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Software for Forecast of Consumer Demand for Retailers

Forecasting consumer demand is extremely important for retail companies, since non-optimal planning of purchases results in significant losses.

Non-optimal purchases of goods results in:

  • freezing of funds,
  • decrease in assets turnover,
  • decrease in Return On Capital,
  • decrease in liquidity.

On the other hand, insufficient or belated purchases of goods leads to decreases in sales volumes and negatively impacts customer loyalty.

Effective planning means optimal time and volume of purchases. Sales forecasting is normally performed either by experts or specialized systems. Inefficiency of expert forecasting can be explained by the fact that an expert has to process huge volumes of data and take into account a great number of factors. Inexactitude in a forecast, even for a day, can lead to additional losses.

The most effective systems of forecasting the demand are based on neural networks. Using neural networks for forecasting is appropriate and effective for several reasons:

  • neural networks are intended for working with unstable, non-linear and quickly changing processes,
  • neural networks allows working effectively with "noisy" information and summarize large volumes of data,
  • the system has a functionality for evaluating the result deviation and classifying errors.

In the neural network, demand is forecast based on:

  • sales volume for the previous period,
  • day status (work day, weekend, holiday),
  • seasonality,
  • current volume of trade balance,
  • supply periodicity,
  • minimum incompressible stock,
  • etc.

Depending on the needs of a retail network, short-term and medium-term forecasts of demand can be created using an unlimited number of different input parameters.

Using systems based on neural networks ensures:

  • increase in sales and in customer loyalty,
  • optimization of stocks, decrease in the quantity of goods with low liquidity,
  • increase in turnover means and decrease in losses for the retailer,
  • minimization of human factor influence in forecasting.

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