Demand Forecasting: How to Slash Inventory Using Machine Learning & Signal Processing

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The spreadsheet-based manual forecasting approach that is still used in many retail organizations is not suitable for staying ahead of the competition and improving their bottom line. Manual adjustments of forecasts in this method leave all inventory spend decisions to human judgement, which pushes towards generalized decisions rather than going with product, vendor, and market-specific indicators.

What makes this paper different is that it demonstrates the inventory optimization issue with a real-world problem and breaks down the recommended solution into digestible steps. This free white paper includes:

  • A walkthrough of what non-predictive inventory planning looks like
  • Limitations of traditional time-series forecasting
  • A step-by-step guide of how signal processing works
  • A toolkit for addressing data complexity and data scarcity challenges
  • Formulas for converting sales forecast to SKU-level demand forecast
  • Deep-dive of integrated demand planning to avoid lost opportunities from overstocking and understocking

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