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Forecasting Part 3

Forecasting Part 3. By Anita Lee-Post. Selecting a forecasting model. Forecast accuracy. A good forecast is accurate but not perfect, i.e., actual value  forecast value Overall accuracy measures: 1. Mean absolute deviation 2. Mean squared error

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Forecasting Part 3

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  1. ForecastingPart 3 By Anita Lee-Post

  2. Selecting a forecasting model

  3. Forecast accuracy • A good forecast is accurate but not perfect, i.e., actual value  forecast value • Overall accuracy measures: 1. Mean absolute deviation 2. Mean squared error • Forecast accuracy has to be monitored by using a “tracking signal”

  4. Overall error measures • Mean absolute deviation (MAD): • Mean squared error (MSE): • The forecast technique giving the lowest MAD/MSE is preferred • MSE magnifies large errors through the squaring process

  5. Tracking signal A way to monitor forecast accuracy is by comparing a measure called: against predetermined control limits (usually +/-4 MAD) in a control chart

  6. Tracking signal continued Signal exceeded limit Tracking signal Upper control limit = +4MAD + 0 0 MAD - Lower control limit = -4MAD Time

  7. Correlation coefficient • Correlation coefficient, r, measures the direction and strength of the linear relationship between the independent (x) and dependent (y) variables

  8. Correlation coefficient continued r = 1 r = -1 Y Y Y = a + b X i i Y = a + b X i i X X r = .89 r = 0 Y Y Y = a + b X Y = a + b X X X i i i i • r = +1: a perfect positive linear relationship • r = 0: no relationship • r = -1: a perfect negative linear relationship

  9. Using Excel for forecasting • Enter the following demand figures for C&A’s product in an Excel worksheet

  10. Using Excel for forecastingcontinued • Invoke the data analysis tool: • Tools  Data Analysis If “Data Analysis” is not found, then • Tools  Add-ins  select “Analysis ToolPak”

  11. Using Excel for forecastingcontinued 3. Select “Moving Average” from the list of data analysis options” to compute a 3-month moving average:

  12. Using Excel for forecastingcontinued 4. Fill in the Moving Average Parameters: • Input Range: cell range of the time series • Labels in First Row: leave it unchecked if your cell range above contains data points only • Interval: parameter n (number of data points used in moving average computation) • Output Range: starting cell address for forecast values (need to offset the input range by one row)

  13. Using Excel for forecastingcontinued • Excel-generated moving average forecasts:

  14. Using Excel for forecastingcontinued 4. Fill in the Exponential Smoothing Parameters: • Input Range: cell range of the time series • Damping factor: 1-a, the smoothing constant • Labels: leave it unchecked if your cell range above contains data points only • Output range: starting cell address for forecast values (no offset is needed)

  15. Using Excel for forecastingcontinued • Excel-generated exponential smoothing forecasts: Copy the formula in cell C7 to cell C8 to compute the forecast for July

  16. Using Excel for forecastingcontinued 4. Fill in the Regression Parameters: • Input Y Range: cell range of the dependent variable • Input X Range: cell range of the independent variable • Labels: have it checked as column headings are included in our input ranges • Output range: starting cell address for regression analysis output

  17. Excel-generated regression analysis report: Enter the formula =D17+D18*A8 in cell B8 to compute the forecast for July

  18. Excel can be used to compute MAD and MSE:

  19. Excel can be used to compute MAD and MSE:

  20. Excel can be used to compute Tracking Signals:

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