Data Envelopment Analysis (Dea) Approach In Efficiency Transport Manufacturing Industry in Malaysia

  • Authors

    • Mohd Fahmy-Abdullah
    • Basri Abdul Talib
    2018-09-01
    https://doi.org/10.14419/ijet.v7i3.20.19270
  • Data Envelopment Analysis, technical efficiency, transportation, manufacturing, industry
  • The objective of this study was to measure of technical efficiency, transport manufacturing industry in Malaysia score using the data envelopment analysis (DEA) from 2005 to 2010. The efficiency score analysis used only two inputs, i.e., capital and labor and one output i.e., total of sales. The results shown that the average efficiency score of the Banker, Charnes, Cooper - Variable Returns to Scale (BCC-VRS) model is higher than the Charnes, Cooper, Rhodes - Constant Return to Scale (CCR-CRS) model. Based on the BCC-VRS model, the average efficiency score was at a moderate level and only four sub-industry that recorded an average efficiency score more than 0.50 percent during the period study. The implication of this result suggests that the transport manufacturing industry needs to increase investment, especially in human capital such as employee training, increase communication expenses such as ICT and carry out joint ventures as well as research and development activities to enhance industry efficiency.

     

  • References

    1. [1] Adhikary, B. K. 2011. FDI, trade openness, capital formation, and economic growth in Bangladesh: a linkage analysis. International Journal of Business and Management, 6: 16.

      [2] Agarwal, S., Yadav, S. P., & Singh, S. P. 2010. DEA based estimation of the technical efficiency of state transport undertakings in India. Opsearch, 47: 216-230.

      [3] Annual National Account Report (GDP) 2005 – 2011. 2012. Putrajaya: Department of Statistic.

      [4] Avkiran, N. K. 200). Investigating technical and scale efficiencies of Australian universities through data envelopment analysis. Socio-Economic Planning Sciences, 35: 57-80.

      [5] 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.

      [6] Boame, A. K. 2004. The technical efficiency of Canadian urban transit systems. Transportation Research Part E: Logistics and Transportation Review, 40: 401-416.

      [7] Charnes, A., Cooper, W.W., Rhodes, E. 1978. Measuring the Efficiency of Decision Making Unit. European Journal of Operational Research, Vol. 2, 429-444.

      [8] Coelli, T. J., & Battese, G. E. 1996. Identification of factors which influence the technical inefficiency of Indian farmers. Australian Journal of Agricultural Economics, 40(2), 103-128.

      [9] Cooper, W. W., Seiford, L. M., & Tone, K. 2007. The Basic CCR Model. In Data Envelopment Analysis A Comprehensive Text with Models, Applications, References and DEA-Solver Software. 2nd edition. New York: Springer Science Business Media, Inc.

      [10] Cullinane, K., Wang, T. F., Song, D. W., & Ji, P. 2006. The technical efficiency of container ports: comparing data envelopment analysis and stochastic frontier analysis. Transportation Research Part A: Policy and Practice, 40: 354-374.

      [11] Economic Census 2006. 2007. Putrajaya: Jabatan Perangkaan Malaysia

      [12] Economic Census 2011. 2012. Putrajaya: Jabatan Perangkaan Malaysia.

      [13] Fahmy-Abdullah, M., Ismail, R., N. Sulaiman, N., & Talib, B. A. 2017. Technical Efficiency in Transport Manufacturing Firms: Evidence from Malaysia. Asian Academy of Management Journal, 22: 57-77.

      [14] Farrell, M. J. 1957. The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120: 253-290.

      [15] Ismail, R., & Jajri, I. 2008. Analysis of Technical Efficiency Change, Technology Change, Total Factor Productivity in Transport Equipment Industry in Malaysia. Sains Humanika, 49.

      [16] Jarboui, S. A. M. I., Pascal, F., & Younes, B. 2013. Public road transport efficiency: a stochastic frontier analysis. Journal of Transportation Systems Engineering and Information Technology, 13: 64-71.

      [17] Khalifah, N. A. 2013. Ownership and technical efficiency in Malaysia's automotive industry: A stochastic frontier production function analysis. The Journal of International Trade & Economic Development, 22: 509-535.

      [18] Kim, S., Lim, H., & Park, D. 2007. The effect of imports and exports on total factor productivity in Korea. Research Institute of Economy, Trade and Industry Discussion Paper Series, (07-E).

      [19] Mahadevan, R. 2004. The economics of productivity in Asia and Australia. Edward Elgar Publishing.

      [20] Mankiw, N. G., Phelps, E. S., & Romer, P. M. 1995. The growth of nations. Brookings papers on economic activity, 1995, pp: 275-326.

      [21] MITI. 2013. National Automotive Policy 2013. Ministry of International Trade and Industry.

      [22] National Automotive Policy. 2014. Ministry of International Trade and Industry.

      [23] Nunamaker, T. R. (1985). Using data envelopment analysis to measure the efficiency of nonâ€profit organizations: A critical evaluation. Managerial and decision Economics, 6(1), 50-58.

      [24] Productivity Report 2012 and 2013. Produktivitas Nasional Corporation, Petaling Jaya: Kuala Lumpur.

      [25] Research logistics quarterly, 9: 181-186.

      [26] Sun, X., Lv, X., & Li, L. 2015. Sufficient and Comprehensive Measurement of Automobile Manufacturing Industry Performance Applying Bi-objective Super-efficiency DEA.

      [27] Yu, M. M., & Fan, C. K. 2009. Measuring the performance of multimode bus transit: A mixed structure network DEA model. Transportation Research Part E: Logistics and Transportation Review, 43: 501-515.

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    Fahmy-Abdullah, M., & Abdul Talib, B. (2018). Data Envelopment Analysis (Dea) Approach In Efficiency Transport Manufacturing Industry in Malaysia. International Journal of Engineering & Technology, 7(3.20), 339-343. https://doi.org/10.14419/ijet.v7i3.20.19270