Executive Summary
A mid-size omnichannel retailer transformed its inventory planning by migrating to a Snowflake-native data and ML stack. Using Fivetran for automated ingestion, dbt for modular transformations, and Snowflake (Cortex and Snowpark ML) for data storage and forecasting, the team delivered accurate SKU-store level forecasts with governed, versioned models. The implementation reduced stockouts, cut holding costs, and streamlined procurement decisions, while improving data reliability and accelerating delivery.
Business Challenges
- Volatile demand across channels with frequent promotions and seasonal peaks
- Inconsistent data across POS, ERP, and marketing systems leading to unreliable forecasts
- Manual planning cycles and limited visibility into forecast drivers and accuracy
- The need for near-real-time insights to adjust procurement and replenishment decisions
Objectives
- Centralize and standardize data with minimal ops overhead
- Produce reliable SKU-store forecasts with confidence intervals
- Enable transparent model governance and continuous improvement
- Deliver actionable recommendations to planners in their BI tools
Solution Overview
We implemented a modern analytics and ML platform centered on Snowflake, with Fivetran and dbt enabling rapid, reliable pipelines and Snowflake ML capabilities powering time-series forecasting.
Data ingestion with Fivetran
- Prebuilt connectors load POS, ERP, marketing, pricing, and inventory data into Snowflake RAW schemas on automated schedules (typically 15–60 minutes).
- For niche sources, REST API connectors and staged file ingestion are supported; Snowpipe Auto-Ingest handles file-based feeds.
Transformations and modeling with dbt
- A layered dbt project (stg_ → dim_/fct_ → mart_) conforms data, enforces quality, and assembles a single source of truth for demand forecasting.
- Features include rolling demand signals, seasonal indicators, promo flags, price elasticity proxies, store-region calendars, and external factors (holidays, weather).
Forecasting with Snowflake ML
- Baseline approach: Snowflake Cortex forecasting functions for fast, scalable time-series predictions at SKU-store-day/week horizons with prediction intervals.
- Custom approach (as needed): Snowpark ML in Python for ARIMA/Prophet/gradient boosting ensembles; training runs inside Snowflake compute with the Snowflake Feature Store ensuring consistent features online/offline.
- Models are versioned and governed in the Snowflake Model Registry.
Orchestration and MLOps
- dbt Cloud jobs orchestrate transformations; Snowflake Tasks and Streams trigger incremental scoring when upstream data changes.
- Accuracy, drift, and freshness metrics are logged to monitoring tables; automated alerts notify analysts of anomalies.
Insights delivery and actionability
- Power BI connects directly to Snowflake marts for planners to review forecasts, risk signals, and procurement recommendations
- Scenario analysis lets users test service levels, lead times, and promo plans; approved recommendations are written back to Snowflake for auditability.
Architecture
Implementation Details
#1: Data Integration and Standardization
- Tools : Fivetran, Snowflake, dbt
- What we did :
- Configured Fivetran connectors for POS, ERP, and marketing platforms; raw data landed in RAW schema with source-specific naming.
- Built dbt staging models (stg_) to normalize timestamps/time zones, enforce data types, deduplicate keys, handle returns, and reconcile SKU/store IDs.
- Applied dbt tests (not_null, unique, accepted_values) and schema contracts to ensure quality upstream of ML.
#2: Feature Engineering & Analytical Models
- Tools : dbt, Snowflake
- What we did :
- Constructed dimensional models (dim_store, dim_product, dim_calendar) with SCD Type 2 for attribute history.
- Built fact layers (fct_sales_daily, fct_inventory, fct_promotions) with outlier handling, and created a unified training table (fct_training_base) combining :
- Lagged demand and rolling statistics
- Seasonal/week-of-year/month indicators
- Promotion flags and price ratio features
- Store-region attributes and trading calendars
- Holiday/weather signals where applicable
#3: Forecasting Models in Snowflake
- Tools : Snowflake Cortex and Snowpark ML, Snowflake Model Registry, Snowflake Feature Store
- What we did :
- Employed Cortex time-series functions to generate p10/p50/p90 forecasts at SKU-store for weekly/daily horizons.
