The Efficiency of Railways Specialized in Transporting Iron Ore and Pellets
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Abstract
Objective: the objective is to compare the relative efficiency of the railways specialized in transporting iron ore (MFe) and pellets (PLMFe), which are part of the assets of mining companies and pellet plants considering the 2016 scenario. Methods: the methods used were the data envelopment analysis (DEA) technique, with the application of the output-oriented constant returns scale (CRS) model; the initial combinatorial multicriteria method for choosing the input variables; and Tobit regression as a validation strategy for the DEA model. Results: of the twelve railways evaluated, three railways were identified as efficient: Estrada de Ferro Carajás, Fortescue, and Mount Newman. Conclusions: the applied model was considered a good method to evaluate the efficiency of railways specialized in transporting MFe and PLMFe, as it determined the efficiency of each railway, suggesting the necessary increase in the output variable or adjustments in the input variables so that the railways reach the efficiency frontier. With that, companies can use the results of this study to guide future improvements to make their railways more efficient or maintain them on the frontier of efficiency.
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References
Banker, R. D., Charnes, A., Cooper, W. W., Swarts, J., & Thomas, D. (1989). An introduction to data envelopment analysis with some of its models and their uses. In P. A. Copley (Ed.), Research in governmental and nonprofit accounting (Vol. 5, pp. 125-163). Greenwich, CT: JAI Press.
Bogetoft, P., & Otto, L. (2011). Benchmarking with DEA, SFA, and R (Vol. 157). New York: Springer-Verlag.
Caldas, M. A. F., Gabriele, P. D., Carvalhal, R. L., & Ramos, T. G. (2012, September). A eficiência do transporte ferroviário de cargas: Uma análise do Brasil e dos Estados Unidos. Proceedings of Congreso Latino Ibero-Americano de Investigación Operativa e Simpósio Brasileiro de Pesquisa Operacional (CLAIO-SBPO), Rio de Janeiro, RJ, Brazil, 16. Retrieved from http://www.din.uem.br/sbpo/sbpo2012/pdf/arq0333.pdf
Cantos, P., Pastor, J. M., & Serrano, L. (2012). Evaluating European railway deregulation using different approaches. Transport Policy, 24, 67-72. https://doi.org/10.1016/j.tranpol.2012.07.008
Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444. https://doi.org/10.1016/0377-2217(78)90138-8
Chernick M. R. (2008) Bootstrap methods: A guide for practitioners and researchers. Hoboken, NJ: Wiley.
Cinca, C. S., Molinero, C. M., & Callén, Y. F. (2016). Input and output search in DEA: The case of financial institutions. In S.-N Hwang & H.-S. Lee (Eds.), Handbook of operations analytics using data envelopment analysis (pp. 51-87). Boston: Springer.
Garside, M. (2020). Iron ore - statistics & facts. Statista. Retrieved from https://www.statista.com/topics/1919/iron-ore/
Gujarati, D. N. (2000). Econometria básica. São Paulo, SP: Makron Books.
Kutlar, A., Kabasakal, A., & Sarikaya, M. (2013). Determination of the efficiency of the world railway companies by method of DEA and comparison of their efficiency by Tobit analysis. Quality & Quantity, 47(6), 3575-3602. https://doi.org/10.1007/s11135-012-9741-0
Lin, L. C., & Tseng, C. C. (2007). Operational performance evaluation of major container ports in the Asia-Pacific region. Maritime Policy & Management, 34(6), 535-551. https://doi.org/10.1080/03088830701695248
Marchetti, D., & Wanke, P. (2017). Brazil’s rail freight transport: Efficiency analysis using two-stage DEA and cluster-driven public policies. Socio-Economic Planning Sciences, 59, 26-42. https://doi.org/10.1016/j.seps.2016.10.005
Mello, J. D. S., Gomes, E. G., Meza, L. A., & Lins, M. E. (2004). Selección de variables para el incremento del poder de discriminación de los modelos DEA. Revista de la Escuela de Perfeccionamiento En Investigación Operativa, (24), 40-52. Retrieved from https://www.alice.cnptia.embrapa.br/bitstream/doc/17494/1/1805.pdf
Merkert, R., Smith, A. S. J., & Nash, C. A. (2010). Benchmarking of train operating firms – a transaction cost efficiency analysis. Transportation Planning and Technology, 33(1), 35-53. https://doi.org/10.1080/03081060903429330
Miles, J., & Shevlin, M. (2001). Applying regression and correlation: A guide for students and researchers. London: Sage.
Motta, G. D. S. (2017). Como escrever um bom artigo tecnológico? Revista de Administração Contemporânea, 21(5), 4-8. https://doi.org/10.1590/1982-7849rac2017170258
Pereira, M. A., Rosa, F. S. da, & Lunkes, R. J. (2015). Análise da eficiência ferroviária no Brasil nos anos entre 2009 a 2013. Transportes, 23(3), 56-63. https://doi.org/10.14295/transportes.v23i3.909
Reis, J. C., Sacramento, K. T., Mello, J. C. C. B. S. de, & Meza, L. A. (2017). Avaliação de eficiência das ferrovias brasileiras: Uma aplicação do método multicritério para seleção de variáveis em DEA e representação gráfica bidimensional. Revista Espacios, 38(14), 16-26. Retrieved from https://www.revistaespacios.com/a17v38n14/17381416.html
Sharma, M. G., Debnath, R. M., Oloruntoba, R., & Sharma, S. M. (2016). Benchmarking of rail transport service performance through DEA for Indian railways. The International Journal of Logistics Management, 27(3), 629-649. Retrieved from https://www.emerald.com/insight/content/doi/10.1108/IJLM-08-2014-0122/full/html?fullSc=1
Senra, L. F. A. C., Nanci, L. C., Mello, J. C. C. B. S. de., & Meza, L. A. (2007). Estudo sobre métodos de seleção de variáveis em DEA. Pesquisa Operacional, 27(2), 191-207. https://doi.org/10.1590/S0101-74382007000200001
Silva, F. G. F., Oliveira, R. L. M., & Marinov, M. (2020). An analysis of the effects on rail operational efficiency due to a merger between Brazilian rail companies: The case of RUMO-ALL. Sustainability, 12(12), 4827. https://doi.org/10.3390/su12124827
Simar L. & Wilson, P. W. (1998) Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric frontier models. Management Science, 44(1), 49-61. Retrieved from http://www.jstor.org/stable/2634426
Wanke, P., Chen, Z., Liu, W., Antunes, J. J., & Azad, M. A. K. (2018). Investigating the drivers of railway performance: Evidence from selected Asian countries. Habitat International, 80, 49-69. https://doi.org/10.1016/j.habitatint.2018.08.004
Yu, M.-M. (2008). Assessing the technical efficiency, service effectiveness, and technical effectiveness of the world’s railways through NDEA analysis. Transportation Research Part A: Policy and Practice, 42(10), 1283-1294. https://doi.org/10.1016/j.tra.2008.03.014
Zhou, H., & Hu, H. (2017). Sustainability evaluation of railways in China using a two-stage network DEA model with undesirable outputs and shared resources. Sustainability, 9(1), 150. https://doi.org/10.3390/su9010150