• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.1-14
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• 2021

It is highly challenging to measure the efficiency of electric vehicle charging stations (EVCSs) because factors affecting operational characteristics of EVCSs are time-varying in practice. For the efficiency measurement, environmental factors around the EVCSs can be considered because such factors affect charging behaviors of electric vehicle drivers, resulting in variations of accessibility and attractiveness for the EVCSs. Considering dynamics of the factors, this paper examines the technical efficiency of 622 electric vehicle charging stations in Seoul using data envelopment analysis (DEA). The DEA is formulated as a multi-period output-oriented constant return to scale model. Five inputs including floating population, number of nearby EVCSs, average distance of nearby EVCSs, traffic volume and traffic congestion are considered and the charging frequency of EVCSs is used as the output. The result of efficiency measurement shows that not many EVCSs has most of charging demand at certain periods of time, while the others are facing with anemic charging demand. Tobit regression analyses show that the traffic congestion negatively affects the efficiency of EVCSs, while the traffic volume and the number of nearby EVCSs are positive factors improving the efficiency around EVCSs. We draw some notable characteristics of efficient EVCSs by comparing means of the inputs related to the groups classified by K-means clustering algorithm. This analysis presents that efficient EVCSs can be generally characterized with the high number of nearby EVCSs and low level of the traffic congestion.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.57-65
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• 2022

DEA(data envelopment analysis) is a technique for evaluation of relative efficiency of decision making units (DMUs) that have multiple input and output. A DEA model measures the efficiency of a DMU by the relative position of the DMU's input and output in the production possibility set defined by the input and output of the DMUs being compared. In this paper, we proposed several DEA models measuring the multi-period efficiency of a DMU. First, we defined the input and output data that make a production possibility set as the spanning set. We proposed several spanning sets containing input and output of entire periods for measuring the multi-period efficiency of a DMU. We defined the production possibility sets with the proposed spanning sets and gave DEA models under the production possibility sets. Some models measure the efficiency score of each period of a DMU and others measure the integrated efficiency score of the DMU over the entire period. For the test, we applied the models to the sample data set from a long term university student training project. The results show that the suggested models may have the better discrimination power than CCR based results while the ranking of DMUs is not different.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.8-14
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• 2019

Most of the data envelopment analysis (DEA) models evaluate the relative efficiency of a decision making unit (DMU) based on the assumption that inputs in a specific period are consumed to produce the output in the same period of time. However, there may be some time lag between the consumption of input resources and the production of outputs. A few models to handle the concept of the time lag effect have been proposed. This paper suggests a new multi-period input DEA model considering the consistent time lag effects. Consistency of time lag effect means that the time delay for the same input factor or output factor are consistent throughout the periods. It is more realistic than the time lag effect for the same output or input factor can vary over the periods. The suggested model is an output-oriented model in order to adopt the consistent time lag effect. We analyze the results of the suggested model and the existing multi period input model with a sample data set from a long-term national research and development program in Korea. We show that the suggested model may have the better discrimination power than existing model while the ranking of DMUs is not different by two nonparametric tests.

• Journal of the East Asian Society of Dietary Life
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• v.20 no.5
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• pp.823-835
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• 2010

The research aimed to measure the efficiency of using multi stores in a foodservice company using by DEA (data envelopment analysis) which is a new management science technique. The study also attempted to identify relevant variables affecting DEA efficiency in order to suggest methods for improving efficiency. The data were collected from 148 contract foodservice operations, which were operated in similar fashion in October 2009. The DEA efficiency was calculated as an output-oriented BCC Model. Sales, and CSI (customer satisfaction index) were used as output variables whereas food cost, labor cost, and management expense were used as input variables to calculate the DEA efficiency. Operation process variables of the unit consisted of the were consist of ratio of regular employee, ratio of housekeeper, meal counts, meal price, food cost per meal, contract period, number of menu items, forecasting accuracy, order accuracy, inventory turnover, use of processed food, deviation of food cost, number of new menus, and number of events. According to the BCC score and profitability, units were classified into four groups: High efficiency-high profitability (HEHP), High efficiency-low profitability (HELP), Low efficiency-high profitability (LEHP), and Low efficiency-low profitability (LELP). The HEHP group contained 54 units, which mostly contracted management fee type and had a high meal price. The units were also very large and, served three meals. Twenty of the units were operated with high labor cost: most of these were factories and hospitals. The LEHP group contained 20 units, that were mainly office stores of large scale and medium price. Fifty-four LELP group had a low meal price. A high performance group must have high efficiency, profitability, and satisfaction. The BCC score was over 0.969, the meal price was over 4,116 won, the food cost was over 2,077 won, and meal counts per month were over 10,212 meals.

• Journal of Technology Innovation
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• v.11 no.2
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• pp.151-173
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• 2003

This paper analyses the efficiency and productivity of R&D system across time (1991${\sim}$2000) and 16 OECD countries using multi-output and multi-input non-parametric frontier methods such as DEA (data envelopement analysis) and Malmquist productivity indexes. Malmquist productivity indexes are decomposed into two components measures, namely technical change and efficiency change. To calculate R&D efficiency and productivity, we used R&D stock and the number or researchers as R&D input proxies and the number of adjusted SCI papers and U.S. patent applications as R&D output proxies. Empirical result shows that Switzerland, Canada, U.S., Australia's R&D efficiencies are the highest and Korea's R&D productivity growth is the highest in the sample for the period. Technical efficiency growth was a more important source of productivity growth than technological innovation.

