Teaching Data Envelopment Analysis using Applichem—New Perspective on a Popular Operations Case

Combining ranking scales and selecting variables in the DEA context: The case of industrial branches

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.

Data envelopment analysis (DEA) is a data-oriented approach for performance evaluation and benchmarking when trade-offs on multiple performance measures and benchmarks are not completely available. There are relative efficiencies and inefficiencies in using DEA in classifying funds of hedge funds (FOF). FOFs provide absolute returns and have non-linear returns because of long-short positions, derivatives, and option-like fee contracts, resulting in significant skewness and kurtosis. Thus the inclusion of FOFs in investor portfolios calls for performance appraisal methodologies that are appropriate for handling the asymmetric returns they produce, such as DEA, and not comparison to traditional passive and active benchmarks. While selecting FOFs can be arduous, DEA can shed light on and further validate the process. <b>TOPICS:</b>Real assets/alternative investments/private equity, statistical methods

Data envelopment analysis (DEA) is a methodology for evaluating the relative efficiencies of peer decision-making units (DMUs), in a multiple input/output setting. While it is generally assumed that all outputs are impacted by all inputs, there are many situations where this may not be the case. For example, in a food manufacturing setting, certain foods are exempt from nutrition labeling and as a result are not influenced by labeling resources. This chapter extends the conventional DEA methodology to allow for the measurement of technical efficiency in situations where only partial input-to-output impacts exist. The new methodology involves viewing the DMU as a business unit, consisting of a set of mutually exclusive subunits, each of which can be treated in the conventional DEA sense.

Multi-valued measures in DEA in the presence of undesirable outputs

Purpose The purpose of this paper is to provide an outline of the major contributions in the literature on the determination of the least distance in data envelopment analysis (DEA). The focus herein is primarily on methodological developments. Specifically, attention is mainly paid to modeling aspects, computational features, the satisfaction of properties and duality. Finally, some promising avenues of future research on this topic are stated. Design/methodology/approach DEA is a methodology based on mathematical programming for the assessment of relative efficiency of a set of decision-making units (DMUs) that use several inputs to produce several outputs. DEA is classified in the literature as a non-parametric method because it does not assume a particular functional form for the underlying production function and presents, in this sense, some outstanding properties: the efficiency of firms may be evaluated independently on the market prices of the inputs used and outputs produced; it may be easily used with multiple inputs and outputs; a single score of efficiency for each assessed organization is obtained; this technique ranks organizations based on relative efficiency; and finally, it yields benchmarking information. DEA models provide both benchmarking information and efficiency scores for each of the evaluated units when it is applied to a dataset of observations and variables (inputs and outputs). Without a doubt, this benchmarking information gives DEA a distinct advantage over other efficiency methodologies, such as stochastic frontier analysis (SFA). Technical inefficiency is typically measured in DEA as the distance between the observed unit and a “benchmarking” target on the estimated piece-wise linear efficient frontier. The choice of this target is critical for assessing the potential performance of each DMU in the sample, as well as for providing information on how to increase its performance. However, traditional DEA models yield targets that are determined by the “furthest” efficient projection to the evaluated DMU. The projected point on the efficient frontier obtained as such may not be a representative projection for the judged unit, and consequently, some authors in the literature have suggested determining closest targets instead. The general argument behind this idea is that closer targets suggest directions of enhancement for the inputs and outputs of the inefficient units that may lead them to the efficiency with less effort. Indeed, authors like Aparicio et al. (2007) have shown, in an application on airlines, that it is possible to find substantial differences between the targets provided by applying the criterion used by the traditional DEA models, and those obtained when the criterion of closeness is utilized for determining projection points on the efficient frontier. The determination of closest targets is connected to the calculation of the least distance from the evaluated unit to the efficient frontier of the reference technology. In fact, the former is usually computed through solving mathematical programming models associated with minimizing some type of distance (e.g. Euclidean). In this particular respect, the main contribution in the literature is the paper by Briec (1998) on Hölder distance functions, where formally technical inefficiency to the “weakly” efficient frontier is defined through mathematical distances. Findings All the interesting features of the determination of closest targets from a benchmarking point of view have generated, in recent times, the increasing interest of researchers in the calculation of the least distance to evaluate technical inefficiency (Aparicio et al., 2014a). So, in this paper, we present a general classification of published contributions, mainly from a methodological perspective, and additionally, we indicate avenues for further research on this topic. The approaches that we cite in this paper differ in the way that the idea of similarity is made operative. Similarity is, in this sense, implemented as the closeness between the values of the inputs and/or outputs of the assessed units and those of the obtained projections on the frontier of the reference production possibility set. Similarity may be measured through multiple distances and efficiency measures. In turn, the aim is to globally minimize DEA model slacks to determine the closest efficient targets. However, as we will show later in the text, minimizing a mathematical distance in DEA is not an easy task, as it is equivalent to minimizing the distance to the complement of a polyhedral set, which is not a convex set. This complexity will justify the existence of different alternatives for solving these types of models. Originality/value As we are aware, this is the first survey in this topic.

