Stocking Under Random Demand and Product Variety: Exact Models and Heuristics

Vashkar Ghosh, Anand Paul and Lingjiong Zhu

Production and Operations Management, 2022, vol. 31, issue 3, 1006-1032

Abstract: Efficient inventory management in the face of product variety is an important part of retail operations management. In this study, we analyze the optimal stocking policy for a retailer, in a setup with a single horizontally differentiated product with an arbitrary number of product variants, stochastic demand, and two‐level consumer choice. The demands for individual product variants are negatively correlated conditional on the total demand. We assume that each customer will purchase one unit of a preferred product variant, if it is in stock, and will seek to buy a second choice product, if the former is not in stock. We formulate an exact model, with Poisson customer arrivals. In order to maintain tractability and characterize an optimal policy analytically, we develop a benchmark model which does not explicitly account for the stochastic nature of customer arrival times. In this model, which is a heuristic approximation of the exact model, we find simple conditions under which the objective of maximizing expected profit is jointly concave in the stocking levels of the product variants; under these conditions we prove that the optimal stocking levels are simply scaled versions of the optimal newsvendor quantities. We then analytically establish a connection between the exact and benchmark models. We develop a dynamic Monte Carlo simulation experiment to gain further insights on the impact of different performance measures on the effectiveness of the optimal policy in the benchmark model and its performance in reference to the exact optimal policy.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1111/poms.13592

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:popmgt:v:31:y:2022:i:3:p:1006-1032

Ordering information: This journal article can be ordered from
http://onlinelibrary ... 1111/(ISSN)1937-5956

Access Statistics for this article

Production and Operations Management is currently edited by Kalyan Singhal

More articles in Production and Operations Management from Production and Operations Management Society
Bibliographic data for series maintained by Wiley Content Delivery ().