Statistically monitoring inventory accuracy in large warehouse and retail environments

dc.contributor.authorHuschka, Andrew
dc.date.accessioned2011-11-29T20:16:09Z
dc.date.available2011-11-29T20:16:09Z
dc.date.graduationmonthDecemberen_US
dc.date.issued2011-11-29
dc.date.published2011en_US
dc.description.abstractThis research builds upon previous efforts to explore the use of Statistical Process Control (SPC) in lieu of cycle counting. Specifically a three pronged effort is developed. First, in the work of Huschka (2009) and Miller (2008), a mixture distribution is proposed to model the complexities of multiple Stock Keeping Units (SKU) within an operating department. We have gained access to data set from a large retailer and have analyzed the data in an effort to validate the core models. Secondly, we develop a recursive relationship that enables large samples of SKUs to be evaluated with appropriately with the SPC approach. Finally, we present a comprehensive set of type I and type II error rates for the SPC approach to inventory accuracy monitoring.en_US
dc.description.advisorJohn R. Englishen_US
dc.description.degreeMaster of Scienceen_US
dc.description.departmentDepartment of Industrial & Manufacturing Systems Engineeringen_US
dc.description.levelMastersen_US
dc.identifier.urihttp://hdl.handle.net/2097/13157
dc.language.isoen_USen_US
dc.publisherKansas State Universityen
dc.subjectInventory accuracyen_US
dc.subjectSPCen_US
dc.subjectControl charten_US
dc.subjectCycle countingen_US
dc.subject.umiIndustrial Engineering (0546)en_US
dc.titleStatistically monitoring inventory accuracy in large warehouse and retail environmentsen_US
dc.typeThesisen_US

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