The use for the competition theory of the industrial investment decisions—a case study of the Taiwan IC assembly industry

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The use for the competition theory of the industrial investment

decisions—a case study of the Taiwan IC assembly industry

Hsi-che Teng, Ying-fang Huang


Department of Industrial Engineering and Management, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan, ROC

a r t i c l e

i n f o

Article history: Received 25 April 2012 Accepted 15 August 2012 Available online 31 August 2012 Keywords: Co-opetition theory Forecasting Grey theory Semi-conductor industry

a b s t r a c t

This study empirically analyzes model accuracy, and applies grey forecasting to handle non-linear problems, insufficient data resources and forecasting involving small samples, and to construct the co-opetition diffusion model for the Lotka–Volterra (L.V.) system. Furthermore, this study examines historical data comprising revenue trends in the Taiwanese IC assembly industry during the past ten years and selects from a range of forecasting models.

Empirical study uses MAPE to precisely analyze revenue trends in the L.V. dynamic co-opetition diffusion model relation to the IC assembly industry. The nine companies will be selected from 4 to 11 of the modeling, the results of the LV model 64 accuracy test, its accuracy is higher than 95% accounted for 59 times, five times better than the grey prediction, showing LV competing diffusion model not only with grey prediction, and better than the traditional grey forecasting model to make a higher accuracy of the predicted value. Like grey forecasting, MAPE can promptly respond even given insufficient data. Additionally, MAPE is able to provide more accurate forecasting values than the traditional Grey forecasting model. This study demonstrates the applicability of the dynamic co-opetition theory forecasting model to the Taiwanese IC assembly industry and provides management with a reference for use in decisions aimed to increase managerial competitiveness.

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1. Introduction

Under different research backgrounds, this study uses different forecasting tools that produce forecasts of different effectiveness. This study uses Grey theory statistics to achieve considerable accuracy and easy computation, and to test forecasting advantages and disadvantages of Lotka–Voltera (herein after referred as L.V.) in the short term. After collecting the relevant literature, this study uses the background of the Taiwan IC assembly industry as a prerequisite and divides the collected time series into two parts for evaluation. First, this study substitutes the revenue statistics of the main IC assembly makers in Taiwan into the L.V. system forecast-ing model, then adopts the traditional grey theory GM (1,1) shadow model, and finally evaluates the advantages and disadvan-tages of various models to select the best model obtained by model construction during different steps.

This study uses the ‘‘Semiconductor Yearbook’’ along with other references to obtain operational statistics for the main IC assembly makers in Taiwan from 2000 to 2010. Based on limited data, this research adopts the L.V. forecasting model to test its applicability, compares it with the Grey model, which is good for

short-term forecasting, and discusses the best forecasting method under different forecasting backgrounds.

2. Literature review 2.1. Lotka–Voltera

L.V. system is a diffusion model by using competition to influence both parties to increase their competitiveness made by Lotka (1925) and Volterra (1926). Generally diffusion models can be divided into Bass and L.V. models. These two types of models differ in that the Bass diffusion model does not consider limitations associated with manufacturer production ability. Additionally, the product diffusion process is mainly affected by market structure, manufacturer decision making and consumers. Since the proposal of the co-epetition theory, the L.V. model has been applied in situations involving market co-epetition. In the co-epetition type, the L.V. model can describe market change produced by decision making at every time point. Recently, the L.V. model has even been used in forecasting. The statistical method can be used to estimate the parameters of the L.V. model. These parameters are then used to estimate open and compatible product features in the market, and to help enterprises achieve sales.

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Int. J. Production Economics

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influenced by number of data model construction and achieves better accuracy than Grey forecasting.

5. Conclusions

This study concludes the following based on the model evaluation results:

SIMPLY The L.V. model is less limited:

This study finds that the newly developed L.V. forecasting model can be used in applications other than traditional forecasting. Data substitution does not necessarily consider data number and does not have to use too many historical data for estimate.

L.V. computation is simple and easy:

Compared with the traditional Grey model, the L.V. model does not involve complicated matrix computation and nor does it require software assistance to obtain results. L.V. computation is very convenient.

The L.V. model has excellent forecasting accuracy:

In Table 1, the L.V. model takes up 59 items in the 95% of accuracy, while the Grey model accounts for 5 items. However the accuracy of Grey model falls between 85% and 95%. Though the Grey model has a certain forecasting level, it remains inferior to the L.V. model.

The L.V. model can be used to forecast trends in the semi-conductor industry:

Empirical results from applying the L.V. model to the semi-conductor industry demonstrate that both the L.V. and Grey models can make forecasts under conditions of limited or

incomplete data. However the L.V. model is more accurate than the Grey model, and is also better at short-term forecasting.

Appendix A (Table A1). References

Hung, K.C., 2010. Rey model with rolling mechanism for radio-wave path-loss forecasting in suburban environment. Journal of Grey System 13 (2). Ken, M.L., 2009. A study of applying grey predictive module into sales revenue

prediction of convenience chain stores an investigation of 7-ELEVEN and Family-Mart. Journal of Grey System 12 (3).

Wen, K.L., 2004. Grey System: Modeling and Prediction. Yang’s Scientific Research Institute, USA.

Further reading

Burghes, D.N., Wood, A.D., 1981. Mathematical Models in the Social Management and Life Sciences. Halsted Press, New York.

Chow, Hwee Kwan, Choy, Keen Meng, 2009. Population Dynamical Behavior of Lotka Volterra System Under Regime Switching.

Deng, J.L., 1989. Introduction of grey system theory. Journal of Grey System Theory 1 (1).

Romer, David, 2000. Advanced Macroeconomics. McGraw-Hill, Boston. Table A1

Full name and abbreviation of Taiwan main IC assembly companies.

Abbreviation Full name Stock code

ASE Advanced Semiconductor Engineering Inc. 2311 SPIL Siliconware Precision Industries Co. 2325

GEI Greatek Electronics Inc. 2441

PTI Powertech Technology Inc. 6239

WAE Walton Advanced Engineering Inc. 8110

OSE Orient Semiconductor Electronic 2329

LPI Lingsen Precision Industries, LTD 2369 SMC Sigurd Microelectronics Corporation 6257 TICP Taiwan IC Packaging Corporation 3372 Table 1

Advantage and disadvantage comparison of model comparison of the model advantages and disadvantages (Unit item).

Model L.V. forecasting model Grey forecasting model

Accuracy 95% 59 5

90–95% 2 27

85–90% 1 24

80–85% 2 5

80% 0 3

H.-c. Teng, Y.-f. Huang / Int. J. Production Economics 141 (2013) 335–338 338