Calculating a Forecast

Calculating A Forecast

For a company being able to calculate a forecast for a product is very important. The relevance to a forecast is for the company to be able to understand and predict a consumers buying behavior while avoiding stock-outs or over stocking the inventory (Hoover, 2009).

The first step for being able to adequately calculate a forecast is to choose a proper metric. The most popular is the MAPE or the Means Absolute Percentage Error. This type of metric is considered a scale-in-dependent and can be used to gauge and equate exactitude for a wide variety of items (Hoover, 2009).

The ABC Floral Shop wants to calculate a forecast for the demand of geraniums for a three and five year moving average using the current consumer demands for the geraniums over the past two weeks. To do this will use Excel Spreadsheets and enter the information given for the sales in the past two weeks. After this we will start by forecasting for the three year moving average: then calculating the sums.

B2 + B3 + B4 = 491 (200 + 134 + 157)

This formula is continued for the entire duration of the information we received, which was a two-week period or fourteen days. Here are the figures and predictions that were calculated using Excel and the information provided.

Day Demand 3PMT 3PMA 5PMT 5PMA

1 200

2 134 491 163.6666667

3 157 456 152 833 166.6

4 165 499 166.3333333 758 151.6

5 177 467 155.6666667 770 154

6 125 448 149.3333333 763 152.6

7 146 421 140.3333333 780 156

8 150 478 159.3333333 800 160