Abstract
This study was motivated by challenges facing inventory managers when deciding the ordering policy for various items. It is difficult to find an appropriate ordering policy for many types of items. We propose a model that changes conventional multi-criteria ABC analysis so that it is suitable for use by inventory managers. We indicate that categorizing items based on their statistical characteristics leads to an ordering policy suitable for each item. We propose a method for deciding the ordering policy based on important shipping statistics and a classification technique. For this method, we analyze the relation between shipping statistics and the ordering policy for searching important shipping statistics. We classify items by shipping statistics and then decide the ordering policy for each item. In the numerical experiment, we used actual shipment data to calculate many shipping statistics that represent the characteristics of each item. Next, we found the important shipping statistics from Random Forests and applied them to decide the ordering policy. Finally, for confirming the importance of important shipping statistics, we tested the performance of Random Forests and other classifying methods using the important shipping statistics. It was found that the performance of each classifying method was improved.
