© 2008 prentice hall, inc.4 – 1 operations management chapter 4 – forecasting delivered by:...

© 2008 Prentice Hall, Inc. 4 – 1

Operations ManagementOperations ManagementChapter 4 – Chapter 4 – ForecastingForecasting

Delivered by:Delivered by:

Eng.Mosab I. TabashEng.Mosab I. Tabash


What is Forecasting?What is Forecasting?

Process of Process of predicting a future predicting a future eventevent

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

InventoryInventory

PersonnelPersonnel

FacilitiesFacilities

Sales will be $200 Million!


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 Purchasing, job scheduling, workforce

levels, job assignments, production levelslevels, job 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, New product planning, facility location,

research and developmentresearch and development

Forecasting Time HorizonsForecasting Time Horizons


Distinguishing DifferencesDistinguishing Differences

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

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


Influence of Product Life Influence of Product Life CycleCycle

Introduction and growth require longer Introduction and growth require longer forecasts than maturity and declineforecasts than maturity and decline

As product passes through life cycle, As product passes through life cycle, forecasts are useful in projectingforecasts are useful in projecting Staffing levelsStaffing levels

Inventory levelsInventory levels

Factory capacityFactory capacity

Introduction – Growth – Maturity – Decline


Types of ForecastsTypes of Forecasts

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

money supply, housing starts, etc.money 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 products and Predict sales of existing products and

servicesservices


Strategic Importance of Strategic Importance of ForecastingForecasting

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

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

Supply Chain Management – Good Supply Chain Management – Good supplier relations and price supplier relations and price advantagesadvantages


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


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 more accurate than forecasts are more accurate than individual product forecastsindividual product forecasts


Forecasting ApproachesForecasting Approaches

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

New technologyNew technology

Involves intuition, experienceInvolves intuition, experience e.g., forecasting sales on Internete.g., forecasting sales on Internet

Qualitative MethodsQualitative Methods


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


Overview of Qualitative Overview of Qualitative MethodsMethods

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

sometimes augment by statistical sometimes augment by statistical modelsmodels

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


Overview of Qualitative Overview of Qualitative MethodsMethods

Sales force compositeSales force composite Estimates from individual Estimates from individual

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

Consumer Market SurveyConsumer Market Survey Ask the customerAsk the customer


Involves small group of high-level experts Involves small group of high-level experts and managersand managers

Group estimates demand by working Group estimates demand by working togethertogether

Combines managerial experience with Combines managerial experience with statistical modelsstatistical models

Relatively quickRelatively quick

‘‘Group-think’Group-think’disadvantagedisadvantage

Jury of Executive OpinionJury of Executive Opinion


Sales Force CompositeSales Force Composite

Each salesperson projects his or Each salesperson projects his or her salesher sales

Combined at district and national Combined at district and national levelslevels

Sales reps know customers’ wantsSales reps know customers’ wants

Tends to be overly optimisticTends to be overly optimistic


Delphi MethodDelphi Method

Iterative group Iterative group process, process, continues until continues until consensus is consensus is reachedreached

3 types of 3 types of participantsparticipants Decision makersDecision makers StaffStaff RespondentsRespondents

Staff(Administering

survey)

Decision Makers(Evaluate

responses and make decisions)

Respondents(People who can make valuable

judgments)


Consumer Market SurveyConsumer Market Survey

Ask customers about purchasing Ask customers about purchasing plansplans

What consumers say,What consumers say,

and what they actually and what they actually

do are often differentdo are often different

Sometimes difficult to answerSometimes difficult to answer

How many hours will you use the

Internet next week?

How many hours will you use the

Internet next week?

© 1995 Corel Corp.


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


Set of evenly spaced numerical dataSet of evenly spaced numerical data

Obtained by observing response variable at Obtained by observing response variable at regular time periodsregular time periods

Forecast based only on past values, no other Forecast based only on past values, no other variables importantvariables important

Assumes that factors influencing past and Assumes that factors influencing past and present will continue influence in futurepresent will continue influence in future

Time Series ForecastingTime Series Forecasting

ExampleYear: 1993 1994 1995 1996 1997Sales:78.7 63.5 89.7 93.2 92.1


Trend

Seasonal

Cyclical

Random

Time Series ComponentsTime Series Components


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


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 Typically several years duration duration

Trend ComponentTrend Component

Mo., Qtr., Yr.

Response

© 1984-1994 T/Maker Co.


Regular pattern of up and Regular pattern of up and down fluctuationsdown fluctuations

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

Occurs within a single year Occurs within a single year

Seasonal ComponentSeasonal Component

Mo., Qtr.

Response

Summer

© 1984-1994 T/Maker Co.


