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# Demand Forecasting and Forecasting Error

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Forecasting Exercises:
(P. 60 Attachment 1) Exhibit 11.16 Gas-Mart Monthly Milk Sales Data
? Create a graph of sales
? Create 3-month moving average forecast and add it to the graph
? Create weighted moving average forecast (60% most recent, 30% two periods back , 10% 3 periods back) and add it to your graph
? Create a single exponential smoothing forecast (alpha = .2 and taken as a cell parameter) and add it to the graph
? Regress milk sales on time and plot the associated linear regression line on the graph
? Evaluate the forecast accuracy using each of these methods:
o Mean Squared Error
o Mean Absolute Deviation
o Mean Absolute Percentage Error
(P. 79 Attachment 2) (Exercise #12 parts a, b, c, d)
****Just an FYI. The first problem (Gas Mart) is based on the attached example. There is an error on this example. Specifically, the attachment calculation of the 3-period weighted moving average applies the largest weights to the oldest data and the smallest weight to the newest data (opposite of the correct way). Please create in Excel format.

#### Solution Preview

The graphs are being created with fictitious data to show forecasting trends, moving average, weighted moving average, exponential moving average, and trend line with regression equation. The Excel formulas can be used to calculate any above mentioned forecasting method. Please see the attached Excel spreadsheet.

n-Month Moving Average: The average of the n most recent months.

Weighted Moving Average: If we are calculating weighted moving average for 3 months then we give weights to each months and set the weights to such that the oldest month gets the least weight and the most recent month gets ...

#### Solution Summary

The solution provides a brief explanation of each of the forecasting methods (Moving Average, Weighted Moving Average, Exponential Smoothing) as well as measures (Running Sum of Forecast Errors, Tracking Signal, Mean Squared Error, Mean Absolute Percentage Error, Mean Absolute Deviation) of forecast error. The attached Excel spreadsheet shows the formulas and the graphs to calculate these measures.

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