This research presents a statistical methodology to construct an early automotive warranty issue detection model based on upstream supply chain information. This is contrary to extant methods that are mostly reactive and only rely on data available from the OEMs (original equipment manufacturers). For any upstream supply chain information with direct history from warranty claims, the research proposes hazard rate models to link upstream supply chain information as explanatory covariates for early detection of warranty issues. For any upstream supply chain information without direct warranty claims history, we introduce Bayesian hazard rate models to account for uncertainties of the explanatory covariates. In doing so, it improves both the accuracy of warranty issue detection as well as the lead time for detection. The proposed methodology is illustrated and validated using real-world data from a leading global Tier-one automotive supplier.
","abstract_html":"<p>This research presents a statistical methodology to construct an early automotive warranty issue detection model based on upstream supply chain information. This is contrary to extant methods that are mostly reactive and only rely on data available from the OEMs (original equipment manufacturers). For any upstream supply chain information with direct history from warranty claims, the research proposes hazard rate models to link upstream supply chain information as explanatory covariates for early detection of warranty issues. For any upstream supply chain information without direct warranty claims history, we introduce Bayesian hazard rate models to account for uncertainties of the explanatory covariates. In doing so, it improves both the accuracy of warranty issue detection as well as the lead time for detection. The proposed methodology is illustrated and validated using real-world data from a leading global Tier-one automotive supplier.</p>","abstract_has_math":false,"creators":["Zhou, Chongwen"],"institution":null,"degree_name":"Ph.D.","degree_level":"Open Access Dissertation","degree_discipline":"Industrial and Manufacturing Engineering","degree_department":null,"school":null,"contributors":["Ratna B. Chinnam"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-01-01T08:00:00Z","date_published":"2011-01-01T08:00:00Z","updated_at":"2026-07-24T05:58:57Z","subjects":["Bayesian analysis, early detection, hazard rate, proportional hazard model, supply chain, warranty","Industrial Engineering"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.wayne.edu/oa_dissertations/404","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ratna B. Chinnam"]},{"key":"dc:creator","label":"Author","values":["Zhou, Chongwen"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2011-01-01T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial and Manufacturing Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Bayesian analysis, early detection, hazard rate, proportional hazard model, supply chain, warranty","Industrial Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.wayne.edu/oa_dissertations/404"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This research presents a statistical methodology to construct an early automotive warranty issue detection model based on upstream supply chain information. This is contrary to extant methods that are mostly reactive and only rely on data available from the OEMs (original equipment manufacturers). For any upstream supply chain information with direct history from warranty claims, the research proposes hazard rate models to link upstream supply chain information as explanatory covariates for early detection of warranty issues. For any upstream supply chain information without direct warranty claims history, we introduce Bayesian hazard rate models to account for uncertainties of the explanatory covariates. In doing so, it improves both the accuracy of warranty issue detection as well as the lead time for detection. The proposed methodology is illustrated and validated using real-world data from a leading global Tier-one automotive supplier.
"]},{"key":"dc:title","label":"Title","values":["Hazard rate models for early warranty issue detection using upstream supply chain information"]}]}],"canonical_facts":{"dc:contributor":["Ratna B. Chinnam"],"dc:creator":["Zhou, Chongwen"],"dc:date.available":["2011-01-01T08:00:00Z"],"dc:description.abstract":["This research presents a statistical methodology to construct an early automotive warranty issue detection model based on upstream supply chain information. This is contrary to extant methods that are mostly reactive and only rely on data available from the OEMs (original equipment manufacturers). For any upstream supply chain information with direct history from warranty claims, the research proposes hazard rate models to link upstream supply chain information as explanatory covariates for early detection of warranty issues. For any upstream supply chain information without direct warranty claims history, we introduce Bayesian hazard rate models to account for uncertainties of the explanatory covariates. In doing so, it improves both the accuracy of warranty issue detection as well as the lead time for detection. The proposed methodology is illustrated and validated using real-world data from a leading global Tier-one automotive supplier.
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