Journal of Applied Mathematics

An Empirical Study of Hybrid DEA and Grey System Theory on Analyzing Performance: A Case from Indian Mining Industry

Lai-Wang Wang, Thanh-Tuyen Tran, and Nhu-Ty Nguyen

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text

Abstract

India, which has long been recognized as a well-endowed nation in natural mineral resources, is a major minerals producer. According to the report of Indian Ministry of Mines 2013, Indian mining and metals sector ranked the fourth among the mineral producer countries, behind China, United States, and Russia and had in fact led the economy into recovery from the global financial crisis. Since this industry has turned into a significant issue, this paper attempts to rank the performance of 23 Indian mining and metal companies and to evaluate and measure the productivity change of these sectors during different time periods (2010–2014). Besides, the authors would like to choose one advanced model of MPI to see the performance of these companies in the past-present period and the 4-year future period (2015–2018) by using forecasting results of Grey system theory. The results revealed that from the past to future period the National Mineral Development Corporation, Hindalco Industries Limited, and Coal India always keep their highest best rankings among 23 DMUs regarding performance scores. This study contributes better insights of Indian mining industry as it is the core of the economy.

Article information

Source
J. Appl. Math., Volume 2015 (2015), Article ID 395360, 15 pages.

Dates
First available in Project Euclid: 15 April 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1429105028

Digital Object Identifier
doi:10.1155/2015/395360

Zentralblatt MATH identifier
07000922

Citation

Wang, Lai-Wang; Tran, Thanh-Tuyen; Nguyen, Nhu-Ty. An Empirical Study of Hybrid DEA and Grey System Theory on Analyzing Performance: A Case from Indian Mining Industry. J. Appl. Math. 2015 (2015), Article ID 395360, 15 pages. doi:10.1155/2015/395360. https://projecteuclid.org/euclid.jam/1429105028


Export citation

References

  • K. Singh and K. Kalirajan, “A decade of economic reforms in India: the mining sector,” Resources Policy, vol. 29, no. 3-4, pp. 139–151, 2004.
  • A. Das, “Who extracts minerals more efficiently–-public or private firms? A study of Indian mining industry,” Journal of Policy Modeling, vol. 34, no. 5, pp. 755–766, 2012.
  • M. Kulshreshtha and J. K. Parikh, “Study of efficiency and productivity growth in opencast and underground coal mining in India: a DEA analysis,” Energy Economics, vol. 24, no. 5, pp. 439–453, 2002.
  • H. Fang, J. Wu, and C. Zeng, “Comparative study on efficiency performance of listed coal mining companies in China and the USčommentComment on ref. [7?]: We deleted reference [8] in the original manuscript, which was a repetition of [7?]. Consequently we will replace all the citations of [8] within text with those of [7?]. Please check all highlighted cases throughout.,” Energy Policy, vol. 37, no. 12, pp. 5140–5148, 2009.
  • M. Kulshreshtha and J. K. Parikh, “A study of productivity in the Indian coal sector,” Energy Policy, vol. 29, no. 9, pp. 701–713, 2001.
  • Y. Kasap, A. Konuk, R. N. Gasimov, and A. M. Kiliç, “The effects of non-controllable factors in efficiency evaluation of Turkish coal enterprises,” Energy Exploration and Exploitation, vol. 25, no. 6, pp. 429–450, 2007.
  • X. Xue, Q. Shen, Y. Wang, and J. Lu, “Measuring the productivity of the construction industry in China by using DEA-based malmquist productivity indices,” Journal of Construction Engineering and Management, vol. 134, no. 1, pp. 64–71, 2008.
  • I. E. Tsolas, “Performance assessment of mining operations using nonparametric production analysis: a bootstrapping approach in DEA,” Resources Policy, vol. 36, no. 2, pp. 159–167, 2011.
  • H. Berkman and V. R. Eleswarapu, “Short-term traders and liquidity :: a test using Bombay Stock Exchange. Paper presented at the WFA and the APFA/PACAP Conferences,” Journal of financial Economics, vol. 47, no. 3, pp. 339–355, 1998.
  • M. Jain, P. L. Meena, and T. N. Mathur, “Impact of foreign institutional investment on stock market with special reference to BSE, a study of last one decade,” Asian Journal of Research in Banking and Finance, vol. 2, no. 4, pp. 31–47, 2012.
  • C. N. Wang, N. T. Nguyen, and T. T. Tran, “Integrated DEA models and grey system theory to evaluate past-to-future performance: a case of Indian electricity industry,” The Scientific World Journal, vol. 2014, Article ID 638710, 23 pages, 2014.
  • J. Zhu, “Multi-factor performance measure model with an application to Fortune 500 companies,” European Journal of Operational Research, vol. 123, no. 1, pp. 105–124, 2000.
  • T.-Y. Chen and L.-H. Chen, “DEA performance evaluation based on BSC indicators incorporated: the case of semiconductor industry,” International Journal of Productivity and Performance Management, vol. 56, no. 4, pp. 335–357, 2007.
  • A. Charnes, W. W. Cooper, and E. Rhodes, “Measuring the efficiency of decision making units,” European Journal of Operational Research, vol. 2, no. 6, pp. 429–444, 1978.
  • R. Banker, A. Charnes, and W. W. Cooper, “Some models for estimating technical and scale inefficiency in data envelopment analysis,” Management Science, vol. 30, no. 9, pp. 1078–1092, 1984.
  • R. Fare, S. Grossfopf, and C. A. K. Lovell, Production Frontiers, Cambridge University Press, New York, NY, USA, 1994.
  • K. A. Tone, “Slacks-based measure of efficiency in data envelopment analysis,” European Journal of Operational Research, vol. 130, no. 3, pp. 498–509, 2001.
  • K. A. Tone, “A slacks-based measure of super-efficiency in data envelopment analysis,” European Journal of Operational Research, vol. 143, no. 1, pp. 32–41, 2002.
  • K. F. Lam, H. W. Mui, and H. K. Yuen, “A note of minimizing absolute percentage error in combined forecasts,” Computers and Operations Research, vol. 28, no. 11, pp. 1141–1147, 2001.
  • R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” International Journal of Forecasting, vol. 22, no. 4, pp. 679–688, 2006.
  • W. Chia-Nan and N. N. Ty, “Forecasting the manpower requirement in Vietnamese tertiary institutions,” Asian Journal of Empirical Research, vol. 3, no. 5, pp. 563–575, 2013.
  • F.-Y. Lo, C.-F. Chien, and J. T. Lin, “A DEA study to evaluate the relative efficiency and investigate the district reorganization of the Taiwan Power Company,” IEEE Transactions on Power Systems, vol. 16, no. 1, pp. 170–178, 2001.
  • L. M. Seiford and J. Zhu, “Modeling undesirable factors in efficiency evaluation,” European Journal of Operational Research, vol. 142, no. 1, pp. 16–20, 2002.
  • N. K. Avkiran, “Association of DEA super-efficiency estimates with financial ratios: investigating the case for Chinese banks,” Omega, vol. 39, no. 3, pp. 323–334, 2011.
  • Y. Chen and H. D. Sherman, “The benefits of non-radial vs. radial super-efficiency DEA: an application to burden-sharing amongst NATO member nations,” Socio-Economic Planning Sciences, vol. 38, no. 4, pp. 307–320, 2004. \endinput