Achieving MAX-MIN Fair Cross-efficiency scores in Data Envelopment Analysis

Algorithmic decision making is gaining popularity in today's business. The need for fast, accurate, and complex decisions forces decision-makers to take advantage of algorithms. However, algorithms can create unwanted bias or undesired consequences that can be averted. In this paper, we propose a MAX-MIN fair cross-efficiency data envelopment analysis (DEA) model that solves the problem of high variance cross-efficiency scores. The MAX-MIN cross-efficiency procedure is in accordance with John Rawls’s Theory of justice by allowing efficiency and cross-efficiency estimation such that the greatest benefit of the least-advantaged decision making unit is achieved. The proposed mathematical model is tested on a healthcare related dataset. The results suggest that the proposed method solves several issues of cross-efficiency scores. First, it enables full rankings by having the ability to discriminate between the efficiency scores of DMUs. Second, the variance of cross-efficiency scores is reduced, and finally, fairness is introduced through optimization of the minimal efficiency scores

Assessing the Efficiency of Public Universities through DEA. A Case Study

[EN] This paper presents the results of an efficiency study of Colombian public universities in 2012, conducted using the methodology of Data Envelopment Analysis (DEA) and the models CCR, BCC and SBM under output orientation. The main objective is to determine technical, pure technical, scale and mix efficiencies using data acquired from the Ministry of National Education. An analysis of the results shows the extent to which outputs of inefficient Higher Education Institutions (HEIs) could be improved and the possible cause of this inefficiency. The universities were also ranked using a Pareto efficient cross-efficiency model and a study was made of changes to overall productivity between 2011 and 2012. The results showed Tolima, Caldas and UNAD to be the best-performing universities, with Universidad del Pacífico as the worst performer. Malmquist index was applied to analyze the change in productivity from 2011 to 2012. The Universidad de La Guajira showed great improvement in technical efficiency between 2011 and 2012.Monica Martinez-Gomez has been funded by the research project GVA/20161004: Project of Conselleria d'Educacio, Investigacio, Cultura i Esport de la Generalitat Valenciana, through the project "Validacion de la competencia transversal de innovacion mediante un modelo de Medida formativo"Visbal-Cadavid, D.; Martínez-Gómez, M.; Guijarro, F. (2017). Assessing the Efficiency of Public Universities through DEA. A Case Study. Sustainability. 9(8):1-19. https://doi.org/10.3390/su9081416S11998Bayraktar, E., Tatoglu, E., & Zaim, S. (2013). Measuring the relative efficiency of quality management practices in Turkish public and private universities. Journal of the Operational Research Society, 64(12), 1810-1830. doi:10.1057/jors.2013.2Mayston, D. J. (2017). Convexity, quality and efficiency in education. Journal of the Operational Research Society, 68(4), 446-455. doi:10.1057/jors.2015.91Palomares-Montero, D., García-Aracil, A., & Castro-Martínez, E. (2008). Assessment of Higher Education Institutions: A Bibliographic Review of Indicatorsâ Systems. Revista española de Documentación Científica, 31(2). doi:10.3989/redc.2008.v31.i2.425Witte, K. D., & López-Torres, L. (2017). Efficiency in education: a review of literature and a way forward. Journal of the Operational Research Society, 68(4), 339-363. doi:10.1057/jors.2015.92Barra, C., & Zotti, R. (2016). Measuring Efficiency in Higher Education: An Empirical Study Using a Bootstrapped Data Envelopment Analysis. International Advances in Economic Research, 22(1), 11-33. doi:10.1007/s11294-015-9558-4Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. doi:10.1016/0377-2217(78)90138-8Agasisti, T., & Bianco, A. D. (2009). Measuring efficiency of Higher Education