Data‐driven framework for warranty claims forecasting with an application for automotive components

Mohammad Babakmehr; Sascha Baumanns; Abdallah Chehade; Thomas Hochkirchen; Mahdokht Kalantari; Vasiliy Krivtsov; David Schindler

doi:10.1002/eng2.12764

journal article Open Access Sep 25, 2023

Data‐driven framework for warranty claims forecasting with an application for automotive components

Mohammad Babakmehr Sascha Baumanns Abdallah Chehade

Thomas Hochkirchen Mahdokht Kalantari Vasiliy Krivtsov

David Schindler

Engineering Reports Vol. 6 No. 5 · Wiley

View at Publisher Save 10.1002/eng2.12764

Abstract

AbstractAutomakers spend billions of dollars annually towards warranty costs, and warranty reduction is typically high on their priorities. An accurate understanding of warranty performance plays a critical role in controlling and steering the business, and it is of crucial importance to fully understand the actual situation as well as be able to predict future performance, for example, to set up adequate financial reserves or to prioritize improvement actions based on expected forthcoming claims. Data maturation, a major nuisance causing changes in performance metrics with observation time, is one of the factors complicating warranty data analysis and typically leads to over‐optimistic conclusions. In this paper, we propose a sequence of steps, decomposing and addressing the main reasons causing data maturation. We first compensate for reporting delay effects using a Cox regression model. For the compensation of heterogeneous build quality, sales delay, and warranty expiration rushes, a constrained quadratic optimization approach is presented, and finally, a sales pattern forecast is provided to properly weigh adjusted individual warranty key performance indicators. The results are shown to dramatically improve prior modeling approaches.

Topics

No keywords indexed for this article. Browse by subject →

References

48

[1]

10.1016/j.ress.2018.05.002

[2]

A performance-based warranty for products subject to competing hard and soft failures

Xiaolin Wang, Bin Liu, Xiujie Zhao

International Journal of Production Economics 10.1016/j.ijpe.2020.107974

[3]

10.1109/tr.2009.2019673

[4]

10.1016/j.ress.2019.05.006

[5]

10.1016/0360‐8352(92)90031‐e

[6]

10.1002/qre.1282

[7]

10.1016/j.ress.2020.107097

[8]

10.1016/j.ress.2004.05.003

[9]

Kaminskiy M (1997)

[10]

Krivtsov V "A Bayesian estimation procedure of reliability function for lifetime distributions" Int J Performability Eng (2017)

[11]

10.1002/qre.1399

[12]

10.1080/15458830.1996.11770751

[13]

10.1016/j.ress.2005.12.004

[14]

10.1016/j.ress.2012.06.008

[15]

10.1016/j.ejor.2011.03.009

[16]

10.1007/978‐3‐030‐30241‐2_55

[17]

10.1016/j.ress.2021.108010

[18]

10.1016/j.ress.2021.108180

[19]

10.1080/00401706.1991.10484834

[20]

KleynerDEA.Warranty Data Maturity Patterns: Knowing when your Data Is Ready. 2019.

[21]

10.1016/j.engfailanal.2021.105657

[22]

10.1016/j.ress.2021.107530

[23]

KrivtsovV.Field Data Analysis & Statistical Warranty Forecasting. IEEE Cat No CFP11RAM‐CDR 2011.

[24]

10.1201/9781315382425

[25]

Krivtsov V (2016)

[26]

10.2307/3315826.n1

[27]

SchaferH SantanaE HadenA BiasiniR.A Commute in Data: the comma2k19 Dataset. 2018.

[28]

Kalbfleisch JD "Regression models for right truncated data with applications to AIDS incubation times and reporting lags" Stat Sin (1991)

[29]

Kalbfleisch JD "Statistical analysis of warranty claims data" Prod Warranty Handb (1996)

[30]

10.1111/j.1751-5823.1998.tb00405.x

[31]

10.1109/tbme.2019.2909027

[32]

TeoHG.Data Mining in Automotive Warranty Analysis. 2010.

[33]

10.1016/s0925-5273(97)00034-0

[34]

10.1002/(sici)1099-1638(199803/04)14:2<103::aid-qre147>3.0.co;2-5

[35]

10.1016/s0169-7161(01)20023-6

[36]

10.1002/asmb.469

[37]

Lim TJ "Nonparametric estimation of the product reliability from grouped warranty data with unknown start‐up time" Int J Ind Eng Appl Pract (2003)

[38]

10.1108/02656710510610820

[39]

10.1504/ijrs.2008.021067

[40]

10.1002/qre.1302

[41]

10.1108/jqme-03-2017-0023

[42]

10.1002/asmb.643

[43]

10.1016/j.ress.2004.01.001

[44]

10.1109/tr.2006.879648

[45]

Mittman E "A hierarchical model for heterogenous reliability field data" Dent Tech (2019)

[46]

Lewis‐Beck C "Prediction of future failures for heterogeneous reliability field data" Dent Tech (2022)

[47]

Regression Models and Life-Tables

D. R. Cox

Journal of the Royal Statistical Society Series B:... 1975 10.1111/j.2517-6161.1972.tb00899.x

[48]

Extremely randomized trees

Pierre Geurts, Damien Ernst, Louis Wehenkel

Machine Learning 10.1007/s10994-006-6226-1

Metrics

6

Citations

48

References

Details

Published: Sep 25, 2023
Vol/Issue: 6(5)
License: View

Authors

M

Mohammad Babakmehr

Ford Motor Company Dearborn Michigan USA

S

Sascha Baumanns

Ford Research and Innovation Center Aachen Aachen Germany

A

Abdallah Chehade

Department of Industrial and Manufacturing Systems Engineering University of Michigan‐Dearborn Dearborn Michigan USA

T

Thomas Hochkirchen

Ford Research and Innovation Center Aachen Aachen Germany

M

Mahdokht Kalantari

Ford Motor Company Dearborn Michigan USA

V

Vasiliy Krivtsov

Ford Motor Company Dearborn Michigan USA; University of Maryland College Park Maryland USA

D

David Schindler

Ford Research and Innovation Center Aachen Aachen Germany

Cite This Article

Mohammad Babakmehr, Sascha Baumanns, Abdallah Chehade, et al. (2023). Data‐driven framework for warranty claims forecasting with an application for automotive components. Engineering Reports, 6(5). https://doi.org/10.1002/eng2.12764

Data‐driven framework for warranty claims forecasting with an application for automotive components

You May Also Like