4 – 1 Operations Management Chapter 4 - Forecasting Chapter 4 - Forecasting © 2006 Prentice Hall,...

4 – 1

Operations ManagementOperations ManagementChapter 4 - ForecastingChapter 4 - Forecasting

© 2006 Prentice Hall, Inc.

PowerPoint presentation to accompanyPowerPoint presentation to accompany Heizer/Render Heizer/Render Principles of Operations Management, 6ePrinciples of Operations Management, 6eOperations Management, 8e Operations Management, 8e

4 – 2

OutlineOutline

What Is Forecasting?What Is Forecasting? Forecasting Time HorizonsForecasting Time Horizons

Types Of ForecastsTypes Of Forecasts

The Strategic Importance Of The Strategic Importance Of ForecastingForecasting

Seven Steps In The Forecasting Seven Steps In The Forecasting SystemSystem

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Outline – ContinuedOutline – Continued

Forecasting ApproachesForecasting Approaches Overview of Qualitative MethodsOverview of Qualitative Methods

Overview of Quantitative MethodsOverview of Quantitative Methods

Time-series ForecastingTime-series Forecasting

Forecasting In The Service SectorForecasting In The Service Sector

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What is Forecasting?What is Forecasting?

Process of trying to Process of trying to predict a future eventpredict a future event

Underlying basis of Underlying basis of all business all business decisionsdecisions ProductionProduction

InventoryInventory

PersonnelPersonnel

FacilitiesFacilities

??

4 – 5

Short-range forecastShort-range forecast Up to 1 year, generally less than 3 monthsUp to 1 year, generally less than 3 months Purchasing, job scheduling, workforce levels, Purchasing, job scheduling, workforce levels,

job assignments, production levelsjob assignments, production levels

Medium-range forecastMedium-range forecast 3 months to 3 years3 months to 3 years Sales and production planning, budgetingSales and production planning, budgeting

Long-range forecastLong-range forecast 33++ years years New product planning, facility location, research New product planning, facility location, research

and developmentand development

Forecasting Time HorizonsForecasting Time Horizons

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Distinguishing DifferencesDistinguishing Differences

Medium/long rangeMedium/long range forecasts deal with more forecasts deal with more comprehensive issues and support comprehensive issues and support management decisions regarding planning management decisions regarding planning and products, plants and processesand products, plants and processes

Short-termShort-term forecasting usually employs forecasting usually employs different methodologies than longer-term different methodologies than longer-term forecastingforecasting

Short-termShort-term forecasts tend to be more forecasts tend to be more accurate than longer-term forecastsaccurate than longer-term forecasts

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Types of ForecastsTypes of Forecasts

Economic forecastsEconomic forecasts Address business cycle – inflation rate, money Address business cycle – inflation rate, money

supply, housing starts, etc.supply, housing starts, etc.

Technological forecastsTechnological forecasts Predict rate of technological progressPredict rate of technological progress

Impacts development of new productsImpacts development of new products

Demand forecastsDemand forecasts Predict sales of existing productPredict sales of existing product

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Strategic Importance of Strategic Importance of ForecastingForecasting

Human Resources – Hiring, training, Human Resources – Hiring, training, laying off workerslaying off workers

Capacity – Capacity shortages can result Capacity – Capacity shortages can result in undependable delivery, loss of in undependable delivery, loss of customers, loss of market sharecustomers, loss of market share

Supply-Chain Management – Good Supply-Chain Management – Good supplier relations and price advancesupplier relations and price advance

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Seven Steps in ForecastingSeven Steps in Forecasting

Determine the use of the forecastDetermine the use of the forecast

Select the items to be forecastedSelect the items to be forecasted

Determine the time horizon of the Determine the time horizon of the forecastforecast

Select the forecasting model(s)Select the forecasting model(s)

Gather the dataGather the data

Make the forecastMake the forecast

Validate and implement resultsValidate and implement results

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The Realities!The Realities!