institutions. International Journal of Management and Decision Making, 10(5/6), 443. doi:10.1504/ijmdm.2009.026687Agasisti, T., Barra, C., & Zotti, R. (2016). Evaluating the efficiency of Italian public universities (2008–2011) in presence of (unobserved) heterogeneity. Socio-Economic Planning Sciences, 55, 47-58. doi:10.1016/j.seps.2016.06.002Da Silva e Souza, G., & Gomes, E. G. (2015). Management of agricultural research centers in Brazil: A DEA application using a dynamic GMM approach. European Journal of Operational Research, 240(3), 819-824. doi:10.1016/j.ejor.2014.07.027Gökşen, Y., Doğan, O., & Özkarabacak, B. (2015). A Data Envelopment Analysis Application for Measuring Efficiency of University Departments. Procedia Economics and Finance, 19, 226-237. doi:10.1016/s2212-5671(15)00024-6Katharaki, M., & Katharakis, G. (2010). A comparative assessment of Greek universities’ efficiency using quantitative analysis. International Journal of Educational Research, 49(4-5), 115-128. doi:10.1016/j.ijer.2010.11.001Podinovski, V. V., & Wan Husain, W. R. (2015). The hybrid returns-to-scale model and its extension by production trade-offs: an application to the efficiency assessment of public universities in Malaysia. Annals of Operations Research, 250(1), 65-84. doi:10.1007/s10479-015-1854-0Thanassoulis, E., Kortelainen, M., Johnes, G., & Johnes, J. (2011). Costs and efficiency of higher education institutions in England: a DEA analysis. Journal of the Operational Research Society, 62(7), 1282-1297. doi:10.1057/jors.2010.68Wu, J., Chu, J., Sun, J., & Zhu, Q. (2016). DEA cross-efficiency evaluation based on Pareto improvement. European Journal of Operational Research, 248(2), 571-579. doi:10.1016/j.ejor.2015.07.042Kwon, H.-B., & Lee, J. (2015). Two-stage production modeling of large U.S. banks: A DEA-neural network approach. Expert Systems with Applications, 42(19), 6758-6766. doi:10.1016/j.eswa.2015.04.062Tao, L., Liu, X., & Chen, Y. (2012). Online banking performance evaluation using data envelopment analysis and axiomatic fuzzy set clustering. Quality & Quantity, 47(2), 1259-1273. doi:10.1007/s11135-012-9767-3Tsolas, I. E., & Charles, V. (2015). Incorporating risk into bank efficiency: A satisficing DEA approach to assess the Greek banking crisis. Expert Systems with Applications, 42(7), 3491-3500. doi:10.1016/j.eswa.2014.12.033Wanke, P., & Barros, C. (2014). Two-stage DEA: An application to major Brazilian banks. Expert Systems with Applications, 41(5), 2337-2344. doi:10.1016/j.eswa.2013.09.031Aristovnik, A., Seljak, J., & Mencinger, J. (2014). Performance measurement of police forces at the local level: A non-parametric mathematical programming approach. Expert Systems with Applications, 41(4), 1647-1653. doi:10.1016/j.eswa.2013.08.061Fang, L., & Li, H. (2015). Centralized resource allocation based on the cost–revenue analysis. Computers & Industrial Engineering, 85, 395-401. doi:10.1016/j.cie.2015.04.018Du, J., Cook, W. D., Liang, L., & Zhu, J. (2014). Fixed cost and resource allocation based on DEA cross-efficiency. European Journal of Operational Research, 235(1), 206-214. doi:10.1016/j.ejor.2013.10.002Lozano, S. (2015). A joint-inputs Network DEA approach to production and pollution-generating technologies. Expert Systems with Applications, 42(21), 7960-7968. doi:10.1016/j.eswa.2015.06.023Woo, C., Chung, Y., Chun, D., Seo, H., & Hong, S. (2015). The static and dynamic environmental efficiency of renewable energy: A Malmquist index analysis of OECD countries. Renewable and Sustainable Energy Reviews, 47, 367-376. doi:10.1016/j.rser.2015.03.070Azadeh, A., Motevali Haghighi, S., Zarrin, M., & Khaefi, S. (2015). Performance evaluation of Iranian electricity distribution units by using stochastic data envelopment analysis. International Journal of Electrical Power & Energy Systems, 73, 919-931. doi:10.1016/j.ijepes.2015.06.002Omrani, H., Gharizadeh Beiragh, R., & Shafiei Kaleibari, S. (2015). Performance assessment of Iranian electricity distribution companies by