Ch12 Russell
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Copyright 2012 John Wiley & Sons, Copyright 2012 John Wiley & Sons, Inc. Inc. Forecasting Forecasting Operations Management - 7 th Edition Chapter 12 Chapter 12 Roberta Russell & Bernard W. Taylor, III Roberta Russell & Bernard W. Taylor, III
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Copyright 2012 John Wiley & Sons, Inc.ForecastingOperations Management - 7th EditionChapter 12Roberta Russell & Bernard W. Taylor, III
Copyright 2009 John Wiley & Sons, Inc.
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Copyright 2012 John Wiley & Sons, Inc.12-*Lecture OutlineStrategic Role of Forecasting in Supply Chain Management Components of Demand ForecastingTime Series MethodsForecast AccuracyTime Series Forecasting Using ExcelRegression Methods
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Copyright 2012 John Wiley & Sons, Inc.12-*ForecastingPredicting the futureQualitative forecast methodssubjectiveQuantitative forecast methodsbased on mathematical formulas
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Copyright 2012 John Wiley & Sons, Inc.12-*Forecasting and Supply Chain ManagementAccurate forecasting determines how much inventory a company must keep at various points along its supply chainContinuous replenishmentsupplier and customer share continuously updated datatypically managed by the supplierreduces inventory for the companyspeeds customer deliveryVariations of continuous replenishmentquick responseJIT (just-in-time)VMI (vendor-managed inventory)stockless inventory
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Copyright 2012 John Wiley & Sons, Inc.12-*ForecastingQuality ManagementAccurately forecasting customer demand is a key to providing good quality serviceStrategic PlanningSuccessful strategic planning requires accurate forecasts of future products and markets
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Copyright 2012 John Wiley & Sons, Inc.12-*Types of Forecasting MethodsDepend ontime framedemand behaviorcauses of behavior
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Copyright 2009 John Wiley & Sons, Inc.12-*Time FrameIndicates how far into the future is forecastShort- to mid-range forecasttypically encompasses the immediate futuredaily up to two yearsLong-range forecastusually encompasses a period of time longer than two years
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Copyright 2009 John Wiley & Sons, Inc.12-*Demand BehaviorTrenda gradual, long-term up or down movement of demandRandom variationsmovements in demand that do not follow a patternCyclean up-and-down repetitive movement in demandSeasonal patternan up-and-down repetitive movement in demand occurring periodically
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Copyright 2009 John Wiley & Sons, Inc.12-*Forms of Forecast Movement
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Copyright 2009 John Wiley & Sons, Inc.12-*Forecasting MethodsTime seriesstatistical techniques that use historical demand data to predict future demandRegression methodsattempt to develop a mathematical relationship between demand and factors that cause its behaviorQualitativeuse management judgment, expertise, and opinion to predict future demand
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Copyright 2009 John Wiley & Sons, Inc.12-*Qualitative MethodsManagement, marketing, purchasing, and engineering are sources for internal qualitative forecastsDelphi methodinvolves soliciting forecasts about technological advances from experts
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Copyright 2009 John Wiley & Sons, Inc.12-*Forecasting ProcessNoYes
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Copyright 2009 John Wiley & Sons, Inc.12-*Time SeriesAssume that what has occurred in the past will continue to occur in the futureRelate the forecast to only one factor - timeIncludemoving averageexponential smoothinglinear trend line
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Copyright 2012 John Wiley & Sons, Inc.12-*Moving AverageNaive forecastdemand in current period is used as next periods forecastSimple moving averageuses average demand for a fixed sequence of periodsstable demand with no pronounced behavioral patternsWeighted moving averageweights are assigned to most recent data
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Copyright 2009 John Wiley & Sons, Inc.12-*Moving Average:Nave Approach
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Copyright 2009 John Wiley & Sons, Inc.12-*Simple Moving Average
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Copyright 2009 John Wiley & Sons, Inc.12-*3-month Simple Moving Average
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Copyright 2009 John Wiley & Sons, Inc.12-*5-month Simple Moving Average
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Copyright 2009 John Wiley & Sons, Inc.12-*Smoothing Effects
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Copyright 2009 John Wiley & Sons, Inc.12-*Weighted Moving AverageAdjusts moving average method to more closely reflect data fluctuationsn
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Copyright 2009 John Wiley & Sons, Inc.12-*Weighted Moving Average ExampleMONTH WEIGHT DATA
August 17%130September 33%110October 50%90
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Copyright 2009 John Wiley & Sons, Inc.12-*Averaging method Weights most recent data more stronglyReacts more to recent changesWidely used, accurate methodExponential Smoothing
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Copyright 2009 John Wiley & Sons, Inc.12-*Ft +1 = Dt + (1 - )Ftwhere:Ft +1 =forecast for next periodDt =actual demand for present periodFt =previously determined forecast for present period=weighting factor, smoothing constantExponential Smoothing (cont.)
