02 demand forecasting
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MODULEMODULE--22MODULEMODULE--22
DemandDemand ForecastingForecasting
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OverviewOverview
IntroductionIntroduction
Qualitative Forecasting MethodsQualitative Forecasting Methods
Quantitative Forecasting ModelsQuantitative Forecasting Models
How to Have a Successful Forecasting SystemHow to Have a Successful Forecasting System
Computer Software for ForecastingComputer Software for Forecasting
Forecasting in Small Businesses and StartForecasting in Small Businesses and Start--UpUp
VenturesVentures
WrapWrap--Up: What WorldUp: What World--Class Producers DoClass Producers Do
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IntroductionIntroductionIntroductionIntroduction
Demand estimatesDemand estimates for products and servicesfor products and services
are the starting point for all the other planningare the starting point for all the other planning
in operations management.in operations management.
Management teams developManagement teams develop sales forecastssales forecastsbased in part on demand estimates.based in part on demand estimates.
The sales forecasts become inputs to bothThe sales forecasts become inputs to both
business strategy andbusiness strategy and production resourceproduction resourceforecastsforecasts..
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Forecasting is an Integral PartForecasting is an Integral Part
of Business Planningof Business Planning
Forecasting is an Integral PartForecasting is an Integral Part
of Business Planningof Business Planning
ForecastForecast
Method(s)Method(s)
DemandDemand
EstimatesEstimates
SalesSales
ForecastForecast
ManagementManagement
TeamTeam
Inputs:Inputs:
Market,Market,
Economic,Economic,
OtherOther
BusinessBusiness
StrategyStrategy
Production ResourceProduction Resource
ForecastsForecasts
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Some Reasons WhySome Reasons Why
Forecasting is Essential in OMForecasting is Essential in OM
Some Reasons WhySome Reasons Why
Forecasting is Essential in OMForecasting is Essential in OM
New Facility PlanningNew Facility Planning It can take 5 years to designIt can take 5 years to design
and build a new factory or design and implement aand build a new factory or design and implement a
new production process.new production process.
Production PlanningProduction Planning Demand for products varyDemand for products varyfrom month to month and it can take several monthsfrom month to month and it can take several months
to change the capacities of production processes.to change the capacities of production processes.
Workforce SchedulingWorkforce Scheduling Demand for services (andDemand for services (and
the necessary staffing) can vary from hour to hourthe necessary staffing) can vary from hour to hourand employees weekly work schedules must beand employees weekly work schedules must be
developed in advance.developed in advance.
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LongLong--Range ForecastsRange ForecastsLongLong--Range ForecastsRange Forecasts
Time spans usually greaterTime spans usually greater
than one yearthan one year
Necessary to support strategicNecessary to support strategic
decisions about planningdecisions about planning
products, processes, andproducts, processes, andfacilitiesfacilities
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Examples of Production Resource ForecastsExamples of Production Resource ForecastsExamples of Production Resource ForecastsExamples of Production Resource Forecasts
LongLong
RangeRange
MediumMedium
RangeRange
ShortShortRangeRange
YearsYears
MonthsMonths
Days,Days,WeeksWeeks
Product Lines,Product Lines,
Factory CapacitiesFactory Capacities
ForecastForecast
HorizonHorizon
TimeTime
SpanSpan
Item BeingItem Being
ForecastedForecasted
Unit ofUnit of
MeasureMeasure
Product Groups,Product Groups,
Depart. CapacitiesDepart. Capacities
Specific Products,Specific Products,Machine CapacitiesMachine Capacities
Dollars,Dollars,
TonsTons
Units,Units,
PoundsPounds
Units,Units,HoursHours
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Forecasting MethodsForecasting Methods
Qualitative ApproachesQualitative Approaches
Quantitative ApproachesQuantitative Approaches
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NonNon--Statistical (Qualitative) ApproachesStatistical (Qualitative) Approaches
Usually based on judgments about causal factors thatUsually based on judgments about causal factors that
underlie the demand of particular products or servicesunderlie the demand of particular products or services
Do not require a demand history for the product orDo not require a demand history for the product or
service, therefore are useful for new products/servicesservice, therefore are useful for new products/services Approaches vary in sophistication from scientificallyApproaches vary in sophistication from scientifically
conducted surveys to intuitive hunches about futureconducted surveys to intuitive hunches about future
eventsevents
The approach/method that is appropriate depends on aThe approach/method that is appropriate depends on aproducts life cycle stageproducts life cycle stage
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Qualitative MethodsQualitative MethodsQualitative MethodsQualitative Methods
Educated guessEducated guess intuitive hunchesintuitive hunches Executive committee consensusExecutive committee consensus
Delphi methodDelphi method
Survey of sales forceSurvey of sales force
Survey of customersSurvey of customers
Historical analogyHistorical analogy Market research orMarket research or scientificallyscientifically
conducted surveysconducted surveys
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Qualitative MethodsQualitative MethodsQualitative MethodsQualitative Methods
Educated guessEducated guess intuitive hunchesintuitive hunches
Executive committee consensusExecutive committee consensus
Delphi methodDelphi method
Survey of sales forceSurvey of sales force Survey of customersSurvey of customers
Historical analogyHistorical analogy
Market researchMarket research sscientifically conducted surveyscientifically conducted surveys
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Quantitative Forecasting ApproachesQuantitative Forecasting ApproachesQuantitative Forecasting ApproachesQuantitative Forecasting Approaches
Based on the assumption that the forces thatBased on the assumption that the forces that
generated the past demand will generate thegenerated the past demand will generate the
future demand, i.e., history will tend to repeatfuture demand, i.e., history will tend to repeat
itselfitself Analysis of the past demand pattern provides aAnalysis of the past demand pattern provides a
good basis for forecasting future demandgood basis for forecasting future demand
Majority of quantitative approaches fall in theMajority of quantitative approaches fall in thecategory ofcategory oftime series analysistime series analysis
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AA time seriestime series is a set of numbersis a set of numberswhere the order or sequence of thewhere the order or sequence of the
numbers is important, e.g., historicalnumbers is important, e.g., historical
demanddemand
Analysis of the time series identifiesAnalysis of the time series identifies
patternspatterns Once the patterns are identified, theyOnce the patterns are identified, they
can be used to develop a forecastcan be used to develop a forecast
Time Series AnalysisTime Series AnalysisTime Series AnalysisTime Series Analysis
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Components of a Time SeriesComponents of a Time SeriesComponents of a Time SeriesComponents of a Time Series
TrendsTrends are noted by an upward orare noted by an upward or
downward sloping line.downward sloping line.
CycleCycle is a data pattern that may coveris a data pattern that may cover
several years before it repeats itself.several years before it repeats itself.
