The following study is, in addition to a reassessment of literature and an analysis based on non-parametrical techniques based on linear programming. The analysis based on the Data Envelopment Analisys (DEA) technique will be used to see whether the model that we have used has a significant importance, if there are any substantial differences between the efficiency scores obtained or estimated through various methods. The theoretical part, based on the DEA technique will be analysed under the influence of both the works of Farell(1957), and also Charnes, Cooper, Rhodes(1978), Banker, Charnes, Cooper(1984) and other newer models. The dissolution of efficiency scores obtained through the CRS-DEA model has been studied for a long time into two different components: One is linked with the scale inefficiency and the other one represents the pure technical inefficiency. This dissolution can be done by using the CRS model with technology when not all the companies are operating at the optimum level, i.e. through the simultaneous application on the same set of data of the CRS and VRS models. In this study, the main non-parametrical Data Envelopment Analysis method is presented (Wu, Fan, Zhou, Zhou, 2012; Halkos, Tzeremes, 2009) and its application on a group of 42 companies (The headquarters of a top commercial bank in Romania - S.C. BRD GROUPE SOCIÉTÉ GÉNÉRALE ), based on the information gained in the years 2016-2017. This paper is original because it combines the already developed method with new techniques, in order to link together economic factors and operational research and leaves more room for future researches with the purpose of further assessing and changing the performance of every decisional unit under the influence of the environmental factors.
Aigner, D. J., Chu, S.F. (1968), On estimating the industry production function. American Economic Review, 58, 226-239.
Agrell, P.J., Hatami-Marbini, A. (2013), Frontier-based performance analysis models for supply chain management: State of the art and research directions, Computers & Industrial Engineering, 66, 567-583.
Alene, A. D., Manyong, V.M., Gockowski, J. (2006), The production efficiency of intercropping annual and perennial crops in southern Ethiopia: A comparison of distance functions and production frontiers, Agricultural Systems, 91, 51-70.
Asmild, M., Tam, F. (2007), Estimating global frontier shifts and global Malmquist indices, Journal of Productivity Analysis, 27, 137-148.
Bampatsou, C., Papadopoulos, S., Zervas, E. (2013), Technical efficiency of economic systems of EU-15 countries based on energy consumption, Energy Policy, 55(4), 426-434
Banker, R. D., Charnes, A., Cooper, W. W. (1984), Some models for estimating technical and scale inefficiencies in data envelopment analysis, Management Science, 30, 1078-1092.
Charnes, A., Cooper, W.W., Rhodes, E. (1978), Measuring efficiency of decision making units, European Journal of Operational Research, 3(2), 429-444.
Charnes, A., Cooper, W.W., Schinnar, A.. (1977), Transforms and approximations in cost and production function relations, Research Report CCS 284, Austin, TX, University of Texas Center for Cybernetic Studies.
Coelli, T.J., Rao, D.S.P., O’Donnell, C.J., Battese, G.E. (2005), An introduction to efficiency and productivity analysis, Springer science and business media, New York.
Cooper, W.W., Seiford, L.M., Tone, K. (2000), Data Envelopment Analysis, Kluwer Academic Publishers, Boston.
Dantzig, G. B., (1951), Maximization of a linear function of variables subject to linear inequalities, In T. C. Koopmans (Ed.), Activity analysis of production and allocation, New York: Wiley.
Donaldson, C. (2010), The state of the art of costing health care for economic evaluation, Community Health Stud, 14, 341-356. http://dx.doi.org/10.1111/j.1753-6405.1990.tb00045.x
Egilmez, G., Kucukvar, M., Tatari, O., Bhutta, M.K.S. (2014), Supply chain sustainability assessment of the U.S. food manufacturing sectors: A life cycle-based frontier approach, Resources, Conservation and Recycling,. 82(1),8-20.
Farrell, M. J. (1957), The measurement of productive efficiency, Journal of the Royal Statistical Society, Series A, 120 (III), 253-281.
Färe, R., Grabowski, R., Grasskopf, S., Kraft, S. (1997), Efficiency of a fixed but allocable input: A non-parametric approach, Economics Letters, 56, 187-193.
