An integrated model for online product placement and inventory control problem in a drop-shipping optional environment. (March 2018)

• Record Type: Journal Article
• Authors: Chen, Yan-Kwang; Chiu, Fei-Rung; Lin, Wen-Hsiang; Huang, Yueh-Chuen
• Is Part Of: Computers & industrial engineering. Volume 117(2018)
• Page Start: 71
• Page End: 80
• Publication Date: 2018-03
• Subjects: Product placement -- Inventory control -- Optimization -- Drop shipping; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.01.020
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We model the online product placement and inventory control problem under the drop-shipping option. Optimal reorder points for first-page products are higher than those for the following-page products. Higher drop-shipping proportion leads a smaller order quantity and a lower reorder point. An increase in space elasticity on the first page raises the total profit. An increase in space elasticity on the second page decreases the total profit. Abstract: The growth of the Internet has revolutionized how retailers operate. Proper management of product display on product listing pages and inventory control not only increases profit but also decreases cost. Online retailers typically sell their products by using the following inventory strategies: drop-shipping, in-house, and hybrid (a combination of drop-shipping and in-house) strategies. This paper addresses the problem of retailers selling various items of the same product category by displaying a product in an array format over multiple pages. It is assumed that the visual stimuli of the image size and display position significantly affect product demand. In addition, the substitution effect of the items on product demand is considered. A continuous-review order quantity reorder point ( Q, r ) inventory model with a visual-attention-dependent demand rate is developed for solving the product placement and inventory control problem in a drop-shipping environment that allows retailers to use the hybrid strategy if a shortage occurs during lead time. A genetic algorithm (GA) is then proposed for deriving a solution to the model. The validity of the proposed model is illustrated using numerical examples. Furthermore, the effects of the model parameters on the solution for the model are examined.