|dc.contributor.advisor||Malzahn, Don E.||
|dc.description||Thesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Industrial, Systems, and Manufacturing Engineering||
|dc.description.abstract||This research deals with the inventory classification via multi-criteria optimization
models. The idea behind this study is to educate readers about what factors must be
considered when selecting inventory classification models. The factors that are more or less
important depending on the datasets and variability in the input variables have been explored
in this study.
It has been learned that demand increase can make an initial inventory classification
invalid, which is a critical factor considered in this study. This research shows how to first
determine demand increase levels specific to an inventory item, which can change its
classification. The next step is to test the feasibility of the model in classifying inventory items.
Here, a discriminating power test has been introduced. Then, methods have been proposed to
compare the performance of different multi-criteria models. Model selection criteria specific to
datasets have been introduced and are intended to enhance customer service levels (CSLs) as
well as reduce safety stock costs at the same time. Sensitivity analysis is used to show readers
that varying the levels of input variables affects the performance of the selected model.
Regression analysis is used to determine those levels that show when an originally selected
model no longer gives the highest customer service.
It can be concluded that although there are many classification models available in the
literature, it is not possible to recommend any one model for obtaining the best performance in
all cases. The procedure explained in this study should be followed in order to select the best
model for a given dataset, thus improving service levels and reducing inventory cost.||
|dc.format.extent||xvi, 148 pages||
|dc.publisher||Wichita State University||
|dc.rights||Copyright 2017 by Qamar Iqbal
All Rights Reserved||
|dc.title||Multi-criteria inventory classification using weighted linear optimization||