Performance Evaluation of Microfinance Providers Using Data Envelopment Analysis: The Profitability Perspective

Measuring performances of the hospitals under Ministry of Health is a must for the efficient use of the resources. To serve this purpose, data envelopment analysis, capable of measuring relative effectiveness of decision units by processing more than one input and output, has been used. By means of data envelopment analysis technique, we have aimed to use the resources efficiently. For this reason, the model has been run input oriented. With the study, effectiveness and profitability measurements were performed in the hospitals under Ministry of Health. For this purpose, by using 2007 data on 5 financial inputs and 1 financial output variables from 16 different hospitals, effectiveness measurement was carried out and profit/loss values of the units were evaluated. According to the results obtained improvement for each unit is described and effects on their profitability are also stated. Considering the profit / loss values of the hospitals, it is concluded that they should cut their expenses at the rate of 10, 43% in order to reach break-even point regarding profit and loss balance. Key words: Data envelopment analysis, hospitals, performance measurement, financial evaluation.

Data envelopment analysis (DEA) is a mathematical method based on production theory and the principles of linear programming. It enables one to assess how efficiently a firm, organization, agency, or such other unit uses the resources available (inputs) to generate a set of outputs relative to other units in the data set. Recent papers by different writers present different applications of DEA in the transportation engineering domain. All of these papers are published in transportation journals. These papers are mainly aimed at addressing the transportation-related issues and thus do not focus too much on the DEA concept itself. It can be asserted that DEA is very likely to be used more and more in the transportation engineering domain. Given this, there is a need for the transportation professionals to fully understand the DEA concept. It is essential for such a community to identify cases where the application of this innovative and powerful method can be useful to help the decision-making process, to accurately apply DEA in a particular setting, to derive meaningful conclusions from the obtained results, and to acknowledge the limitations of DEA in certain cases so as to approach the results with caution. The purpose of this paper is to illustrate to the civil engineering, more specifically to the transportation engineering community the use of this powerful approach in performing comparative performance measurement. Within this context, this paper will address a transportation-related problem by using the DEA approach. Different from the other papers containing transportation-related DEA applications (as mentioned above), this paper will discuss, in detail, the steps that need to be taken to generate the DEA model and solve it.

Efficiency-based rank assessment for electric power industry: A combined use of Data Envelopment Analysis (DEA) and DEA-Discriminant Analysis (DA)

Data envelopment analysis of urban efficiency—Interpretative methods to make DEA a heuristic tool

European pilchard captures constitute an important source of income for the Portuguese fishing sector. This raw material is used for a varied range of final seafood products, such as canning, fresh seafood consumption or bait for other fisheries. The Portuguese purse seining fleet, which concentrates most of the pilchard landings, has recently obtained the Marine Stewardship Council certification scheme, demonstrating the effort of the fleet to comply with sustainable fishing practices. However, this scheme does not consider the human inputs to the fishery, such as fuel consumption or the use of other materials and resources. Consequently, life-cycle methods, such as Life Cycle Assessment (LCA), have arisen to provide a thorough assessment of the environmental profile of fishing fleets. In the current study, LCA is combined with Data Envelopment Analysis (DEA), a management tool, in order to understand the eco-efficiency of the individual vessels that compose this fleet. A 5-step LCA + DEA method, previously used to assess resembling vessel samples, was used to examine the efficiency of 20 purse seiners in the northern Portugal for years 2011 and 2012. Individual Life Cycle Inventories (LCIs) were gathered for each vessel to perform, thereafter, the Life Cycle Impact Assessment (LCIA). DEA matrices were generated based on the LCI in order to obtain the efficiency values for each unit. Finally, based on the efficiency projections provided by the DEA model, a new LCIA was performed for inefficient vessels in order to calculate the potential environmental benefits of operating at higher levels of efficiency. The average efficiency of the fleet in the two different years of assessment was slightly above 60 %. Moreover, individual vessels showed a fairly low standard deviation across the 2 years of assessment, demonstrating that units with higher levels of efficiency tend to maintain these values through the analysed window. In fact, this result, given its strong correlation with fuel use, appears to have relation with the existence of a certain level of “skipper effect”. Important environmental benefits, mainly linked to the optimisation of fuel resources, could be attained if inefficient vessels were to operate efficiently, especially in terms of two main impact categories: climate change and fossil depletion. The results in this study confirm that fishing small-pelagic fish shows low energy intensity as compared to other fisheries. However, despite this worldwide tendency, the use of LCA + DEA confirms that substantial improvements in terms of optimising energy and material inputs, as well as in reducing environmental impacts, can be attained in these fishing fleets.