an integrated cooperative game data envelopment analysis principal component analysis approach. International Journal of Electrical Power & Energy Systems, 64, 617-625. doi:10.1016/j.ijepes.2014.07.045Escorcia Caballero, R., Visbal Cadavid, D., & Agudelo Toloza, J. M. (2015). Eficiencia en las instituciones educativas públicas de la ciudad de Santa Marta (Colombia) mediante "Análisis Envolvente de Datos. Ingeniare. Revista chilena de ingeniería, 23(4), 579-593. doi:10.4067/s0718-33052015000400009Grosskopf, S., Hayes, K., & Taylor, L. L. (2014). Applied efficiency analysis in education. Economics and Business Letters, 3(1), 19. doi:10.17811/ebl.3.1.2014.19-26Huguenin, J.-M. (2015). Determinants of school efficiency. International Journal of Educational Management, 29(5), 539-562. doi:10.1108/ijem-12-2013-0183Avilés Sacoto, S., Güemes Castorena, D., Cook, W. D., & Cantú Delgado, H. (2015). Time-staged outputs in DEA. Omega, 55, 1-9. doi:10.1016/j.omega.2015.01.019De Witte, K., & Rogge, N. (2011). Accounting for exogenous influences in performance evaluations of teachers. Economics of Education Review, 30(4), 641-653. doi:10.1016/j.econedurev.2011.02.002Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30(9), 1078-1092. doi:10.1287/mnsc.30.9.1078Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498-509. doi:10.1016/s0377-2217(99)00407-5Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253. doi:10.2307/2343100Scheel, H., & Scholtes, S. (2003). Continuity of DEA Efficiency Measures. Operations Research, 51(1), 149-159. doi:10.1287/opre.51.1.149.12803Andersen, P., & Petersen, N. C. (1993). A Procedure for Ranking Efficient Units in Data Envelopment Analysis. Management Science, 39(10), 1261-1264. doi:10.1287/mnsc.39.10.1261Fang, H.-H., Lee, H.-S., Hwang, S.-N., & Chung, C.-C. (2013). A slacks-based measure of super-efficiency in data envelopment analysis: An alternative approach. Omega, 41(4), 731-734. doi:10.1016/j.omega.2012.10.004Doyle, J., & Green, R. (1994). Efficiency and Cross-efficiency in DEA: Derivations, Meanings and Uses. Journal of the Operational Research Society, 45(5), 567-578. doi:10.1057/jors.1994.84Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: Critique and extensions. New Directions for Program Evaluation, 1986(32), 73-105. doi:10.1002/ev.1441Yang, G., Yang, J., Liu, W., & Li, X. (2013). Cross-efficiency aggregation in DEA models using the evidential-reasoning approach. European Journal of Operational Research, 231(2), 393-404. doi:10.1016/j.ejor.2013.05.017Zerafat Angiz, M., Mustafa, A., & Kamali, M. J. (2013). Cross-ranking of Decision Making Units in Data Envelopment Analysis. Applied Mathematical Modelling, 37(1-2), 398-405. doi:10.1016/j.apm.2012.02.038Banker, R. D., & Chang, H. (2006). The super-efficiency procedure for outlier identification, not for ranking efficient units. European Journal of Operational Research, 175(2), 1311-1320. doi:10.1016/j.ejor.2005.06.028Thanassoulis, E., Shiraz, R. K., & Maniadakis, N. (2015). A cost Malmquist productivity index capturing group performance. European Journal of Operational Research, 241(3), 796-805. doi:10.1016/j.ejor.2014.09.002Wijesiri, M., & Meoli, M. (2015). Productivity change of microfinance institutions in Kenya: A bootstrap Malmquist approach. Journal of Retailing and Consumer Services, 25, 115-121. doi:10.1016/j.jretconser.2015.04.004Eskelinen, J. (2017). Comparison of variable selection techniques for data envelopment analysis in a retail bank. European Journal of Operational Research, 259(2), 778-788. doi:10.1016/j.ejor.2016.11.009Jenkins, L., & Anderson, M. (2003). A multivariate statistical approach to reducing the number of variables in data envelopment analysis. European Journal of Operational Research, 147(1), 51-61. doi:10.1016/s0377-2217(02)00243-6Land, K. C., Knox Lovell, C. A., & Thore, S. (1994). Productive efficiency under capitalism and state socialism: Technological Forecasting and Social Change, 46(2), 139-152. doi:10.1016/0040-1625(94)90022-

