Product complexity has been studied as an important factor to decrease the cost and time of the development process. With this purpose, prior research has included the development of design complexity metrics as a method to assess and decrease complexity. Recent studies have also focused on the comparison of complexity metrics for the particular case of medical devices development (MDD). However, the major issue relevant to MDD has not been addressed; the relationship between FDA regulations and the device complexity is not clarified. Therefore, to increase MDD safety and decrease the time to market, we must understand the regulatory decision process and rules. In this paper, we investigate the relation between different complexity metrics and FDA’s decision time using a sample of 100 hip replacement devices. Bayesian network learning is used to explore in detail local relationships between different variables, both complexity measures and product variables. This relationship was found significant for the first two clusters of the analysis. However, for a third cluster it is speculated that FDA decision time does not depend solely upon the degree of medical device complexity. Company or organization relevant variables could be playing a greater role than just complexity. Additional questions are drawn based on the results that must be investigated.
