Deep Generative Demand Learning for Newsvendor and Pricing

TL;DR

This paper introduces a deep generative learning approach using conditional deep generative models to improve inventory and pricing decisions in the newsvendor problem, handling demand influenced by features without structural assumptions.

Contribution

It proposes a novel cDGM-based method for demand forecasting and decision optimization, providing theoretical guarantees and demonstrating effectiveness through simulations and a real-world case study.

Findings

Accurate demand forecasting with cDGMs improves profit estimation.

The approach converges to optimal inventory and pricing decisions.

Demonstrated effectiveness in complex scenarios including textual features.

Abstract

We consider data-driven inventory and pricing decisions in the feature-based newsvendor problem, where demand is influenced by both price and contextual features and is modeled without any structural assumptions. The unknown demand distribution results in a challenging conditional stochastic optimization problem, further complicated by decision-dependent uncertainty and the integration of features. Inspired by recent advances in deep generative learning, we propose a novel approach leveraging conditional deep generative models (cDGMs) to address these challenges. cDGMs learn the demand distribution and generate probabilistic demand forecasts conditioned on price and features. This generative approach enables accurate profit estimation and supports the design of algorithms for two key objectives: (1) optimizing inventory for arbitrary prices, and (2) jointly determining optimal pricing and inventory levels. We provide theoretical guarantees for our approach, including the consistency of profit estimation and convergence of our decisions to the optimal solution. Extensive simulations-ranging from simple to complex scenarios, including one involving textual features-and a real-world case study demonstrate the effectiveness of our approach. Our method opens a new paradigm in management science and operations research, is adaptable to extensions of the newsvendor and pricing problems, and holds potential for solving other conditional stochastic optimization problems.