EOQ estimation for imperfect quality items using association rule mining with clustering

• Published in: Decision Science Letters Vol. 4; no. 4; pp. 497 - 508
• Main Authors: Mittal, Mandeep, Pareek, Sarla, Agarwal, Reshu
• Format: Journal Article
• Language: English
• Published: Growing Science 01.09.2015
• Subjects: Apriori algorithm; Clustering; Data mining; EOQ; Imperfect quality items
• ISSN: 1929-5804; 1929-5812
• DOI: 10.5267/j.dsl.2015.5.008

Timely identification of newly emerging trends is needed in business process. Data mining techniques like clustering, association rule mining, classification, etc. are very important for business support and decision making. This paper presents a method for redesigning the ordering policy by including cross-selling effect. Initially, association rules are mined on the transactional database and EOQ is estimated with revenue earned. Then, transactions are clustered to obtain homogeneous clusters and association rules are mined in each cluster to estimate EOQ with revenue earned for each cluster. Further, this paper compares ordering policy for imperfect quality items which is developed by applying rules derived from apriori algorithm viz. a) without clustering the transactions, and b) after clustering the transactions. A numerical example is illustrated to validate the results.