Modern investment theory has empirically proved that stock returns can be explained by several factors such as market risk, firm size, and book-to-market ratio. Other unknown factors affecting stock returns are also believed to still exist yet to be found. We believe that one of such factors is the operational efficiency of firms in transforming inputs to outputs, considering the fact that operations is a fundamental and primary function of any type of businesses. To support this belief, this study intends to empirically study the relationship between firm efficiency and stock price performance. Firm efficiency is measured using data envelopment analysis (DEA) with inputs and outputs obtained from financial statements. We employ cross-efficiency evaluation to enhance the discrimination power of DEA with a secondary objective function of aggressive formulation. Using the CAPM-based performance regression model, we test the performance of equally weighted portfolios of different sizes selected based upon DEA cross-efficiency scores along with a buy & hold trading strategy. For the empirical test, we collect financial data of domestic firms listed in KOSPI over the period of 2000~2016 from well-known financial databases. As a result, we find that the porfolios with highly efficient firms included outperform the benchmark market portfolio after controlling for the market risk, which indicates that firm efficiency plays a important role in explaining stock returns.

This paper was to evaluate social enterprises’ management efficiency with Data Envelope Analysis (DEA). The data was based on the 168 social enterprises’ of annual performance reports published in 2015. The research focused on to measure both financial efficiency and social impact of the companies simultaneously. To apply DEA, the paper classified the enterprises into seven types based on types of socal impacts which each company provides before the estimation of the efficiency. The research results showed that group D, which employes disadvantaged people, provides social services and shares resources was the most efficient group and had higest net worths in Pure Technical Efficiency. In contrast, Group B, which only employs social advantage people and provides social service, was the least efficient one. The research suggests a practical and efficient framework in measuring social enterprises’ management efficiency, including both the financial performance and social impacts simultaneously with their self-publishing reports. Because the Korea Social Enterprise Promotion Agency does not open business reports which social enterprises submit each year, there are basic limitations on researchers attempting to analyse with data from all social enterprises in Korea. Thus, this study dealt with only 10% of the social enterprises which self-published their performance report on the Korea Social Enterprise Promotion Agency’s web site. Regardless of these limitations, this study suggested substantial methods to estimate management efficiency with the self-published reports. Because self-publishing is increasing each year, it will be the main source of information for researchers in examining and evaluating social enterprises’ financial performance or social contribution. The research suggests a practical and efficient framework in measuring social enterprises’ management efficiency, including both the financial performance and social impacts simultaneously with their self-publishing reports. The research results suggest not only list of efficient enterprises but also methods of improvement for less efficient enterprises.

Conventional data envelopment analysis (DEA) models require that inputs and outputs are given as crisp values. Very often, however, some of inputs and outputs are given as imprecise data where they are only known to lie within bounded intervals. While a typical approach to addressing this situation for optimization models such as DEA is to conduct sensitivity analysis, it provides only a limited ex-post measure against the data imprecision. Robust optimization provides a more effective ex-ante measure where the data imprecision is directly incorporated into the model. This study aims to apply robust optimization approach to DEA models with imprecise data. Based upon a recently developed robust optimization framework which allows a flexible adjustment of the level of conservatism, we propose two robust optimization DEA model formulations with imprecise data; multiplier and envelopment models. We demonstrate that the two models consider different risks regarding imprecise efficiency scores, and that the existing DEA models with imprecise data are special cases of the proposed models. We show that the robust optimization for the multiplier DEA model considers the risk that estimated efficiency scores exceed true values, while the one for the envelopment DEA model deals with the risk that estimated efficiency scores fall short of true values. We also show that efficiency scores stratified in terms of probabilistic bounds of constraint violations can be obtained from the proposed models. We finally illustrate the proposed approach using a sample data set and show how the results can be used for ranking DMUs.

This study intends to propose a non-oriented DEA based game cross-efficiency approach for supplier selection. With a discussion on the choice of DEA models and approaches that are most appropriate for supplier selection, we propose a game cross efficiency model based upon the non-oriented variable returns-to-scale RAM DEA by adapting the existing game cross efficiency model based upon the oriented constant returns-to-scale CCR DEA. We develop the RAM game cross efficiency model and a convergent iterative solution procedure to find the best game cross efficiency scores that constitute a Nash equilibrium. We illustrate the proposed model with two data sets of supplier selection, and demonstrate that significantly different results are obtained when compared with the existing approaches.