Energy efficiency analysis based on DEA integrated ISM: A case study for Chinese ethylene industries

This study reports the performance of 23 Electrical Distribution Divisions (EDD's) of an Indian state, Uttarakhand for the period of 2005–2006 using the concepts of Additive Model (AM), Slack Based Modelling (SBM) and Two-stage Data Envelopment Analysis (DEA). Slack Based Modelling helps in quantifying the slacks, which is a hidden source of inefficiency that might be present in BCC efficient DMU's. Following the slack quantification, a two-stage DEA is applied. The results obtained from AM and SBM indicate that numerous EDD's have scope for improvement in overall efficiency by incorporating the slacks that have been identified in the particular EDD. Further the two stage DEA modelling suggest that the inefficiency is primarily due to the intermediate measures.

Operational performance evaluation and process improvement in hotels have been the focus of several studies, a number of which have used Data Envelopment Analysis (DEA) to evaluate the operational performance of hotels in different destinations. This study sets out to evaluate the operational efficiency of twenty-three tourist hotels in Taipei over five years (2003 to 2007) using DEA models and the cross-efficiency evaluation method. Efficiency measures are based on four input measures: the total number of hotel rooms, the total number of employees, food and beverage (F and B) capacity, and total operating cost. The three output measures are guest room revenue, F and B revenue, and other revenue. The efficiency scores derived from the DEA models are subject to clustering analysis to identify benchmarks for efficiency improvement in poorly performing hotels. The contributions and managerial implications of the study are discussed. Key words: Data envelopment analysis (DEA),

This paper proposes data envelopment analysis (DEA) as a suitable data analysis tool to overcome facility management (FM) benchmarking difficulties: FM performance benchmarking analysis is often unsophisticated, relying heavily on simple statistical representation, linking hard cost data with soft customer satisfaction data is often problematic. A case study is presented to show that DEA can provide FM personnel with an objective view on performance improvements. An objective of the case study is to investigate the relative efficiency of nine facilities with the same goals and to determine the most efficient facility. The case is limited to nine buildings in FM on four inputs and nine output criteria. The paper concludes by demonstrating that DEA-generated improvement targets can be applied when formulating FM outsourcing policies, strategies and improvements. Facility manager can apply DEA-generated improvement targets in formulating FM outsourcing policies, specifications development, FM strategy and planning. FM benchmarking with DEA can enhance continuous improvement in service efficiency and cost saving. This will help reduce utility cost as well as pollution. This paper fills the gap in the research of FM benchmarking by applying DEA which studies both soft and hard data simultaneously. It also contributes to a future research of a trade-off sensitivity test between FM cost, services performance and reliability.

Data envelopment analysis (DEA) provides a relative efficiency measure for peer decision making units (DMUs) with multiple inputs and outputs. While DEA has been proven an effective approach in identifying the best practice frontiers, its flexibility in weighting multiple inputs and outputs and its nature of self-evaluation have been criticized. The cross efficiency method was developed as a DEA extension to rank DMUs with the main idea being to use DEA to do peer evaluation, rather than in pure self-evaluation mode. However, cross efficiency scores obtained from the original DEA model are generally not unique, and depend on which of the alternate optimal solutions to the DEA linear programs is used. The current chapter discusses various cross efficiency approaches in dealing with non-unique solutions from DEA

Performance or efficiency evaluation is of great importance for effective supply chain management, especially in the context of sustainable development and platform economy. In existing literature, two-stage network data envelopment analysis (DEA) has been widely used for supply chain efficiency evaluation. Although existing supply chain DEA usually assumes two-stage systems resolve the inherent conflicts between two stages, e.g. the supplier and manufacturer in a two-echelon supply chain, the relation of supply chain DEA to traditional ‘black box' DEA remains unclear. Moreover, the coordination effect between the two stages is ignored. In this paper, we define the coordination efficiency and propose a DEA-based approach for measuring the coordination effect of supply chain systems. We prove that the proposed multiplier DEA approach is equivalent to the envelopment DEA one. The proposed approach in this paper is verified via a numerical example from a supplier–manufacturer sustainable supply chain on resin companies finally.

The government of Bangladesh has been trying to encourage potato consumption to reduce pressure on rice consumption and earn foreign currency along with ensuring zero hunger that helps to achieve the Sustainable Development Goal. It is necessary to use farmers' resources and current technology more efficiently to meet the demand. Therefore, the authors aimed to evaluate the farm-level efficiency of potato farming in Bangladesh. The Cobb-Douglas Stochastic Frontier Analysis (SFA) and the input-oriented Data Envelopment Analysis (DEA) methods are used to compute farm-level technical, allocative, and economic efficiencies and inefficiency of potato farming. The primary data were collected through interviews of 300 potato farmers from Munshigonj, Rangpur, Dinajpur, and Joypurhat districts of Bangladesh. The findings revealed that the efficiency score of the SFA model is higher than the DEA model, which implies that the SFA frontier fits better than the DEA frontier. In the case of DEA, variable returns to scale (VRS) technical efficiency (TE) enveloped data more closely than constant returns to scale (CRS) TE. Results of efficiency suggest significant economic, technical, and allocative inefficiencies in potato farming and there is a scope to increase potato production levels through efficiency improvement. Inefficiency analysis shows that infrastructure and socio-economic factors jointly influence potato production variability. The authors suggest for using the SFA to find efficiencies in the agriculture sector. To achieve efficiency in potato production, the government needs to pay attention for improving the allocative and economic efficiencies along with emphasizing to choose the appropriate technology and efficient use of resources for the scale of operation.

