신선·시즌 상품의 최적 주문량 산정 문제에 대한 실증적 분석 : 소매유통업에서 뉴스벤더 모델의 적용
An Empirical Analysis on Optimal Oder Quantity of Perishable and Seasonal Products : A Practical Application of Newsvendor Model in Retail
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Although retailers deals with a large number of single-term inventory items, but few cases have been considered in the areas of practical decision making. However, recent moves to strengthen fair trade have created a real need for single-period inventory decision-making problems. This study addresses the problem of ordering quantity decisions that are expected to maximize profits using classical newsvendor models. The research target is data on seasonal and perishable products from retail. We also use data from retailers to actually apply the newsvendor model and calculate the results to compare performance. It also suggests solutions for estimating demand for products sold in order to apply newsvendor models that utilize actual demand ratio versus forecast demand. This study would like to examine the effectiveness of this research through data analysis and make some suggestions for applying it to reality.
Abstract
Although retailers deals with a large number of single-term inventory items, but few cases have been considered in the areas of practical decision making. However, recent moves to strengthen fair trade have created a real need for single-period inventory decision-making problems. This study addresses the problem of ordering quantity decisions that are expected to maximize profits using classical newsvendor models. The research target is data on seasonal and perishable products from retail. We also use data from retailers to actually apply the newsvendor model and calculate the results to compare performance. It also suggests solutions for estimating demand for products sold in order to apply newsvendor models that utilize actual demand ratio versus forecast demand. This study would like to examine the effectiveness of this research through data analysis and make some suggestions for applying it to reality.
참고문헌 (16)
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