Data Envelopment Analysis

A benchmark approach for baseball evaluation

Data envelopment analysis (DEA) is a method for measuring efficiency of peer decision-making units (DMUs). Conventional DEA evaluates the performance of each DMU using a set of most favourable weights. As a result, traditional DEA models can be considered methods for the analysis of the best relative efficiency or analysis of the optimistic efficiency. DEA efficient DMUs obtained from conventional DEA models create an efficient production frontier. Traditional DEA can be used to identify units with good performance in the most desirable scenarios. There is a similar approach that evaluates the performance indicators of each DMU using a set of most unfavourable weights. Accordingly, such models can be considered models for analysing the worst relative efficiency or pessimistic efficiency. This approach uses the inefficient production frontier for determining the worst relative efficiency that can be assigned to each DMU. DMUs lying on the inefficient production frontier are referred to as DEA inefficient while those neither on the efficient frontier nor on the inefficient frontier are declared DEA inefficient. It can be argued that both relative efficiencies should be considered simultaneously and any approach with only one of them would be biased. This paper proposed the integration of both efficiencies as an interval so that the overall performance score would belong to this interval. It was shown that efficiency interval provided more information than either of the two efficiencies, which was illustrated using two numerical examples.

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.

كفاءة نسبية | مؤسسات تعليم عالي جزائرية | تحليل تطويقي للبيانات | عوائد حجم ثابتة | عوائد حجم متغيرة | Relative Efficiency | Algerian Institutions of Higher Education | Data Envelopment Analysis | Constant Returns to Scale | Variable Returns to Scale

PurposeThe purpose of this paper is to benchmark the Indian mobile telecommunication service providers for relative efficiencies. In this paper, a method for benchmarking performance of mobile telecom utilities based on data envelopment analysis (DEA) is presented. The paper discusses some concepts between quality performance and benchmarking and the results include performance efficiency and sensitivity‐based classification of utilities. Also, peer‐to‐peer comparison of inefficient with efficient utilities is provided. Based on these results, inefficient utilities can develop strategic plans to improve performance.Design/methodology/approachThe authors use DEA to measure comparative efficiencies of mobile telecom companies and two different DEA models, CCR and BCC, were applied to evaluate the relative efficiency of mobile telecom operators in India. Sensitivity‐based classification of utilities is carried out by removing one or more inputs or outputs from the base model to construct a new DEA model. Comparisons of DEA efficiencies from the base model with the structurally perturbed models show the impact on efficiency. Data include annual and quarterly reports showing various quality parameters.FindingsDEA is used to derive the benchmarks based on the comparison of the 126 utilities which include public sector undertaking (PSU) operators (MTNL and BSNL) and private operators of the Indian mobile telecommunication sector. The result includes performance efficiency and peer‐to‐peer comparison of inefficient utilities with efficient utilities. Based on these results, inefficient utilities can develop strategic plans to improve their performance. Sensitivity analysis, based on removal of one or more variables from the base model to determine changes in DEA efficiencies is done for selecting the strength of parameters of utilities for performance improvement.Practical implicationsBenchmarking of service utilities in the telecom sector is virtually non‐existent at the national level in India. This research identifies the different variables and then a model is prepared for benchmarking of the service providers in India. Based on the efficiency analysis, benchmarks can be set, and utility efficiency scores can be obtained based on the set benchmarks. These scores can help develop a strategic plan for mitigating the factors that contribute to the system inefficiencies.Originality/valueThis paper is one of the few published studies that benchmark the performance of mobile telecom services in India. This research promises to be amongst the first of the works carried out taking specific parameters of mobile telecom utilities of India.

