Olamide Raimat Amosu
Papers
1
Total Citations
33
H-Index
1
About
Olamide Raimat Amosu is a leading researcher at the intersection of artificial intelligence and supply chain optimization. Her work focuses on leveraging AI to revolutionize inventory management and demand forecasting, addressing critical inefficiencies in traditional, data-limited systems. Her most cited paper, "AI-enhanced inventory and demand forecasting" (2024, 33 citations), demonstrates how machine learning models can dynamically predict customer demand, reduce waste, and optimize stock levels—a breakthrough for retail and logistics sectors. Beyond this, Amosu’s research explores the broader integration of predictive analytics into operational frameworks, offering scalable solutions for real-time decision-making. Her contributions have been recognized for their practical impact, bridging the gap between theoretical AI advancements and tangible business outcomes. With a growing citation record and a reputation for translating complex algorithms into actionable strategies, Amosu is shaping the future of smart supply chains. Her work not only advances academic knowledge but also provides a roadmap for industries seeking resilience and efficiency in an increasingly data-driven world.
Research Focus
Key Achievements
Top Papers
- 1