An Approach for Estimating the Opportunity Cost Using Temporal Association Rule Mining and Clustering

Abstract

Timely identification of newly emerging trends is needed in business process. Data mining techniques are best suited for the classification, useful patterns extraction and predications which are very important for business support and decision making. Some research studies have also extended the usage of this concept in inventory management to determine opportunity cost based on association rules. Yet, not many research studies have considered the application of the data mining approach on evaluating penalty cost which is also a significant factor to the manager for optimal inventory control. In this paper, two different cases for evaluating penalty cost based on cross-selling effect are presented. An example is illustrated to validate the results.