Forecasts are seldom perfectForecasts are seldom perfect

Most techniques assume an Most techniques assume an underlying stability in the systemunderlying stability in the system

Product family and aggregated Product family and aggregated forecasts are generally more accurate forecasts are generally more accurate than individual product forecaststhan individual product forecasts

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Forecasting ApproachesForecasting Approaches

Used when situation is vague and Used when situation is vague and little data existlittle data exist New productsNew products

New technologyNew technology

Involves intuition, experienceInvolves intuition, experience e.g., forecasting demand for a new e.g., forecasting demand for a new

productproduct

Qualitative MethodsQualitative Methods

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Forecasting ApproachesForecasting Approaches

Used when situation is ‘stable’ and Used when situation is ‘stable’ and historical data existhistorical data exist Existing productsExisting products

Current technologyCurrent technology

Involves mathematical techniquesInvolves mathematical techniques e.g., forecasting sales of color e.g., forecasting sales of color

televisionstelevisions

Quantitative MethodsQuantitative Methods

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Overview of Qualitative MethodsOverview of Qualitative Methods

Jury of executive opinionJury of executive opinion Pool opinions of high-level executives, Pool opinions of high-level executives,

sometimes augment by statistical sometimes augment by statistical modelsmodels

Delphi methodDelphi method Panel of experts, queried iterativelyPanel of experts, queried iteratively

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Overview of Qualitative MethodsOverview of Qualitative Methods

Sales force compositeSales force composite Estimates from individual salespersons Estimates from individual salespersons

are reviewed for reasonableness, then are reviewed for reasonableness, then aggregated aggregated

Consumer Market SurveyConsumer Market Survey Ask the customerAsk the customer

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Overview of Quantitative Overview of Quantitative ApproachesApproaches

1.1. Naive approachNaive approach

2.2. Moving averagesMoving averages

3.3. Exponential Exponential smoothingsmoothing

4.4. Trend projectionTrend projection

5.5. Linear regressionLinear regression

Time-Series Time-Series ModelsModels

Associative Associative ModelModel

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Set of evenly spaced numerical dataSet of evenly spaced numerical data Obtained by observing response Obtained by observing response

variable at regular time periodsvariable at regular time periods

Forecast based only on past valuesForecast based only on past values Assumes that factors influencing past Assumes that factors influencing past

and present will continue influence in and present will continue influence in futurefuture

Relates the forecast to only one Relates the forecast to only one factor – time factor – time

Time Series ForecastingTime Series Forecasting

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Trend

Seasonal

Cyclical

Random

Time Series ComponentsTime Series Components

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Components of DemandComponents of DemandD

eman

d f

or

pro

du

ct o

r se

rvic

e

| | | |1 2 3 4

Year

Average demand over four years

Seasonal peaks

Trend component

Actual demand

Random variation

Figure 4.1Figure 4.1

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Persistent, overall upward or Persistent, overall upward or downward patterndownward pattern

Changes due to population, Changes due to population, technology, age, culture, etc.technology, age, culture, etc.

Typically several years duration Typically several years duration

Trend ComponentTrend Component

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Regular pattern of up and down Regular pattern of up and down fluctuationsfluctuations

Due to weather, customs, etc.Due to weather, customs, etc.

Occurs within a single year Occurs within a single year

Seasonal ComponentSeasonal Component

Number ofPeriod Length Seasons

Week Day 7Month Week 4-4.5Month Day 28-31Year Quarter 4Year Month 12Year Week 52

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Repeating up and down movementsRepeating up and down movements

Affected by business cycle, political, Affected by business cycle, political, and economic factorsand economic factors

Multiple years durationMultiple years duration

Often causal or Often causal or associative associative relationshipsrelationships

Cyclical ComponentCyclical Component

00 55 1010 1515 2020

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Erratic, unsystematic, ‘residual’ Erratic, unsystematic, ‘residual’ fluctuationsfluctuations

Due to random variation or Due to random variation or unforeseen eventsunforeseen events

