A Bayesian Networks Approach to Estimate Engineering Change Propagation Risk and Duration.

An engineering change (EC) is an alteration made to a system that has been released following a system design process. EC propagation is a series of ECs occurring due to dependencies among components of a product. ECs can consume up to 50% of the overall engineering efforts during the development of a complex system. Therefore, EC propagation prediction received considerable attention in past decades as the product development industries started to suffer from the negative impacts of change propagation. This paper evaluates the current approaches to EC propagation prediction and presents a dynamic Bayesian networks (DBNs) approach to estimate change propagation risk (CPR) as well as a novel approach to estimate EC durations. Literature research shows that although some studies have used design structure matrices to estimate CPR and the total redesign duration (TRD) due to change propagation, an approach that allows iteration while accounting for the conjunction of all impacts has not been explored. This paper aims to fill the gaps for calculating CPR using DBN and evaluating change propagation paths from a Split-and task outcome logic, which accounts for the conjunction of all component relationships. This paper compares the proposed method results with the existing CPR and engineering change duration estimation methods using a real-world dataset from a U.S. Navy shipbuilding program. The results indicate that the CPR can be calculated using the proposed method without the shortcomings of the existing method and the accuracy for estimating engineering change durations is increased. [ABSTRACT FROM AUTHOR]