Decision-Making with Temporal Association Rule Mining and Clustering in Supply Chains

Abstract

Timely identification of recently rising patterns is required in business process. Data mining methods are most appropriate for the characterization, valuable examples extraction, and predications which are essential for business support and decision-making. Some research studies have also expanded the use of this idea in inventory management. However, not very many research analyzes have considered the utilization of the data mining approach for supply chain inventory management. In this chapter, two unique cases for supply chain inventory management dependent on cross-selling effect are presented. First, the cross-selling effect in different clusters is characterized as a basis for deciding the significance of items. Second, the cross-selling in different time periods is considered as a criterion for ranking inventory items. An example is devised to approve the outcomes. It is illustrated that by using this modified approach, the ranking of items may get affected resulting in higher profit.