Short duration and Short duration and nonrepeating nonrepeating

Random ComponentRandom Component

MM TT WW TT FF

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Naive ApproachNaive Approach

Assumes demand in next period is Assumes demand in next period is the same as demand in most recent the same as demand in most recent periodperiod e.g., If May sales were 48, then June e.g., If May sales were 48, then June

sales will be 48sales will be 48

Sometimes cost effective and Sometimes cost effective and efficientefficient

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MA is a series of arithmetic means MA is a series of arithmetic means

Used if little or no trendUsed if little or no trend

Used often for smoothingUsed often for smoothingProvides overall impression of data Provides overall impression of data

over timeover time

Moving Average MethodMoving Average Method

Moving average =Moving average =∑∑ demand in previous n periodsdemand in previous n periods

nn

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JanuaryJanuary 1010FebruaryFebruary 1212MarchMarch 1313AprilApril 1616MayMay 1919JuneJune 2323JulyJuly 2626

ActualActual 3-Month3-MonthMonthMonth Shed SalesShed Sales Moving AverageMoving Average

(12 + 13 + 16)/3 = 13 (12 + 13 + 16)/3 = 13 22//33

(13 + 16 + 19)/3 = 16(13 + 16 + 19)/3 = 16(16 + 19 + 23)/3 = 19 (16 + 19 + 23)/3 = 19 11//33

Moving Average ExampleMoving Average Example

101012121313

((1010 + + 1212 + + 1313)/3 = 11 )/3 = 11 22//33

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Graph of Moving AverageGraph of Moving Average

| | | | | | | | | | | |

JJ FF MM AA MM JJ JJ AA SS OO NN DD

Sh

ed S

ales

Sh

ed S

ales

30 30 –28 28 –26 26 –24 24 –22 22 –20 20 –18 18 –16 16 –14 14 –12 12 –10 10 –

Actual Actual SalesSales

Moving Moving Average Average ForecastForecast

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Used when trend is present Used when trend is present Older data usually less importantOlder data usually less important

Weights based on experience and Weights based on experience and intuitionintuition

Weighted Moving AverageWeighted Moving Average

WeightedWeightedmoving averagemoving average ==

∑∑ ((weight for period nweight for period n)) x x ((demand in period ndemand in period n))

∑∑ weightsweights

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JanuaryJanuary 1010FebruaryFebruary 1212MarchMarch 1313AprilApril 1616MayMay 1919JuneJune 2323JulyJuly 2626

ActualActual 3-Month Weighted3-Month WeightedMonthMonth Shed SalesShed Sales Moving AverageMoving Average

[(3 x 16) + (2 x 13) + (12)]/6 = 14[(3 x 16) + (2 x 13) + (12)]/6 = 1411//33

[(3 x 19) + (2 x 16) + (13)]/6 = 17[(3 x 19) + (2 x 16) + (13)]/6 = 17[(3 x 23) + (2 x 19) + (16)]/6 = 20[(3 x 23) + (2 x 19) + (16)]/6 = 2011//22

Weighted Moving AverageWeighted Moving Average

101012121313

[(3 x [(3 x 1313) + (2 x ) + (2 x 1212) + () + (1010)]/6 = 12)]/6 = 1211//66

Weights Applied Period

3 Last month2 Two months ago1 Three months ago6 Sum of weights

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Increasing Increasing nn smooths the forecast but smooths the forecast but makes it less sensitive to changesmakes it less sensitive to changes

Do not forecast trends wellDo not forecast trends well

Require extensive historical dataRequire extensive historical data

Potential Problems WithPotential Problems With Moving Average Moving Average

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Moving Average And Moving Average And Weighted Moving AverageWeighted Moving Average

30 30 –

25 25 –

20 20 –

15 15 –

10 10 –

5 5 –

Sa

les

de

man

dS

ale

s d

em

and

| | | | | | | | | | | |

JJ FF MM AA MM JJ JJ AA SS OO NN DD

Actual Actual salessales

Moving Moving averageaverage

Weighted Weighted moving moving averageaverage


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Form of weighted moving averageForm of weighted moving average Weights decline exponentiallyWeights decline exponentially