SeasonalitySeasonality is a data pattern that repeatsis a data pattern that repeats
itself over the period of one year or less.itself over the period of one year or less. Random fluctuation (noise)Random fluctuation (noise) results fromresults from
random variation or unexplained causes.random variation or unexplained causes.
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Seasonal PatternsSeasonal PatternsSeasonal PatternsSeasonal Patterns
Length of TimeLength of Time Number ofNumber of
Before Pattern Length ofBefore Pattern Length of SeasonsSeasons
Is RepeatedIs Repeated SeasonSeason in Patternin Pattern
YearYear Quarter Quarter 44
YearYear MonthMonth 1212
YearYear Week Week 5252
MonthMonth DayDay 2828--3131
WeekWeek DayDay 77
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Quantitative Forecasting ApproachesQuantitative Forecasting Approaches
Linear RegressionLinear Regression Simple Moving AverageSimple Moving Average
Weighted Moving AverageWeighted Moving Average Exponential SmoothingExponential Smoothing
(exponentially weighted moving(exponentially weighted moving
average)average) Exponential Smoothing with TrendExponential Smoothing with Trend
(double exponential smoothing)(double exponential smoothing)
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Simple Linear RegressionSimple Linear RegressionSimple Linear RegressionSimple Linear Regression
Linear regression analysis establishes aLinear regression analysis establishes a
relationship between a dependent variable andrelationship between a dependent variable and
one or more independent variables.one or more independent variables.
InIn simple linear regression analysissimple linear regression analysis there isthere isonly one independent variable.only one independent variable.
If the data is a time series, the independentIf the data is a time series, the independent
variable is the time period.variable is the time period. The dependent variable is whatever we wish toThe dependent variable is whatever we wish to
forecast.forecast.
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Simple Linear RegressionSimple Linear RegressionSimple Linear RegressionSimple Linear Regression
Regression EquationRegression Equation
This model is of the form:This model is of the form:
Y = a +Y = a + bXbX
Y = dependent variableY = dependent variable
X = independent variableX = independent variable
a = ya = y--axis interceptaxis intercept
b = slope of regression lineb = slope of regression line
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Simple Linear RegressionSimple Linear RegressionSimple Linear RegressionSimple Linear Regression
Constants a and bConstants a and b
The constants a and b are computed using theThe constants a and b are computed using the
following equations:following equations:
2
2 2
x y- x xya =
n x -( x)
2 2
xy- x yb = n x -( x)
n
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Simple Linear RegressionSimple Linear RegressionSimple Linear RegressionSimple Linear Regression
Once theOnce the aa and b values areand b values are
computed, a future value of Xcomputed, a future value of X
can be entered into thecan be entered into theregression equation and aregression equation and a
corresponding value of Y (thecorresponding value of Y (theforecast) can be calculated.forecast) can be calculated.
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Example: College EnrollmentExample: College EnrollmentExample: College EnrollmentExample: College Enrollment
Simple Linear RegressionSimple Linear Regression
At a small regional college enrollments have grownAt a small regional college enrollments have grown
steadily over the past six years, as evidenced below.steadily over the past six years, as evidenced below.
Use time series regression to forecast the studentUse time series regression to forecast the student
enrollments for the next three years.enrollments for the next three years.
StudentsStudents StudentsStudents
YearYear Enrolled (1000s)Enrolled (1000s) YearYear Enrolled (1000s)Enrolled (1000s)
11 2.52.5 44 3.23.2
22 2.82.8 55 3.33.3
33 2.92.9 66 3.43.4
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Example: College EnrollmentExample: College EnrollmentExample: College EnrollmentExample: College Enrollment
Simple Linear RegressionSimple Linear Regression
xx yy xx22 xyxy
11 2.52.5 11 2.52.5
22 2.82.8 44 5.65.633 2.92.9 99 8.78.7
44 3.23.2 1616 12.812.8
55 3.33.3 2525 16.516.5
66 3.43.4 3636 20.420.477x=21x=21 77y=18.1y=18.1 77xx22=91=91 77xy=66.5xy=66.5
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Example: College EnrollmentExample: College EnrollmentExample: College EnrollmentExample: College Enrollment
Simple Linear RegressionSimple Linear Regression
Y = 2.387 + 0.180XY = 2.387 + 0.180X
2
91(18.1) 21(66.5)2.387
6(91) (21)a
! !
6(66.5 21(18.10.180
105b
! !
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Example: College EnrollmentExample: College EnrollmentExample: College EnrollmentExample: College Enrollment
Simple Linear RegressionSimple Linear Regression
YY77
= 2.387 + 0.180(7) = 3.65 or3,650 students= 2.387 + 0.180(7) = 3.65 or3,650 students
YY88 = 2.387 + 0.180(8) = 3.83 or3,830 students= 2.387 + 0.180(8) = 3.83 or3,830 students
YY99 = 2.387 + 0.180(9) = 4.01 or4,010 students= 2.387 + 0.180(9) = 4.01 or4,010 students
Note: Enrollment is expected to increase by 180Note: Enrollment is expected to increase by 180
students per year.students per year.
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Simple Linear RegressionSimple Linear RegressionSimple Linear RegressionSimple Linear Regression
(CAUSAL FORECASTING METHODS)(CAUSAL FORECASTING METHODS)
SimpleSimple linear regression can also be usedlinear regression can also be used
when the independent variable X representswhen the independent variable X representsa variable other than timea variable other than time..
In this case, linear regression isIn this case, linear regression isrepresentative of a class of forecastingrepresentative of a class of forecasting
models calledmodels called causal forecasting modelscausal forecasting models..
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Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.
Simple Linear RegressionSimple Linear Regression Causal ModelCausal ModelThe manager of RPC wants to project the firmsThe manager of RPC wants to project the firms
sales for the next 3 years. He knows that RPCs longsales for the next 3 years. He knows that RPCs long--
range sales are tied very closely to national freight carrange sales are tied very closely to national freight car
loadings. On the next slide are 7 years of relevantloadings. On the next slide are 7 years of relevanthistorical data.historical data.
Develop a simple linear regression modelDevelop a simple linear regression model
between RPC sales and national freight car loadings.between RPC sales and national freight car loadings.
Forecast RPC sales for the next 3 years, given that theForecast RPC sales for the next 3 years, given that the
rail industry estimates car loadings of 250, 270, andrail industry estimates car loadings of 250, 270, and
300 million.300 million.
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Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.
