A new algorithm of inventory classification based on the association rules

Abstract

Today, engineering science and information technologies have given us powerful computational tools to support production planning and inventory control. In history, the ABC classification is usually used for inventory items aggregation because the number of inventory items is so large that it is not computationally feasible to set stock and service control guidelines for each individual item. A fundamental principle in ABC classification is that, ranking all inventory items with respect to a notion of profit based on historical transactions. The difficulty is that the profit of one item not only comes from its own sales, but also from its influence on the sales of other items, that is, the 'cross-selling effect'. In this paper a new algorithm of inventory classification based on the association rules is presented. By using the Support-Confidence framework the consideration of cross-selling effect is introduced to generate a new criterion that is then used to rank inventory items. A numeral example is used to explain the new algorithm and empirical experiments are implemented to evaluate its effectiveness and utility.