Most recent data weighted mostMost recent data weighted most

Requires smoothing constant Requires smoothing constant (()) Ranges from 0 to 1Ranges from 0 to 1

Subjectively chosenSubjectively chosen

Involves little record keeping of past Involves little record keeping of past datadata

Exponential SmoothingExponential Smoothing

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Exponential SmoothingExponential Smoothing

New forecast =New forecast = last period’s forecastlast period’s forecast+ + ((last period’s actual demand last period’s actual demand

– – last period’s forecastlast period’s forecast))

FFtt = F = Ft t – 1– 1 + + ((AAt t – 1– 1 - - F Ft t – 1– 1))

wherewhere FFtt == new forecastnew forecast

FFt t – 1– 1 == previous forecastprevious forecast

== smoothing (or weighting) smoothing (or weighting) constant constant (0 (0 1) 1)

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Exponential Smoothing Exponential Smoothing ExampleExample

Predicted demand Predicted demand = 142= 142 Ford Mustangs Ford MustangsActual demand Actual demand = 153= 153Smoothing constant Smoothing constant = .20 = .20

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New forecastNew forecast = 142 + .2(153 – 142)= 142 + .2(153 – 142)

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New forecastNew forecast = 142 + .2(153 – 142)= 142 + .2(153 – 142)

= 142 + 2.2= 142 + 2.2

= 144.2 ≈ 144 cars= 144.2 ≈ 144 cars

4 – 36

Effect ofEffect of Smoothing Constants Smoothing Constants

Weight Assigned toWeight Assigned to

MostMost 2nd Most2nd Most 3rd Most3rd Most 4th Most4th Most 5th Most5th MostRecentRecent RecentRecent RecentRecent RecentRecent RecentRecent

SmoothingSmoothing PeriodPeriod PeriodPeriod PeriodPeriod PeriodPeriod PeriodPeriodConstantConstant (()) (1 - (1 - )) (1 - (1 - ))22 (1 - (1 - ))33 (1 - (1 - ))44

= .1= .1 .1.1 .09.09 .081.081 .073.073 .066.066

= .5= .5 .5.5 .25.25 .125.125 .063.063 .031.031

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Impact of Different Impact of Different

225 225 –

200 200 –

175 175 –

150 150 –| | | | | | | | |

11 22 33 44 55 66 77 88 99

QuarterQuarter

De

ma

nd

De

ma

nd

= .1= .1

Actual Actual demanddemand

= .5= .5

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Choosing Choosing

The objective is to obtain the most The objective is to obtain the most accurate forecast no matter the accurate forecast no matter the techniquetechnique

We generally do this by selecting the We generally do this by selecting the model that gives us the lowest forecast model that gives us the lowest forecast errorerror

Forecast errorForecast error = Actual demand - Forecast value= Actual demand - Forecast value

= A= Att - F - Ftt

4 – 39

Common Measures of ErrorCommon Measures of Error

Mean Absolute Deviation Mean Absolute Deviation ((MADMAD))

MAD =MAD =∑∑ |actual - forecast||actual - forecast|

nn

Mean Squared Error Mean Squared Error ((MSEMSE))

MSE =MSE =∑∑ ((forecast errorsforecast errors))22

nn

4 – 40

Common Measures of ErrorCommon Measures of Error

Mean Absolute Percent Error Mean Absolute Percent Error ((MAPEMAPE))

MAPE =MAPE =100 100 ∑∑ |actual |actualii - forecast - forecastii|/actual|/actualii

nn

nn

i i = 1= 1

4 – 41

Comparison of Forecast Error Comparison of Forecast Error

RoundedRounded AbsoluteAbsolute RoundedRounded AbsoluteAbsoluteActualActual ForecastForecast DeviationDeviation ForecastForecast DeviationDeviation