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


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


Naive ApproachNaive Approach

Assumes demand in next Assumes demand in next period is the same as period is the same as demand in most recent perioddemand in most recent period e.g., If January sales were 68, then e.g., If January sales were 68, then

February sales will be 68February sales will be 68

Sometimes cost effective and Sometimes cost effective and efficientefficient

Can be good starting pointCan be good starting point


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


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

ActualActual 3-Month3-MonthMonthMonth car Salescar 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


Graph of Moving AverageGraph of Moving Average

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

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

Car

s S

ales

Car

s 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


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


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

ActualActual 3-Month Weighted3-Month WeightedMonthMonth car Salescar 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 ago

6 Sum of weights


Increasing n smooths the forecast Increasing n smooths the forecast but makes it less sensitive to but makes it less sensitive to changeschanges

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


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

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


Actual Actual salessales

Moving Moving averageaverage

Weighted Weighted moving moving averageaverage



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


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)


You’re organizing a planning meeting. You’re organizing a planning meeting. You want to forecast attendance for You want to forecast attendance for 2000 using exponential smoothing 2000 using exponential smoothing ((αα= .10). The1995 forecast was 175.= .10). The1995 forecast was 175.

19951995 1801801996 1996 16816819971997 15915919961996 17517519991999 190190

© 1995 Corel Corp.

Exponential Smoothing Exponential Smoothing ExampleExample


Ft = Ft-1 + ·(At-1 - Ft-1)

TimeTime ActualForecast, F t

(αα = = .10.10))

19951995 180 175.00 (Given)

19961996 168168

19971997 159159

19981998 175175

19991999 190190

20002000 NANA

175.00 +175.00 +

Exponential Smoothing Exponential Smoothing SolutionSolution


Ft = Ft-1 + ·(At-1 - Ft-1)

TimeTime ActualActualForecast, Forecast, FFtt

((αα= = .10.10))

19951995 180180 175.00 (Given)175.00 (Given)

19961996 168168 175.00 +175.00 + .10.10(180 (180 - 175.00- 175.00)) = 175.50 = 175.50

19971997 159159

19981998 175175

19991999 190190

20002000 NANA



Ft = Ft-1 + ·(At-1 - Ft-1)

Time ActualForecast, F t

(α = .10)

19951995 180180 175.00 (Given)175.00 (Given)

19961996 168168 175.00 + .10(180 - 175.00) = 175.50175.00 + .10(180 - 175.00) = 175.50

19971997 159159 175.50 + .10(168 - 175.50) = 174.75175.50 + .10(168 - 175.50) = 174.75

19981998 175175 174.75 + .10(159 - 174.75) = 173.18174.75 + .10(159 - 174.75) = 173.18

19991999 190190 173.18 + .10(175 - 173.18) = 173.36173.18 + .10(175 - 173.18) = 173.36

20002000 NANA 173.36173.36 + + .10.10(190(190 - 173.36- 173.36) = 175.02) = 175.02



Year

Sales

140150160170180190

93 94 95 96 97 98

Actual

Forecast

Exponential Smoothing Exponential Smoothing GraphGraph


Forecast errorForecast error

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


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


Exponential Smoothing with Exponential Smoothing with Trend AdjustmentTrend Adjustment

When a trend is present, exponential When a trend is present, exponential smoothing must be modifiedsmoothing must be modified

Forecast Forecast including including ((FITFITtt)) = = trendtrend

ExponentiallyExponentially ExponentiallyExponentiallysmoothed smoothed ((FFtt)) + + ((TTtt)) smoothedsmoothedforecastforecast trendtrend


Exponential Smoothing with Exponential Smoothing with Trend AdjustmentTrend Adjustment

FFtt = = ((AAtt - 1 - 1) + (1 - ) + (1 - )()(FFtt - 1 - 1 + + TTtt - 1 - 1))

TTtt = = ((FFtt - - FFtt - 1 - 1) + (1 - ) + (1 - ))TTtt - 1 - 1

Step 1: Compute FStep 1: Compute Ftt

Step 2: Compute TStep 2: Compute Ttt

Step 3: Calculate the forecast FITStep 3: Calculate the forecast FITtt == F Ftt + + TTtt


Exponential Smoothing with Exponential Smoothing with Trend Adjustment ExampleTrend Adjustment Example

ForecastForecastActualActual SmoothedSmoothed SmoothedSmoothed IncludingIncluding

MonthMonth((tt)) Demand Demand ((AAtt)) Forecast, FForecast, Ftt Trend, TTrend, Ttt Trend, FITTrend, FITtt

11 1212 1111 22 13.0013.0022 171733 202044 191955 242466 212177 313188 282899 3636

1010

Table 4.1Table 4.1





11 1212 1111 22 13.0013.0022 171733 202044 191955 242466 212177 313188 282899 3636

