Poisson Inventory Models with Many Items: An Empirical Bayes Approach

TL;DR

This paper applies empirical Bayes methods to optimize inventory decisions for many Poisson-demand items, emphasizing the importance of posterior demand estimates and analyzing when grouping items is advantageous.

Contribution

It introduces a practical empirical Bayes framework for inventory management with Poisson demand, highlighting the benefits of posterior estimates over simple rate estimates.

Findings

Posterior demand estimates improve inventory decisions.

Empirical Bayes is effective with around 100 items.

Grouping items can be beneficial depending on context.

Abstract

We consider inventory decisions with many items, each of which has Poisson demand. The rate of demand for individual items is estimated on the basis of observations of past demand. The problem is to determine the items to hold in stock and the amount of each one. Our setting provides a natural framework for the application of the empirical Bayes methodology. We show how to do this in practice and demonstrate the importance of making posterior estimates of different demand levels, rather than just estimating the Poisson rate. We also address the question of when it is beneficial to separately analyse a group of items which are distinguished in some way. An example occurs when looking at inventory for a book retailer, who may find it advantageous to look separately at certain types of book (e.g. biographies). The empirical Bayes methodology is valuable when dealing with items having Poisson demand, and can be effective even with relatively small numbers of distinct items (e.g. 100). We discuss the best way to apply an empirical Bayes methodology in this context, and also show that doing this in the wrong way will reduce or eliminate the potential benefits.