An evaluation of cross-efficiency methods: With an application to warehouse performance

Cross-efficiency measurement is an extension of Data Envelopment Analysis that allows for tie-breaking ranking of the Decision Making Units (DMUs) using all the peer evaluations. In this article we examine the theory of cross-efficiency measurement by comparing a selection of methods popular in the literature. These methods are applied to performance measurement of European warehouses. We develop a cross-efficiency method based on a rank-order DEA model to accommodate the ordinal nature of some key variables characterizing warehouse performance. This is one of the first comparisons of methods on a real-life dataset and the first time that a model allowing for qualitative variables is included in such a comparison. Our results show that the choice of model matters, as one obtains statistically different rankings from each one of them. This holds in particular for the multiplicative and game-theoretic methods whose results diverge from the classic method. From a managerial perspective, focused on the applicability of the methods, we evaluate them through a multidimensional metric which considers their capability to rank DMUs, their ease of implementation, and their robustness to sensitivity analyses. We conclude that standard weight-restriction methods, as initiated by Sexton et al. [48], perform as well as recently introduced, more sophisticated alternativesSpanish Ministry of Science and Innovation (Ministerio de Ciencia e Innovación), the State Research Agency (Agencia Estatal de Investigación) and the European Regional Development Fund (Fondo Europeo de Desarrollo Regional) under grants EIN2020-11226

An Integrated Fuzzy Clustering Cooperative Game Data Envelopment Analysis Model with application in Hospital Efficiency

Hospitals are the main sub-section of health care systems and evaluation of hospitals is one of the most important issue for health policy makers. Data Envelopment Analysis (DEA) is a nonparametric method that has recently been used for measuring efficiency and productivity of Decision Making Units (DMUs) and commonly applied for comparison of hospitals. However, one of the important assumption in DEA is that DMUs must be homogenous. The crucial issue in hospital efficiency is that hospitals are providing different services and so may not be comparable. In this paper, we propose an integrated fuzzy clustering cooperative game DEA approach. In fact, due to the lack of homogeneity among DMUs, we first propose to use a fuzzy C-means technique to cluster the DMUs. Then we apply DEA combined with the game theory where each DMU is considered as a player, using Core and Shapley value approaches within each cluster. The procedure has successfully been applied for performances measurement of 288 hospitals in 31 provinces of Iran. Finally, since the classical DEA model is not capable to distinguish between efficient DMUs, efficient hospitals within each cluster, are ranked using combined DEA model and cooperative game approach. The results show that the Core and Shapley values are suitable for fully ranking of efficient hospitals in the healthcare systems

Evaluating the eco-efficiency of wastewater treatment plants: comparison of optimistic and pessimistic approaches

The assessment of wastewater treatment plant (WWTP) performance has gained the interest of water utilities and water regulators. Eco-efficiency has been identified as a powerful indicator, as it integrates economic and environmental variables into a single index. Most previous studies have employed traditional data envelopment analysis (DEA) for the evaluation of WWTP eco-efficiency. However, DEA allows the selection of input and output weights for individual WWTPs for the calculation of eco-efficiency scores. To overcome this limitation, we employed the double-frontier and common set of weights methods to evaluate the eco-efficiency of a sample of 30 WWTPs in Spain. The WWTPs were ranked based on eco-efficiency scores derived under several scenarios including best- and worst-case scenarios; this approach to performance assessment is reliable and robust. Twenty-six of the 30 WWTPs were not classified as eco-efficient, even under the most favorable scenario, indicating that these facilities have substantial room for the reduction of costs and greenhouse gas emissions. The ranking of WWTPs varied according to the scenario used for evaluation, which has notable consequences when eco-efficiency scores are used for regulatory purposes. The findings of this study are relevant for water regulators and water utilities, as they demonstrate the importance of weight allocation for eco-efficiency score estimation

