| his research addresses several quality control problems which arise in a variety of manufacturing, healthcare, service, finance, and other industries given the existence of human and automated attribute detection error. Several mathematical and economic models are developed for various types of single and multiple inspection screening policies in order first to examine inherent tradeoffs between type I errors, type II errors, and all associated inspection, false-rejection, and false-acceptance costs and then, ultimately, to help identify the minimum expected total cost policy and the optimal amount of inspection for any particular scenario. While originally motivated by industrial problems, these models also have been adapted to various non-manufacturing concerns, including service processes and laboratory cancer screening policies. In particular, similar methods are developed and used to analyze the policy for screening Pap smears for early indications of cervical cancer currently required by the Congressional Laboratory Improvements Amendments Act of 1988 (CLIA'88), to compare this policy with possible alternatives, and to develop an algorithm that identifies the optimal policy in any given scenario.;Results show that the mandated CLIA policy never is optimal and always increases total costs, that overall sensitivity of CLIA never can be improved beyond a certain mathematical bound, that CLIA's 10% minimum requirement nor any other amount of partial resampling ever is optimal, that multiple readings in some realistic cases can result in very significant benefits, and that the proposed use of automated rescreening technology recently approved by the FDA may not result in improvements over CLIA nor the optimal policy derived here. Sensitivity analyses indicate that the improvement possible by switching to this optimal policy ranges from 90,000 to 165,000 fewer false-negatives and... |
