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1.

Effects of learning on retailer ordering policy for imperfect quality items with trade credit financing Pages 49-62 Right click to download the paper Download PDF

Authors: Mahesh Kumar Jayaswal, Isha Sangal, Mandeep Mittal, Sarthak Malik

DOI: 10.5267/j.uscm.2018.5.003

Keywords: EPQ, Learning effects, Imperfect items, Trade-credit financing

Abstract:
Learning curves monitor the performance of workers for the given new task as well as it is a mathematical representation of the same learning process which can be analyzed after frequent repetitions. Now-a-days learning curve is a promotion effective tool for management concern with designing and controlling the process of imperfect production and redesigning unbalanced business operations in the production of goods or services related to scheduling, uncontrolled inventory management, quality management as well as inspection. Learning effect has direct impact in calculation of profit or loss. Generally, a business seller, in order to increase his sale prefers to lend his products to buyers for a definite period of time. There is no penalty before or during this definite time period however after the duration of lending time period is over, he will assign some extra charges. For this action, seller offers a trade credit financing period to his buyer. Assuming when buyer receives a lot he separates the defective and non-defective items by a screening process and defective items are then sold at a discounted price. The percentage of defective items decreases per lot according to learning curve. Seller too plans which condition is beneficial for good coordination of retailers and analysts. Different cases are explained broadly in this model to get maximum profit. In this paper, a fiscal construction feature model for imperfect quality items with trade credit policy is analyzed under the effects of learning. Total profit function per cycle has been derived with the help of involvement of different costs and related parameters for the retailers and a numerical example given ahead shows the verification of results. The impacts of key parameters of the model are studied by sensitivity analysts to deduce managerial insights.
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Journal: USCM | Year: 2019 | Volume: 7 | Issue: 1 | Views: 2066 | Reviews: 0

 

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