Industry: Retail & Supply Chain
Client Size: National retail chain with 500+ stores

Challenge

The client faced inventory mismanagement, leading to:
❌ $5M in lost sales annually due to stockouts
❌ Overstocking in 20% of SKUs, increasing storage costs
❌ No AI-driven demand forecasting

Solution

We developed a custom AI-powered demand forecasting model using:

🔹 AI Model Development:

• Built a Time-Series Forecasting Model using XGBoost, Prophet, and LSTMs
• Integrated real-time POS data, market trends, and weather data
• Used Feature Engineering on historical sales, promotions, and regional demand

🔹 Cloud & Data Infrastructure:

• Centralized ETL pipeline using Apache Airflow + AWS Glue
• Stored training data in Amazon Redshift for fast querying
• Deployed the model via AWS SageMaker with AutoML tuning

🔹 Automated Inventory Optimization:

• Developed an AI-powered replenishment engine that suggests restocking levels dynamically
• Integrated Power BI dashboards for real-time business intelligence

Results

✅ 25% reduction in stockouts → $1.2M increase in revenue
✅ 20% decrease in excess inventory → Lower carrying costs
✅ 95% forecasting accuracy achieved within 6 months

📌 Tech Stack: XGBoost, Prophet, LSTM, Apache Airflow, AWS Glue, Amazon Redshift, AWS SageMaker, Power BI

🔹 “AI-driven forecasting helped us optimize inventory, cut costs, and boost sales!” – [Client Testimonial]