The objective of this paper is to present a framework for assessing and improving safety object performance based on dependability. The main idea is to employ Principle Component Analysis (PCA) and Importance Analysis (IA) to provide insight on safety object performance. The validity of the model is verified and validated by Data Envelopment Analysis (DEA). Furthermore, a non-parametric correlation method, namely, Spearman correlation experiment shows a high level of correlation between the findings of PCA and DEA. At first PCA is used for assessing the performance of safety objects and ranking them. IA is then performed for the worst safety objectwhich could have most impact on the overall system effectiveness to classify their components based on the Component Criticality Measures (CCM). The analysis of the classified components can ferret out the leading causes and common-cause events to pave a way toward decreasing failure interdependency and magnitude of incidents which ultimately maximize overall operational effectiveness.