AI-Powered Demand Forecasting for a Multi-Location Retail Chain
Client Overview
A national retail chain operating over 200 stores across urban and suburban areas, offering a wide assortment of consumer goods—from apparel to household essentials. The company faced recurring inventory imbalances that impacted both profitability and customer satisfaction.
Business Challenge
The retailer was grappling with:
- Inconsistent product availability across locations
- Overstocking of low-demand items and frequent stockouts of bestsellers
- High dependency on manual forecasting methods that lacked accuracy and adaptability
- Supply chain inefficiencies leading to higher holding costs and lost sales
These issues resulted in reduced margins and operational friction, especially during promotions and seasonal spikes.
Objectives
Vithobha was engaged to modernize the retailer’s demand forecasting with AI, aiming to:
- Enhance forecast precision at the store and SKU level
- Support supply chain optimization with timely and accurate demand signals
- Reduce inventory-related costs and improve product availability
- Empower decision-makers with real-time, actionable insights
AI-Powered Solution
We implemented a scalable AI-based demand forecasting solution that adapted to regional and category-level dynamics:
📈 Time-Series & Ensemble Forecasting
- Trained advanced forecasting models (ARIMA, Prophet, XGBoost ensembles)
- Tailored models to category, region, and store type
- Factored in past sales, holidays, promotions, and product lifecycles
🌍 Local & Macroeconomic Signal Integration
- Incorporated external variables like weather data, inflation trends, local events, and mobility patterns
- Enhanced responsiveness to real-world factors affecting customer demand
☁️ Cloud-Based Automation
- Deployed on a cloud-native stack for scalability and daily model retraining
- Automated data ingestion from POS and ERP systems
- Daily forecasts pushed to merchandising and logistics teams
📊 Interactive Dashboards
- Delivered visual analytics via Power BI and Tableau
- Store managers and planners could view SKU-level forecasts, anomalies, and recommendations in real time
Results Achieved
Within 3 months, the retailer experienced measurable impact:
- 31% reduction in overstock-related holding costs
- 22% fewer stockouts of high-demand items
- Real-time visibility into forecast trends across 100+ stores
- Faster replenishment cycles based on forecast-driven insights
- 💰 Improved revenue margins during peak sale periods
Key Takeaway
By shifting to AI-powered demand forecasting, the retailer gained a data-driven, proactive inventory strategy—leading to operational efficiency, increased customer trust, and greater profitability across the board.