Efficiency decomposition for multi-level multi-components production technologies

This paper addresses the efficiency measurement of firms composed by multiple components, and assessed at different decision levels. In particular it develops models for three levels of decision/production: the subunit (production division/process), the DMU (firm) and the industry (system). For each level, inefficiency is measured using a directional distance function and the developed measures are contrasted with existing radial models. The paper also investigates how the efficiency scores computed at different levels are related to each other by proposing a decomposition into exhaustive and mutually exclusive components. The proposed method is illustrated using data on Portuguese hospitals. Since most of the topics addressed in this paper are related to more general network structures, avenues for future research are proposed and discussed.info:eu-repo/semantics/publishedVersio

Data Service Efficiency of Mobile Network Operators using Data Envelopment Analysis

The ubiquity of mobile devices such as smartphones, tablets, laptops, and mobile routers drive unprecedented mobile data traffic year after year. However, the actual mean mobile data usage (volume) per subscriber varies significantly between Mobile Network Operators (MNOs) and countries. Understanding these differences is important for both MNO's evolving business models and telecom regulation in general. A potential driver for these differences is MNO efficiency in delivering mobile data services. Where efficiency is measured relative to non-financial inputs (subscribers and spectrum) and output (total data volume). Given this context, this study estimates and analyzes the efficiency of data service delivery (data usage by subscribers) of 94 mobile operators from 28 countries by using the Data Envelopment Analysis (DEA) method. The study demonstrates that many countries have a single highly efficient MNO due to that MNO's effort to gain market share. While in a few other countries all MNOs are high efficiency likely due to country-level initiatives. Furthermore, major economic disparities between countries highly influence country-level efficiency scores. Finally, a case study between Finland and India highlights their similar high-efficiency scores but very dissimilar data service markets

Health care performance management : insights from applications of data envelopment analysis

The comprehensive measurement of efficiency and performance in the Health Service in the UK has become one of the most important managerial developments of recent years. The reasons for this development were examined, particularly in relation to the difficulties involved with performance assessment in such a context. The most widely utilised techniques were evaluated from the perspective of the Health Care Manager and a number of serious limitations were identified. In response to these limitations, the technique of Data Envelopment Analysis was evaluated as an alternative. It has been proposed as an appropriate and useful tool for the assessment of efficiency, although the literature on DEA showed limited practical application to public sector services in the UK. The many facets of the technique were investigated and literature on its application to hospital data was reviewed. A two-stage application procedure for the DEA technique was developed in response to this evaluation, to be used in the measurement hospital efficiency. The procedure was based on a deep theoretical understanding of the DEA methodology. The most important elements of the process were related to selection of the initial sample, the identification of the variables to be included in the DEA model and the definition of the weight restrictions to be incorporated. Input from Health Care Managers was used to guide the application and data from a sample of acute hospitals in Scotland was utilised in the analysis. The application procedure showed how the practicalities of the DEA technique could be enhanced, in particular through the inclusion of weight restrictions. This led to the development of efficiency strategies for the inefficient hospitals, which could be related to the policy objectives or managerial structure of the hospitals in the sample. It was concluded that there were many potential benefits of the DEA approach to efficiency assessment and the two-stage application procedure defined here, which could be seen to fulfil many of the requirements of the Health Care Manager. It was determined that combining theoretical and practical issues can enhance the applicability of the DEA methodology

Bootstrapping in Network Data Envelopment Analysis

Data Envelopment Analysis (DEA) is a linear-programming method used to measure the relative efficiency of firms. The objective of this thesis is (i) to study the efficiency of the railway transport process in Europe considering its inner structure and the impact of railway noise on humans and (ii) to study the performance of bootstrapping approaches in obtaining DEA efficiency estimates when the production process has a network structure and the relation between the different stages is considered. First, the railway transport process is divided into two stages, related to assets and service provision, respectively. The negative impact of railways on people is measured as the number of people that are exposed to high levels of railway noise. The number of rail wagons in each country that is retrofitted with more silent braking technology is used as a proxy to measure the effort to reduce railway noise pollution. Data is extracted from Eurostat (2016), ERA 006REC1072 Impact Assessment (2018), and EEA (2020) and the additive efficiency decomposition approach is used. Based on the results, asset-efficient countries are usually service-efficient, but the inverse does not hold. Sensitivity analysis revealed that efficiency rankings are robust to alterations in the decomposition weight restrictions. Subsampling bootstrap was chosen as the most appropriate as it does not require any restrictive assumptions. The performance of subsampling is examined through Monte Carlo simulations for various sample and subsample sizes for general two-stage series structures. Results indicate great sensitivity both to the sample and subsample size, as well as to the data generating process-higher than in one-stage structures. A practical approach is suggested to overcome some result inconsistencies that are due to the peculiarities of the additive decomposition algorithm. The method is applied to obtain confidence interval estimates for the overall and stage efficiency scores of European railways