Simple Linear RegressionSimple Linear Regression Causal ModelCausal Model
RPC SalesRPC Sales Car LoadingsCarLoadings
YearYear ($millions)($millions) (millions)(millions)
11 9.59.5 12012022 11.011.0 13513533 12.012.0 13013044 12.512.5 150150
55 14.014.0 17017066 16.016.0 19019077 18.018.0 220220
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Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.
Simple Linear RegressionSimple Linear Regression Causal ModelCausal Model
xx yy xx22 xyxy
120120 9.59.5 14,40014,400 1,1401,140
135135 11.011.0 18,22518,225 1,4851,485
130130 12.012.0 16,90016,900 1,5601,560
150150 12.512.5 22,50022,500 1,8751,875
170170 14.014.0 28,90028,900 2,3802,380
190190 16.016.0 36,10036,100 3,0403,040
220220 18.018.0 48,40048,400 3,9603,960
1,1151,115 93.093.0 185,425185,425 15,44015,440
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Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.
Simple Linear RegressionSimple Linear Regression Causal ModelCausal Model
Y = 0.528 + 0.0801XY = 0.528 + 0.0801X
2
185,425(93 1, 115(15,440a 0.528
7(185,425 (1,115
! !
2
7(15, 440 1,115(93b 0.0801
7(185,425 (1,115
! !
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Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.
Simple Linear RegressionSimple Linear Regression Causal ModelCausal Model
YY88 = 0.528 + 0.0801(250) = $20.55 million= 0.528 + 0.0801(250) = $20.55 million
YY99 = 0.528 + 0.0801(270) = $22.16 million= 0.528 + 0.0801(270) = $22.16 million
YY1010 = 0.528 + 0.0801(300) = $24.56 million= 0.528 + 0.0801(300) = $24.56 million
Note: RPC sales are expected to increase byNote: RPC sales are expected to increase by$80,100 for each additional million national freight$80,100 for each additional million national freightcar loadings.car loadings.
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Multiple Regression AnalysisMultiple Regression AnalysisMultiple Regression AnalysisMultiple Regression Analysis
Multiple regression analysis is used when there areMultiple regression analysis is used when there aretwo or more independent variables.two or more independent variables.
An example of a multiple regression equation is:An example of a multiple regression equation is:
Y = 50.0 + 0.05XY = 50.0 + 0.05X11 + 0.10X+ 0.10X22 0.03X0.03X33
where: Y = firms annual sales ($millions)where: Y = firms annual sales ($millions)
XX11 = industry sales ($millions)= industry sales ($millions)
XX22 = regional per capita income ($thousands)= regional per capita income ($thousands)
XX33
= regional per capita debt ($thousands)= regional per capita debt ($thousands)
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Coefficient ofCorrelation (Coefficient ofCorrelation (rr))Coefficient ofCorrelation (Coefficient ofCorrelation (rr))
The coefficient of correlation,The coefficient of correlation, rr, explains the, explains therelative importance of the relationship betweenrelative importance of the relationship between
xx andandyy..
The sign ofThe sign ofrrshows the direction of theshows the direction of the
relationship.relationship.
The absolute value ofThe absolute value ofrrshows the strength ofshows the strength of
the relationship.the relationship.
The sign ofThe sign ofrris always the same as the sign ofis always the same as the sign of
b.b.
rrcan take on any value betweencan take on any value between 1 and +1.1 and +1.
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Coefficient ofCorrelation (Coefficient ofCorrelation (rr))Coefficient ofCorrelation (Coefficient ofCorrelation (rr))
Meanings of several values ofMeanings of several values ofrr::
--1 a perfect negative relationship (as1 a perfect negative relationship (asxx goes up,goes up,yy
goes down by one unit, and vice versa)goes down by one unit, and vice versa)+1 a perfect positive relationship (as+1 a perfect positive relationship (asxx goes up,goes up,yy
goes up by one unit, and vice versa)goes up by one unit, and vice versa)
0 no relationship exists between0 no relationship exists betweenxx andandyy
+0.3 a weak positive relationship+0.3 a weak positive relationship
--0.8 a strong negative relationship0.8 a strong negative relationship
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Coefficient ofCorrelation (Coefficient ofCorrelation (rr))Coefficient ofCorrelation (Coefficient ofCorrelation (rr))
rr is computed byis computed by::
2 2 2 2
( ) ( )
n xy x yr
n x x n y y
!
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Coefficient of Determination (Coefficient of Determination (rr22))Coefficient of Determination (Coefficient of Determination (rr22))
The coefficient of determination,The coefficient of determination, rr22, is the square of, is the square ofthe coefficient of correlation.the coefficient of correlation.
The modification ofThe modification ofrrtoto rr22 allows us to shift fromallows us to shift from
subjective measures of relationship to a more specificsubjective measures of relationship to a more specific
measure.measure.
rr22 is determined by the ratio of explained variation tois determined by the ratio of explained variation to
total variationtotal variation::
22
2( )( )Y yry y
!
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Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.
Coefficient of CorrelationCoefficient of Correlation
xx yy xx22 xyxy yy22
120120 9.59.5 14,40014,400 1,1401,140 90.2590.25
135135 11.011.0 18,22518,225 1,4851,485 121.00121.00
130130 12.012.0 16,90016,900 1,5601,560 144.00144.00
150150 12.512.5 22,50022,500 1,8751,875 156.25156.25
170170 14.014.0 28,90028,900 2,3802,380 196.00196.00
190190 16.016.0 36,10036,100 3,0403,040 256.00256.00
220220 18.018.0 48,40048,400 3,9603,960 324.00324.00
1,1151,115 93.093.0 185,425185,425 15,44015,440 1,287.501,287.50
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Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.
Coefficient of CorrelationCoefficient of Correlation
rr = .9829= .9829
2 2
7( ) ( )
7( 25) ( 5) 7( 287.5) ( )r
!
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Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.
Coefficient of DeterminationCoefficient of Determination
rr22 = (.9829)= (.9829)22 = .= .966966
96.6% of the variation in RPC sales is96.6% of the variation in RPC sales is
explained by national freight carexplained by national freight car
loadings.loadings.
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Ranging ForecastsRanging ForecastsRanging ForecastsRanging Forecasts
Forecasts for future periods are only estimatesForecasts for future periods are only estimatesand are subject to error.and are subject to error.
One way to deal with uncertainty is to developOne way to deal with uncertainty is to develop
bestbest--estimate forecasts and theestimate forecasts and the rangesranges withinwithinwhich the actual data are likely to fall.which the actual data are likely to fall.
The ranges of a forecast are defined by theThe ranges of a forecast are defined by the
upper and lower limits of a confidenceupper and lower limits of a confidenceinterval.interval.
