Using the principal component analysis method as a tool in contractor pre-qualification

Contractor pre-qualification can be regarded as a complicated, two-group, non-linear classification problem. It involves a variety of subjective and uncertain information extracted from various parties such as contractors, pre-qualifiers and project teams. Non-linearity, uncertainty and subjectivity are the three predominant characteristics of the contractor pre-qualification process. This makes the process more of an art than a scientific evaluation. In addition to non-linearity, uncertainty and subjectivity, contractor pre-qualification is further complicated by the large number of contractor pre-qualification criteria (CPC) used in current practice and the multicollinearity existing between contractor attributes. An alternative empirical method using principal component analysis (PCA) is proposed for contractor pre-qualification in this study. The proposed method may alleviate the existing amount of multicollinearity and largely reduce the dimensionality of the pre-qualification data set. The applicability and potential of PCA for contractor pre-qualification has been examined by way of two data sets: (1) 73 pre-qualification cases (37 qualified and 36 disqualified) collected in England and (2) 85 (45 qualified and 40 disqualified) pre-qualification cases relating to 10 public sector projects in Hong Kong. The PCA-based results demonstrated that strong and positive inter-correlations existed between most of the qualifying variables, with the minimum correlation coefficient being 0.121 and the maximum being 0.899, and that qualified and disqualified contractors could be satisfactorily separated.