AI Inventory Manager
Forecast demand, prevent stockouts, and free up cash tied in excess inventory.
- Project range
- $7,000–16,000
- AWS running cost
- $70–350/mo
- Time to deploy
- 4–6 weeks
- Best-fit industries
- Retail, Wholesale
Executive summary
A forecasting and replenishment engine that learns each SKU's demand pattern — including seasonality and promotions — and recommends what to order, how much, and when. It flags slow movers, prevents both stockouts and overstock, and explains its recommendations so buyers stay in control of working capital.
Business problem
Inventory is managed on gut feel and stale spreadsheets. Popular items sell out and cost sales, while cash sits frozen in slow-moving stock. Reorder points are static, seasonality is guessed at, and no one has time to forecast every SKU by hand.
Architecture
AWS services
Amazon EventBridge
Messaging- — Schedule data syncs and forecast runs
- — Trigger reorder alerts
AWS Lambda
Compute- — Run forecasting and replenishment logic
- — Assemble purchase-order recommendations
Amazon S3
Storage- — Sales history and model input data lake
- — Lifecycle retention
Demand model
Compute- — Per-SKU forecasting with seasonality and trend
- — Safety-stock and reorder-point calculation
Amazon Bedrock
AI / ML- — Explain recommendations in plain language
- — Detect and describe demand anomalies
Amazon DynamoDB
Database- — Live SKU state, reorder points, and recommendations
Amazon QuickSight
Observability- — Inventory health and forecast-accuracy dashboards
ERP / supplier adapters
Integration- — Pull sales and stock data, push purchase orders
Amazon CloudWatch
Observability- — Logs, metrics, and cost alarms
Data flow
- 1
On a schedule, EventBridge triggers a sync of POS, order, and supplier data into S3.
- 2
The demand model forecasts each SKU, accounting for seasonality, trend, and promotions.
- 3
Lambda computes safety stock and reorder points, and Bedrock explains notable changes and anomalies.
- 4
Recommendations and live SKU state are stored in DynamoDB and visualized in QuickSight.
- 5
Approved orders are pushed to the ERP or supplier as purchase orders, closing the loop.
Security considerations
- Sales and supplier data encrypted at rest and in transit.
- Least-privilege IAM; ERP and supplier credentials in Secrets Manager.
- Recommendations are explainable and require buyer approval before ordering.
- Full audit trail of forecasts, overrides, and orders.
Cost considerations
- Forecast compute scales with SKU count and run frequency — batch runs keep it low.
- Bedrock is used selectively for explanations and anomalies, not every SKU.
- S3, DynamoDB, and EventBridge are inexpensive at rest.
Scalability
- Serverless and batch-oriented; scales to tens of thousands of SKUs.
- New locations and suppliers onboard as configuration and adapters.
- Forecast horizon and service-level targets are tunable per category.
Deployment roadmap
Phase 1 — Data & targets
Weeks 1–2- — Connect sales history and set service-level goals
- — Provision AWS foundation and data lake
Phase 2 — Build & validate
Weeks 3–5- — Build forecasting and replenishment
- — Backtest accuracy against history
Phase 3 — Integrate & launch
Week 6- — Wire ERP and supplier ordering
- — Launch dashboards and reorder alerts
Future enhancements
- Multi-echelon optimization across warehouses and stores.
- Supplier lead-time learning and dynamic safety stock.
- Markdown and promotion planning for slow movers.
- Working-capital and turnover reporting into the executive dashboard.