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Ranging ForecastsRanging ForecastsRanging ForecastsRanging Forecasts
The ranges or limits of a forecast are estimated by:The ranges or limits of a forecast are estimated by:
Upper limit = Y + t(Upper limit = Y + t(ssyxyx))
Lower limit = YLower limit = Y -- t(t(ssyxyx))
where:where:
Y = bestY = best--estimate forecastestimate forecast
t = number of standard deviations from the meant = number of standard deviations from the mean
of the distribution to provide a givenof the distribution to provide a given probaproba--bilitybility of exceeding the limits through chanceof exceeding the limits through chance
ssyxyx = standard error of the forecast= standard error of the forecast
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Ranging ForecastsRanging ForecastsRanging ForecastsRanging Forecasts
TheThe standard error (deviation) of the forecaststandard error (deviation) of the forecast isiscomputed as:computed as:
2
yx
y - a y - b xys = n - 2
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Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.
Ranging ForecastsRanging Forecasts
Recall that linear regression analysisRecall that linear regression analysis
provided a forecast of annual sales forprovided a forecast of annual sales for
RPC in year8 equal to $20.55 million.RPC in year8 equal to $20.55 million.
Set the limits (ranges) of the forecastSet the limits (ranges) of the forecast
so that there is only a 5 percentso that there is only a 5 percent
probability of exceeding the limits byprobability of exceeding the limits by
chance.chance.
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Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.
Ranging ForecastsRanging Forecasts
Step 1: Compute the standard error of theStep 1: Compute the standard error of the
forecasts,forecasts, ssyxyx..
Step 2: Determine the appropriate value for t.Step 2: Determine the appropriate value for t.
n = 7,n = 7, soso degrees of freedom = ndegrees of freedom = n 2 = 5.2 = 5.Area in upper tail = .05/2 = .025Area in upper tail = .05/2 = .025
Appendix B, Table 2 shows t = 2.571.Appendix B, Table 2 shows t = 2.571.
1 87.5 .528(93) .0801(15, 0) .57487 2
yxs ! !
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Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.Example: Railroad Products Co.
Ranging ForecastsRanging Forecasts
Step 3: Compute upper and lower limits.Step 3: Compute upper and lower limits.
Upper limit = 20.55 + 2.571(.5748)Upper limit = 20.55 + 2.571(.5748)
= 20.55 + 1.478= 20.55 + 1.478= 22.028= 22.028
Lower limit = 20.55Lower limit = 20.55 -- 2.571(.5748)2.571(.5748)
= 20.55= 20.55 -- 1.4781.478
= 19.072= 19.072
WeWe are 95% confidentare 95% confident that thethat the actual sales for year8actual sales for year8will be between $19.072 and $22.028 million.will be between $19.072 and $22.028 million.
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Seasonalized Time Series Regression AnalysisSeasonalized Time Series Regression AnalysisSeasonalized Time Series Regression AnalysisSeasonalized Time Series Regression Analysis
Select a representative historical data set.Select a representative historical data set. Develop a seasonal index for each season.Develop a seasonal index for each season.
Use the seasonal indexes toUse the seasonal indexes to dede--seasonalizeseasonalize thethe
data.data. PerformPerform linear regressionlinear regression analysis on theanalysis on the dede--
seasonalizedseasonalized data.data.
Use the regression equation to compute theUse the regression equation to compute theforecasts.forecasts.
Use theUse the seasonalizedseasonalized indexes to reapply theindexes to reapply the
seasonal patterns to the forecasts.seasonal patterns to the forecasts.
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Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.
SeasonalizedSeasonalized Times Series RegressionTimes Series RegressionAnalysisAnalysis
An analyst at CPC wants to developAn analyst at CPC wants to develop
next years quarterly forecasts of salesnext years quarterly forecasts of sales
revenue for CPCs line of Epsilonrevenue for CPCs line of Epsilon
Computers. She believes that the mostComputers. She believes that the most
recent 8 quarters of sales (shown on therecent 8 quarters of sales (shown on the
next slide) are representative of nextnext slide) are representative of next
years sales.years sales.
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Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.
SeasonalizedSeasonalized Times Series Regression AnalysisTimes Series Regression Analysis
Representative HistoricalRepresentative Historical Sales DataSales Data SetSet
YearYear Qtr.Qtr. ($mil.)($mil.) Year Year Qtr.Qtr. ($mil.)($mil.)
11 11 7.47.4 22 11 8.38.3
11 22 6.56.5 22 22 7.47.4
11 33 4.94.9 22 33 5.45.4
11 44 16.116.1 22 44 18.018.0
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Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.
Seasonalized Times Series Regression AnalysisSeasonalized Times Series Regression Analysis
Compute the Seasonal IndexesCompute the Seasonal Indexes
Quarterly SalesQuarterly Sales
YearYear Q1Q1 Q2Q2 Q3Q3 Q4Q4 TotalTotal
11 7.47.4 6.56.5 4.94.9 16.116.1 34.934.9
22 8.38.3 7.47.4 5.45.4 18.018.0 39.139.1
TotalsTotals 15
.7
15
.7
13
.913
.9 10.3
10.3 34
.134
.174
.074
.0Qtr. Avg.Qtr. Avg. 7.857.85 6.956.95 5.155.15 17.0517.05 9.259.25
Seas.Ind.Seas.Ind. .849.849 .751.751 .557.557 1.8431.843 4.0004.000
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Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.
SeasonalizedSeasonalized Times Series Regression AnalysisTimes Series Regression Analysis
DeseasonalizeDeseasonalize the Datathe Data
Quarterly SalesQuarterly Sales
YearYear Q1Q1 Q2Q2 Q3Q3 Q4Q411 8.728.72 8.668.66 8.808.80 8.748.74
22 9.789.78 9.859.85 9.699.69 9.779.77
(Quarterly Sales) / index = de(Quarterly Sales) / index = de--seasonlizedseasonlized salessales
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Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.
Seasonalized Times Series Regression AnalysisSeasonalized Times Series Regression Analysis
Perform Regression on Deseasonalized DataPerform Regression on Deseasonalized Data
Yr.Yr. Qtr.Qtr. xx yy xx22 xyxy
11 11 11 8.728.72 11 8.728.7211 22 22 8.668.66 44 17.3217.3211 33 33 8.808.80 99 26.4026.4011 44 44 8.748.74 1616 34.9634.9622 11 55 9.789.78 2525 48.9048.90
22 22 66 9.859.85 3636 59.1059.1022 33 77 9.699.69 4949 67.8367.8322 44 88 9.779.77 6464 78.1678.16
TotalsTotals 3636 74.0174.01 204204 341.39341.39
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Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.