- Trained Snowpark ML models (e.g., gradient boosting) for categories requiring specialized behavior (promo-heavy items, sparse series), leveraging Feature Store for consistent feature definitions.
- Registered all models in Snowflake Model Registry with metadata, training cutoff, and lineage back to dbt models.
#4: Deployment, Scoring, and Monitoring
- Tools : Snowflake Tasks and Streams, dbt Cloud, Power BI
- What we did :
- Scheduled nightly and intra-day scoring Tasks; Streams detect upstream changes to trigger incremental refreshes.
- Wrote forecasts to fct_forecast with versioning, horizons, and prediction intervals; maintained met_forecast_accuracy for MAPE/WAPE/sMAPE by product class and region.
- Implemented champion/challenger model management; automatic rollback triggers if accuracy degrades beyond thresholds.
- Enabled email/Slack alerts for data freshness issues, forecast drift, or model performance regression.
#5: Consumption and Decision Support
- Tools : Power BI
- What we did :
- Delivered dashboards for demand forecasts, inventory risk heatmaps, purchase order recommendations, and service-level trade-off analysis.
- Added scenario inputs for lead time, service levels, and promotional plans; calculated safety stock and reorder points from forecast error distributions.
- Supported writeback of approved procurement decisions to Snowflake with user/timestamp for auditability.
Data Models and Tables (examples)
| Staging | stg_sales_transactionsstg_productsstg_storesstg_promotions |
|---|---|
| Core | dim_product (SCD2)dim_store (SCD2)dim_calendarfct_sales_dailyfct_inventory_positions |
| Feature base | fct_training_base (all engineered features per SKU-store-date) |
| Forecast output | fct_forecast (sku, store, date, horizon, p10, p50, p90, version_id, model_id, training_cutoff) |
| Monitoring | met_forecast_accuracymet_data_freshnessmet_model_drift |
Governance and Cost Management
- Separate warehouses for ingestion, transformations, training, and BI; autosuspend/auto-resume tuned per workload.
- Access control via Snowflake roles; row-level security on marts by region/brand where required.
- CI/CD with dbt Cloud and Git; pre-production Snowflake database for UAT before promoting models and marts.
Results
96% Improved Forecast Accuracy
Achieved 96% accuracy in demand predictions, reducing forecasting errors by 25% compared to the previous model, resulting in better alignment of supply with demand.
$1.2M Reduction in Inventory Costs
Lowered annual holding costs by $1.2 million, optimizing procurement and reducing overstock by 18%, leading to a leaner inventory.
22% Faster Supply Chain Efficiency
Streamlined procurement processes, reducing lead times by 22% (from 14 days to 11 days), ensuring faster replenishment cycles and improved supplier coordination.
$3.5M Revenue Growth
Avoided stockouts during peak periods, leading to an 8% increase in annual revenue, equating to an additional $3.5 million in sales for the year.
20% Boost in Operational Efficiency Through Actionable Insights
Real-time dashboards empowered stakeholders with over 150 key performance indicators (KPIs), enabling quicker decision-making and improving operational efficiency by 20% across the supply chain.
Tech Stack
Snowflake
- Elastic compute isolation via virtual warehouses, separation of storage and compute, Time Travel, secure data sharing, and robust governance features.
Fivetran
- Managed connectors and scheduling reduce pipeline maintenance; predictable costs with MAR-based pricing.
dbt
- Transformation-as-code with built-in testing, documentation, and CI/CD for reliable analytics engineering.
Power BI
- Widely adopted enterprise BI with strong governance and security, ideal for executive and operational reporting.
Team
- Project Manager: 1
- Data Engineer : 1
- Database Administrator : 1
- Data Scientist : 1
- Data Analyst : 1
