Forecasting Demand - What is yours Goals ???

 

   

Demand Forecasting --  Lets Begin with Basic Definition.. So as name suggest it is  process of estimating customer demand in advance for a defined period of time.. Actually it provide an approx. Figure that how much Inventory a should keep in stock to fulfil customer orders.

  Ok Let "s think How to do Forecasting !!! 

 

Ouch !!! Do not think that much Below are some Common Method of Forecasting.. 

There are many types of forecasting methods in common use. The selection of the method to use depends on the industry of a firm and the length of time that is being forecasted. 

The simplest forecasting technique is the Naive method.- It simply states that demand this next period will be what demand was the last period. 

This is not a useful method for most industries. It is however effective in some industries, such as the fast food industry. In that industry the store manager predicts sales for the next Monday based on the sales of the prior Monday and orders an appropriate amount of food and schedules the necessary staff.. 

Time Series Forecast - A common set of forecasting techniques are called time-series forecasts. A time series is any group of data that is arranged in sequence according to the time it was gathered. For example, the monthly demand for a product is timeseries data. There is a large number of time-series forecasting methods.

    * The moving average is a simple times-series method. It is just the average of the demand for a set of the most recent periods. For example, if demand for the last 4 weeks was 100, 120, 130, and 120 for weeks 1 to 4, respectively, and if the number of periods to be averaged is n, then n 4 and the moving average is calculated as:

                                  MA (d1 d2 d3 d4)/n (100 120 130 120)/4 117.5 = 118

 *   Weighted moving average- A second time-series forecasting method is the. Instead of simply taking the average of a set number of periods, this method weights the more recent periods heavier than the older periods. Continuing to use the example above, where demand for the last 4 weeks was 100, 120, 130, and 120 for weeks 1 to 4, respectively, and with weights (w1, w2, w3, and w4) of .1, .2, .3, and .4 for weeks 1 to 4, respectively, the weighted moving average is: 

                 WMA w1d1 w2d2 w3d3 w4d4 .1 * (100) .2 * (120) .3 * (130) .4 * (120)  = 121

Again Confused ??  Which one to choose ??  Below is trick.. 

At this point you have probably noticed that all the different forecasts made with different forecasting models gave different forecasts. Which one should you use? Usually managers select the forecasting model that they will use to make forecasts using empirical evidence of how well it has made forecasts in the past. To evaluate the accuracy of each forecasting model, managers measure the forecast error. The manager will then select the forecast model that provides the least amount of error. The forecast error is the difference between actual demand and the forecast of the demand.

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