Explores how machine learning and AI models can be applied to demand forecasting in supply chains, quantifying the downstream impact on inventory levels, carrying costs, and service levels across MENA distribution networks.
Investigates how BI systems and data dashboards translate into measurable supply chain performance gains — including lead time reduction, order accuracy, and cost-to-serve improvements across diverse industry sectors.
Benchmarks various ML algorithms — from ARIMA to gradient boosting and LSTM networks — on retail demand forecasting tasks, measuring accuracy, training cost, and adaptability to seasonal patterns in developing-market retail chains.
Evaluates the operational and strategic impact of real-time IoT tracking across logistics and warehousing, examining how sensor data and connected devices improve transparency, reduce disputes, and enable proactive exception management.
Active member of IEEE — the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. IEEE membership connects me with a global community of engineers, researchers, and innovators, supporting my ongoing work at the intersection of technology, AI, and supply chain systems.