Bank Efficiency has been an important issue in the process of Indonesia's banking recovery after facing banking crisis. In most of the transition countries such Indonesia the relative comparison of banks by size, type of ownership has at some point been an issue. How good it is to let new banks enter the market? Should the state owned banks be sold to public or foreigners? Do small banks have future in the era of globalization? How good the intermediation function has been done by banks? These, and other questions continue to dominate discussions of Indonesia 's banking sector. In this paper I analyze efficiency of eleven largest banks in Indonesia using Data Envelopment Analysis (DEA). DEA is a methodology for analyzing relative efficiency and managerial performance of productive units having same multiple inputs and multiple outputs. It allows us to compare relative efficiency of banks by determining the efficient banks, which span the frontier. The most important advantage of DEA over traditional econometric frontier studies is that it is a non parametric, deterministic method and therefore doses not require a priori assumptions about the analytical form of the production function. Therefore the probability of misspecification of the production technology is zero. The disadvantage is that, being a non parametric method, it is more sensitive to possible mismeasurement problems. I measured relative efficiency of eleven largest banks in Indonesia according to their size and type of ownership. Eleven largest banks in Indonesia dominate over 60% of banking sector. I found that in 2002, no state owned banks reached efficient, three of six largest private owned banks reached efficient frontier and Citibank as one and only foreign owned banks reached efficient. In 2003, only one of four state owned bank reached efficient. Three of six private owned banks reached efficient. Citibank still reached efficient. Based on this result, type of ownership appears to have correlation with efficiency. Foreign owned bank is the most efficient, and that state owned banks are the less efficient. Private owned banks are in the middle. Only fifty percent of private owned banks reached efficient.

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

Returns to Scale and Damages to Scale with Strong Complementary Slackness Conditions in DEA Assessment: Japanese Corporate Effort on Environment Protection

The concept of presence was initially associated with an instant messaging service, allowing an end user to recognize the presence of a peer online to send or receive messages. Now the technology has grown up to include various services like monitoring performance of any type of end user device, and services are accessible from anywhere, any time. The need for enhanced value remains the driving force behind these services, for example, Voice over Internet Protocol (VoIP) services, which is drawing tremendous research interest in services performance evaluation, measurement, benchmarking, and monitoring. Monitoring service level parameters happens to be one of the most interesting application-oriented research issues because various service consumers at the customer companies/end users’ level are finding it very difficult to design and monitor an effective SLA (Service Level Agreement) with the presence-enabled service providers. This chapter focuses on to these specific issues and presents a new approach of SLA monitoring through Data Envelopment Analysis (DEA). This extreme point approach actually can work much better in the context of SLA monitoring than general central-tendency-based statistical tools, a fact which has been corroborated by similar application examples of DEA presented in this chapter and has therefore it acts as the primary motivation to propose this new approach. Towards this end, this chapter first builds up the context of presence-enabled services (Day, Rosenburg, & Sugano, 2000), its SLA and SLA parameters, and the monitoring requirements. Then it explains the basics of DEA and its application in various other engineering and services context. Ultimately, a DEA application framework for monitoring an SLA of presence-enabled services is proposed which can serve as a clear guideline for the customers of presence-enabled services, not only for SLA monitoring but also at various other stages of implementing presence-enabled services frameworks. This approach exploits the definitive suitability of the application of DEA methods to presence-enabled service monitoring problems, and can be easily implemented by the industry practitioners.

This paper reports an innovative use of a popular operations management case to illustrate the usefulness and application of a powerful analysis technique called Data Envelopment Analysis (DEA). DEA is often the only available tool that enables managers to evaluate the relative efficiencies of various business units in the presence of multiple performance measures. Applichem is a case study about a multinational chemical company, with six manufacturing plants located all over the world. The manufacturing plants' efficiencies are highly varied and in the presence of excessive capacity, management is having a difficult time determining which plants must be shut down. This case is often studied from an optimization perspective, with the objective of matching customer demands with plant capacities at the lowest possible cost. The case involves multiple measures of performance (e.g. labor cost, material cost, etc.), which makes it ideal for introducing and demonstrating DEA from a practical perspective. This paper details how such an analysis can be presented in a business classroom.

