|
Activity Number:
|
481
- Modeling, Analysis, and Assessment
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Quality and Productivity Section
|
|
Abstract #328866
|
Presentation
|
|
Title:
|
Prediction of Warranty Returns Based on Modeling Seasonal Recurrent Event Data
|
|
Author(s):
|
Qianqian Shan* and William Meeker
|
|
Companies:
|
and Iowa State University
|
|
Keywords:
|
repairable systems;
NHPP;
clustering;
seasonality
|
|
Abstract:
|
Warranty return data from repairable systems, such as vehicles, result in recurrent event data. The non-homogeneous Poisson process (NHPP) model is used widely to describe such data. Seasonality, however, complicates the modeling of recurrent-event data. This paper provides a general approach for the application of NHPP models to predict warranty returns. A hierarchical clustering method is used to stratify the population into groups that are more homogeneous than the overall population. The stratification facilitates modeling the recurrent-event data with both time-varying and time-constant covariates. We demonstrate and validate the models using vehicle warranty claim data. The results show that our approach provides significant improvements in predictive power.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2018 program
|
