The Change Prediction Method is an approach that has been proposed in the literature as a way to assess the risk of change propagation. This approach requires experts to define the elements of the design structure matrix and provide both impact and likelihood values for each subsystem interaction. The combined risk values produced by the Change Prediction Method indicate where high probabilities of propagation may exist, but the results rely heavily on the supplied expert data. This study explores how potential variability in expert data impacts the rank order of returned risk values from the Change Prediction Method. Results are presented that indicate significant changes in rank order, highlighting both the importance of expert data accuracy and the insights that can be gained from the Change Prediction Method as a design tool.

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