Cisco was interested in how field failure categories relate to manufacturing test failures to better predict the tests that should be performed on a product after a return by a customer and what tests will be failed by a product before and after a return by a customer based on what type of failure occurred in the field. A study was conducted on one product type and 5 years of field data and the associated test data were captured. For each combination of field failure, shipping status, test area result, and test area a statistical model of the population proportion was created. The 95% and 99% confidence intervals were found. The critical test areas and a ranking of the criticality of the test areas were arranged from the confidence intervals. These findings will reduce the time spent on unnecessary tests.
","abstract_html":"<p>Cisco was interested in how field failure categories relate to manufacturing test failures to better predict the tests that should be performed on a product after a return by a customer and what tests will be failed by a product before and after a return by a customer based on what type of failure occurred in the field. A study was conducted on one product type and 5 years of field data and the associated test data were captured. For each combination of field failure, shipping status, test area result, and test area a statistical model of the population proportion was created. The 95% and 99% confidence intervals were found. The critical test areas and a ranking of the criticality of the test areas were arranged from the confidence intervals. These findings will reduce the time spent on unnecessary tests.</p>","abstract_has_math":false,"creators":["Carter, Alyssa M"],"institution":null,"degree_name":"MS in Industrial Engineering","degree_level":null,"degree_discipline":"Industrial and Manufacturing Engineering","degree_department":null,"school":null,"contributors":["Reza Pouraghabagher"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-06-01T07:00:00Z","date_published":"2017-06-01T07:00:00Z","updated_at":"2026-07-24T01:31:49Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2017.58"],"render_values":[{"text":"10.15368/theses.2017.58","href":"https://doi.org/10.15368/theses.2017.58","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1749","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Reza Pouraghabagher"]},{"key":"dc:creator","label":"Author","values":["Carter, Alyssa M"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2020-06-14T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial and Manufacturing Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Industrial Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1749","10.15368/theses.2017.58"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Cisco was interested in how field failure categories relate to manufacturing test failures to better predict the tests that should be performed on a product after a return by a customer and what tests will be failed by a product before and after a return by a customer based on what type of failure occurred in the field. A study was conducted on one product type and 5 years of field data and the associated test data were captured. For each combination of field failure, shipping status, test area result, and test area a statistical model of the population proportion was created. The 95% and 99% confidence intervals were found. The critical test areas and a ranking of the criticality of the test areas were arranged from the confidence intervals. These findings will reduce the time spent on unnecessary tests.
"]},{"key":"dc:title","label":"Title","values":["Application of Statistical Predictive Models for Field Failure and Cisco Testing Data (Big Data Source: Cisco)"]}]}],"canonical_facts":{"dc:contributor":["Reza Pouraghabagher"],"dc:creator":["Carter, Alyssa M"],"dc:date.available":["2020-06-14T07:00:00Z"],"dc:description.abstract":["Cisco was interested in how field failure categories relate to manufacturing test failures to better predict the tests that should be performed on a product after a return by a customer and what tests will be failed by a product before and after a return by a customer based on what type of failure occurred in the field. A study was conducted on one product type and 5 years of field data and the associated test data were captured. For each combination of field failure, shipping status, test area result, and test area a statistical model of the population proportion was created. The 95% and 99% confidence intervals were found. The critical test areas and a ranking of the criticality of the test areas were arranged from the confidence intervals. These findings will reduce the time spent on unnecessary tests.
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