Multiple Criteria Inventory Classification Under Fuzzy Environment

Introduction

Inventory has been looked at as a major cost and source of uncertainty due to the volatility within the commodity market and demand for the value-added product. Inventory is held by manufacturing companies for a number of reasons, such as to allow for flexible production schedules and to take advantage of economies of scale when ordering stock (Nahmias, 2004). The efficient management of inventory systems is therefore a crucial element in the operation of any production or manufacturing company (Chase et al., 2006). Classification of inventory is a crucial element in the operation of any production company. Because of the huge number of inventory items in many companies, great attention is directed to inventory classification into the different classes, which consequently require the application of different management tools and policies. ABC inventory management deals with classification of the items in an inventory in decreasing order of annual dollar volume. The ABC classification process is an analysis of a range of items, such as finished products or customers into three categories: A- outstandingly important; B- of average importance; C- relatively unimportant as a basis for a control scheme. Each category can and sometimes should be handled in a different way, with more attention being devoted to category A, less to B, and less to C (Muller, 2003).

Sometimes, only one criterion is not a very efficient measure for decision-making. Therefore, multiple criteria decision making methods are used (Flores & Whybark, 1986, 1987). Apart from other criteria like lead time of supply, part criticality, availability, stock out penalty costs, ordering cost, scarcity, durability, substitutability, reparability etc has been taken into consideration (Flores & Whybark, 1986, 1987; Zhou & Fan, 2007). More studies have been done on multi-criteria inventory classification in the past 20 years. So many different methods for classifying inventory and taking into consideration multiple criteria have been used and developed (Guvenir & Erel, 1998; Partovi & Anandarajan, 2002; Lei et al., 2005; Ramanathan, 2006; Zhou & Fan, 2007; Ng, 2007; Jamshidi & Jain, 2008; Chu et al., 2008; Hadi-Vencheh, 2010; Çebi et al., 2010; Yu, 2011).

Flores and Whybark (1986, 1987) proposed the bi-criteria matrix approach, wherein annual dollar usage by a joint-criteria matrix is combined with another criterion. Though this approach is interesting, it accompanies some limitations. Their approach becomes increasingly complicated for three or more criteria to classify inventory items and also weights of all criteria taken into account equal. Flores et al. (1992) have proposed the use of joint criteria matrix for two criteria. The resulting matrix requires the development of nine different policies and for more than two criteria it becomes impractical to use the procedure. Analytic Hierarchy Process (AHP) developed by Saaty (1980) has been successfully applied to Multicriteria inventory classification by Flores et al. (1992). The advantage of the AHP is that it can incorporate many criteria and ease of use on a massive accounting and measurement system, but its shortcoming is that a significant amount of subjectivity is involved in pairwise comparisons of criteria. They have used the AHP to reduce multiple criteria to a univariate and consistent measure. However, Flores et al. (1992) has taken average unit cost and annual dollar usage as two different criteria among others. The problem with this approach is that the annual dollar usage and the unit price of items are usually measured in different units. On the other hand, for the applicability of this approach, the unit of a criterion must not change from item to item.