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Advances in Decision Sciences
Volume 2017 (2017), Article ID 5028919, 11 pages
https://doi.org/10.1155/2017/5028919
Research Article

Selecting a Multicriteria Inventory Classification Model to Improve Customer Order Fill Rate

1Industrial, Systems and Manufacturing Engineering Department, Wichita State University, 1845 Fairmount St, Wichita, KS 67260, USA
2College of Engineering and Information Technology, University of Arkansas at Little Rock, 2801 S University Ave, Little Rock, AR 72204, USA

Correspondence should be addressed to Qamar Iqbal; ude.atihciw.srekcohs@labqixq

Received 13 January 2017; Accepted 5 March 2017; Published 5 April 2017

Academic Editor: Panos Pardalos

Copyright © 2017 Qamar Iqbal et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Multicriteria models have been proposed for inventory classification in previous studies. However, it is important to make a decision when a particular multicriteria inventory classification model should be preferred over other models and also if the highest performing model remains the highest performing at all times. Companies always look for ways to improve customer order fulfillment process. This paper shows how better inventory classification can improve customer order fill rate in variable settings. The method to compare the inventory classification models with regard to improving customer order fill rate is proposed. The cut-off point is calculated which indicates when a model currently in use should be dropped in favor of another model to increase revenue by filling more orders. Sensitivity analysis is also performed to determine how holding cost and demand uncertainty affect the performance metric. Finally, regression analysis and hypothesis testing inform the decision-maker of how a model’s performance differs from other models at various values of holding cost and standard deviation of demand.