Demand Forecasting

2573 Words Aug 31st, 2009 11 Pages
Demand forecasting
Demand Forecasting is the activity of estimating the quantity of a product or service that consumers will purchase. Demand forecasting involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data from test markets. Demand forecasting may be used in making pricing decisions, in assessing future capacity requirements, or in making decisions on whether to enter a new market.

Necessity for forecasting demand
Often forecasting demand is confused with forecasting sales. But, failing to forecast demand ignores two important phenomena. There is a lot of debate in the demand planning literature as how to measure and
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Time series forecasting is a collection of methods for projecting forward from historic observations. A very simple example is a moving average. Of course, different methods are appropriate for different business conditions. The Holt’s Method is most suitable for basic or staple merchandise, while the Winter’s Method works best for seasonal merchandise, and Croston’s Method is appropriate for merchandise with little turnover. In all, there are more than a dozen methods to use, depending on your current situation. What is common across all methods is that the only data consumed in producing the forecast is derived of the learnings from previous similar situations. They permit modeling seasonal demand fluctuations, trend growth or decay, and lifecycle phenomena. Using time series methods, you need to utilize prior observations of demand. A good source of these observations is a point-of-sale system. These systems capture sales/transaction information, so it is necessary to make two adjustments in order to create your time series forecast. The first is to adjust the sales quantity to reflect the sales that you could have achieved if there had been no inventory defects. This may be as simple as extrapolating across weeks in which the item was out of stock, or as complex as dynamically adjusting sales when daily stock values fell
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