Seasonalized Times Series Regression AnalysisSeasonalized Times Series Regression Analysis
Perform Regression on Deseasonalized DataPerform Regression on Deseasonalized Data
Y = 8.357 + 0.199XY = 8.357 + 0.199X
2
204(74.01 36(341.39a 8.357
8(204 (36
! !
2
8(341.39 36(74.01b 0.199
8(204 (36
! !
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Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.
Seasonalized Times Series Regression AnalysisSeasonalized Times Series Regression Analysis
Compute the Deseasonalized ForecastsCompute the Deseasonalized Forecasts
YY99 = 8.357 + 0.199(9) = 10.148= 8.357 + 0.199(9) = 10.148
YY1010 = 8.357 + 0.199(10) = 10.347= 8.357 + 0.199(10) = 10.347
YY1111 = 8.357 + 0.199(11) = 10.546= 8.357 + 0.199(11) = 10.546
YY1212 = 8.357 + 0.199(12) = 10.745= 8.357 + 0.199(12) = 10.745
Note: Average sales are expected to increase byNote: Average sales are expected to increase by
.199 million (about $200,000) per quarter..199 million (about $200,000) per quarter.
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Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.Example: Computer Products Corp.
Seasonalized Times Series Regression AnalysisSeasonalized Times Series Regression Analysis
Seasonalize the ForecastsSeasonalize the Forecasts
Seas.Seas. Deseas.Deseas. Seas.Seas.
Yr.Yr. Qtr.Qtr. IndexIndex ForecastForecast ForecastForecast
33 11 .849.849 10.14810.148 8.628.62
33 22 .751.751 10.34710.347 7.777.77
33 33 .557.557 10.54610.546 5.875.8733 44 1.8431.843 10.74510.745 19.8019.80
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ShortShort--Range ForecastsRange ForecastsShortShort--Range ForecastsRange Forecasts
Time spans ranging from a fewTime spans ranging from a fewdays to a few weeksdays to a few weeks
Cycles, seasonality, and trendCycles, seasonality, and trendmay have little effectmay have little effect
Random fluctuation is mainRandom fluctuation is maindata componentdata component
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Evaluating ForecastEvaluating Forecast--Model PerformanceModel PerformanceEvaluating ForecastEvaluating Forecast--Model PerformanceModel Performance
ShortShort--range forecastingrange forecastingmodels are evaluated on themodels are evaluated on the
basis of three characteristics:basis of three characteristics: Impulse responseImpulse response
NoiseNoise--dampening abilitydampening ability AccuracyAccuracy
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Evaluating ForecastEvaluating Forecast--Model PerformanceModel PerformanceEvaluating ForecastEvaluating Forecast--Model PerformanceModel Performance
Impulse Response and NoiseImpulse Response and Noise--DampeningDampeningAbilityAbility
If forecasts have little periodIf forecasts have little period--toto--period fluctuation,period fluctuation,
they are said to bethey are said to be noise dampeningnoise dampening..
Forecasts that respond quickly to changes in dataForecasts that respond quickly to changes in data
are said to have a highare said to have a high impulse responseimpulse response..
A forecast system that responds quickly to dataA forecast system that responds quickly to data
changes necessarily picks up a great deal ofchanges necessarily picks up a great deal ofrandom fluctuation (random fluctuation (noisenoise).).
Hence, there is aHence, there is a tradetrade--off between high impulseoff between high impulse
response and high noise dampening.response and high noise dampening.
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Evaluating ForecastEvaluating Forecast--Model PerformanceModel Performance
Accuracy :Accuracy :
Accuracy is the typical criterion forAccuracy is the typical criterion for
judging the performance of ajudging the performance of aforecasting approachforecasting approach
Accuracy is how well theAccuracy is how well the
forecasted values match the actualforecasted values match the actualvaluesvalues
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Monitoring AccuracyMonitoring AccuracyMonitoring AccuracyMonitoring Accuracy
Accuracy of a forecasting approach needs toAccuracy of a forecasting approach needs tobe monitored to assess the confidence you canbe monitored to assess the confidence you can
have in its forecasts and changes in the markethave in its forecasts and changes in the market
may require reevaluation of the approachmay require reevaluation of the approach Accuracy can be measured in several waysAccuracy can be measured in several ways
Standard error of the forecast (coveredStandard error of the forecast (covered
earlier in slide4
1)earlier in slide4
1) Mean absolute deviation (MAD)Mean absolute deviation (MAD)
Mean squared error (Mean squared error (MSEMSE))
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Monitoring AccuracyMonitoring AccuracyMonitoring AccuracyMonitoring Accuracy
Mean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)
n
e io snoeviationabsoluteouMAD
n
i i
i=1
Actual demand -Forecast demand
MAD =n
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Mean Squared Error (Mean Squared Error (MSEMSE))
MSEMSE = (= (SSyxyx))22
A small value forA small value for SSyxyx
(standard error of the(standard error of the
forecast) meansforecast) means data points are tightly groupeddata points are tightly grouped
around the line and error range is small.around the line and error range is small.
When the forecast errors are normallyWhen the forecast errors are normally
distributed, the values of MAD anddistributed, the values of MAD and ssyxyx are related:are related:
MSEMSE = 1.25(MAD)= 1.25(MAD)
Monitoring AccuracyMonitoring Accuracy
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ShortShort--Range Forecasting MethodsRange Forecasting MethodsShortShort--Range Forecasting MethodsRange Forecasting Methods
(Simple) Moving Average(Simple) Moving Average
Weighted Moving AverageWeighted Moving Average
Exponential SmoothingExponential Smoothing
Exponential SmoothingExponential Smoothingwith Trendwith Trend
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Simple Moving AverageSimple Moving Average
AnAn averaging period (APaveraging period (AP) is given or) is given orselectedselected
The forecast for the next period is theThe forecast for the next period is the
arithmetic average of the AP most recentarithmetic average of the AP most recentactual demandsactual demands
It is called a simple average because eachIt is called a simple average because each
period used to compute the average isperiod used to compute the average isequally weightedequally weighted
. . . more. . . more
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Simple Moving AverageSimple Moving AverageSimple Moving AverageSimple Moving Average
It is called moving because as new demandIt is called moving because as new demanddata becomes available, the oldest data is notdata becomes available, the oldest data is not
usedused
By increasing the AP, the forecast is lessBy increasing the AP, the forecast is lessresponsive to fluctuations in demand (lowresponsive to fluctuations in demand (low
impulse response and high noise dampening)impulse response and high noise dampening)
By decreasing the AP, the forecast is moreBy decreasing the AP, the forecast is moreresponsive to fluctuations in demand (highresponsive to fluctuations in demand (high
impulse response and low noise dampening)impulse response and low noise dampening)
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Weighted Moving AverageWeighted Moving Average
This is a variation on the simpleThis is a variation on the simplemoving average where the weightsmoving average where the weights
used to compute the average are notused to compute the average are not
equal.equal.
