Evaluating and forecasting performance using past data of an industry: An analysis of electronic manufacturing services industry

Tran, Thanh-Tuyen

International Journal of Advanced and Applied Sciences

Int. j. adv. appl. sci.

EISSN: 2313-3724

Print ISSN: 2313-626X

Volume 3, Issue 12 (December 2016), Pages: 5-20

Title: Evaluating and forecasting performance using past data of an industry: An analysis of electronic manufacturing services industry

Author(s): Thanh-Tuyen Tran *

Affiliation(s):

Scientific Research Office, Lac Hong University, Bien Hoa City, Dong Nai, Vietnam

https://doi.org/10.21833/ijaas.2016.12.002

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Abstract:

In this paper, the slacks-based measure of super efficiency (super-SBM) is employed to evaluate the rankings and identify the best performers among 18 chosen EMS providers. Both profitability and marketability efficiency are concerned in order to give a comprehensive view in performance of these companies. Besides, Non-radial Malmquist is also applied to analyze the inter-temporal efficiency change which is decomposed into “catch-up” and “frontier-shift” effects. In addition, GM (1, 1) will be utilized for forecasting the future variables which give the managers a further look on the development potential and situation of these EMSs in near future. The results found that the number of efficient companies and the order of ranking change every year. Hon Hai seems to keep its highest best rankings among 18 DMUs most of the time regarding performance scores. In recent years and next few years, the efficiency of profitability stage is higher than in marketability when considering each separate year with super SBM model. However, there are more companies showing efficient score on marketability model than on profitability in cross – period performance with Malmquist index, which means that the increasing market value productivity of EMSs have been being more and more improved. Finally, a decision-making matrix will be designed to help EMS authorities identify their status and position in the industry. Some recommendations for EMSs in how to enhance precisely its performance to create company value and success are also suggested here. The integration of Data Envelopment Analysis (DEA) and Grey model this research is expect to contribute a better insights into performance evaluation of EMSs in recent years and next few years.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: EMS industry, Performance efficiency, Grey forecasting, Data envelopment analysis

Article History: Received 5 September 2016, Received in revised form 12 November 2016, Accepted 18 November 2016

Digital Object Identifier: https://doi.org/10.21833/ijaas.2016.12.002

Citation:

Tran TT (2016). Evaluating and forecasting performance using past data of an industry: An analysis of electronic manufacturing services industry. International Journal of Advanced and Applied Sciences, 3(12): 5-20

http://www.science-gate.com/IJAAS/V3I12/Tran.html

References:

Andersen P and Petersen NC (1993). A procedure for ranking efficient units in data envelopment analysis. Management Science, 39(10): 1261-1264. https://doi.org/10.1287/mnsc.39.10.1261

Charnes A, Cooper WW and 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

Doyle J and Green R (1994). Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. Journal of the Operational Research Society, 45(5): 567-578. https://doi.org/10.1057/jors.1994.84

Golany B and Roll Y (1989). An application procedure for DEA. Omega, 17(3): 237-250. https://doi.org/10.1016/0305-0483(89)90029-7

Julong D (1989). Introduction to grey system theory. The Journal of Grey System, 1(1): 1-24.

Nguyen NT and Tran TT (2015). Mathematical development and evaluation of forecasting models for accuracy of inflation in developing countries: A case of Vietnam. Discrete Dynamics in Nature and Society, 2015: Article ID 858157. https:// doi.org/10.1155/2015/858157 https://doi.org/10.1155/2015/858157

Nguyen NT and Tran TT (2016). Facilitating an advanced product layout to prioritize hot lots in 450 mm wafer foundry in the semiconductor industry. International Journal of Advanced and Applied Sciences, 3(6): 14-23.

Nguyen NT, Tran TT, Wang CN and Nguyen NT (2015). Optimization of strategic alliances by integrating DEA and grey model. Journal of Grey System, 27(1): 38-56.

Seiford LM and Zhu J (1998). Stability regions for maintaining efficiency in data envelopment analysis. European Journal of Operational Research, 108(1):127-139. https://doi.org/10.1016/S0377-2217(97)00103-3

Seiford LM and Zhu J (1999). Profitability and marketability of the top 55 US commercial banks. Management Science, 45(9): 1270-1288. https://doi.org/10.1287/mnsc.45.9.1270

Tofallis C (1996). Improving discernment in DEA using profiling. Omega, 24(3): 361-364. https://doi.org/10.1016/0305-0483(95)00065-8

Tone K (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3): 498-509. https://doi.org/10.1016/S0377-2217(99)00407-5

Tone K (2002). A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research, 143(1): 32-41. https://doi.org/10.1016/S0377-2217(01)00324-1

Zhu J (2014). Quantitative models for performance evaluation and benchmarking: data envelopment analysis with spreadsheets. Springer, Academic Publishers, Boston, USA: 213. https://doi.org/10.1007/978-3-319-06647-9