The purpose of this paper is to develop a benchmark of performance standards for Chinese third‐party logistics providers (3PLs) in the emerging market. It also intends to identify various factors that significantly affect the operational efficiency of the Chinese 3PLs and propose ways to improve the competitiveness of 3PLs.
This paper proposes data envelopment analysis (DEA) to measure the operational efficiency of ten leading 3PLs in China, relative to prior periods and their key competitors. In particular, this paper develops both the Charnes, Cooper and Rhodes model and the Banker, Charnes and Cooper model that are designed to derive weights without being fixed in advance. It also uses step‐wise regression analysis to identify factors influencing the performances of Chinese 3PLs.
First, the declining efficiency within some Chinese 3PLs coincides with a steep decline in domestic transportation activities due to the SARS outbreak and the slow adaptation of state‐owned enterprise into a more market‐based economy. Second, the sales opportunity and the level of technical expertise are directly correlated with the operational efficiency of 3PLs, whereas the size of 3PLs has no direct bearing on the 3PL's performance. Third, in contrast with the 3PL industry in the USA, the Chinese 3PLs tend to focus on traditional service offerings such as port management, transportation, and warehousing rather than playing the role as the integrator or the lead service provider.
This paper is the first attempt to utilize DEA to develop performance benchmarks for 3PLs in the emerging foreign market. The proposed DEA can be easily modified or extended to similar settings in other Asian countries such as India and East European countries.
Zhou, G., Min, H., Xu, C. and Cao, Z. (2008), "Evaluating the comparative efficiency of Chinese third‐party logistics providers using data envelopment analysis", International Journal of Physical Distribution & Logistics Management, Vol. 38 No. 4, pp. 262-279. https://doi.org/10.1108/09600030810875373
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