Case Studies → Retail
Demand forecasting that cut stockouts across 200+ stores
We built a forecasting platform on Databricks that combined POS, weather, and promotional data into a single demand signal.
Client challenge
What was wrong with the legacy environment
Store-level demand forecasts relied on spreadsheets and static rules
POS, promotional, and weather data lived in separate systems with no shared model
Forecast refreshes took days, too slow to react to demand shifts
No visibility into forecast accuracy by store or category
Modernization approach
How Karsient approached the transformation
Karsient built a unified demand-signal pipeline that blends POS transactions, promotional calendars, and external weather data into a single feature set, replacing spreadsheet-driven planning with a governed, automated model.
Architecture
The modern target architecture
Batch and near-real-time ingestion from POS and inventory systems
Bronze/Silver/Gold medallion layers for raw, cleaned, and modelled demand data
Forecasting models orchestrated on a daily schedule via Databricks Workflows
Power BI dashboards for store- and category-level forecast accuracy
Migration
Migration & re-engineering strategy
Legacy spreadsheet workflows were replaced store-cluster by store-cluster, with each cluster's new forecast validated against the prior manual process for two full cycles before cutover.
Engineering improvements
What changed under the hood
Automated daily forecast refresh replacing manual weekly cycles
A single demand-signal feature store shared across planning teams
Parallel processing across store clusters cutting refresh time
Forecast-accuracy monitoring built into the pipeline itself
Business impact
Measurable outcomes
22% reduction in stockouts across 200+ stores
Forecast refresh time cut from days to hours
One consistent demand model replacing per-region spreadsheets
Planners now spend time on exceptions, not data assembly
Technology
Technology used
More stories
Other client engagements
Want results like these?