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Retail Demand Forecasting

Business Need​

Predict weekly demand at store and item levels for a major retailer to optimize inventory management, reduce stockouts, and minimize excess inventory costs across hundreds of locations.

Solution Overview​

This solution leverages Abacus.AI's forecasting capabilities combined with custom aggregation logic to deliver accurate demand predictions:

Step 1: Connect & Ingest Data

  • Data Source: Historical sales data is ingested from BigQuery using our native database connector
  • Data Structure: Transactional sales data includes timestamps, store IDs, product SKUs, quantities sold, and promotional indicators
  • Update Frequency: Weekly updates ensure the model always has access to the latest sales trends

Step 2: Multi-Level Aggregations

  • Store-Level Weekly: Weekly transactions are aggregated to weekly store totals to capture overall store performance patterns
  • Item-Level Weekly: Simultaneously, item-level weekly aggregations capture product-specific demand trends
  • Feature Engineering: Additional features include holiday indicators, and promotional flags
💡Pro Tip

Dual-level aggregation allows the model to learn both macro-level store patterns and micro-level product behaviors, significantly improving forecast accuracy for seasonal and promotional events.

Step 3: Build Forecasting Models

Using the Forecasting and Planning use case, separate models are trained for store-level and item-level forecasts. The platform automatically handles seasonality detection, trend analysis, and feature engineering.

Step 4: Normalization & Reconciliation

A custom feature group normalizes item-level forecasts based on store-level aggregates to ensure consistency. This prevents scenarios where individual item forecasts exceed total store capacity or violate business constraints.

Step 5: Export Predictions

Forecasted demand is written back to BigQuery in a structured format, ready for consumption by inventory management systems, supply chain planning tools, and executive dashboards.

How It's Used in Practice​

This forecasting solution operates as a production-grade system supporting weekly operations:

Weekly Forecast Refresh:

  • Ingests the latest week of sales data from BigQuery
  • Generates 26-week rolling forecasts for all active store-item combinations
  • Applies normalization logic to ensure forecast consistency
  • Writes results to dedicated BigQuery tables

Quarterly Model Retraining:

  • Retrains forecasting models to adapt to changing seasonal patterns
  • Incorporates new promotional strategies and market trends
  • Validates model performance against held-out test sets

Real-Time Access:

  • Supply chain teams query BigQuery directly for procurement decisions

✨Key Outcomes

Key achievements:

📊 40% reduction in error​

Compared to previous solution

💰 $1+M annual savings​

Reduced excess inventory carrying costs

Additional Information​