All work
Case study · illustrative example
Demand analytics & forecasting for a retail chain
A data pipeline and forecasting model that replaced manual weekly planning with daily store-level demand forecasts and a decision dashboard.
- Client
- Retail chain, 40 stores
- Role
- Data scientist & data engineer
- Timeline
- 12 weeks
- Stack
- Pythonpandasscikit-learnPostgreSQLAirflowMetabase
Problem
Weekly ordering decisions were made from spreadsheets exported from the POS system. Stock-outs on fast movers and overstock on slow movers were both common.
Approach & architecture
- Built an ELT pipeline that consolidates POS, promotions and calendar data into a clean warehouse model, orchestrated with Airflow.
- Trained gradient-boosted models per product category with backtesting on 18 months of history; forecasts published daily to a dashboard with confidence bands and reorder suggestions.
Result
- Forecast error (MAPE) fell by ~28% on the pilot categories compared with the previous manual method.
- Planners moved from a weekly to a daily cycle without extra headcount.
What made it robust
- Data-quality checks and freshness alerts on every pipeline run.
- Model performance monitored monthly; retraining is a one-command job.
- Full documentation of data lineage for the finance team.
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