Author(s)

E. A. Beardslee & T. B. Trafalis

Abstract

Over the past decade, several data mining techniques have come into use within a variety of data intensive fields. Mining transaction data to discover interesting patterns or help forecast future rare events based upon historical records is of key interest. Among the classic problems facing any supply chain is that of determining how much of a given item to keep on the shelf of a warehouse or a store, and at what level to initiate replenishment of this supply. This problem has been well examined and extensively researched. However, applying current data mining techniques to support the determination of effective inventory stock levels and economic re-order points has yet to be explored. In this paper, Naïve Bayes, Bayes Network, SVM, MLP, Logistic, and J48 decision tree data mining techniques are compared with respect to their effectiveness when tasked with predicting stock shortage conditions indicated by the reduced transaction history of a bench stock inventory. These comparisons have been made using training and test data sets drawn from a set of 1.8 million transactions. Each method is applied to the same set of transaction data that spans three years of bench stock inventory activity. This set of transactions contains orders, receipts, stock outages, and stock shortages but it does not include issue transactions from the inventory. Since the time of issue or removal of the item from the inventory is not known, determining the expected demand becomes very difficult. The effectiveness of the data mining predictive capability is measured with respect to the accuracy of the prediction and the lead-time provided to the inventory planner for the given prediction. The results show similar performance among most of the data mining algorithms using \“actual event” prediction evaluation criteria. Keywords: data mining, inventory, supply chain, Naïve Bayes, SVM, prediction, stock shortage, transaction, time-series, rare event.

Keywords

data mining, inventory, supply chain, Naïve Bayes, SVM, prediction, stock shortage, transaction, time-series, rare event.