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© 2003 Anita Lee-Post Forecasting Part 3 By Anita Lee-Post By

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© 2003 Anita Lee-Post Selecting a forecasting model

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© 2003 Anita Lee-Post Forecast accuracy A good forecast is accurate but not perfect, i.e., actual value forecast valueA good forecast is accurate but not perfect, i.e., actual value forecast value Overall accuracy measures:Overall accuracy measures: 1. Mean absolute deviation 2. Mean squared error Forecast accuracy has to be monitored by using a “tracking signal”Forecast accuracy has to be monitored by using a “tracking signal” A good forecast is accurate but not perfect, i.e., actual value forecast valueA good forecast is accurate but not perfect, i.e., actual value forecast value Overall accuracy measures:Overall accuracy measures: 1. Mean absolute deviation 2. Mean squared error Forecast accuracy has to be monitored by using a “tracking signal”Forecast accuracy has to be monitored by using a “tracking signal”

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© 2003 Anita Lee-Post Overall error measures 1.Mean absolute deviation (MAD): 2.Mean squared error (MSE): The forecast technique giving the lowest MAD/MSE is preferredThe forecast technique giving the lowest MAD/MSE is preferred MSE magnifies large errors through the squaring processMSE magnifies large errors through the squaring process 1.Mean absolute deviation (MAD): 2.Mean squared error (MSE): The forecast technique giving the lowest MAD/MSE is preferredThe forecast technique giving the lowest MAD/MSE is preferred MSE magnifies large errors through the squaring processMSE magnifies large errors through the squaring process

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© 2003 Anita Lee-Post 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 A way to monitor forecast accuracy is by comparing a measure called: against predetermined control limits (usually +/-4 MAD) in a control chart

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© 2003 Anita Lee-Post Tracking signal continued Time Lower control limit = -4MAD Upper control limit = +4MAD Signal exceeded limit Tracking signal 0 MAD + 0 -

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© 2003 Anita Lee-Post Correlation coefficient Correlation coefficient, r, measures the direction and strength of the linear relationship between the independent (x) and dependent (y) variablesCorrelation coefficient, r, measures the direction and strength of the linear relationship between the independent (x) and dependent (y) variables

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© 2003 Anita Lee-Post Correlation coefficient continued r = +1: a perfect positive linear relationship r = 0: no relationship r = -1: a perfect negative linear relationship r = +1: a perfect positive linear relationship r = 0: no relationship r = -1: a perfect negative linear relationship

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© 2003 Anita Lee-Post Using Excel for forecasting 1.Enter the following demand figures for C&A’s product in an Excel worksheet JanFebMarAprMayJun

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© 2003 Anita Lee-Post Using Excel for forecasting continued 2.Invoke the data analysis tool: Tools Data Analysis If “Data Analysis” is not found, then Tools Add-ins select “Analysis ToolPak” 2.Invoke the data analysis tool: Tools Data Analysis If “Data Analysis” is not found, then Tools Add-ins select “Analysis ToolPak”

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© 2003 Anita Lee-Post Using Excel for forecasting continued 3. Select “Moving Average” from the list of data analysis options” to compute a 3-month moving average:

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© 2003 Anita Lee-Post Using Excel for forecasting continued Input Range: cell range of the time seriesInput Range: cell range of the time series Labels in First Row: leave it unchecked if your cell range above contains data points onlyLabels 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)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)Output Range: starting cell address for forecast values (need to offset the input range by one row) Input Range: cell range of the time seriesInput Range: cell range of the time series Labels in First Row: leave it unchecked if your cell range above contains data points onlyLabels 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)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)Output Range: starting cell address for forecast values (need to offset the input range by one row) 4. Fill in the Moving Average Parameters:

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© 2003 Anita Lee-Post Using Excel for forecasting continued Excel-generated moving average forecasts: Excel-generated moving average forecasts:

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© 2003 Anita Lee-Post Using Excel for forecasting continued Input Range: cell range of the time seriesInput Range: cell range of the time series Damping factor: 1- the smoothing constantDamping factor: 1- the smoothing constant Labels: leave it unchecked if your cell range above contains data points onlyLabels: leave it unchecked if your cell range above contains data points only Output range: starting cell address for forecast values (no offset is needed)Output range: starting cell address for forecast values (no offset is needed) Input Range: cell range of the time seriesInput Range: cell range of the time series Damping factor: 1- the smoothing constantDamping factor: 1- the smoothing constant Labels: leave it unchecked if your cell range above contains data points onlyLabels: leave it unchecked if your cell range above contains data points only Output range: starting cell address for forecast values (no offset is needed)Output range: starting cell address for forecast values (no offset is needed) 4. Fill in the Exponential Smoothing Parameters:

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© 2003 Anita Lee-Post Using Excel for forecasting continued Excel-generated exponential smoothing forecasts: Excel-generated exponential smoothing forecasts: Copy the formula in cell C7 to cell C8 to compute the forecast for July

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© 2003 Anita Lee-Post Using Excel for forecasting continued Input Y Range: cell range of the dependent variableInput Y Range: cell range of the dependent variable Input X Range: cell range of the independent variableInput X Range: cell range of the independent variable Labels: have it checked as column headings are included in our input rangesLabels: have it checked as column headings are included in our input ranges Output range: starting cell address for regression analysis outputOutput range: starting cell address for regression analysis output Input Y Range: cell range of the dependent variableInput Y Range: cell range of the dependent variable Input X Range: cell range of the independent variableInput X Range: cell range of the independent variable Labels: have it checked as column headings are included in our input rangesLabels: have it checked as column headings are included in our input ranges Output range: starting cell address for regression analysis outputOutput range: starting cell address for regression analysis output 4. Fill in the Regression Parameters:

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© 2003 Anita Lee-Post Excel-generated regression analysis report: Enter the formula =D17+D18*A8 in cell B8 to compute the forecast for July

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© 2003 Anita Lee-Post Excel can be used to compute MAD and MSE: ABCDE 1 MonthDemand3-month Moving Average Absolute DeviationSquared Error …………… 5 Apr800720=ABS(B5-C5)=(B5-C5)^2 6 May900770=ABS(B6-C6)=(B6-C6)^2 7 Jun =ABS(B7-C7)=(B7-C7)^2 8 9 MAD=AVERAGE(D5:D7) 10 MSE =) = AVERAGE(E5:E7 )

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© 2003 Anita Lee-Post Excel can be used to compute MAD and MSE: ABCDE 1 MonthDemandExp. Smooth. ( Absolute DeviationSquared Error …………… 5 Apr =ABS(B5-C5)=(B5-C5)^2 6 May =ABS(B6-C6)=(B6-C6)^2 7 Jun =ABS(B7-C7)=(B7-C7)^2 8 9 MAD=AVERAGE(D5:D7) 10 MSE=AVERAGE(E5:E7)

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© 2003 Anita Lee-Post Excel can be used to compute Tracking Signals: ABCDEF 1 MonthDemand3-month Moving Average ErrorCumulative Sum of Error Tracking Signal ……………… 5 Apr800720=B5-C5=D5=E5/$D$9 6 May900770=B6-C6=E5+D6=E6/$D$9 7 Jun =B7-C7=E6+D7=E7/$D$9 8 9MAD116

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