$12 Million Lost Annually to Bad Forecasts. Our AI Agents Turned That Into $12 Million Saved — Across 800+ Stores.
Industry
Retail
Service
AI/ML & Demand Forecasting
Locations
800+ stores
Team
Agilityx consultants + AI agents
Accuracy Gain
23% improvement
The Situation
A national retail chain with over 800 locations was suffering from a critical disconnect between its inventory levels and actual consumer demand. The legacy forecasting process was almost entirely manual, relying on static spreadsheets that failed to account for localized trends, regional weather events, or social media-driven product spikes.
This resulted in an annual loss of $12M due to a combination of frequent stock-outs on high-velocity items and massive markdowns on over-stocked goods. The retailer required an Agentic AI solution built on a Databricks Lakehouse foundation to develop an automated, SKU-level demand-sensing engine.
The goal was to deploy agents that could not only predict future demand but also automatically trigger supplier restock alerts to optimize inventory turnover across all regions.
The Approach
Data Prep(Phase 1)
AI Agents
Unified transaction, weather, and inventory data into a medallion lakehouse layer.
Consultants
Defined the business logic for SKU-level demand sensing across regions.
Modeling(Phase 2)
AI Agents
Evaluated 500+ model variations using automated experimentation frameworks.
Consultants
Selected the optimal ML architecture to balance accuracy with interpretability.
Agent Build(Phase 3)
AI Agents
Developed 'Agentic' workflows that automatically trigger supplier restock alerts.
Consultants
Integrated forecasting engine into the client's core ERP and supply chain systems.
Validation(Phase 4)
AI Agents
Agents continuously monitored model drift and alerted on forecasting anomalies.
Consultants
Validated the $12M savings impact against actual procurement data.
Enablement(Phase 5)
AI Agents
Created training materials for regional store managers on AI-driven restocking.
Consultants
Built the MLOps pipeline, including model versioning, monitoring, and drift detection.
Traditional vs. Agilityx
| Dimension | Traditional | Agilityx |
|---|---|---|
| Forecast Accuracy | 65% baseline | 88% accuracy (23% gain) |
| Restock Speed | Weekly manual orders | Real-time automated alerts |
| Model Testing | Weeks of manual tuning | 48 hours via AI agents |
| Financial Impact | Incremental gains | $12M annual ROI |
Forecast Accuracy
Traditional
65% baseline
Agilityx
88% accuracy (23% gain)
Restock Speed
Traditional
Weekly manual orders
Agilityx
Real-time automated alerts
Model Testing
Traditional
Weeks of manual tuning
Agilityx
48 hours via AI agents
Financial Impact
Traditional
Incremental gains
Agilityx
$12M annual ROI
The Outcomes
$12M
Annual Savings
Drastically reduced inventory spoilage and stock-out events through optimized forecasting.
23%
Accuracy Boost
Achieved industry-leading SKU-level precision across 15+ product categories.
Agentic
Operations
Automating the 'heavy lifting' allowed managers to focus on customer experience.
Sustainable
AI Ownership
The client now owns the feature stores and pipelines to evolve the models internally.
"Agilityx's AI-augmented execution allowed us to move from strategy to a production-ready model in record time. For the first time, our managers are operating on the same predictive engine."
SVP Supply Chain
National Retail Chain
Facing a similar challenge? Let's talk.
Book a 30-minute discovery call and let's discuss how the Build With model can work for your organization.