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Copyright 2009 John Wiley & Sons, Inc.12-*Effect of Smoothing Constant0.0 1.0 If = 0.20, then Ft +1 = 0.20Dt + 0.80 FtIf = 0, then Ft +1 = 0Dt + 1 Ft = Ft Forecast does not reflect recent dataIf = 1, then Ft +1 = 1Dt + 0 Ft =Dt Forecast based only on most recent data
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Copyright 2009 John Wiley & Sons, Inc.12-*Exponential Smoothing (=0.30)
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Copyright 2009 John Wiley & Sons, Inc.12-*Exponential Smoothing (cont.)
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Copyright 2009 John Wiley & Sons, Inc.12-*Exponential Smoothing (cont.)
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Copyright 2009 John Wiley & Sons, Inc.12-*AFt +1= Ft +1 + Tt +1whereT = an exponentially smoothed trend factor
Tt +1 = (Ft +1 - Ft) + (1 - ) TtwhereTt = the last period trend factor= a smoothing constant for trendAdjusted Exponential Smoothing
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Copyright 2009 John Wiley & Sons, Inc.12-*Adjusted Exponential Smoothing (=0.30)
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Copyright 2009 John Wiley & Sons, Inc.12-*Adjusted Exponential Smoothing: Example
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Copyright 2009 John Wiley & Sons, Inc.12-*Adjusted Exponential Smoothing Forecasts
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Copyright 2009 John Wiley & Sons, Inc.12-*y = a + bx
wherea = interceptb = slope of the linex = time periody = forecast for demand for period xLinear Trend Line
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Copyright 2009 John Wiley & Sons, Inc.12-*Least Squares Example
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Copyright 2009 John Wiley & Sons, Inc.12-*Least Squares Example (cont.)
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Copyright 2009 John Wiley & Sons, Inc.12-*
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Copyright 2009 John Wiley & Sons, Inc.12-*Seasonal AdjustmentsRepetitive increase/ decrease in demandUse seasonal factor to adjust forecast
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Copyright 2009 John Wiley & Sons, Inc.12-*Seasonal Adjustment (cont.)
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Copyright 2009 John Wiley & Sons, Inc.12-*Seasonal Adjustment (cont.)