Public sports services crucially determine the transformation of China into a robust sports country. The enhancement of public sports service efficiency fundamentally manifests its high-quality development. We utilize the data of 30 provinces in China in 2020 as the research object, and we apply data envelope analysis (DEA) to measure and evaluate the efficiency level of China’s public sports services as well as Fuzzy Set Qualitative Comparative Analysis (fsQCA) to identify the critical influencing factors; moreover, we explore the grouping pathways that affect the efficiency improvement of public sports services. The results of the study indicate the following: (1) Although the efficiency of public sports services in China is high, regional differences are apparent. (2) GDP per capita, government support, and years of education are the key factors for efficiency improvement. (3) Population-government-led, economic-resource input, and economic-educational dual-drive are the core pathways for enhancing efficiency. Therefore, the following recommendations are made: boosting the efficiency of China’s public sports services by focusing on the level of regional economic development and rationally allocating public sports resources; increasing the investment of public sports funds to enhance the quality and level of services; strengthening the government’s purchasing efforts to promote the market-based operation of public sports services; and increasing the average years of education to strengthen the public’s awareness and participation in sports. This study integrates Data Envelopment Analysis (DEA) for efficiency assessment and fuzzy-set Qualitative Comparative Analysis (fsQCA) for causal analysis in public sports services, effectively addressing the limitations of single-method approaches. Thus, the scope of research is broadened, and a novel perspective is offered. DEA highlights efficiency disparities within Chinese public sports services, whereas the input–output system enhances the accuracy of evaluations. FsQCA identifies key drivers, inhibitors, and contextual combinations that assist policymakers. Overall, this study enriches theoretical understanding and provides practical guidance, facilitating high-quality development and addressing diverse sports demands.

Universities play an important role in the social and economical development of a country. Therefore, governments usually provide the financial resources universities need. On the other hand, universities should be efficient in satisfying the government's conditions of functional resources. Finding a transparent and systematic way to distributing the funds to each university is a major challenge for government. With participation in higher education amongst young people rising, governments around the world have been faced with increasing pressure on their finances, giving rise to the need to operate universities with a higher degree of efficiency. Data Envelopment Analysis (DEA) is a powerful method widely used in the evaluation of performance of Decision Making Units (DMUs). These can be business units, government agencies, police departments, hospitals, educational institutions, and even people DEA have been used in the assessment of athletic, sales and student performance). This paper provides an introduction to DEA and some important methodological extensions that have improved its effectiveness as a productivity analysis tool. Data Envelopment Analysis (DEA) techniques are used to estimate technical and scale efficiency of individual Saudi Arabia universities 2010. The purpose of this paper is to present basic principles of DEA and evaluate its application possibilities to assess the performance of nineteen Saudi Arabia universities. DEA is a choice between constant returns to scale CRS and variable returns to scale VRS. The CRS efficiency score represents technical efficiency, which measures inefficiencies due to input/output configuration and as well as size of operations. On the other hand, the VRS efficiency score represent pure technical efficiency, that is, a measure of efficiency without scale efficiency. The results found that the number of universities with maximum relative efficiency was ten out of nineteen universities when CRS was used. The number of universities with maximum relative efficiency was fifteen out of nineteen universities when VRS was used. The percentage of inefficiency was determined for each inefficient university, together with the extent of inputs that could be reduced and the extent of outputs that could be increased in these universities in order for them to be fully efficient.

This study examines the benefits of data envelopment analysis (DEA) in evaluating the performance of decision making units (DMUs). DEA is a mathematical programming tool applied in performance measurement. The problem identified is establishing business support units as value adding business units. A case is made for applying DEA when evaluating the performance of such business support units. To this end, a literature review of the results of applications of DEA to the evaluation of information technology and purchasing supply chain management functions was conducted. The findings indicate the benefits of DEA are that the method identifies efficient performers in a given population and, therefore, allows for benchmarking against the ’best in class’ performer. This as opposed to more commonly used parametric methods, such as regression analysis, which result in a comparator that represents the average performance for a given population, therefore, allowing only for measurement against the average. In addition, the findings indicate that in respect of business support units, the DEA methodology allows for the incorporation of intermediate outcomes, which facilitates the measurement of the contribution of these units to overall company performance. Although the DEA methodology has been widely applied, it is still not as well known or generally applied as the more common approaches. The recommendations made in this paper will be beneficial in bringing DEA to the attention of decision-makers. The recommendations will also raise awareness of the potential benefits to be realised when applying the method in developing performance measurement frameworks for business support units. Keywords: performance measurement, data envelopment analysis, decision making units, business support units. JEL Classification: C61, L25