ch12

Chapter 12Forecasting

Copyright 2011 John Wiley & Sons, Inc.

Lecture OutlineStrategic Role of Forecasting in Supply Chain Management Components of Forecasting DemandTime Series MethodsForecast AccuracyTime Series Forecasting Using ExcelRegression MethodsCopyright 2011 John Wiley & Sons, Inc.12-*


Supply Chain ManagementAccurate forecasting determines inventory levels in the supply chainContinuous replenishmentsupplier & 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 inventoryCopyright 2011 John Wiley & Sons, Inc.12-*


ForecastingQuality ManagementAccurately forecasting customer demand is a key to providing good quality service

Strategic PlanningSuccessful strategic planning requires accurate forecasts of future products and marketsCopyright 2011 John Wiley & Sons, Inc.12-*


Types of Forecasting MethodsDepend on

time frame

demand behavior

causes of behaviorCopyright 2011 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 years

Long-range forecastusually encompasses a period of time longer than two yearsCopyright 2011 John Wiley & Sons, Inc.12-*


Demand BehaviorTrenda gradual, long-term up or down movement of demand

Random variationsmovements in demand that do not follow a pattern

Cyclean up-and-down repetitive movement in demand

Seasonal patternan up-and-down repetitive movement in demand occurring periodically12-*

Forms of Forecast Movement12-*

Forecasting MethodsTime seriesstatistical techniques that use historical demand data to predict future demand

Regression methodsattempt to develop a mathematical relationship between demand and factors that cause its behavior

Qualitativeuse management judgment, expertise, and opinion to predict future demand

12-*

Qualitative MethodsManagement, marketing, purchasing, and engineering are sources for internal qualitative forecasts

Delphi methodinvolves soliciting forecasts about technological advances from experts12-*

Forecasting Process12-*

Time SeriesAssume that what has occurred in the past will continue to occur in the future

Relate the forecast to only one factor time

Includemoving average

exponential smoothing

linear trend line12-*

Moving AverageNaive forecastdemand in current period is used as next periods forecast

Simple moving averageuses average demand for a fixed sequence of periodsstable demand with no pronounced behavioral patterns

Weighted moving averageweights are assigned to most recent dataCopyright 2011 John Wiley & Sons, Inc.12-*


We sold 250 wheels lastweek.... Now, next week we should sell.At : Actual demand in period tF(t+1) : Forecast of demand for period t+1F(t+1) = At Naive forecasts250 wheels

Seasonal Adjustments12-*Repetitive increase and decrease in demandUse seasonal factor to adjust forecastHoliday season: toys, sport equipments, clothing, wine, fruit, etc.

Forecast AccuracyForecast errordifference between forecast and actual demandMADmean absolute deviationMAPDmean absolute percent deviationCumulative errorAverage error or biasCopyright 2011 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 frequentlyCopyright 2011 John Wiley & Sons, Inc.12-*


Regression MethodsLinear regressionmathematical 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 variablesCopyright 2011 John Wiley & Sons, Inc.12-*


Linear Regression ExampleCopyright 2011 John Wiley & Sons, Inc.12-*|||||||||||01234567891060,000 50,000 40,000 30,000 20,000 10,000 Linear regression line, y = 18.46 + 4.06xWins, xAttendance, yy= 18.46 + 4.06(7)= 46.88, or 46,880 Attendance forecast for 7 wins


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

Copyright 2011 John Wiley & Sons, Inc.12-*


Copyright 2011 John Wiley & Sons, Inc.12-*Copyright 2011 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.


Auto sales at Carmens Chevrolet are shown below. Develop a 3-week moving average.

WeekAuto Sales18210394115106137-

WeekAuto SalesThree-Week Moving Average1821039411(8 + 9 + 10) / 3 = 9510(10 + 9 + 11) / 3 = 10613(9 + 11 + 10) / 3 = 107-(11 + 10 + 13) / 3 = 11 1/3

Carmens decides to forecast auto sales by weighting the three weeks as follows:

Weights AppliedPeriod3Last week2Two weeks ago1Three weeks ago6Total

WeekAuto SalesThree-Week Moving Average1821039411[(3*9) + (2*10) + (1*8)] / 6 = 9 1/6510[(3*11) + (2*9) + (1*10)] / 6 = 10 1/6613[(3*10) + (2*11) + (1*9)] / 6 = 10 1/67-[(3*13) + (2*10) + (1*11)] / 6 = 11 2/3

Exponential smoothing

Sheet1

Exponential Smoothing

Alpha =0.4

PeriodActualForecast

60

6560

5562.00

5859.20

6458.72

60.83

Sheet1

Sheet2

Sheet3

.2.05 Smoothing constant

]F-CAST2.XLC

100010001000

105610001000

10901002.81011.2

11001007.161028.64

11981011.8021047.208

12451021.11191084.4476

12591032.3063051129.22522

13001043.640989751174.563959

13051056.45894026251225.83576105

12901068.88599324941275.5439729975

13001079.94169358691319.7667743476

13541090.94460890761363.7784356302

&F

Page &P

Demand

F (.05)

F(.2)

Example 1

Example 2

MonthActual Battery SalesForecastJanuary2022February21March15April14May13June16

Given the forecast demand and actual demand for 10-foot fishing boats, compute the tracking signal and MAD.Example 3

YearForecast DemandActual Demand1787127580383101484845886068573

*******************************

ch12

Documents

Transcript of ch12