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AI-Driven Optimization of Order Procurement and Inventory Management in Supply Chains

Gizem Yeldan1✉,
Gökçe Yılmaz2,
Gökhan Kayatürk3
1KOÇTAŞ R&D Center
2KOÇTAŞ R&D Center
3KOÇTAŞ R&D Center
Published:October 31, 2024
DOI: 10.56038/ejrnd.v4i3.605
Vol. 4, No. 3

Abstract

This project focuses on the development of an advanced artificial intelligence-based system for optimizing order procurement and inventory management in supply chains. The system utilizes machine learning algorithms to analyze historical sales data, customer behavior, and market trends, enabling it to predict optimal procurement times and quantities for imported products. The primary objective is to reduce excess stock, prevent stockouts, and improve overall inventory turnover. By integrating features such as substitute product tracking, import forecasting, and truckload optimization, the system enhances decision-making processes and ensures efficient supply chain operations. Furthermore, the inclusion of real-time data for demand forecasting aims to improve the accuracy of predictions and adapt to dynamic market conditions. The project also envisions future advancements, including the incorporation of external factors like seasonal variations and promotional campaigns, as well as the automation of supply chain processes. Ultimately, the system aims to provide more accurate, data-driven insights for decision-making, leading to enhanced operational efficiency and a more responsive and resilient supply chain.

Keywords
Artificial IntelligenceInventory ManagementSupply Chain OptimizationMachine LearningProcurement Forecasting

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Cite This Article
Yeldan, G., Yılmaz, G., Kayatürk, G. (2024). AI-Driven Optimization of Order Procurement and Inventory Management in Supply Chains. *The European Journal of Research and Development*, 4(3), 46-56. https://doi.org/10.56038/ejrnd.v4i3.605

Bibliographic Info

JournalThe European Journal of Research and Development
Volume4
Issue3
Pages46–56
PublishedOctober 31, 2024
eISSN2822-2296