Design failures often incur substantial cost overruns in the shipbuilding industry. Precautions against possible design failures facilitate on-time delivery and improved productivity. However, few studies have investigated the use of accumulated knowledge to prevent ship design failure. In addition, existing associative classification (AC) methods pay little attention to the rule consolidation process whereby discriminative association rules can be aggregated. In this study, we propose a new AC method that considers both support and confidence, while the number of matching features is taken into account not only to identify specific rules that capture useful associations, but also to enhance predictive performance by effectively aggregating relevant rules. We present an empirical case for the Korean shipbuilding industry by applying the proposed method to help reduce design failures by providing a designer with the most relevant revision history for a given design task so that unnecessary rectification can be avoided. The comparative results showed that the proposed method returns the best prediction accuracy among competing AC processes.