Multiobjective centralized DEA approach to Tokyo 2020 Olympic Games

"Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License...."There exist two types of Data Envelopment Analysis (DEA) approaches to the Olympic Games: conventional and fixed-sum outputs (FSO). The approach proposed in this paper belongs to the latter category as it takes into account the total number de medals of each type awarded. Imposing these constraints requires a centralized DEA perspective that projects all the countries simultaneously. In this paper, a multiobjective FSO approach is proposed, and the Weighted Tchebychef solution method is employed. This approach aims to set all output targets as close as possible to their ideal values. In order to choose between the alternative optima, a secondary goal has been considered that minimizes the sum of absolute changes in the number of medals, which also renders the computed targets to be as close to the observed values as possible. These targets represent the output levels that could be expected if all countries performed at their best level. For certain countries, the targets are higher than the actual number of medals won while, for other countries, these targets may be lower. The proposed approach has been applied to the results of the Tokyo 2020 Olympic Games and compared with both FSO and non-FSO DEA method

The assessment of dynamic efficiency of decision making units using data envelopment analysis

The concept of a "production function" as means to measuring efficiency began in 1928 with the seminal paper by Cobb and Douglas (1928). However, until the 1950s, production functions were largely used as a tool for studying the functional distribution of income between capital and labour. Farrell's argument (1957) provides an intellectual basis for redirecting attention from the production function specifically to the deviation from that function as a measure of efficiency. He developed a method so that we can measure efficiency in terms of distance to the "best DMU" on the frontier isoquant. Charnes, Cooper and Rhodes (1978) generalised Farrell's concept to multiple - input multiple - output situations and reformulated it using mathematical programming and thus derived an efficiency measurement known as Data Envelopment Analysis (DEA). Therefore DEA is a linear programming based method for comparing Decision Making Units (DMUs) such as schools, hospitals, etc. In the method originally proposed by Charnes, Cooper and Rhodes (1978) the efficiency of a DMU is defined as a ratio of the weighted sum of outputs to the weighted sum of inputs. Thus in the original DEA approach the notion of time dimension has been ignored. This thesis proposes a IDEA based method for assessing the comparative efficiencies of DMUs operating production processes where input - output levels are inter - temporally dependent. One cause of inter - temporal dependence between input and output levels is stock input which influences output levels over many production periods. Such DMUs cannot be assessed by traditional or 'static' DEA. The method developed in the study overcomes the problem of inter - temporal input - output dependence by using input - output 'paths' mapped out by operating DMUs over time as the basis of assessing them. The aim of this thesis is, therefore, firstly, to address that traditional or "static" IDEA fails to capture the efficiency of DMUs with inter - temporal input - output dependence. Secondly the thesis develops an approach for measuring efficiency under inter - temporal input - output dependence by defining an inter - temporal Production Possibility Set (PPS). The method developed uses path of input - output levels associated with DMUs rather than input - output DMUs observed at one point in time as static IDEA does. Using this PPS, an assessment framework is developed which parallels that of static DEA. The thesis develops mathematical programming models which use input - output paths to measure efficiency, identify peers and target of performance of DMUs. The approach is illustrated using simulated and real data

An integrated fuzzy AHP/DEA approach for performance evaluation of territorial units in Turkey

Due to the differences between regions and sub-regions in the countries, some problems come out especially in economic and social life. The issue of differences of regions has been widely implemented to evaluate the economic performance of Turkey in many disciplines. The objective of this paper is to evaluate the efficiency of 26 sub-regions of NUTS-2 classification using integration Fuzzy Analytic Hierarchy Process (FAHP) with Data Envelopment Analysis (DEA). The integrated FAHP/DEA method comprises two stages. In the first stage, linguistic terms are used to determine the decision makers’ opinion and are converted to quantitative forms by using FAHP methods. Subsequently, in the second stage, DEA method is applied to obtain relative efficiency of sub-regions in Turkey. The integrated FAHP/DEA method is illustrated with a real case study