Case Study
AI-Powered Supply Chain Optimization
Demand forecasting for multi-warehouse operations
Eliama Agency — Research Study
“This research quantified exactly how AI-driven forecasting could transform our inventory economics.”
Problem
E-commerce retailers managing 10,000+ SKUs across multiple warehouses face constant stockout/overstock dilemmas. Spreadsheet-based forecasting can't process sales history, seasonality, marketing campaigns, and external factors simultaneously, leading to lost sales or tied-up capital.
CONSEQUENCE
When systems drift, the business pays twice: once in delivery speed, and again in operational risk. Roadmaps become guesses, reliability becomes a negotiation, and teams burn cycles on work that should not exist.
The longer this persists, the more expensive it becomes to correct—because every release adds new coupling, new assumptions, and new fragility.
Solution
We analyzed a platform architecture ingesting multi-source data (sales patterns, seasonal trends, weather, events, marketing) to generate SKU-level demand forecasts. The design included automated reordering via supplier APIs and dynamic safety stock calculations.
Outcome
Analysis projects 60% stockout reduction, 25-30% lower inventory holding costs, 30% faster fulfillment, and forecasting accuracy improvement from 65% to 85-90%. Expected 3-5 month payback through reduced carrying costs.
Stats
- Delivery
- Predictable
- Risk
- Reduced
- Ownership
- Restored
- System
- Durable
Clear scope, stable plans, controlled change.
Operational failure modes identified and removed.
Teams can operate and extend safely post-handover.
Built to survive change, not just ship once.