This allows more recent demand dataThis allows more recent demand data
to have a greater effect on the movingto have a greater effect on the moving
average, therefore the forecast.average, therefore the forecast.
. . . more. . . more
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Weighted Moving AverageWeighted Moving AverageWeighted Moving AverageWeighted Moving Average
The weights must add to 1.0The weights must add to 1.0and generally decrease in valueand generally decrease in value
with the age of the data.with the age of the data. The distribution of the weightsThe distribution of the weights
determine the impulse responsedetermine the impulse responseof the forecast.of the forecast.
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The weights used to compute the forecast (movingThe weights used to compute the forecast (movingaverage) are exponentially distributed.average) are exponentially distributed.
The forecast is the sum of the old forecast and aThe forecast is the sum of the old forecast and a
portion (portion (EE) of the forecast error (A) of the forecast error (A tt--11 -- FFtt--11).).
FFtt = F= Ftt--11 ++ EE(A(A tt--11 -- FFtt--11))
. . . more. . . more
Exponential SmoothingExponential Smoothing
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Exponential SmoothingExponential SmoothingExponential SmoothingExponential Smoothing
The smoothing constant,The smoothing constant, EE,,must be between 0.0 and 1.0.must be between 0.0 and 1.0.
A largeA large EEprovides a highprovides a highimpulse response forecast.impulse response forecast.
A smallA small EEprovides a lowprovides a lowimpulse response forecast.impulse response forecast.
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Example: Central Call CenterExample: Central Call CenterExample: Central Call CenterExample: Central Call Center
Moving AverageMoving AverageCCCCCC wishes to forecast the number ofwishes to forecast the number of
incoming calls it receives in a day from theincoming calls it receives in a day from the
customers of one of its clients, BMI.customers of one of its clients, BMI. CCCCCC
schedules the appropriate number of telephoneschedules the appropriate number of telephone
operators based on projected call volumes.operators based on projected call volumes.
CCCCCC believes that the most recent 12 daysbelieves that the most recent 12 daysof call volumes (shown on the next slide) areof call volumes (shown on the next slide) are
representative of the near future call volumes.representative of the near future call volumes.
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Example: Central Call CenterExample: Central Call CenterExample: Central Call CenterExample: Central Call Center
Moving AverageMoving Average Representative Historical DataRepresentative Historical Data
DayDay CallsCalls DayDay CallsCalls
11 159159 77 20320322 217217 88 195195
33 186186 99 188188
44 161161 1010 168168
55 173173 1111 19819866 157157 1212 159159
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Example: Central Call CenterExample: Central Call CenterExample: Central Call CenterExample: Central Call Center
Moving AverageMoving Average
Use the moving average methodUse the moving average method
with an AP =3
days to develop awith an AP =3
days to develop aforecast of the call volume in Day 13.forecast of the call volume in Day 13.
FF1313 = (168 + 198 + 159)/3 = 175.0 calls= (168 + 198 + 159)/3 = 175.0 calls
C C CC C CC C CC C C
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Example: Central Call CenterExample: Central Call CenterExample: Central Call CenterExample: Central Call Center
Weighted Moving AverageWeighted Moving AverageUse the weighted moving average method with anUse the weighted moving average method with an
AP = 3 days and weights of .1 (for oldest datum), .3,AP = 3 days and weights of .1 (for oldest datum), .3,
and .6 to develop a forecast of the call volume in Dayand .6 to develop a forecast of the call volume in Day13.13.
FF1313 = .1(168) + .3(198) + .6(159) = 171.6 calls= .1(168) + .3(198) + .6(159) = 171.6 calls
Note: The WMA forecast is lower than the MA
Note: The WMA forecast is lower than the MAforecast because Dayforecast because Day 12s12s relatively low call volumerelatively low call volume
carries almost twice as much weight in the WMAcarries almost twice as much weight in the WMA
(.60) as it does in the MA (.33).(.60) as it does in the MA (.33).
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EXPONENTIAL SMOOTHINGEXPONENTIAL SMOOTHINGEXPONENTIAL SMOOTHINGEXPONENTIAL SMOOTHING
It takes the forecast for the prior period andIt takes the forecast for the prior period andadds an adjustment to obtain the forecast for theadds an adjustment to obtain the forecast for the
next period.next period.
This adjustment is a proportion of the forecastThis adjustment is a proportion of the forecasterror in the prior period and computed byerror in the prior period and computed by
multiplying the forecast error in the prior periodmultiplying the forecast error in the prior period
by a constant that is between zero and one.by a constant that is between zero and one.
This constant (This constant () is called the smoothing) is called the smoothing
constant. Its value is estimated or derived.constant. Its value is estimated or derived.
E l C l C ll CE l C l C ll CE l C l C ll CE l C l C ll C
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Example: Central Call CenterExample: Central Call CenterExample: Central Call CenterExample: Central Call Center
Exponential SmoothingExponential SmoothingIf a smoothing constant value of .25 isIf a smoothing constant value of .25 is
used and the exponential smoothingused and the exponential smoothing
forecast for Day 11 was 180.76 calls, whatforecast for Day 11 was 180.76 calls, what
is the exponential smoothing forecast foris the exponential smoothing forecast for
Day 13?Day 13?
FF1212 = 180.76 + .25(198= 180.76 + .25(198 180.76) = 185.07180.76) = 185.07
FF1313 = 185.07 + .25(159= 185.07 + .25(159 185.07) = 178.55185.07) = 178.55
E l C l C ll CE l C l C ll CE l C l C ll CE l C l C ll C
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Example: Central Call CenterExample: Central Call CenterExample: Central Call CenterExample: Central Call Center
Forecast AccuracyForecast Accuracy based on MADbased on MAD(mean absolute deviation) :(mean absolute deviation) :
Which forecasting method (Which forecasting method (the AP = 3the AP = 3
moving averagemoving average oror thethe EE = .= .2525(smoothing constant),(smoothing constant), exponential smoothingexponential smoothing))
is preferred, based on the MAD over the mostis preferred, based on the MAD over the most
recent 9 days? (Assume that the exponentialrecent 9 days? (Assume that the exponential
smoothing forecast for Day 3 is the same assmoothing forecast for Day 3 is the same as
the actual call volume.)the actual call volume.)
