Statistically monitoring inventory accuracy in large warehouse and retail environments

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dc.contributor.author Huschka, Andrew
dc.date.accessioned 2011-11-29T20:16:09Z
dc.date.available 2011-11-29T20:16:09Z
dc.date.issued 2011-11-29
dc.identifier.uri http://hdl.handle.net/2097/13157
dc.description.abstract This 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.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Inventory accuracy en_US
dc.subject SPC en_US
dc.subject Control chart en_US
dc.subject Cycle counting en_US
dc.title Statistically monitoring inventory accuracy in large warehouse and retail environments en_US
dc.type Thesis en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Industrial & Manufacturing Systems Engineering en_US
dc.description.advisor John English en_US
dc.subject.umi Industrial Engineering (0546) en_US
dc.date.published 2011 en_US
dc.date.graduationmonth December en_US


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