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Copyright 2009 John Wiley & Sons, Inc.12-*Forecast AccuracyForecast errordifference between forecast and actual demandMADmean absolute deviationMAPDmean absolute percent deviationCumulative errorAverage error or bias
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Copyright 2009 John Wiley & Sons, Inc.12-*Mean Absolute Deviation (MAD)where t= period number Dt= demand in period t Ft= forecast for period t n= total number of periods= absolute value
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Copyright 2009 John Wiley & Sons, Inc.12-*MAD Example
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Copyright 2009 John Wiley & Sons, Inc.12-*Other Accuracy Measures
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Copyright 2009 John Wiley & Sons, Inc.12-*Comparison of Forecasts
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Copyright 2009 John Wiley & Sons, Inc.12-*Forecast ControlTracking signalmonitors the forecast to see if it is biased high or low
1 MAD 0.8 Control limits of 2 to 5 MADs are used most frequently
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Copyright 2009 John Wiley & Sons, Inc.12-*Tracking Signal Values
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Copyright 2009 John Wiley & Sons, Inc.12-*Tracking Signal Plot3 2 1 0 -1 -2 -3 |||||||||||||0123456789101112Tracking signal (MAD)Period
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Copyright 2009 John Wiley & Sons, Inc.12-*Statistical Control ChartsUsing we can calculate statistical control limits for the forecast errorControl limits are typically set at 3
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Copyright 2009 John Wiley & Sons, Inc.12-*Statistical Control Charts
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Copyright 2009 John Wiley & Sons, Inc.12-*Time Series Forecasting using ExcelExcel can be used to develop forecasts:Moving averageExponential smoothingAdjusted exponential smoothingLinear trend line
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Copyright 2009 John Wiley & Sons, Inc.12-*Exponentially Smoothed and Adjusted Exponentially Smoothed Forecasts
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Copyright 2009 John Wiley & Sons, Inc.12-*Demand and exponentially smoothed forecast
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Copyright 2009 John Wiley & Sons, Inc.12-*Data Analysis option
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Copyright 2009 John Wiley & Sons, Inc.12-*Computing a Forecast with Seasonal Adjustment
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Copyright 2009 John Wiley & Sons, Inc.12-*OM Tools
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Copyright 2009 John Wiley & Sons, Inc.12-*Regression MethodsLinear regressiona mathematical technique that relates a dependent variable to an independent variable in the form of a linear equationCorrelationa measure of the strength of the relationship between independent and dependent variables
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Copyright 2009 John Wiley & Sons, Inc.12-*Linear Regression
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Copyright 2009 John Wiley & Sons, Inc.12-*Linear Regression Examplexy(WINS)(ATTENDANCE) xyx2
436.3145.216640.1240.636641.2247.236853.0424.064644.0264.036745.6319.249539.0195.025747.5332.549
49346.72167.7311
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Copyright 2009 John Wiley & Sons, Inc.12-*Linear Regression Example (cont.)
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Copyright 2009 John Wiley & Sons, Inc.12-*Linear Regression Example (cont.)
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Copyright 2009 John Wiley & Sons, Inc.12-*Correlation and Coefficient of DeterminationCorrelation, rMeasure of strength of relationshipVaries between -1.00 and +1.00Coefficient of determination, r2Percentage of variation in dependent variable resulting from changes in the independent variable
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Copyright 2009 John Wiley & Sons, Inc.12-*Computing Correlation
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Copyright 2009 John Wiley & Sons, Inc.12-*Regression Analysis with Excel
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Copyright 2009 John Wiley & Sons, Inc.12-*Regression Analysis with Excel (cont.)
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Copyright 2009 John Wiley & Sons, Inc.12-*Regression Analysis with Excel (cont.)
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Copyright 2009 John Wiley & Sons, Inc.12-*Multiple RegressionStudy the relationship of demand to two or more independent variables
y = 0 + 1x1 + 2x2 + kxkwhere0 =the intercept1, , k=parameters for the independent variablesx1, , xk=independent variables
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Copyright 2009 John Wiley & Sons, Inc.12-*Multiple Regression with Excel
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Copyright 2009 John Wiley & Sons, Inc.12-*Copyright 2009 John Wiley & Sons, Inc. All rights reserved. Reproduction or translation of this work beyond that permitted in section 117 of the 1976 United States Copyright Act without express permission of the copyright owner is unlawful. Request for further information should be addressed to the Permission Department, John Wiley & Sons, Inc. The purchaser may make back-up copies for his/her own use only and not for distribution or resale. The Publisher assumes no responsibility for errors, omissions, or damages caused by the use of these programs or from the use of the information herein.
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