Publication Date


Series Number

Preprint #2014-05


Many industrial products consist of multiple components that are necessary for system operation. There is an abundance of literature on modeling the lifetime of such components through competing risks models. During the life-cycle of a product, it is common for there to be incremental design changes to improve reliability, to reduce costs, or due to changes in availability of certain part numbers. These changes can affect product reliability, but are often ignored in system lifetime modeling. By incorporating this information about changes in part numbers over time (information that is readily available in most product production databases), better accuracy can be achieved in predicting time to failure, thus yielding more accurate fieldfailure predictions. This paper presents methods for estimating parameters and predictions for this generational model and a comparison with existing methods through the use of simulation. Our results indicate that the generational model has important practical advantages and outperforms the existing methods in predicting field failures.


This preprint was published as Caleb King, Yili Hong, and William Q. Meeker, "Product Component Genealogy Modeling and Field-Failure Prediction".