Research Article Selecting a Multicriteria Inventory Classification Model to Improve Customer Order Fill Rate Qamar Iqbal, 1 Don Malzahn, 1 and Lawrence E. Whitman 2 1 Industrial, Systems and Manufacturing Engineering Department, Wichita State University, 1845 Fairmount St, Wichita, KS 67260, USA 2 College 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; qxiqbal@shockers.wichita.edu Received 13 January 2017; Accepted 5 March 2017; Published 5 April 2017 Academic Editor: Panos Pardalos Copyright © 2017 Qamar Iqbal et al. Tis 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. Multicriteria models have been proposed for inventory classifcation in previous studies. However, it is important to make a decision when a particular multicriteria inventory classifcation 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 fulfllment process. Tis paper shows how better inventory classifcation can improve customer order fll rate in variable settings. Te method to compare the inventory classifcation models with regard to improving customer order fll rate is proposed. Te cut-of point is calculated which indicates when a model currently in use should be dropped in favor of another model to increase revenue by flling more orders. Sensitivity analysis is also performed to determine how holding cost and demand uncertainty afect the performance metric. Finally, regression analysis and hypothesis testing inform the decision-maker of how a model’s performance difers from other models at various values of holding cost and standard deviation of demand. 1. Introduction In order to efciently manage inventory, companies use an ABC classifcation by assigning items to one of three classes so that specifc inventory control policies can be applied [1]. Class A is considered very important, class B is seen as moderately important, and class C is considered the least important [2]. Te traditional ABC classifcation is based on Pareto analysis, and the criterion typically used is annual dollar value usage [3]. Tis criterion is most frequently used in practice because managers pay more attention to the inventory that has a high dollar value [4]. Te traditional ABC method is easy to use because it only considers dollar usage. Te following studies have suggested that using other criteria in combination makes inventory classifcation more efective. Te criteria that are ofen employed in multicriteria methods include lead time, commonality, obsolescence, and criticality [2, 5–8]. Te ana- lytic hierarchy process (AHP), distance-based modeling, and neural network techniques have also been used in diferent studies [6, 9–11]. In later studies, optimization models are used to classify inventory. Tese are discussed in the literature review. Te study focuses on optimization models. Since the single-criteria method is easy to use, it is most widely employed in classifying inventory. Te multicriteria literature claims that more than one criterion should be used to enhance the inventory classifcation. Tis argument cannot be validated unless the results of the single-criteria and multicriteria models are compared quantitatively with a common metric. Inventory classifcation directly afects the ability of inventory to satisfy customer orders. Poor inventory clas- sifcation, on one hand, will result in inventory buildup of those items that are not needed to meet customer demand; on the other hand, this may cause inventory shortages of items when they are needed the most. Te metric that is usually used in the industry is customer order fll rate, which is defned as the probability of flling an entire customer order within a specifed period [12]. A comparison of order fll rates obtained by inventory classifcation from diferent Hindawi Advances in Decision Sciences Volume 2017, Article ID 5028919, 11 pages https://doi.org/10.1155/2017/5028919