Master of Science / Department of Industrial & Manufacturing Systems
Engineering / John R. English / Inventory accuracy is critical for almost all industrial environments such as distribution, warehousing, and retail. It is quite common for companies with exceptional inventory accuracy to use a technique called cycle counting. For many organizations, the time and resources to complete cycle counting are limited or not available. In this work, we promote statistical process control (SPC) to monitor inventory accuracy. Specifically, we model the complex underlying environments with mixture distributions to demonstrate sampling from a mixed but stationary process. For our particular application, we concern ourselves with data that result from inventory adjustments at the stock keeping unit (SKU) level when a given SKU is found to be inaccurate. We provide estimates of both the Type I and Type II errors when a classic c chart is used. In these estimations, we use both analytical as well as simulation results, and the findings demonstrate the environments that might be conducive for SPC approaches.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/1407 |
| Date | January 1900 |
| Creators | Huschka, Kyle |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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