Demand Forecasting at Scale with Production MLOps Pipeline
How a logistics enterprise reduced inventory waste by 45% and improved on-time delivery using ML-powered demand forecasting.
The Challenge
A pan-India logistics company was struggling with inventory mismatch — overstocking in some hubs, stock-outs in others — costing crores in idle inventory and missed SLAs. Their forecasting was based on spreadsheets and intuition, with no data-driven demand signals.
- ✕Inventory mismatch across 40+ distribution hubs nationwide
- ✕No ML infrastructure — all forecasting done in Excel
- ✕Seasonal demand spikes causing SLA breaches and customer churn
- ✕Siloed data across WMS, TMS, and ERP with no unified pipeline
Solution Architecture
WMS + TMS + ERP → Kafka streaming → Spark ETL → Snowflake → Feature Store → Ensemble ML model (XGBoost + LSTM) → MLflow registry → Airflow scheduling → Dashboard (Looker) → Automated retraining every 2 weeks.
Client Testimonial
Before Zgrow, we were guessing demand. Now we have a live ML pipeline predicting stock needs 3 weeks ahead across all our hubs. Our SLA breach rate dropped by 38% in the first quarter. This is exactly the kind of transformation we needed.
🛠 Tech Stack Used
Want Similar Results?
Let's discuss how we can deliver this for your business.
Book Free Consultation