TonnageTonnage withwith forfor withwith forforQuarterQuarter UnloadedUnloaded = .10 = .10 = .10 = .10 = .50 = .50 = .50 = .50

11 180180 175175 55 175175 5522 168168 176176 88 178178 101033 159159 175175 1616 173173 141444 175175 173173 22 166166 9955 190190 173173 1717 170170 202066 205205 175175 3030 180180 252577 180180 178178 22 193193 131388 182182 178178 44 186186 44

8484 100100

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RoundedRounded AbsoluteAbsolute RoundedRounded AbsoluteAbsoluteActualActual ForecastForecast DeviationDeviation ForecastForecast DeviationDeviationTonageTonage withwith forfor withwith forfor

QuarterQuarter UnloadedUnloaded = .10 = .10 = .10 = .10 = .50 = .50 = .50 = .50

11 180180 175175 55 175175 5522 168168 176176 88 178178 101033 159159 175175 1616 173173 141444 175175 173173 22 166166 9955 190190 173173 1717 170170 202066 205205 175175 3030 180180 252577 180180 178178 22 193193 131388 182182 178178 44 186186 44

8484 100100

MAD =∑ |deviations|

n

= 84/8 = 10.50

For = .10

= 100/8 = 12.50

For = .50

4 – 43




11 180180 175175 55 175175 5522 168168 176176 88 178178 101033 159159 175175 1616 173173 141444 175175 173173 22 166166 9955 190190 173173 1717 170170 202066 205205 175175 3030 180180 252577 180180 178178 22 193193 131388 182182 178178 44 186186 44

8484 100100MADMAD 10.5010.50 12.5012.50

= 1,558/8 = 194.75

For = .10

= 1,612/8 = 201.50

For = .50

MSE =∑ (forecast errors)2

n

4 – 44




11 180180 175175 55 175175 5522 168168 176176 88 178178 101033 159159 175175 1616 173173 141444 175175 173173 22 166166 9955 190190 173173 1717 170170 202066 205205 175175 3030 180180 252577 180180 178178 22 193193 131388 182182 178178 44 186186 44

8484 100100MADMAD 10.5010.50 12.5012.50MSEMSE 194.75194.75 201.50201.50

= 45.62/8 = 5.70%

For = .10

= 54.8/8 = 6.85%

For = .50

MAPE =100 ∑ |deviationi|/actuali

n

n

i = 1

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RoundedRounded AbsoluteAbsolute RoundedRounded AbsoluteAbsoluteActualActual ForecastForecast DeviationDeviation ForecastForecast DeviationDeviation

TonnageTonnage withwith forfor withwith forforQuarterQuarter UnloadedUnloaded = .10 = .10 = .10 = .10 = .50 = .50 = .50 = .50

11 180180 175175 55 175175 5522 168168 176176 88 178178 101033 159159 175175 1616 173173 141444 175175 173173 22 166166 9955 190190 173173 1717 170170 202066 205205 175175 3030 180180 252577 180180 178178 22 193193 131388 182182 178178 44 186186 44

8484 100100MADMAD 10.5010.50 12.5012.50MSEMSE 194.75194.75 201.50201.50

MAPEMAPE 5.70%5.70% 6.85%6.85%

4 – 46

Trend ProjectionsTrend Projections

Fitting a trend line to historical data points to Fitting a trend line to historical data points to project into the medium-to-long-rangeproject into the medium-to-long-range

Linear trends can be found using the least Linear trends can be found using the least squares techniquesquares technique

y y = = a a + + bxbx^̂

where where yy= computed value of the = computed value of the variable to be predicted (dependent variable to be predicted (dependent variable)variable)aa= y-axis intercept= y-axis interceptbb= slope of the regression line= slope of the regression linexx= the independent variable= the independent variable

^̂

4 – 47

Least Squares MethodLeast Squares Method

Time periodTime period

Va

lue

s o

f D

ep

end

en

t V

ari

able


DeviationDeviation11







Actual observation Actual observation (y value)(y value)

