A Hybrid Data Mining Approach for Product Complexity Analysis

This paper proposes a hybrid data mining approach to quantitatively analyze product complexity of prefabricated construction components from product nonconforming quality performance data. The proposed model is constructed in three steps, which (1) measure product complexity by introducing a Bayesian-based nonconforming quality performance indicator; (2) score each type of product complexity by developing a Hellinger distance-based distribution similarity measurement; and (3) cluster products into homogeneous complexity groups by using the agglomerative hierarchical clustering technique. An illustrative example is provided to demonstrate the proposed approach, and a case study of an industrial company in Edmonton, Canada, is conducted to validate the feasibility and applicability of the proposed model. This research inventively defines and investigates product complexity from the perspective of product quality performance. The research outcomes provide valuable insights for practitioners to better analyze and manage product complexity. In addition to this practical contribution, a novel hierarchical clustering technique is devised. This technique is capable of clustering uncertain data (i.e., probability distributions) and has the potential to be generalized to cluster all types of uncertain data.