid analysis utilizing DEA along with C5.0. We can generate classification rules C5.0 in order to classify any new Port without perturbing previously existing evaluation structures by proposed methodology.

• The Journal of the Korea Contents Association
• /
• v.20 no.10
• /
• pp.496-509
• /
• 2020

This study analyzes 5-year efficiency of the game industry, from 2014 to 2018 which is aimed at 25 online and mobile game companies, that are emerging as a new growth engine of a national economy to come and as a core areas of late entertainment industry. The DEA is used for static efficiency analyze and the DEA-Window is used for dynamic efficiency analyze. This study uses assets, the number of employees and costs as input variables and it also uses operating profits and sales as output variables. The main results show that scale efficiency presents a resonable result over 0.85 on a total average except 2014. However, there has not been a year that is over 0.80 of the whole period in technical efficiency. Also, in terms of business scale, there is a huge efficiency gap between high rank companies and low rank companies and the average trend of efficiency has been increased from 2014 to 2016 but it has been decreased since 2017.

• The Journal of the Korea Contents Association
• /
• v.9 no.7
• /
• pp.276-284
• /
• 2009

This paper applies DEA windows analysis in order to determine the efficiency of the Districts of Busan Metropolitan City over time for 5 years (2003 - 2007). This paper used such factors including Number of solutions of civil petitions, Local Tax Collection, and Financial Independent rate as output, Total Labor Costs, Government Employees, Population and Expenditure as inputs. This study concludes that the efficiency of the different District can fluctuate over time to different extents and efficiency of most District decreases from 2003. Indeed, the empirical results reveal that substantial inefficiency exists in some Districts at some point in time. In consequence, this validates the necessity for using DEA windows analysis in preference to an analysis based upon cross-sectional data. This paper shows that Districts' efficiency decreases as Window goes to 1 to 3.

• Journal of Korea Port Economic Association
• /
• v.31 no.3
• /
• pp.155-166
• /
• 2015

This study selected container terminals of Gwangyang and Busan Ports to evaluate the influence of efficient container terminals. For the study, after data envelopment analysis (DEA) using the CCR and BCC models, the decision-making unit (DMU) system was used to define nodes; and with the use of a reference group in DEA (BCC model) and a lambda value, this study created a social network and analyzed the influences of efficient DMUs through a centrality analysis of eigenvectors. The results are presented as follows: First, as a result of the DEA, CCR efficiencies in PNC, HJNC, and HPNT container terminals of Busan Port were 1 and BCC efficiencies at Singamman Terminal, Wooam Terminal, PNC, HJNC, HPNT, and BNCT container terminals of Busan Port were 1. Second, as a result of undertaking social network analysis (SNA), according to an eigenvector centrality analysis, HJNC Terminal was referred to the most (influence score of 0.515), which indicates that it is the most influential as a container terminal. The influence of PNC Terminal was 0.512, while that of Wooam Terminal was 0.379. CJ Korea Express in Gwangyang Port was ranked fourth in influence, but its influence score of 0.256 indicates that it was the most influential of the container terminals at Gwangyang Port.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.163-170
• /
• 2006

This paper presents a approach to the measurement of efficiency. Data envelopment analysis(DEA), as it is called, has particular applicability in the service sector. Applying mathematical programming techniques, DEA enables relative efficiency ratings to be derived within a set of analysed units. This paper investigates the efficiency employing DAE-CCR Model and DEA-BCC Model on data for 15 container terminals covering 1998$^{\sim}$2005 in Korea Results of this paper, suggests to some plan for operation strategy in Container terminals.

• Journal of Korea Port Economic Association
• /
• v.32 no.1
• /
• pp.165-178
• /
• 2016

Container port nowadays becomes one of the most vital link of the transportation chain, plays an important role in trading with other countries. Therefore, evaluating the operational efficiency of container ports to reflect their status and to reveal their position in this competitive environment is very important for port development. Although there have been lots of methods used to measure efficiency in the past, the DEA (Data Envelopment Analysis) model is still the most commonly applied approach. However, the data used in the model sometimes is complex and uncertain to handle using the basic DEA model. In this paper, we applied an uncertainty theory to create an uncertainty DEA model (UDEA), which can solve the limitation of the traditional one. This study mainly focuses on measuring efficiency of 41 container ports by applying proposed an UDEA model. The results show that among 41 container ports, only six container ports are regarded to have efficient operation through the clustering, meanwhile others have technical and scale inefficiencies. We found out that an UDEA model is better to analysis efficiency than existing DEA model.

• Journal of Korea Port Economic Association
• /
• v.32 no.2
• /
• pp.91-103
• /
• 2016

The purpose of this study is to analyze the efficiency and productivity of domestic trucking transportation companies using DEA-CCR, BCC, and Malmquist indexes. Here, we analyze the top 14 domestic trucking transportation companies, based on cargo volume. The number of freight agents, trucks, and assets are used as input variables, and cargo volumes and sales are used as output variables. The efficiency of trucking transportation companies is examined using a DEA approach, and Malmquist indexes are applied to analyze productivity. According to the DEA results, the efficiency levels of the CCR, BCC, and scales for three companies (DMU 4, 5, and 10) are 1, indicating that these companies are operated efficiently. At the same time, the Malmquist indexes show that all companies have values smaller than 1, except for the period 2012-2013, indicating that their productivity decreased. Furthermore, the TECI indexes were all larger than 1, except for the period 2012-2013, indicating that the companies are efficient. Lastly, all TCI indexes are smaller than 1, except for the period 2012-2013, indicating regressing trends.