Trend line, y = a + bxTrend line, y = a + bx^̂

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Time periodTime period

Va

lue

s o

f D

ep

end

en

t V

ari

able









Actual observation Actual observation (y value)(y value)

Trend line, y = a + bxTrend line, y = a + bx^̂

Least squares method minimizes the sum of the

squared errors (deviations)

4 – 49


Equations to calculate the regression variablesEquations to calculate the regression variables

b =b =xy - nxyxy - nxy

xx22 - nx - nx22

y y = = a a + + bxbx^̂

a = y - bxa = y - bx

4 – 50

Least Squares ExampleLeast Squares Example

b b = = = 10.54= = = 10.54∑∑xy - nxyxy - nxy

∑∑xx22 - nx - nx22

3,063 - (7)(4)(98.86)3,063 - (7)(4)(98.86)

140 - (7)(4140 - (7)(422))

aa = = yy - - bxbx = 98.86 - 10.54(4) = 56.70 = 98.86 - 10.54(4) = 56.70

TimeTime Electrical Power Electrical Power YearYear Period (x)Period (x) DemandDemand xx22 xyxy

19991999 11 7474 11 747420002000 22 7979 44 15815820012001 33 8080 99 24024020022002 44 9090 1616 36036020032003 55 105105 2525 52552520042004 66 142142 3636 85285220052005 77 122122 4949 854854

∑∑xx = 28 = 28 ∑∑yy = 692 = 692 ∑∑xx22 = 140 = 140 ∑∑xyxy = 3,063 = 3,063xx = 4 = 4 yy = 98.86 = 98.86

4 – 51


b b = = = 10.54= = = 10.54xy - nxyxy - nxy

xx22 - nx - nx22

3,063 - (7)(4)(98.86)3,063 - (7)(4)(98.86)

140 - (7)(4140 - (7)(422))

aa = = yy - - bxbx = 98.86 - 10.54(4) = 56.70 = 98.86 - 10.54(4) = 56.70

TimeTime Electrical Power Electrical Power YearYear Period (x)Period (x) DemandDemand xx22 xyxy

19991999 11 7474 11 747420002000 22 7979 44 15815820012001 33 8080 99 24024020022002 44 9090 1616 36036020032003 55 105105 2525 52552520042004 66 142142 3636 85285220052005 77 122122 4949 854854

xx = 28 = 28 yy = 692 = 692 xx22 = 140 = 140 xyxy = 3,063 = 3,063xx = 4 = 4 yy = 98.86 = 98.86

The trend line is

y = 56.70 + 10.54x^

4 – 52


| | | | | | | | |19991999 20002000 20012001 20022002 20032003 20042004 20052005 20062006 20072007

160 160 –

150 150 –

140 140 –

130 130 –

120 120 –

110 110 –

100 100 –

90 90 –

80 80 –

70 70 –

60 60 –

50 50 –

YearYear

Po

wer

dem

and

Po

wer

dem

and

Trend line,Trend line,y y = 56.70 + 10.54x= 56.70 + 10.54x^̂

4 – 54

Forecasting in the Service Forecasting in the Service SectorSector

Presents unusual challengesPresents unusual challenges Special need for short term recordsSpecial need for short term records

Needs differ greatly as function of Needs differ greatly as function of industry and productindustry and product

Holidays and other calendar eventsHolidays and other calendar events

Unusual eventsUnusual events

4 – 55

Fast Food Restaurant Fast Food Restaurant ForecastForecast

20% 20% –

15% 15% –

10% 10% –

5% 5% –

11-1211-12 1-21-2 3-43-4 5-65-6 7-87-8 9-109-1012-112-1 2-32-3 4-54-5 6-76-7 8-98-9 10-1110-11

(Lunchtime)(Lunchtime) (Dinnertime)(Dinnertime)

Hour of dayHour of day

Per

cen

tag

e o

f sa

les

Per

cen

tag

e o

f sa

les


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