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Demand Forecasting: Using Time and Resources More Sensibly

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Demand Forecasting Optimize resource deployment

Forecasting Usecase Evaluation 

Complementary to the previous article  The following framework helps to save time and resources in Demand Forecasting more targeted. Each point in the diagram represents a product, a customer, a market, etc.. Behind each point there is a time series for which a demand forecast is to be created.

The usual dimensions, to distinguish the data points are Volume and volatility. Volume is the quantity of sales.

High volume means lots of sales and therefore (in most cases) more important products, markets or customers.

Volatility indicates the extent to which the time series data fluctuate respectively "erratic"are. There are usually forces at work here that cannot be read out of the data, e.g. economic effects or strategy changes by major customers. Critical is how the relationship between "Signal vs Noise" is in the data. If there is too much noise, the time series may not be "forecastable" at all.

Apply framework

According to the framework above:
  • Tend to devote few resources to low-volume products, markets, customers, etc.
  • Devote primary resources to high-volume products, markets, customers, etc.
  • For high volume and low volatility, simpler methods are usually sufficient to generate good demand forecasts
  • For high volume and high volatility, the issue is more complex: here, the forecasting capability needs to be evaluated (more on this later) and more advanced methods are generally required

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