Client: Global retail chain with 1,500+ stores
Industry: Retail & eCommerce / Manufacturing & Logistics
Challenge:
Seasonal demand spikes were causing overstock and understock situations. Existing demand forecasting models required manual retraining and could not ingest live sales data, resulting in outdated predictions.

Solution:
We implemented a streaming data-based MLOps architecture that:

• Integrated real-time POS data streams using Kafka
• Enabled automated model retraining every 24 hours
• Used A/B testing to compare forecasting models in production
• Provided store managers with updated demand predictions via API

Results:

• Forecasting accuracy improved by 18%
• Reduced overstocking by 12% and understocking by 9%
• Enabled store-level inventory optimization in near real-time

Key MLOps Features Used:
✅ Real-time data ingestion
✅ Continuous retraining & A/B testing
✅ API-based prediction serving