Abstract
Optimal inspection schemes based on dispersed nonconformity count data are derived to properly discriminate between satisfactory and unsatisfactory batches. The Conway-Maxwell-Poisson distribution is adopted to describe the stochastic behavior of the number of nonconformities per sampled unit. A mixed integer nonlinear programming problem is stated in order to determine the lot sampling plan with a minimal sample size and limited producer and consumer risks. Explicit approximations of the smallest number of units to be tested per lot and the maximum tolerable nonconformity score are presented. A Monte Carlo simulation approach is then used to find the most powerful decision rule for lot disposition. In case of over-dispersion, the suggested perspective allows the practitioners to greatly reduce the required sample size for lot sentencing. The developed methodology is applied to the manufacturing of glass for illustrative and comparative purposes.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Journal of Advanced Manufacturing Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
