Dynamic inventory system with pricing adjustment for price-comparison shoppers
Publication Type
Journal Article
Publication Date
11-2022
Abstract
With numerous price-comparison websites and applications, consumers today are frequently conducting price-comparison shopping. As a result, retailers face an increasing challenge in predicting consumer demand and determining the optimal product price and inventory level accordingly. To address this issue, this paper proposes an inventory model with joint decisions of price and inventory to optimize the retailer's long-run average profit under price-comparison consumer shopping. We first formulate the demand arrival process for a retailer under price-comparison shopping to be affected by not only its own price but also its competitors'. Based on this demand arrival process, we then formulate the retailer's long-run average profit and derive properties of its optimal solution. Our model focuses on capturing the impact of price-comparison consumers on a retailer's optimal price and inventory decisions. In particular, we allow competitors' prices to affect the retailer's demand via two key factors: the manufacturer's suggested price and the variability of the outside lowest price. According to our results, when the suggested price increases, the retailer should lower its price to obtain more price-comparison customers from competitors, whereas when the variability of outside lowest price increases, the retailer should raise its price to increase per unit profit from nonprice-comparison customers.
Keywords
inventory management, price comparison, regenerative process
Discipline
Databases and Information Systems | Management Information Systems
Research Areas
Information Systems and Management
Publication
Applied Stochastic Models in Business and Industry
Volume
39
Issue
2
First Page
251
Last Page
287
ISSN
1524-1904
Identifier
10.1002/asmb.2737
Publisher
Wiley
Citation
CHEN, Wen; KATEHAKIS, Michael; and TANG, Qian.
Dynamic inventory system with pricing adjustment for price-comparison shoppers. (2022). Applied Stochastic Models in Business and Industry. 39, (2), 251-287.
Available at: https://ink.library.smu.edu.sg/sis_research/7730
Additional URL
https://doi.org/10.1002/asmb.2737