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An integrated approach to demand forecasting and inventory optimization in e-commerce

Year: 2026

Type: article

Abstract: This study investigates demand forecasting and inventory optimization in an e-commerce environment with a large assortment of products and highly variable demand. The research focuses on SKU-level demand modeling based on transactional data from an online retail store. The proposed approach combines machine learning methods with a stochastic inventory model. The study demonstrates that integrating machine learning forecasting with stochastic inventory optimization provides an effective decision-support tool for e-commerce, enabling balanced consideration of demand uncertainty, service level, and financial constraints.

Source: Technoeconomics & Management Research

DOI: 10.57809/2026.5.1.16.7

Author: Ermochenko, Semen

Institution: Peter the Great St. Petersburg Polytechnic University

Language: English

Topic: Supply Chain and Inventory Management

Subfield: Management Information Systems

Field: Business, Management and Accounting

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

OpenAlex ID: W7160834102

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