Fried, H.O., Lovell, C.A.K., Schmidt, S.S. (1993), The Measurement of Productive Efficiency: Techniques and Applications, Oxford University Press.
Halkos, G. E., Tzeremes, N. G. (2009), Exploring the existence of Kuznets curve in countries’ environmental efficiency using. DEA window analysis, Ecological Economics, 68, 2168-2176.
Huang, R., Li, Y., (2013), Undesirable input-output two-phase DEA model in an environmental performance audit, Mathematical and Computer Modelling, 58(9), 971-979.
Mirhedayatian, S.M., Azadi, M., Saen, R.F., (2014), A novel network data envelopment analysis model for evaluating green supply chain management, International Journal of Production Economics, 147, 544-554.
Monchuk, D.C., Chen, Z., Bonaparte, Y. (2010), Explaining production inefficiency in China's agriculture using data envelopment analysis and semi-parametric bootstrapping, China Economic Review, 21, 346-354.
Mukherjee, K. (2008), Energy use efficiency in U.S. manufacturing: A nonparametric analysis, Energy Economics, 30(1), 76-96.
Neely, A., Gregory, M., Platts, K. (1995), Performance Measurement System Design, A literature review and research agenda, International Journal of Operation & Production Management, 15 (1995) 80-116. http://dx.doi.org/10.1108/0144357951008 3622
Neely, A. (1999), The Performance Measurement revolution: why now and what next, International Journal of Operation & Production Management, MCB University Press, 19 (2) 205-228. http://dx.doi.org/10.1108/01443579910247437
Nourali, A. E., Davoodabadi, M., Pashazadeh, H. (2014), Regulation and Efficiency & Productivity Considerations in Water&Wastewater Industry: Case of Iran, Procedia Social and Behavioral Sciences, 109(1), 281-289.
Reinhard, S., Knox Lovell, C.A., Thijssen, G.J. (2000), Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA, European Journal of Operational Research, 121, 287-303.
Robisnon, R. (1993), Economic analysis and health care, What does it mean?, Br Med J., 307, 670-673, http://dx.doi.org/10.1136/bmj.307.6905.670
Scheel, H. (2001), Undesirable outputs in efficiency valuations, European Journal Of Operational Research 132, 400-410.
Sharma, K.R., Leung, P., Zaleski, H.M. (1999), Technical, allocative and economic efficiencies in swine production in Hawaii: a comparison of parametric and nonparametric approaches, Agricultural Economics, 20, 23-35.
Simar, L., Wilson, P.W. (2000), Statistical inference in nonparametric frontier models: the state of the art, Journal of Productivity Analysis, 13, 49-78.
Song, M., An, Q., Zhang, W., Wang, Z., Wu, J. (2012), Environmental efficiency evaluation based on data envelopment analysis: A review. Renewable and Sustainable Energy Reviews, 16, 4465-4469.
Sun, J., Wu, J., Guo, D. (2013), Performance ranking of units considering ideal and antiideal DMU with common weights, Applied Mathematical Modelling, 37(5/1), 6301-6310.
Tzouvelekas, V, Pantzios J., Fotopoulos C. (2001), Technical efficiency of alternative farming systems: the case of Greek organic and conventional olive-growing farms, Food Policy 26, 549-569.
Wu, F. , Fan, L. W., Zhou, P., Zhou, D. Q. (2012) , Industrial energy efficiency with CO2 emissions in China: A nonparametric analysis, Energy Policy, 49, 164-172.
Yan, H., Wei, Q., G., H. (2002), DEA models for resource reallocation and production input/output estimation, European Journal of Operation Research, 136(1), 19-31.
Zhou, P., Ang, B.W., Zhou, D.Q. (2012), Measuring economy-wide energy efficiency performance: A parametric frontier approach, Applied Energy, 90(2), 196-200.
Zhu, Z. , Wang, K., Zhang, B. ( 2014 ) , Applying a network data envelopment analysis model to quantify the eco-efficiency of products: a case study of pesticides, Journal of Cleaner Production, 69, 67-73.