| dc.creator |
Huschka, Andrew |
|
| dc.date |
2011-11-29T20:16:09Z |
|
| dc.date |
2011-11-29T20:16:09Z |
|
| dc.date |
2011-11-29 |
|
| dc.date |
2011 |
|
| dc.date |
December |
|
| dc.date.accessioned |
2023-04-10T10:08:03Z |
|
| dc.date.available |
2023-04-10T10:08:03Z |
|
| dc.identifier |
http://hdl.handle.net/2097/13157 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/285379 |
|
| dc.description |
Master of Science |
|
| dc.description |
Department of Industrial & Manufacturing Systems Engineering |
|
| dc.description |
John English |
|
| dc.description |
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. |
|
| dc.format |
application/pdf |
|
| dc.language |
en_US |
|
| dc.publisher |
Kansas State University |
|
| dc.subject |
Inventory accuracy |
|
| dc.subject |
SPC |
|
| dc.subject |
Control chart |
|
| dc.subject |
Cycle counting |
|
| dc.subject |
Industrial Engineering (0546) |
|
| dc.title |
Statistically monitoring inventory accuracy in large warehouse and retail environments |
|
| dc.type |
Thesis |
|