Aplicación del Análisis Envolvente de Datos y Análisis Factorial Múltiple en el estudio del desempeño en las instituciones de educación superior públicas en Colombia y su implicación en la distribución de los recursos

[ES] El establecimiento de estrategias y planes de mejora de todo sistema debe abordar como primera instancia el conocimiento del estado actual del mismo, lo cual se logra mediante la formulación, estudio y análisis de los indicadores de gestión de las dimensiones consideradas importantes para el logro de los objetivos, y ello se hace extensivo al Sistema Universitario Estatal (SUE) colombiano. En este contexto, los resultados de las Instituciones de Educación Superior (IES) están entre los desafíos y retos que tiene el sistema educativo en Colombia. Con el presente trabajo se pretende realizar un análisis comparativo del estado actual de las IES públicas colombianas. Para ello, en primer lugar se realizó un estudio de eficiencia mediante el Análisis Envolvente de Datos (DEA), para posteriormente hacer una propuesta de reestructuración del sector educativo superior público colombiano mediante la implementación del Análisis Envolvente de Datos Inverso en combinación con Algoritmos Genéticos (InvDEA - AG) a través de la identificación de posibles fusiones entre IES ineficientes en una única nueva IES resultante, de manera que esta última posea un cierto nivel de eficiencia técnica preestablecido. En una tercera etapa se realiza una caracterización de las mismas mediante el estudio de los indicadores de resultados establecidos en el Índice de Progreso de la Educación Superior (IPES) desarrollado por el Ministerio de Educación Nacional de Colombia utilizando como herramienta el Análisis Factorial Múltiple (AFM), y finalmente se va a proponer un Índice Sintético de Desempeño basado en los resultados del AFM (IAFM), índice que considera la estructura interna de los indicadores que conforman las dimensiones del sistema de indicadores de gestión de las IES. Los resultados indican que, en términos generales, las universidades que exhiben más debilidades son: Pacifico, Chocó, UFPS-Ocaña, Guajira, Cesar, Amazonía, Sucre, Llanos, Pamplona y Cundinamarca. Los resultados muestran que la dimensión Acceso es la más multidimensional, seguido por Calidad y Logro, siendo el más homogéneo el grupo Recursos. El mejor desempeño en las variables de la dimensión Logro lo tiene la Universidad Nacional de Colombia (UNAL), seguida por Universidad de Antioquia (UDEA). El mejor desempeño en la dimensión Calidad lo posee la Universidad Nacional Abierta y a Distancia (UNAD), mientras que la Universidad Pedagógica Nacional tiene el mejor desempeño en Acceso, y el segundo mejor desempeño en Calidad (compartido con la Universidad Militar).[CA] L'establiment d'estratègies i plans de millora de tot sistema ha d'abordar com a primera instància el coneixement de l'estat actual d'aquest, la qual cosa s'aconsegueix mitjançant la formulació, estudi i anàlisi dels indicadors de gestió de les dimensions considerades importants per a l'assoliment dels objectius, això també és totalment cert en el Sistema Universitari Estatal (SUE) colombià. En aquest context, els resultats de les Institucions d'Educació Superior (IES) estan entre els desafiaments i reptes que té el sistema educatiu a Colòmbia. Amb el present treball es pretén realitzar una anàlisi comparativa de l'estat actual de les IES públiques colombianes, per a això es duu a terme un estudi d'eficiència mitjançant l'Anàlisi Envolupant de Dades (DEA), seguidament es fa una proposta de reestructuració del sector educatiu superior públic colombià mitjançant la implementació de l'Anàlisi Envolupant de Dades Invers en combinació amb Algorismes Genètics (InvDEA - AG) a través de la identificació de possibles fusions entre IES ineficients en una única nova IES resultant, de manera que aquesta última posseïsca un cert nivell d'eficiència tècnica preestablit, també es realitza una caracterització de les mateixes