Utilizing DEA in Scarcity Data for Measuring Financial Efficiency of International Transportation Companies

Financial efficiency is important for international transportation companies, since the operation involves a large amount of capital. However, financial efficiency evaluation can be tricky, when it’s difficult to judge input and output weights objectively. Practically speaking, there’s no need to assess wrights when using data envelopment analysis (DEA). In Taiwan, only a few international transportation companies are listed, especially for those are deemed peer decision-making units (DMUs). When applying DEA, there is a critical restriction, which is the DMUs number should be no less than the number summation of inputs and outputs. Otherwise, the restriction problem would make the relative efficiency of DMUs difficult to discriminate. To resolve the scarcity data tie, five period’s financial data of two listed international transportation companies are adopted as ten DMUs. Through varied DEA models, relative inefficient and efficient DMUs will be found. The above situations of DMUs will provide references of the companies to improve their financial efficiency. Introduction Generally speaking, the financial efficiency influences a company directly, especially for those who need a great amount of capital to operate, such as international transportation companies. An international transportation company transports passengers and cargos all over the world, there’s no doubt that financial efficiency is very critical to the company. Moreover, a company should be aware of the finance situation of their competitors, know its relative financial location and drawbacks, which could led to better efficiency. Some researchers applied multi-criteria decision-making (MCDM) methods [5,11,16,19,20,22] to evaluate international transportation companies’ performances, including finance in the past. However, the MCDM methods of evaluating performance assesses criteria weights subjectively by decision-makers. The subjective assessment of criteria weights might make varied decision-makers distinct and alternatives priorities in decision-makers are not consistent, since a criterion has several different weights. Therefore, it is necessary that alternatives are evaluated objectively and alternative sorts are consistent no matter the criteria weights. To achieve the aim, data envelopment analysis (DEA) [6] ,which measures relative efficiency of peer decision-making units(DMUs) with several inputs and outputs is a useful method. In DEA models [1-4,6-10,12-15,20,21], CCR model proposed by Charnes, Cooper and Rhodes [6] in 1978 was the original of DEA models. Then, Banker, Charnes and Cooper modified CCR model into BCC model [3] in 1984. CCR model evaluated DMUs under constant returns to scale (CRS), whereas BBC model evaluated DMUs under variable returns to scale (VRS). Seiford [17] supposed that CCR and BCC models were the main methods in DEA. To enhance discrimination power of DEA, Andersen and Petersen [2] modified CCR model to be super-efficiency model where a DMU under evaluation was excluded from the reference set. The above model was super-efficiency DEA model in CRS (i.e. CRS super-efficiency DEA model). Likewise, BCC model was improved to be super-efficiency DEA model in VRS [18] (i.e. VRS super-efficiency DEA model). Usually, the VRS super-efficiency model is useful to enhance discrimination of DEA. However, Seiford and Zhu [18] pointed out that infeasibility occurred in the case of VRS super-efficiency model. Thus, Lee, Chu and Zhu [13] proposed an improving VRS super-efficiency model which was always feasible. Based on