Challenge
The client managed procurement, inventory, and logistics operations across multiple disconnected systems. Data from suppliers, warehouses, and logistics providers was fragmented, limiting real-time visibility and slowing decision-making. Adexin needed to unify these workflows while synchronizing different APIs, data formats, and update frequencies. The system also needed to support accurate demand forecasting, growing transaction volumes, and actionable insights without disrupting existing supply chain processes.
The client managed procurement, inventory, and logistics operations across multiple disconnected systems. Data from suppliers, warehouses, and logistics providers was fragmented, limiting real-time visibility and slowing decision-making. Adexin needed to unify these workflows while synchronizing different APIs, data formats, and update frequencies. The system also needed to support accurate demand forecasting, growing transaction volumes, and actionable insights without disrupting existing supply chain processes.
Solution
Adexin developed a custom AI-powered supply chain management platform using its AI-assisted development approach and supply chain expertise. The system connects procurement, inventory, suppliers, warehouses, and logistics providers within a unified ecosystem. It provides real-time supply chain visibility, AI-powered demand forecasting, automated procurement recommendations, supplier performance analytics, logistics optimization, and centralized dashboards. Machine learning models analyze historical data, seasonality, and external factors to forecast demand and recommend when and how much to reorder at SKU level.
Adexin developed a custom AI-powered supply chain management platform using its AI-assisted development approach and supply chain expertise. The system connects procurement, inventory, suppliers, warehouses, and logistics providers within a unified ecosystem. It provides real-time supply chain visibility, AI-powered demand forecasting, automated procurement recommendations, supplier performance analytics, logistics optimization, and centralized dashboards. Machine learning models analyze historical data, seasonality, and external factors to forecast demand and recommend when and how much to reorder at SKU level.
Results
Real-time visibility reached 93% system-wide coverage, reducing data silos by 85%. Stockouts decreased by 32% and overstock by 27%, while AI-driven forecasting improved forecast accuracy by 38%. Supplier efficiency increased by 40%, operational costs decreased by 22%, decision-making speed improved by 50%, and manual workload was reduced by 35%.
Real-time visibility reached 93% system-wide coverage, reducing data silos by 85%. Stockouts decreased by 32% and overstock by 27%, while AI-driven forecasting improved forecast accuracy by 38%. Supplier efficiency increased by 40%, operational costs decreased by 22%, decision-making speed improved by 50%, and manual workload was reduced by 35%.