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