The Challenge.

  • Demand forecasting is one of the most important problems in retail. An accurate forecast can improve planning and reduce inventory costs. ​
  • Often, companies use high-level demand forecasts based on trends from the previous year. ​
  • These methods do not forecast at the product/store level, and often do not account for important factors such as seasonality, promotions, and weather.

The Solution.

  • Built statistical models to forecast sales by product and store up to 24 months in the future.​
  • Used sales of similar products to forecast sales of new products with insufficient historical data.​
  • Included features promotions and weather data into the model for added accuracy.​

The Results.

  • Product-level forecasts lead to a major reduction in out-of-stock.
  • Significant reduction of total inventory.​
  • Improved the buying process by enabling data-driven demand planning and budgeting.
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