mitjançant l'estudi dels indicadors de resultats establits en l'Índex de Progrés de l'Educació Superior (IPES) desenvolupat pel Ministeri d'Educació Nacional de Colòmbia utilitzant com a eina l'Anàlisi Factorial Múltiple (AFM), i finalment es proposa un Índex Sintètic d'Acompliment basat en els resultats del AFM (IAFM), índex que considera l'estructura interna dels indicadors que conformen les dimensions del sistema d'indicadors de gestió de les IES. Els resultats indiquen que, en termes generals, les universitats que exhibeixen més debilitats són: Pacífico, Chocó, UFPS-Ocaña, Guajira, Cesar, Amazonía, Sucre, Llanos, Pamplona i Cundinamarca. Els resultats mostren que la dimensió Accés és la més multidimensional, seguit per Qualitat i Assoliment, i el més homogeni és Recursos. El millor acompliment en les variables de la dimensió Assoliment ho té la Universitat Nacional de Colòmbia (UNAL), seguida per Universitat de Antioquia (UDEA). El millor acompliment en la dimensió Qualitat el posseeix la Universitat Nacional Oberta i a Distància (UNAD), mentre que la Universitat Pedagògica Nacional té el millor acompliment en Accés, i el segon millor acompliment en Qualitat (compartit amb la Universitat Militar).[EN] The establishment of strategies and plans for the improvement of any system should address as a first instance the knowledge of the current state of the same, which is achieved through the formulation, study and analysis of performance indicators of the dimensions considered important for the achievement of objectives, this is also totally true in the Colombian State University System (SUE). In this context, the results of the Higher Education Institutions (HEIs) are among the challenges that the education system has in Colombia. This work intends to carry out a comparative analysis of the current state of Colombian public HEIs. To do this, an efficiency study was first carried out using the Data Envelope Analysis (DEA), then a proposal is made to restructure Higher Public Education Sector in Colombia through the implementation of the Inverse Data Envelopment Analysis in combination with Genetic Algorithms (InvDEA -GA) by identifying possible mergers between inefficient HEI in a single resulting new HEI so that the latter fulfill a global predefined efficiency. In a third stage, a characterization of them is carried out by studying the outcome indicators established in the Progress Index of Higher Education (IPES) developed by the Ministry of National Education of Colombia through Multiple Factor Analysis (MFA) as tool, and finally, a Synthetic Performance Index based on the results of the MFA (IMFA) is proposed, index that considers the internal structure of the indicators that compose the dimensions of the system of indicators of HEIs in Colombia. The results indicate that, in general terms, the universities that exhibit the most weaknesses are: Pacifico, Chocó, UFPS-Ocaña, Guajira, Cesar, Amazonía, Sucre, Llanos, Pamplona y Cundinamarca. The results show that the Access dimension is the most multidimensional, followed by Quality and Achievement, and the most homogeneous is Resources. The best performance in the variables of the Achievement dimension is the Universidad Nacional de Colombia (UNAL), followed by the Universidad de Antioquia (UDEA). The best performance in the Quality dimension is held by the Universidad Nacional Abierta y a Distancia (UNAD), while the Universidad Pedagógica Nacional has the best performance in Access, and the second best performance in Quality (shared with the Universidad Militar Nueva Granada).Visbal Cadavid, DA. (2020). Aplicación del Análisis Envolvente de Datos y Análisis Factorial Múltiple en el estudio del desempeño en las instituciones de educación superior públicas en Colombia y su implicación en la distribución de los recursos [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/140089TESI

Ein DEA-basierter Ansatz zur Messung der Performance bei zentralisierten Managementstrukturen

