Sales Forecasting System (Sports Retailer)
Industry : Manufacturing
Russia's largest sporting goods, clothing, and footwear retailer (hundreds of stores in 100+ cities, tens of thousands of SKUs)
Task
Minimizing financial losses from product shortages and surpluses. Traditional planning based on prior-year data did not account for current market changes: weather conditions, currency fluctuations, holiday calendars, and competitive activity.
AI implementation process
Aggregation of historical data
Aggregation of historical data on sales, promotions, and external factors
Development and training of an ML model
ML model development to identify demand patterns
Testing
Algorithm testing on historical data
Integration into the IT environment
Integration with internal ordering and reporting systems
Client results during the AI implementation process
Revenue growth with AI
Revenue growth without AI
according to McKinsey & Company
Excess inventory reduced by 8%
Forecast accuracy increased to 92%
Logistics planning down to specific weeks
More about the solution
Typically, companies plan purchases based on last year’s data. However, this approach does not account for current market changes: weather, currency fluctuations, the holiday calendar, and competitor activity. Manually correlating and analyzing the impact of all these factors on tens of thousands of products is practically impossible.
How does ML forecasting work? Machine learning models analyze vast amounts of historical data and uncover relationships that are invisible through standard spreadsheet analysis. The system identifies the precise drivers of demand changes — for example, a combination of specific weather conditions, marketing promotions, and current economic indicators.
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