Manufacturing companies' cost competitiveness with respect to equipment management can be achieved by satisfying additional market demands by their own capacity without purchasing additional equipments. In essence, it can be accomplished by making continu

Multi-criteria ABC inventory classification is one of the most widely employed techniques for efficient inventory control, and it considers more than one criterion for categorizing inventory items into groups of different importance. Recently, Ramanathan

Lee[15] examined quantity discount contracts between a manufacturer and a retailer in a stochastic, two-period inventory model where quantity discounts are provided based on the previous order size During the two periods, the retailer faces stochastic(tru

  A solution method for fuzzy linear programs is proposed. A fuzzy linear program is converted to a crisp linear program with average indices being applied to the objective function and constraints. A comparative analysis between the proposed average inde

This study assesses greenhouse gas evolution from construction-material manufacturing facilities and estimates the potential reduction of these gases via the future massive sequestration of carbon dioxide. The scope of the evaluation specifically targets the global-warming potential in terms of kg-CO2 equivalent/tonnage industrial waste. Life cycle assessment (LCA) is a method to quantitatively analyze the input and output of a specific material resource during its life cycle from raw-material acquisition to final disposal as well as its environmental effect(s). LCA comprises four steps: its objective and definition of the scope, the entire life-cycle analysis list, an evaluation of its effects, and life-cycle analysis. The annual inflow of petro-ash reaches 300,000 tons, and this material is transported via screw-driving systems. The composition of the petro-ash is 1.2% volatile compounds, 6.8% fixed carbon and 92% ash contents. A total of 38,181,891 Nm3/yr of carbon dioxide is sequestrated, which is equivalent to 75,000 tons per annum and 304.5 kg/ton of petro-ash waste, with 250 kg/ton of the latter sequestrated as calcium carbonate. The final analysis on the effect of one ton of petro ash in construction materials showed 27.6 kg-CO2 eq emission. According to the final LCA analysis, only 27.6 kg-CO2 eq/ton was emitted by the petro-ash that was used in construction materials if CO2 fixation during carbonate mineralization was considered, where -250 kg-CO2 eq/ton positively contributed to the LCA. In the future, commercial-scale process modification via the realization of continuous processes and the more efficient reduction of carbon dioxide is anticipated.

이산화탄소는 주요 온실가스이며 의심할 여지없이 지구 온난화의 주범이다. 대기의 이산화탄소는 대부분 전기 발전에 사용되는 화석 연료의 연소에 의해 고농도화 되는데, 기후 변화의 억제를 위해서는 대기로부터 이산화탄소를 포획 및 격리가 필수적이다. 한편, 발전소의 소각재는 전기 수요가 증가함에 따라 지속적으로 증가할 것으로 예상된다. 소각재의 증가에 따라 소각재를 활용한 폐기물 자원순환에 관한 관심이 증가하고 있다. 일반적으로 순환 유동층 연소 방법에서는 탈황반응을 위하여 석회석을 인위적으로 첨가하게 되는데, 이 경우, 탈황 반응에 관여하지 않는 잉여 성분은 소각재 중에 CaO 화합물의 형태로 존재한다. CaO와 이산화탄소의 가역적인 반응은 이산화탄소를 제거하는 매우 유망한 방법이다. 특히 상온 상압에서 이루어지기 때문에 매우 경제적이라 할 수 있다. 현재 이와 같은 반응을 토대로 소각재를 이산화탄소와 반응시켜 복합탄산염으로 제조하여 시멘트의 원료로 사용하는 연구가 많이 진행되고 있다. 그러나 대부분의 연구는 다량의 물을 사용하기 때문에 취급이 어렵고, 건조를 위해 많은 에너지가 소모 되는 문제점이 있다. 본 연구에서는 에너지 고효율 공정을 위하여 반건식 복합탄산염 제조를 연구하였으며, 정량분석을 통해 수분의 양이 발전소 소각재의 이산화탄소 포집 특성에 미치는 영향을 연구하였다.