• Journal of Distribution Science
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• v.13 no.4
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• pp.45-53
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• 2015

Purpose - Online shopping enables consumers to conveniently purchase products irrespective of the time and place. As a result, several online shopping companies have emerged to cater to this growing market and, therefore, the competition among them has become increasingly intense. This paper evaluates the comparative efficiency of online shopping companies for a multi-year period (2009-2013), in order to help online shopping managers identify major drivers for enhancing management efficiency and the subsequent competitiveness. Research design, data, and methodology - The researchers collected the data from 2009 to 2013 from the distribution yearbook. This paper analyzes the marketability (sales figures), profitability (business profits), and management conditions (net profits) of domestic online shopping enterprises by incorporating information on human resources (number of employees) and material resources (total assets and capital). Therefore, the number of employees, total assets, and capital are selected as input variables, and sales figures, business profits, and net profits as the output variables. In this study, Data Envelopment Analysis (DEA) was used to measure the comparative efficiency of domestic online shopping companies. In addition, the Malmquist Productivity Index was used to evaluate the trend of change of Decision Making Units' (DMUs') efficiency for a multi-year period. Results - First, as of 2013, Interpark (2.415) was found to be the most efficient online shopping enterprise, followed by Aladdin Communications (2.117), Hyundai Home shopping (1.867), Home&Shopping (1.176), NS Home shopping (1.170), Commerce Planet (1.126), CJ O Shopping (1.105), Ebay Korea (1.088), and GS Home Shopping (1.051). Second, this study recognizes how the management efficiency has changed for the period 2009-2013. Third, the lesser the capital and employees, the more are the net profits, and the better is the management efficiency of domestic online shopping companies. Lastly, the productivity of such companies is influenced by endogenous factors rather than exogenous factors such as shifts in business environment and technological advances. Conclusions - DHC Korea influenced various distribution channels to reach customers through the Internet. Consequently, this helped in increasing the awareness about its products, in addition to an increase in sales. These achievements can be attributed to the characteristics of online shopping companies. Although it is easy for these companies to suggest goods for one-off purchases, they however have difficulties in retaining customers. Overcoming this challenge can be one of the ways to benchmark a successful case of an efficient company. For example, an online shopping company can attract customers by developing a corresponding mobile application as a convenient way to shop online. Additionally, they can satisfy customers by quick delivery of purchased products, which is possible by building an effective logistics network. Our study indicates that the productivity of an online shopping company was influenced by endogenous factors driven by improvements in managerial practices rather than exogenous factors. Accordingly, online shopping companies should adopt strategies to improve their operational efficiency rather than sales volume-oriented management.

• Journal of Distribution Science
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• v.14 no.3
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• pp.73-83
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• 2016

Purpose - The parcel delivery service(courier) industry all over the world has been expanding its market so far, but its growth has been declining in recent years. In this situation, most parcel delivery companies are having trouble with managing themselves because of the pressure from the customer to increase service level and decrease the rate. The purpose of this study is to provide ways to improve competitive advantages of the parcel delivery service industry by evaluating the multi-period operating efficiency of Korea, Japan and global service providers. Research design, data, and methodology - The data for the period of 2011 to 2014 were collected from the annual reports published by parcel delivery companies. In this study, we analyze the marketability (revenue), profitability (operating profits), and management conditions (net profits) of parcel service companies by combining information on human resources (number of employees) and material resources (total assets and equity). Therefore, the number of employees, total assets, and equity are selected as input variables, and revenue, operating profits, and net profits as the output variables. In this study, DEA (Data Envelopment Analysis) is used to measure the comparative efficiency and MPI (Malmquist Productivity Index) is used to analyze the trend of change of the efficiency for a multi-year period. Results - The operational efficiency scores of medium-sized parcel delivery companies in Korea are higher than other larger competitors such as Korean, Japan and Global larger companies. As of 2014, Logen(1.878) was found to be the most efficient parcel delivery enterprise, followed by KGB (1.224), and Kyoungdong(1.002). Otherwise, Hanjin(0.235), CJ(0.262), Hyundai Logistics(0.657), DHL(0.611), UPS(0.766), FedEx(0.498), TNT(0.350), Yamato(0.762) and Sagawa(0.520), larger sized companies, were done inefficiently. The productivity of parcel delivery companies is influenced by endogenous factors as well as exogenous ones such as changes in business environment and technological advances. Conclusions - Korean medium-sized companies have relatively high efficiency scores in operation. That is why they still survive the competitive market in Korea where market restructuring on the industry has been expected to be conducted for many years. The reason why medium-sized couriers had higher efficient scores than larger couriers is that most of couriers spend more operating expenses versus unit price of delivery which is the amount of money that is needed in order to send a package by parcel service. So the delivery unit price must be taken into account by all the expenses associated with the cost of fuel, labor and maintenance expenses for facilities, etc. therefore, the unit price must be increased to strengthen business competitive power. In order for the industry to have more competitive advantage, the companies need to make profits by increasing demand volume and raising the delivery rate to provide high-quality delivery service to customers. And both endogenous and exogenous change must take precedence in order to strengthen their competitiveness.