Reference architecture
Discipline: AI / ML
Industry: Retail and e-commerce
Technology: Machine learning
Cloud: AWS
Region: Illustrative
Improved forecast stability across seasonal categories
Reference architecture
Demand forecasting and replenishment analytics
A multi-region retailer planned replenishment from spreadsheets that could not absorb seasonal volatility or promotions.
Conceptual visualization only
Engagement at a glance
Objectives
- Improve forecast stability across seasonal categories
- Give planners explainable drivers, not just a number
- Automate weekly forecast delivery into existing BI tooling
Data sources
- Historical sales by SKU, store and week
- Promotion and pricing calendars
- Stock-on-hand and lead-time records
- Public holiday and weather series
Architecture and approach
Feature pipelines in Python, gradient-boosted forecasting, BI dashboards and alerting.
Step 1
Baseline
Simple seasonal baselines set the bar every model had to beat.
Step 2
Feature engineering
Lags, promotion flags, price elasticity proxies and calendar effects.
Step 3
Backtesting
Rolling-origin evaluation per category rather than a single global score.
Step 4
Delivery
Weekly automated runs writing forecasts and confidence bands to BI.
Step 5
Monitoring
Drift alerts and periodic retraining triggered by error thresholds.
Technologies used
- Python feature pipelines with Pandas and Polars
- Gradient-boosted forecasting models with backtesting
- AWS Step Functions orchestration and S3 feature storage
- BI dashboards with alerting on forecast drift
Security and governance
- Aggregated commercial data only; no customer personal data
- Least-privilege IAM roles per pipeline stage
- Encrypted storage and audited access to forecast outputs
Output
Reported against the objectives set out for this reference architecture.
Forecast stability
Improved
Largest gains in promotion-heavy seasonal categories.
Planning cycle
Manual → automated
Weekly assembly replaced by a scheduled pipeline.
Explainability
Driver breakdown
Planners see which features moved each forecast.
Lessons learned
- Per-category evaluation exposed problems a single global metric hid.
- Planner trust came from explainability, not from a lower headline error.
Reference architecture illustrating method and architecture only. It is not a verified client project, and no client commissioned it.
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