This 8 page paper explains forecasting techniques for manufacturing companies. The information and data included are provided by the student. The writer begins with a general introduction to forecasting. A different forecasting method is then applied to each product. One method is a simply review of the data, the other is the weighted moving average technique. Data included. Bibliography lists 4 sources.
Name of Research Paper File: MM12_PGmffrc.rtf
Unformatted Sample Text from the Research Paper:
and accurate forecasting can lead a company to increased efficiency and decreased costs. The outcome for any company depends on the accuracy of the forecasting methods they use. Even the
best forecasting, however, is not free from possible errors. Markets change and the needs of consumers change but implementing adequate forecasting techniques helps the company meet the demands of the
market more effectively. There are numerous forecasting techniques from which to select, such as Linear Regression, Linear Programming, Queuing, Simple Moving Average, Weighted Moving Average, Exponential Smoothing, Regression Analysis, Historical
Analogy, Market Research and others. Each has its own value and process. For instance, the Simple Moving Average method of forecasting is related to a time period that contains
a number of data points. These data points are averaged by dividing the sum of the point values by the number of points. Each point has an equal weight. When
the Weighted Moving Average method of forecasting is used, by contrast, each point has a different value, or a different weight. More weight is given to the more recent data
and less weight is ascribed to older data. Exponential Smoothing is also a weighted method wherein the points given to recent data are weighted more; the weight declines exponentially
as data become older. The linear average methods are based on time series analysis (Iowa State University, 2002). In other words, past data are used to predict future needs
(Iowa State University, 2002). The formula for the Simple Moving Average is: Ft = Forecast for the coming period N = Number of periods to be averaged A t-1 =
Actual occurrence in the past period for up to "n" periods (Iowa State University, 2002). As an example, for product #2, we can use the data from the last four