E l C t l C ll C tE l C t l C ll C tE l C t l C ll C tE l C t l C ll C t
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Example: Central Call CenterExample: Central Call CenterExample: Central Call CenterExample: Central Call Center
AP = 3AP = 3 EE = .25= .25DayDay CallsCalls Forec.Forec. |Error||Error| Forec.Forec. |Error||Error|
44 161161 187.3187.3 26.326.3 186.0186.0 25.025.055 173173 188.0188.0 15.015.0 179.8179.8 6.86.8
66 157157 173.3173.3 16.316.3 178.1178.1 21.121.177 203203 163.7163.7 39.339.3 172.8172.8 30.230.288 195195 177.7177.7 17.317.3 180.4180.4 14.614.699 188188 185.0185.0 3.03.0 184.0184.0 4.04.01010 168168 195.3195.3 27.327.3 185.0185.0 17.017.01111 198198 183.7183.7 14.314.3 180.8180.8 17.217.21212 159159 184.7184.7 25.725.7 185.1185.1 26.126.1
MADMAD 20.520.5 18.018.0
E i l S hi i h T dE i l S hi i h T dE i l S hi i h T dE i l S hi i h T d
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Exponential Smoothing with TrendExponential Smoothing with TrendExponential Smoothing with TrendExponential Smoothing with Trend
As we move towardAs we move toward mediummedium--rangerangeforecastsforecasts, trend becomes more important., trend becomes more important.
Incorporating a trend component intoIncorporating a trend component into
exponentially smoothed forecasts isexponentially smoothed forecasts is
calledcalled double exponential smoothingdouble exponential smoothing..
The estimate for the average and theThe estimate for the average and the
estimate for the trend are both smoothed.estimate for the trend are both smoothed.
E ti l S thi ith T dE ti l S thi ith T dE ti l S thi ith T dE ti l S thi ith T d
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Exponential Smoothing with TrendExponential Smoothing with TrendExponential Smoothing with TrendExponential Smoothing with Trend
Model FormModel Form
FTFTtt = S= Stt--11 + T+ Ttt--11
where:where:
FTFTtt = forecast with trend in period t= forecast with trend in period t
SStt--11 = smoothed forecast (average) in period t= smoothed forecast (average) in period t--11
TTtt--11 = smoothed trend estimate in period t= smoothed trend estimate in period t--11
E ti l S thi ith T dE ti l S thi ith T dE ti l S thi ith T dE ti l S thi ith T d
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Exponential Smoothing with TrendExponential Smoothing with TrendExponential Smoothing with TrendExponential Smoothing with Trend
Smoothing the AverageSmoothing the Average
SStt == FTFTtt ++ EE(A(Att FTFTtt))
Smoothing the TrendSmoothing the Trend
TTtt = T= Ttt--11 ++ FF((FTFTtt FTFTtt--11 -- TTtt--11))
where:where: AAtt = actual data in period t= actual data in period t
EE = smoothing constant for the average= smoothing constant for the averageFF = smoothing constant for the= smoothing constant for the trendtrend
Note :Note : Values forValues for andand are estimated orare estimated or
experimentally derived.experimentally derived.
C it i fC it i f S l tiS l ti F ti M th dF ti M th d
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Criteria forCriteria for Selecting aSelecting a Forecasting MethodForecasting Method
CostCost
AccuracyAccuracy
Data availableData available Time spanTime span
Nature of products and servicesNature of products and services
Impulse response and noiseImpulse response and noise
dampeningdampening
C it i fC it i f S l tiS l ti F ti M th dF ti M th dC it i fC it i f S l tiS l ti F ti M th dF ti M th d
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Criteria forCriteria for SelectingSelecting Forecasting MethodForecasting MethodCriteria forCriteria for SelectingSelecting Forecasting MethodForecasting Method
Cost and AccuracyCost and Accuracy There is aThere is a tradetrade--off between cost and accuracyoff between cost and accuracy;;
generally, more forecast accuracy can be obtainedgenerally, more forecast accuracy can be obtained
at a cost.at a cost. HighHigh--accuracy approaches have disadvantages:accuracy approaches have disadvantages:
Use more dataUse more data
Data are ordinarily more difficult to obtainData are ordinarily more difficult to obtain
The models are more costly to design,The models are more costly to design,implement, and operateimplement, and operate
Take longer to useTake longer to use
C it i fC it i f S l tiS l ti F ti M th dF ti M th dC it i fC it i f S l tiS l ti F ti M th dF ti M th d
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Criteria forCriteria for Selecting aSelecting a Forecasting MethodForecasting MethodCriteria forCriteria for Selecting aSelecting a Forecasting MethodForecasting Method
Cost and AccuracyCost and Accuracy Low/ModerateLow/Moderate--Cost ApproachesCost Approaches
statistical models, historicalstatistical models, historical
analogies, executiveanalogies, executive--committeecommittee
consensusconsensus
HighHigh--Cost ApproachesCost Approaches complexcomplexeconometric models, Delphi, andeconometric models, Delphi, and
market researchmarket research
C it i fC it i f S l tiS l ti F ti M th dF ti M th dC it i fC it i f S l tiS l ti F ti M th dF ti M th d
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Criteria forCriteria for Selecting aSelecting a Forecasting MethodForecasting MethodCriteria forCriteria for Selecting aSelecting a Forecasting MethodForecasting Method
Data AvailableData Available Is the necessary data available or can itIs the necessary data available or can it
be economically obtained?be economically obtained?
If the need is to forecast sales of aIf the need is to forecast sales of a newnew
product, then a customer survey mayproduct, then a customer survey may
not be practical; instead,not be practical; instead, historicalhistoricalanalogyanalogy or market research may haveor market research may have
to be used.to be used.
Criteria forCriteria for Selecting aSelecting a Forecasting MethodForecasting MethodCriteria forCriteria for Selecting aSelecting a Forecasting MethodForecasting Method
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Criteria forCriteria for Selecting aSelecting a Forecasting MethodForecasting MethodCriteria forCriteria for Selecting aSelecting a Forecasting MethodForecasting Method
Time SpanTime Span What operations resource is being forecastWhat operations resource is being forecast
and for what purpose?and for what purpose?
ShortShort--term staffing needsterm staffing needs might best bemight best beforecast with moving average or exponentialforecast with moving average or exponential
smoothing models.smoothing models.
LongLong--term factory capacityterm factory capacity needs mightneeds mightbest be predicted with regression orbest be predicted with regression or
executiveexecutive--committee consensus methods.committee consensus methods.
C it i fC it i f S l tiS l ti F ti M th dF ti M th dC it i fC it i f S l tiS l ti F ti M th dF ti M th d
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Criteria forCriteria for Selecting aSelecting a Forecasting MethodForecasting MethodCriteria forCriteria for Selecting aSelecting a Forecasting MethodForecasting Method
Nature of Products and ServicesNature of Products and Services
Is the product/service high cost orIs the product/service high cost or
high volume?
high volume?
