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Primary & Secondary Sales Forecasting: Dairy Producer

Dairy & Milk

Primary & Secondary Sales Forecasting: Dairy Producer

Industry

Food & Beverage

Description

Leveraged machine learning and promotion data to outperform customer demand forecasts at a monthly level.

Scope

  • To improve forecast accuracy and gain demand insights from secondary sales data for both Consumer goods and Food services
  • Leverage Promotion data to fine-tune demand forecast
  • Generate Sell-in forecast based on Sell-in Historical Sales data (3 years of data at weekly level) for 2 countries
  • Forecast Accuracy measured at Product /Market /Opco’s and month level
  • 2 Forecasts generated for M-1 & M-4
  • Data trained considering 2 years of history and validate with 1 year of actuals
  • Improvement in Forecast accuracy in comparison with current demand planner’s forecast for Month – 4 and Month – 1
  • Insights into products with ABC (by volume and value) and XYZ analysis (Coefficient of Variance – Volatility)

Solution

  • The forecast was created at the item, item code for Foods Services, distributor level for Consumer Services
  • Forecasting was done at the daily, weekly, and monthly level
  • Forecasting was done for both M-1 and M-4
  • 6 different algorithmic approaches were used for each product, classic techniques such as ARIMA and Holt-Winters as well as machine learning approaches such as CatBoost and FaceBook Prophet
  • Engineered features of two different types:
    • Statistical features about the time series
    • Business features mostly extracted from price and time

Results

  • 87% accuracy: Consumer Service, Month-4 Forecast
  • 91% accuracy: Consumer Service, Month-1 Forecast
  • 81% accuracy: Food Services, Month-4 Forecast
  • 73% accuracy: Food Services, Month-1 Forecast
  • Machine Learning techniques improves forecast accuracy over the current demand forecaster inputs
  • Promotions had a significant impact on the improvement in forecast accuracy in Consumer Goods

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