Modified Ordering Policy for Items of Imperfect Quality with Allowable Proportionate Discount using Cross Selling Effects and Datamining Techniques
Bhawani Sankar Panigrahi1, Sanjay Kumar2, Pabitra Kumar Tripathy3
1Bhawani Sankar Panigrahi, Research Scholar, Kalinga University, Naya Raipur (Chhattisgarh), India.
2Dr. Sanjay Kumar, Associate Professor, Department of CSE, Kalinga University, Raipur (Chhattisgarh), India.
3Dr. Pabitra Kumar Tripathy, Associate Professor, Department of Computer Science and Engineering, Kalam Institute of Technology, Berhampur (Odisha), India.
Manuscript received on 18 March 2023 | Revised Manuscript received on 22 March 2023 | Manuscript Accepted on 15 April 2023 | Manuscript published on 30 April 2023 | PP: 57-68 | Volume-12 Issue-4, April 2023 | Retrieval Number: 100.1/ijeat.D40810412423 | DOI: 10.35940/ijeat.D4081.0412423
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Abstract: Model of Economic Order Quantity (EOQ) in which cross-selling effects are taken into account and proportional discounts are allowed for products of lesser quality. Here, we introduce cross-selling impact as a means of establishing the ordering policy. To account for the benefits of upselling and cross-selling, we treat groups of frequently purchased items as discrete units for the purposes of calculating EOQ. Furthermore, the cross-selling impacts remain more pronounced when things are defective in nature. Initially, a number of data mining approaches are investigated in order to determine the best approach for establishing the necessary link among the item sets. By factoring in the cross-selling implications, we are able to have a better idea of the EOQ and move the project further. As it is anticipated that every lot contains some level of flaw, the work involves thorough lot-by-lot inspection. The faulty products eventually reached a total profit after varying discounts were applied. Finally, the results of the proposed model are shown through numerical examples.
Keywords: Economic Order Quantity (EOQ), Discounts, Datamining, Techniques.
Scope of the Article: Data Mining