Stochastic Predictive Analytics for Stocks in the Newsvendor Problem

TL;DR

This paper introduces a stochastic predictive model for inventory management in the Newsvendor problem, capable of handling limited data and short-term forecasts, validated with real-world marketplace data.

Contribution

It presents a novel stochastic approach that does not assume demand distribution, improving inventory predictions with limited historical data.

Findings

Effective in short-term forecasting scenarios

Demonstrates robustness with limited data

Validated on real-world electronic marketplace data

Abstract

This work addresses a key challenge in inventory management by developing a stochastic model that describes the dynamic distribution of inventory stock over time without assuming a specific demand distribution. Our model provides a flexible and applicable solution for situations with limited historical data and short-term predictions, making it well-suited for the Newsvendor problem. We evaluate our model's performance using real-world data from a large electronic marketplace, demonstrating its effectiveness in a practical forecasting scenario.