Traditional performance measurement approaches are usually characterized by a number of different limitations. Among other things, these approaches require the subjective determination of weights to aggregate a set of indicators to an overall performance score. Furthermore, traditional approaches are usually not able to incorporate additional improvement potentials that can be received from a centralized management. A performance measurement framework which can overcome these limitations is called data envelopment analysis (DEA). Against this background, this thesis provides a thorough overview of how different degrees of centralization are modeled in the current DEA literature. The systematic literature review identified 135 different approaches that assume a centralized or partially centralized management structure. A concluding discussion of the respective DEA approaches showed two fundamental research gaps. In response to this, this thesis has two fundamental objectives: The first objective is to propose a DEA-based performance measurement approach for measuring performance changes over time. The second objective is to develop another DEA-based approach for comparing the performance of management groups. In contrast to so far developed DEA-models, the here proposed approaches explicitly incorporate the respective management structure. Both DEA approaches thus developed are based on the combination of the metafrontier concept and the Malmquist index. The first approach evaluates productivity changes of operating entities over time and, hence, may indicate potential sources for performance changes. Thereby, the proposed approach preserves the individual characteristics of each local group technology. The second DEA approach proposed here uses the Malmquist index for comparing the performance of management groups. This index accounts for the existence of a central decision maker who can, e.g., undertake resource reallocations to improve the overall performance of its managed group. The applicability and usefulness of both proposed approaches is empirically shown with real-world data from KONE Corporation.Traditionelle Performance Measurement Ansätze gehen mit einer Reihe von Herausforderungen einher. So erfordert die Aggregation unterschiedlicher Kennzahlen zu einem einzelnen Performancemaß die Verwendung von subjektiven Gewichtungen. Darüber hinaus lassen sich in traditionellen Ansätzen nur schwer etwaige Verbesserungspotentiale modellieren, die aus zentralisierten Managementstrukturen resultieren. Eine betriebswirtschaftliche Methode, welche die genannten Limitationen nicht aufweist, ist die Data Envelopment Analysis (DEA). Aufgrund dieser Vorteile wird in dieser Arbeit zunächst ein umfassender Literaturüberblick erarbeitet, wie unterschiedliche Managementstrukturen in einer DEA modelliert werden können. Mithilfe der Literaturrecherche wurden insgesamt 135 unterschiedliche Ansätze ermittelt, die entweder ein vollkommen zentralisiertes oder teilweise zentralisiertes Managementmodell unterstellen. Eine abschließende Diskussion der verschiedenen DEA-Ansätze zeigte allerdings eine Forschungslücke, woraus die beiden folgenden Forschungsziele für diese Arbeit abgeleitet wurden: Einerseits soll ein DEA-basierter Ansatz erarbeitet werden, der zur Messung von Effizienzveränderungen einzelner Produktiveinheiten über die Zeit geeignet ist. Andererseits soll eine DEA-basierte Methode entwickelt werden, welche bei Performancevergleichen zwischen Managementgruppen anwendbar ist. Im Gegensatz zu den bisher in der Literatur diskutierten Ansätzen sollten die entwickelten Methoden dabei die jeweils vorliegende Managementstruktur berücksichtigen. Die entwickelten DEA-Ansätze basieren auf der Kombination des Metafrontier-Konzepts mit dem Malmquist-Index. Der erste Ansatz erlaubt es, Performanceveränderungen von einzelnen Produktiveinheiten über mehrere Zeitperioden zu messen. Im Gegensatz zu konventionellen Metafrontier-basierten Malmquist-Indizes berücksichtigt der vorgeschlagene Ansatz die individuellen Eigenschaften der lokalen Produktionstechnologien. Der zweite vorgeschlagene DEA-Ansatz nutzt den Malmquist-Index für den Vergleich der Performance von Managementgruppen. Der Index berücksichtigt dabei explizit, dass eine zentrale Entscheidungsinstanz existiert, welche Ressourcenumverteilungen durchführen kann. Beide Ansätze werden anhand eines Datensatzes des Unternehmens KONE illustriert