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.graduationmonthDecember
dc.date.issued2011-11-29
dc.date.published2011
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.
dc.description.advisorJohn R. English
dc.description.degreeMaster of Science
dc.description.departmentDepartment of Industrial & Manufacturing Systems Engineering
dc.description.levelMasters
dc.identifier.urihttp://hdl.handle.net/2097/13157
dc.language.isoen_US
dc.publisherKansas State University
dc.rights© the author. This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectInventory accuracy
dc.subjectSPC
dc.subjectControl chart
dc.subjectCycle counting
dc.subject.umiIndustrial Engineering (0546)
dc.titleStatistically monitoring inventory accuracy in large warehouse and retail environments
dc.typeThesis

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