The selection-based problem is a type of decision-making issue which involves opting for a single option among a set of available alternatives. In order to address the selection-based problem in data envelopment analysis (DEA), various integrated mixed binary linear programming (MBLP) models have been developed. Recently, an MBLP model has been proposed to select a unit in DEA with variable returns-to-scale technology. This paper suggests utilizing the linear programming relaxation model rather than the MBLP model. The MBLP model is proved here to be equivalent to its linear programming relaxation problem. To the best of the authors’ knowledge, this is the first linear programming model suggested for selecting a single efficient unit in DEA under the VRS (Variable Returns to Scale) assumption. Two theorems and a numerical example are provided to validate the proposed LP model from both theoretical and practical perspectives.

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 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

Article history: Received October 2, 2012 Received in Revised Format January 23, 2013 Accepted February 15, 2013 Available online February 23 2013 Many supplier selection problems are involved with various criteria such as quality of supplier, price, delivery time, etc. This paper presents a survey on the implementation of using different multi criteria decision making (MCDM) methods for supplier selection problems. The reviews covers recent advances of MCDM techniques such as data envelopment analysis (DEA), analytical hierarchy process, etc. over the period 2000-2012. The review also reveals that nearly 60% of the applications are associated with business unit, 15% is related to economy, 9% is devoted to service and development and 8% is dedicated to research and development. In our survey, DEA has become the most popular technique for supplier selection problem followed by Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytical Hierarchy Process (AHP).

A comparative analysis of DEA as a discrete alternative multiple criteria decision tool

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) method is used to evaluate the efficiency of the bank branches. The present paper prioritized the indicators, using fuzzy questionnaires and by gathering the managers’ ideas, in order to obtain the most important evaluating indicators. After obtaining these indicators, Data Envelopment Analysis method was used to determine the performance bound interval, regarding the fact that the data are related to a certain period of time and their nature is imprecise and fuzzy. Then, Fuzzy Theory method and Fuzzy Data Envelopment Analysis method were employed to rank the branches, and finally the results were presented.

Comprehensive and Technical Efficiency of Insurance and Takaful Companies in Pakistan Insurance and Takaful companies, both, are business organizations existing for the motive of profit. Large capital is required by the shareholders to establish a company; therefore, the company is expected to earn sufficient profit to meet the expectations of the shareholders. Moreover, a company also has its responsibilities towards its customers in terms of quality service. Shareholders are more concerned with their profit and customers regarding the services. Companies pay claims when the covered persons suffer a loss which can be the death of the family’s only bread earner, factory on fire, theft of business goods etc. Therefore, efficient financial performance of insurance and Takaful companies is important to fulfil the requirements of both: shareholders and clients. This study investigates financial efficiency of insurance and Takaful companies via Data Envelopment Analysis during the period 2011 to 2015. We have taken six general (non - life ) insurance &amp; Takaful companies operating in Pakistan with input factors labor cost, assets and output factors premium / contribution received and percentage of claims paid. Results show that insurance and takaful companies are more than 80% efficient in their operations. For further increasing their efficiencies, they need to reduce their input by less than 20% to have same level of output. Comparing efficiencies of insurance and takaful companies, insurance companies are more efficient in terms of pure technical &amp; super efficiency while takaful companies have higher scale efficiency than insurance companies, though the difference in most of the cases is less than 2%. Both types of companies need to improve their efficiencies by decreasing their assets input and labor cost while retaining the same output level of premiums and claims. Keywords: General insurance, Data Envelopment Analysis (DEA); Malmquist index, Takaful, Scale and technical efficiency

Data envelopment analysis (DEA) is a widely used technique for the performance evaluation of decision making units. Cost efficiency is one of the important factors for the financial evaluation of a system. Financial factors are important as they can be used in sustainable supply chain management. There are many cost efficiency models in DEA. In this paper, to evaluate the cost efficiency, we deal with the undesirable outputs by DEA and the piecewise linear theory. The weight restrictions on criteria are incorporated into the proposed model. A case study is given.