Where is the product/service in itsWhere is the product/service in its
life cycle?life cycle?
Does the product/service haveDoes the product/service haveseasonal demand fluctuations?seasonal demand fluctuations?
C it i fC it i f S l tiS l ti F ti M th dF ti M th dC it i fC it i f S l tiS l ti F ti M th dF ti M th d
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Criteria forCriteria for Selecting aSelecting a Forecasting MethodForecasting MethodCriteria forCriteria for Selecting aSelecting a Forecasting MethodForecasting Method
Impulse Response andN
oise DampeningImpulse Response andN
oise Dampening An appropriate balance must beAn appropriate balance must be
achieved between:achieved between:
HowHow responsiveresponsive we want thewe want theforecasting model to be to changes inforecasting model to be to changes in
the actual demand datathe actual demand data
Our desire to suppressOur desire to suppress undesirableundesirablechance variation or noisechance variation or noise in thein the
demand datademand data
Reasons for Ineffective ForecastingReasons for Ineffective ForecastingReasons for Ineffective ForecastingReasons for Ineffective Forecasting
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Reasons for Ineffective ForecastingReasons for Ineffective ForecastingReasons for Ineffective ForecastingReasons for Ineffective Forecasting
N
ot involving a broad cross section of peopleN
ot involving a broad cross section of people Not recognizing that forecasting is integral toNot recognizing that forecasting is integral to
business planningbusiness planning
Not recognizing that forecasts will always beNot recognizing that forecasts will always be
wrongwrong
Not forecasting the right thingsNot forecasting the right things
Not selecting an appropriate forecastingNot selecting an appropriate forecasting
methodmethod
Not tracking the accuracy of the forecastingNot tracking the accuracy of the forecasting
modelsmodels
Monitoring and ControllingMonitoring and ControllingMonitoring and ControllingMonitoring and Controlling
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g gg g
a Forecasting Modela Forecasting Model
g gg g
a Forecasting Modela Forecasting Model
Tracking Signal (TSTracking Signal (TS)) :: The TS measures the cumulative forecast errorThe TS measures the cumulative forecast error
over n periods in terms of MADover n periods in terms of MAD
If the forecasting model is performing well, the TSIf the forecasting model is performing well, the TS
should be around zeroshould be around zero The TS indicates the direction of the forecastingThe TS indicates the direction of the forecasting
error; if the TS iserror; if the TS is positive,positive, increase the forecasts,increase the forecasts,if the TS is negativeif the TS is negative ,, decrease the forecasts.decrease the forecasts.
n
i i1
(Actual demand - Forecast demand )
TS =MAD
i!
Monitoring and ControllingMonitoring and ControllingMonitoring and ControllingMonitoring and Controlling
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g gg g
a Forecasting Modela Forecasting Model
g gg g
a Forecasting Modela Forecasting Model
Tracking SignalTracking Signal The value of the TS can be used toThe value of the TS can be used to
automatically trigger newautomatically trigger new parameter valuesparameter values
of a model, thereby correcting modelof a model, thereby correcting model
performance.performance.
If the limits are set too narrow, theIf the limits are set too narrow, the
parameter values will be changed too often.parameter values will be changed too often.
If the limits are set too wide, the parameterIf the limits are set too wide, the parameter
values will not be changed often enough andvalues will not be changed often enough and
accuracy will suffer.accuracy will suffer.
Computer Software for ForecastingComputer Software for ForecastingComputer Software for ForecastingComputer Software for Forecasting
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Computer Software for ForecastingComputer Software for ForecastingComputer Software for ForecastingComputer Software for Forecasting
Examples of computer software with forecastingExamples of computer software with forecastingcapabilitiescapabilities
Forecast ProForecast Pro
AutoboxAutobox
SmartForecastsSmartForecasts for Windowsfor Windows
SASSAS
SPSSSPSS
SAPSAP
POMPOM SoftwareSoftware LibaryLibary
Primarily forPrimarily for
forecastingforecasting
HaveHave
ForecastingForecastingmodulesmodules
Forecasting in Small BusinessesForecasting in Small BusinessesForecasting in Small BusinessesForecasting in Small Businesses
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gg
and Startand Start--Up VenturesUp Ventures
gg
and Startand Start--Up VenturesUp Ventures
Forecasting for these businesses canForecasting for these businesses can
be difficult for the following reasons:be difficult for the following reasons: Not enough personnel with the time to forecastNot enough personnel with the time to forecast
Personnel lack the necessary skills to develop goodPersonnel lack the necessary skills to develop good
forecastsforecasts
Such businesses are not dataSuch businesses are not data--rich environmentsrich environments
Forecasting for new products/services is alwaysForecasting for new products/services is alwaysdifficult, even for the experienced forecasterdifficult, even for the experienced forecaster
Sources of Forecasting Data and HelpSources of Forecasting Data and HelpSources of Forecasting Data and HelpSources of Forecasting Data and Help
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Sources of Forecasting Data and HelpSources of Forecasting Data and HelpSources of Forecasting Data and HelpSources of Forecasting Data and Help
Government agencies at theGovernment agencies at thelocal, regional, state, andlocal, regional, state, and
federal levelsfederal levels Industry associationsIndustry associations
Consulting companiesConsulting companies
Some Specific Forecasting DataSome Specific Forecasting DataSome Specific Forecasting DataSome Specific Forecasting Data
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Some Specific Forecasting DataSome Specific Forecasting DataSome Specific Forecasting DataSome Specific Forecasting Data
Consumer Confidence IndexConsumer Confidence Index Consumer Price Index (CPI)Consumer Price Index (CPI)
Gross Domestic Product (GDP)Gross Domestic Product (GDP)
Housing StartsHousing Starts Index ofLeading Economic IndicatorsIndex ofLeading Economic Indicators
Personal Income and ConsumptionPersonal Income and Consumption
Producer Price Index (PPI)Producer Price Index (PPI)
PurchasingPurchasing ManagersManagers IndexIndex
Retail SalesRetail Sales
WrapWrap Up: WorldUp: World Class PracticeClass Practice
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WrapWrap--Up: WorldUp: World--Class PracticeClass Practice
Predisposed to have effective methods ofPredisposed to have effective methods offorecasting because they have exceptionalforecasting because they have exceptional
longlong--range business planningrange business planning
Formal forecasting effortFormal forecasting effort
Develop methods to monitor theDevelop methods to monitor the
performance of their forecasting modelsperformance of their forecasting models
Do not overlook the short run.... excellentDo not overlook the short run.... excellent
short range forecasts as wellshort range forecasts as well
End of Module 2End of Module 2
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End of Module-2End of Module-2