Work

Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts

Year: 2016

Type: conference-paper

Abstract: When an item goes out of stock, sales transaction data no longer reflect the original customer demand, since some customers leave with no purchase while others substitute alternative products for the one that was out of stock. Here we develop a Bayesian hierarchical model for inferring the underlying customer arrival rate and choice model from sales transaction data and the corresponding stock levels. The model uses a nonhomogeneous Poisson process to allow the arrival rate to vary throughout the day, and allows for a variety of choice models. Model parameters are inferred using a stochastic gradient MCMC algorithm that can scale to large transaction databases. We fit the model to data from a local bakery and show that it is able to make accurate out-of-sample predictions, and to provide actionable insight into lost cookie sales.

Source: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

DOI: 10.1145/2939672.2939810

Authors: Benjamin Letham, Lydia M. Letham, Cynthia Rudin

Institutions: Massachusetts Institute of Technology, Duke University

Language: English

Topic: Supply Chain and Inventory Management

Subfield: Management Information Systems

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: green

Funder: Massachusetts Institute of Technology

OpenAlex ID: W1621383763

FWCI: 1.722

Cites: 28

Cited by: 12

Related to: 10