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Demand Forecasting: common questions

Demand Forecasting: common questions

Why forecast demand? How? And what’s the difference between forecasting and planning? Your questions answered ….

1.      Why forecast demand?

Demand forecasting should drive better replenishment decisions (i.e. ordering enough stock but not too much) so that cash is not tied up in inventory, but you can meet customer demands with ease. It also helps to align short- and long-term planning in the business.

2.      How can you forecast demand?

Using historical data is a good start, on which you can layer additional data and intelligence such as promotions and out of stock information. You’ll also need to make sure you include information on big one-off events, such as one-off promotions, to adjust the forecast.

3.      How can you forecast demand for a new product?

The method used to forecast sales for a new product depends on what kind of new product it is:

a.      If it’s a product that replaces an existing, similar product, then using historical data for the existing product is a good start.

b.      For a product that is an extended member of an existing product family, then study trends in the product family such as seasonality and trends.

c.       For an entirely new product, look at similar product groups and again pull out trends such as seasonality. Combine this with sales activities, and marketing and promotional planning and use that as a basis to create your new forecast.

You’ll need to keep reviewing forecast and actual data, challenge assumptions and review and adjust for at least six months.

4.      Demand forecasting vs. Sales forecasting – what’s the difference?

Demand forecasting and sales forecasting are terms that are sometimes used interchangeably. Demand forecasting is a process that also can be applied in service sectors outside of sales, for example, forecasting demand volume of calls to a call centre.

5.      Demand forecasting vs. Demand planning – what’s the difference?

Demand forecasting will generate forecast data, demand planning takes that a step further by understanding the reasons behind the forecast, applying assumptions and commercial insight (such as business wins / losses, etc.).