This paper analyzes the cases where the hotels have increased their services and enhanced their work process through IT solutions to cope with computerization globalization. Also the cases have been studies where national hotels use the CRM solution internally to respond effectively to customers requests, increase customer analysis, and build marketing strategies. In particular, this study discusses the introduction of the CRM solutions and CRM sales business and marketing services using a process for utilizing the presumed, CRM by introducing effective DEA(Data Envelopment Analysis). First, the comparison has done regarding the relative efficiency of L Company with the CCR model, then compared L Company's restaurants and facilities' effectiveness through BCC model. L Company reached a conclusion that it is important to precisely create and manage sales data which are the preliminary data for CRM, and for that reason it made it possible to save sales data generated by POS system on each sales performance database. In order to do that, it newly established Oracle POS system and LORIS POS system concerned with restaurants for food and beverage as well as rooms, and made it possible to stably generate and manage sales data and manage. Moreover, it set up a composite database to control comprehensively the results of work processes during a specific period by collecting customer registration information and made it possible to systematically control the information on sales performances. By establishing a system which unifies database and managing it comprehensively, impeccability of data has been greatly enhanced and a problem which generated asymmetric data could be thoroughly solved. Using data accumulated on the comprehensive database, sales data can be analyzed, categorized, classified through data mining engine imbedded in Polaris CRM and the results can be organized on data mart to provide them in the form of CRM application data. By transforming original sales data into forms which are easy to handle and saving them on data mart separately, it enabled acquiring well-organized data with ease when engaging in various marketing operations, holding a morning meeting and working on decision-making. By using summarized data at data mart, it was possible to process marketing operations such as telemarketing, direct mailing, internet marketing service and service product developments for perceived customers; moreover, information on customer perceptions which is one of CRM's end-products could feed back into the comprehensive database. This research was undertaken to find out how effectively CRM has been employed by comparing and analyzing the management performance of each enterprise site and store after introducing CRM to Hotel enterprises using DEA technique. According to the research results, efficiency evaluation for each site was calculated through input and output factors to find out comparative CRM system usage efficiency of L's Company four sites; moreover, with regard to stores, the sizes of workforce and budget application show a huge difference and so does the each store efficiency. Furthermore, by using the DEA technique, it could assess which sites have comparatively high efficiency and which don't by comparing and evaluating hotel enterprises IT project outcomes such as CRM introduction using the CCR model for each site of the related enterprises. By using the BCC model, it could comparatively evaluate the outcome of CRM usage at each store of A site, which is representative of L Company, and as a result, it could figure out which stores maintain high efficiency in using CRM and which don't. It analyzed the cases of CRM introduction at L Company, which is a hotel enterprise, and precisely evaluated them through DEA. L Company analyzed the customer analysis system by introducing CRM and achieved to provide customers identified through client analysis data with one to one tailored services. Moreover, it could come up with a plan to differentiate the service for customers who revisit by assessing customer discernment rate. As tasks to be solved in the future, it is required to do research on the process analysis which can lead to a specific outcome such as increased sales volumes by carrying on test marketing, target marketing using CRM. Furthermore, it is also necessary to do research on efficiency evaluation in accordance with linkages between other IT solutions such as ERP and CRM system.

Data Envelopment Analysis (DEA) aims at assessing the relative efficiency of a number of comparable operating units. Network DEA is used when the units consist of interrelated processes. The simplest network DEA structure is a two-stage serial system. In this paper a well-known Fuzzy DEA approach is extended to this network DEA context. The proposed approach is simpler than the existing approach with which it is compared using a dataset from the literature.

Multiplier bounds in DEA via strong complementary slackness condition solution

In this paper, we examine the relative efficiency of audit production by one of the then Big 6 public accounting firms for a sample of 247 geographically dispersed audits of U.S. companies performed in 1989. To test the relative efficiency of audit production, we use both stochastic frontier estimation (SFE) and data envelopment analysis (DEA). A feature of our research is that we also test whether any apparent inefficiencies in production, identified using SFE and DEA, are correlated with audit pricing. That is, do apparent inefficiencies cause the public accounting firm to reduce its unit price (billing rate) per hour of labor utilized on an engagement? With respect to results, we do not find any evidence of relative (within‐sample) inefficiencies in the use of partner, manager, senior, or staff labor hours using SFE. This suggests that the SFE model may not be sufficiently powerful to detect inefficiencies, even with our reasonably large sample size. However, we do find apparent inefficiencies using the DEA model. Audits range from about 74 percent to 100 percent relative efficiency in production, while the average audit is produced at about an 88 percent efficiency level, relative to the most efficient audits in the sample. Moreover, the inefficiencies identified using DEA are correlated with the firm's realization rate. That is, average billing rates per hour fall as the amount of inefficiency increases. Our results suggest that there are moderate inefficiencies in the production of many of the subject public accounting firm's audits, and that such inefficiencies are economically costly to the firm.