An empirical study on the competitiveness of Small and Medium-sized Manufacturing Enterprises in China

This paper proposes an index system of comprehensive competitiveness assessment for the Small and Medium-sized Manufacturing Enterprises (SMMEs) in order to assess their competitiveness rationally and make the competitive strategy accordingly. An assessment model of SMMEs competitiveness based on BP Neural Network is built on the basis of weighting indices by Analytic Hierarchy Process (AHP) method and calculating SMMEs competitiveness comprehensive index which breaks through the limitation of conventional assessment methods, and also inducts the sensibility analysis to discriminate the importance of each index in the assessment index system. The effectiveness of our methodology was verified with an empirical study by using the data of 135 listed SMMEs which are from annual report in financial database of cninfo consultation. The results indicates that the Artificial Neural Network model can accurately realize the non-linear reflection from input index to output index, and it can avoid the random and subjectivity when determining the indices weight. Consequently, the assessment model of SMMEs competitiveness based on the Artificial Neural Network plays an important role in forecasting the competitiveness comprehensive index, discriminating the importance of each assessment index and discovering the competitive advantages and disadvantages, which is useful to provide a scientific basis for drafting the relevant competitive strategy and improving the competitiveness for the SMMEs.