• Frequent stock discrepancies due to manual tracking and inconsistent store operations.
• Overstock & stockouts, causing revenue loss and increased holding costs.
• Limited visibility across hundreds of retail locations.
• Inefficient restocking process, often reactive rather than predictive.
• Lack of accurate forecasting, making planning difficult for fast-moving and seasonal items.

• Built an AI-driven demand forecasting model analyzing:
  • Historical sales
  • Seasonal trends
  • Weather data
  • Promotion schedules
• Developed an Inventory Optimization Engine that:
  • Predicts restock levels automatically
  • Flags potential stockouts or overstock situations
  • Sends real-time alerts to the retail operations team
• Integrated seamlessly with the client’s ERP through secure APIs.
• Delivered a real-time monitoring dashboard for inventory visibility across all stores.
• Implemented cloud infrastructure and CI/CD pipelines for scalability and uptime.

Impact

• 30% reduction in stock discrepancies across all stores.
• 25% improvement in demand forecasting accuracy, leading to smarter replenishment decisions.
• 40% decrease in overstock and stockout events, improving sales and reducing waste.
• Real-time visibility and automated restocking suggestions significantly improved operational efficiency.
• Enhanced supply chain resilience and readiness for business expansion.