Automobile Industry Strategic Alliance Partner Selection: The Application of a Hybrid DEA and Grey Theory Model

Abstract

Abstract:Finding the right strategic alliance partner is a critical success factor for many enterprises. Therefore, the purpose of this study is to propose an effective approach based on grey theory and data envelopment analysis (DEA) for selecting better partners for alliance. This study used grey forecasting to predict future business performances and used DEA for the partner selection of alliances. This research was implemented with realistic public data in four consecutive financial years (2009)(2010)(2011)(2012) of the world's 20 biggest automobile enterprises. Nissan Motor Co., Ltd was set to be the target decision making unit (DMU). The empirical results showed that, among 19 candidate DMUs, Renault (DMU10) and Daimler (DMU11) were the two feasible beneficial alliance partners for Nissan. Although this research is specifically applied to the automobile industry, the proposed method could also be applied to other manufacturing industries.

Applying Data Envelopment Analysis and Grey Model for the Productivity Evaluation of Vietnamese Agroforestry Industry

“…The MAPE values obtained are shown in Table 6. 2 5.663% DMU 7 2.069% DMU 3 2.256% DMU 8 1.124% DMU 4 4.001% DMU 9 14.513% DMU 5 5.021% DMU 10 7.046%…”

“…Except for DMU 2 and DMU 6 , which had experienced slight efficiency changes, the other DMUs all had experienced some degree of efficiency fluctuations. In terms of "efficiency change", DMU 9 , DMU 4 , and DMU 6 are the top three best companies, while the top three worst companies are DMU 3 , DMU 7 , and DMU 1 . DMU 2 is found with a stable efficiency, but it also had not improved its efficiency in the current and near future.…”

A novel holistic approach for solving the multi-criteria transshipment problem for infectious waste management

“…It has been recognized that the MADM and DEA techniques coincide if input and output variables can be viewed as decision criteria, and DMUs can be viewed as alternatives (Hu et al, 2017;Stewart, 1996). DEA technique is becoming a popular technique since it has the following practical advantages (Fan et al, 2017;Hosseinzadeh Lotfi et al, 2010;Wang et al, 2016): (1) DEA technique is appropriate for evaluating the effectiveness of multiple criteria (multiple inputs and multiple outputs); (2) DEA technique does not require to carry out non-dimensional treatment on the parameters; (3) DEA technique does not need experts to provide weight-related information, because the weights for each variable (Both inputs and outputs) can be gained through mathematical mode; (4) The relationship between input variables and output variables does not need to be considered in the DEA technique and (5) DMUs can be production units, universities, schools, bank branches, hospitals, power plants, etc. However, some of the disadvantages of DEAs are as follows (Berg, 2010): (1) Results of DEA technique are sensitive to the selection of both inputs and outputs; (2) The best specification cannot be tested and (3) If the number of variables are increased, the number of efficient DMUs tends to increase.…”

Partner Selection in Supply Chain of Vietnam’s Textile and Apparel Industry: The Application of a Hybrid DEA and GM (1,1) Approach

“…Because this method can estimate the behavior of data in the future based on a limited amount of available data, this complementary concept appeared in the hybrid DEA and GM (1,1) to study strategic alliance for the electronic manufacturing service industry [11]. In 2016, Wang et al used DEA and a grey theory model to analyze the selection strategic alliance partner in the automobile industry [12]. In 2017, Wang and Nguyen used the grey and DEA model to study enhancing urban development quality based on the results of appraising efficient performance of investors in Vietnam [13].…”