Sarcouncil Journal of Multidisciplinary

Sarcouncil Journal of Multidisciplinary

An Open access peer reviewed international Journal
Publication Frequency- Monthly
Publisher Name-SARC Publisher

ISSN Online- 2945-3445
Country of origin- PHILIPPINES
Frequency- 3.6
Language- English

Keywords

Editors

Retail Inventory Reimagined: Deep Learning for Retail Demand Forecasting and Stock Optimization

Keywords: Retail optimization, deep learning, demand forecasting, artificial intelligence in stock.

Abstract: The retailing industry is undergoing a radical transformation as deep learning technologies are integrated into major aspects of operational processes, such as demand forecasting and inventory management. This paper will discuss how deep learning is enhancing the accuracy of forecasting, optimizing stock levels, and transforming customer experiences. The study draws on existing academic and industry research to explore how intelligent systems enable business model innovation to support sustainable retailing and facilitate real-time responsiveness. It also highlights the advantages of AI-based strategies over traditional approaches, the imperative to integrate business operations, and the importance of upskilling the workforce in the AI era. A comparative analysis, supported by visual representations, provides insights into tangible improvements in supply chain efficiency and forecasting precision. The article concludes with an analysis affirming that the future of smart, data-driven solutions in retail fundamentally relies on deep learning.

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