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Skalar AI
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RetailSeptember 20, 2024

Demand Forecasting Playbook

Our approach to implementing ML-powered demand forecasting for retail inventory optimization.

Target: 25-35% overstock reduction
Target: 30-40% fewer stockouts

Note: This is an illustrative playbook demonstrating our delivery approach. Outcomes shown are representative targets based on industry benchmarks. Actual results vary by retailer, data quality, and implementation scope.

The Challenge

Retail organizations often face a dual inventory problem: excess inventory tying up capital and empty shelves losing sales. Common causes include:

  • Legacy forecasting using simple moving averages
  • Inability to account for seasonal patterns beyond basic holidays
  • No consideration of local events or weather impacts
  • Promotional cannibalization effects ignored

Our Approach

We build ML-powered demand forecasting systems that predict at the store-SKU-day level.

Phase 1: Feature Engineering

Great forecasts require great features. We typically incorporate:

  • Historical sales by store and product (2-3+ years)
  • Weather forecasts by store location
  • Local event calendars
  • Promotional calendars and historical lift patterns
  • Competitor pricing (where available)
  • Economic indicators

Phase 2: Model Architecture

Rather than one monolithic model, we build ensembles:

  • Gradient boosting for stable products
  • Deep learning for products with complex seasonality
  • Separate models for new product launches
  • Automatic model selection based on product characteristics

Phase 3: Integration

Predictions flow directly into:

  • Automatic replenishment orders
  • Store allocation optimization
  • Markdown timing recommendations
  • Promotional planning tools

Target Outcomes

Based on industry benchmarks and similar implementations:

  • 25-35% typical reduction in overstock inventory
  • 30-40% typical reduction in stockout incidents
  • 10-20% improvement in inventory turns
  • Significant working capital freed up

Beyond Forecasting

The biggest impact often comes from changing how merchandising teams work. Instead of spending days building Excel forecasts, teams can focus on strategic decisions:

  • Which new products to test
  • How to allocate promotional budgets
  • Which stores need special attention

Ready to optimize your inventory operations? Contact us to explore the possibilities.

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