Simulating the Demand Reshaping and Substitution Effects of Probabilistic Selling

Abstract

This paper addresses the effect of probabilistic selling on inventory decisions and the expected profit through demand reshaping and demand substitution. By considering a scenario with two higher-priced specific products and one lower-priced probabilistic product, we construct a new newsboy-type inventory model with demand reshaping and substitution. A simulation study is implemented to explore extensively the effects of demand uncertainty, demand correlation, price sensitivity and price discount on the inventory decisions and profitability of probabilistic selling. Finally, we provide insightful managerial implications of the nature of inventory management mechanism of probabilistic selling.

Risk pooling through physical probabilistic selling

“…Endogenizing both capacity and pricing decisions in a single-product system, Wu and Wu (2015) argued that a rm can use a price discount to achieve demand postponement under P S. Therefore, the rm can full customer orders for the probabilistic product after satisfying the spot demand. Zhang et al (2016) simulated the demand reshape and demand substitution eect of P S. In a newsvendor system with two specic products and one probabilistic product, the retailer can achieve demand substitution between the specic products and the probabilistic product by charging a lower price for the option to satisfy the demand for the probabilistic product later. They found that this strategy can benet the retailer with a higher prot and lower inventory through combating demand uncertainty.…”

“…The gure of the probabilistic product was taken in a retail store in Japan. probabilistic product in P P S. Some operational studies on P S endogenize the inventory decision and explore the benets of adopting P S for inventory management through postponing the delivery of the probabilistic product (Petrick et al, 2012;Gönsch and Steinhardt, 2013;Gallego and Phillips, 2004;Wu and Wu, 2015;Fu et al, 2017), dynamic allocation depending on inventory (Elmachtoub and Wei, 2015), or demand substitution (Zhang et al, 2016). However, the above literature considers virtual probabilistic products rather than physical ones.…”

Composition analysis of white mineral pigment based on convolutional neural network and Raman spectrum

“…Convolutional neural networks (CNNs) are a type of deep learning and have been successfully applied in many fields, including computer vision [25,26] and natural language processing. [27,28] In a CNN, high-level features are extracted from lowlevel features by sliding the convolution kernel over the original data. In the context of Raman spectroscopy, the convolution kernel can process several Raman data points simultaneously, allowing the Raman offset to be recognized.…”

Strategy Selection Study of Out-of-stock Substitution and Probabilistic Selling

“…Out-of-stock replacement strategies encourage consumers to choose to purchase another acceptable and available product in the face of product out-of-stocks, thereby reducing the mismatch between retailer inventory and consumer demand [1]. The probabilistic sales strategy uses the retailer's existing products to create a probabilistic product with certain attributes hidden in a certain proportion [2][3][4]. By encouraging consumers to abandon the purchase of certain products, they choose to purchase low-priced but random probabilistic products to reduce inventory and A mismatch between requirements.…”