Journal of Advances in Developmental Research

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The Role of AI-Driven Algorithms in Optimizing Retail Inventory Management

Author(s) Vivek Prasanna Prabu
Country United States
Abstract In the ever-evolving landscape of retail, inventory management plays a critical role in determining operational efficiency, customer satisfaction, and overall profitability. Traditional inventory practices, rooted in manual processes and static rules, are increasingly being outpaced by the complexities of modern retail. Enter artificial intelligence (AI)—a transformative force that is reshaping inventory management through advanced algorithms capable of real-time decision-making, predictive planning, and autonomous optimization. AI-driven algorithms leverage machine learning, neural networks, and statistical models to analyze vast datasets, including sales history, customer behavior, market trends, and supply chain variables. These insights enable retailers to maintain optimal stock levels, minimize overstock and stockouts, and align inventory with fluctuating consumer demand. Beyond demand prediction, AI facilitates dynamic replenishment, automated safety stock calculations, assortment optimization, and anomaly detection. Global retail leaders such as Amazon, Walmart, and H&M have integrated AI into their inventory systems, achieving measurable improvements in inventory turnover, availability, and cost reduction.
By continuously learning from new data inputs, AI systems adapt to evolving consumer behaviors and market conditions. Retailers benefit from proactive stock adjustments that reduce markdowns and improve cash flow. AI also enhances visibility into inventory movement across channels, enabling omnichannel strategies that support customer convenience and fulfillment speed. Additionally, the integration of AI with Internet of Things (IoT) devices has enabled real-time monitoring of stock levels, shelf availability, and warehouse performance. AI’s capacity to forecast anomalies and disruptions allows for risk mitigation and faster recovery. While the benefits are compelling, retailers must also grapple with implementation challenges, including system compatibility, data privacy, and staff training. A successful AI-driven inventory strategy requires collaboration between IT, operations, data science, and merchandising teams. Ethical considerations, such as ensuring algorithmic fairness and avoiding unintended biases, must also be embedded into system design. As AI technologies mature, their impact on inventory management will deepen, fostering smarter, more agile retail ecosystems. This white paper explores the foundational elements, use cases, architecture, and strategic implications of AI-driven inventory optimization in retail. Drawing from real-world case studies and expert insights, it provides a roadmap for organizations looking to harness AI’s potential in building intelligent, resilient inventory ecosystems.
Field Engineering
Published In Volume 14, Issue 2, July-December 2023
Published On 2023-08-10
Cite This The Role of AI-Driven Algorithms in Optimizing Retail Inventory Management - Vivek Prasanna Prabu - IJAIDR Volume 14, Issue 2, July-December 2023. DOI 10.5281/zenodo.15155298
DOI https://doi.org/10.5281/zenodo.15155298
Short DOI https://doi.org/g9cs7p
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