Complexity can be thought of as the level of difficulty in solving mathematically presented problems. Six Sigma practitioners and operations research professionals are often asked to predict the complexity of a hardware or software product by predicting (in man-hours or full-time equivalents) the expected development time, the expected number of customer-facing defects, the expected number of production defects, or the expected level of effort for a new object.

One effective approach I employ to solve this problem involves combining two statistical techniques: cluster analysis and principal component analysis. Employing cluster analysis helps to identify objects that are similar. The advantage of cluster analysis over other statistical techniques such as discriminant analysis is that the groups are determined by the cluster analysis and aren’t predetermined before the analysis. After the groups are established, employing principal component analysis, a data reduction technique, enables the practitioner to map the attributes of an object into a cluster of similar objects.

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