MGMT415-1104A-03: Global Operations Management
American Inter-Continental University
October 29, 2011
In this paper, we will discuss a quantifiable method of forecasting called moving averages. Forecasting entails comparing historical values to predicted values for the future. 3-day and 5-day moving average calculations using Excel will be explained as well as a graph based on the forecasted values will also be shown. Finally, a method to measure error in the forecasting model will be described in detail.
Forecasting: ABC Flower Shop
Forecasting is a very important part of an operations manager’s duties. The demand forecasts are what tell the operations manager whether production will slow down or increase; as a result, he or she would make operational adjustments in accordance with those forecasts. Forecasts are either qualitative or quantitative in nature. In this forecasting example, ABC Flower Shop will use a quantitative method, which will enable it to predict how many flowers it will need to order from suppliers so that it can meet customer demands in the future. Although quantitative methods are designed to be more accurate than a guesstimate, it is important to note that many other factors have a tendency to throw off even the most precise calculations. Moving averages depend on the assumption that the past will repeat itself (Inman, 2011). 3-Day and 5-Day Moving Averages
ABC Flower Shop has acquired data of the past 14 days relating to the sale of geraniums. The quantitative method the store will use is called Moving Averages (MA). The store will use a 3-day and 5-day moving average to determine future customer demands. Table 1: 3-Day and 5-Day Moving Average Forecasts (Geraniums) Days |Demand |3-Day MA |5-Day MA |3-Day MA Error |5-Day MA Error |3-Day Mean Abs. Dev. |5-Day Mean Abs. Dev. | |1 |200 | | | | | | | |2 |134 | | | | | | | |3 |157 | | | | | | | |4 |165 |164 | |1 | |1 | |...