Product Inventory Predictions at Small Medium Enterprise Using Market Basket Analysis Approach-Neural Networks

Abstract

One of the key problems in every company, including small and medium enterprises, is how to determine the inventory level for each product that will be sold to their customers appropriately as it can suppress the build up of inventory as well as avoid the stock out. This study is aimed to understand the behavior of consumers in purchasing the products so it can be used to predict the purchasing for the next period. Later, the prediction is used as a decision support in determining the appropriate amount of inventory for each product. The study was conducted at Karomah Brass, a small and medium enterprise engaged in the sale of antique furniture accessories in which the company doesn’t produce its own products but buys from the supplier. The methods that used in this study are the Market Basket Analysis (MBA) and Artificial Neural Network (ANN) Back propagation. MBA is used to examine the buying behavior of customer while ANN Back propagation is used to predict product inventory's requirements/needs for each product. The results discover that the customers frequently purchase products that serve as a kind of antique closet accessories and if customer bought that certain product, then they will also buy similar products in accordance with 21 rules that have been obtained from the mining of transaction data. Whereas, other result shows the prediction of the amount product inventory requirements/needs for one year to the next.