Design freeze sequencing using Bayesian network framework

Change propagation is the major source of schedule delays and cost overruns in design projects. One way to mitigate the risk of change propagation is to impose a design freeze on components at some point prior to completion of the process. The purpose of this paper is to propose a model-driven approach to optimal freeze sequence identification based on change propagation risk.

A dynamic Bayesian network was used to represent the change propagation process within a system. According to the model, when a freeze decision is made with respect to a component, a probabilistic inference algorithm within the Bayesian network updates the uncertain state of each component. Based on this mechanism, a set of algorithm was developed to derive optimal freeze sequence.

The authors derived the optimal freeze sequence of a helicopter design project from real product development process. The experimental result showed that our proposed method can significantly improve the effectiveness of freeze sequencing compared with arbitrary freeze sequencing.

The methodology identifies the optimal sequence for resolution of entire-system uncertainty in the most effective manner. This mechanism, in progressively updating the state of each component, enables an analyzer to continuously evaluate the effectiveness of the freeze sequence.