1010

Table 4.1Table 4.1

F2 = A1 + (1 - )(F1 + T1)

F2 = (.2)(12) + (1 - .2)(11 + 2)

= 2.4 + 10.4 = 12.8 units

Step 1: Forecast for Month 2





11 1212 1111 22 13.0013.0022 1717 12.8012.8033 202044 191955 242466 212177 313188 282899 3636

1010

Table 4.1Table 4.1

T2 = (F2 - F1) + (1 - )T1

T2 = (.4)(12.8 - 11) + (1 - .4)(2)

= .72 + 1.2 = 1.92 units

Step 2: Trend for Month 2





11 1212 1111 22 13.0013.0022 1717 12.8012.80 1.921.9233 202044 191955 242466 212177 313188 282899 3636

1010

Table 4.1Table 4.1

FIT2 = F2 + T1

FIT2 = 12.8 + 1.92

= 14.72 units

Step 3: Calculate FIT for Month 2





11 1212 1111 22 13.0013.0022 1717 12.8012.80 1.921.92 14.7214.7233 202044 191955 242466 212177 313188 282899 3636

1010

Table 4.1Table 4.1

15.1815.18 2.102.10 17.2817.2817.8217.82 2.322.32 20.1420.1419.9119.91 2.232.23 22.1422.1422.5122.51 2.382.38 24.8924.8924.1124.11 2.072.07 26.1826.1827.1427.14 2.452.45 29.5929.5929.2829.28 2.322.32 31.6031.6032.4832.48 2.682.68 35.1635.16




| | | | | | | | |

11 22 33 44 55 66 77 88 99

Time (month)Time (month)

Pro

du

ct d

eman

dP

rod

uct

dem

and

35 35 –

30 30 –

25 25 –

20 20 –

15 15 –

10 10 –

5 5 –

0 0 –

Actual demand Actual demand ((AAtt))

Forecast including trend Forecast including trend ((FITFITtt))

withwith = .2 = .2 andand = .4 = .4


Trend ProjectionsTrend Projections

Fitting a trend line to historical data points Fitting a trend line to historical data points to project into the medium to long-rangeto project 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 ywhere y= 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

^̂


Least Squares MethodLeast Squares Method

Time periodTime period

Va

lue

s o

f D

ep

end

en

t V

ari

able


DeviationDeviation11

(error)(error)







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

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



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)



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


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

20012001 11 7474 11 747420022002 22 7979 44 15815820032003 33 8080 99 24024020042004 44 9090 1616 36036020052005 55 105105 2525 52552520052005 66 142142 3636 85285220072007 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



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^



| | | | | | | | |20012001 20022002 20032003 20042004 20052005 20062006 20072007 20082008 20092009

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^̂


Seasonal Variations In DataSeasonal Variations In Data

The multiplicative The multiplicative seasonal model seasonal model can adjust trend can adjust trend data for seasonal data for seasonal variations in variations in demanddemand


Seasonal Variations In DataSeasonal Variations In Data

1.1. Find average historical demand for each Find average historical demand for each season season

2.2. Compute the average demand over all Compute the average demand over all seasons seasons

3.3. Compute a seasonal index for each season Compute a seasonal index for each season

4.4. Estimate next year’s total demandEstimate next year’s total demand

5.5. Divide this estimate of total demand by the Divide this estimate of total demand by the number of seasons, then multiply it by the number of seasons, then multiply it by the seasonal index for that seasonseasonal index for that season

Steps in the process:Steps in the process:


Seasonal Index ExampleSeasonal Index Example

JanJan 8080 8585 105105 9090 9494

FebFeb 7070 8585 8585 8080 9494

MarMar 8080 9393 8282 8585 9494

AprApr 9090 9595 115115 100100 9494

MayMay 113113 125125 131131 123123 9494

JunJun 110110 115115 120120 115115 9494

JulJul 100100 102102 113113 105105 9494

AugAug 8888 102102 110110 100100 9494

SeptSept 8585 9090 9595 9090 9494

OctOct 7777 7878 8585 8080 9494

NovNov 7575 7272 8383 8080 9494

DecDec 8282 7878 8080 8080 9494

DemandDemand AverageAverage AverageAverage Seasonal Seasonal MonthMonth 20052005 20062006 20072007 2005-20072005-2007 MonthlyMonthly IndexIndex



JanJan 8080 8585 105105 9090 9494

FebFeb 7070 8585 8585 8080 9494

MarMar 8080 9393 8282 8585 9494

AprApr 9090 9595 115115 100100 9494

MayMay 113113 125125 131131 123123 9494

JunJun 110110 115115 120120 115115 9494

JulJul 100100 102102 113113 105105 9494

AugAug 8888 102102 110110 100100 9494

SeptSept 8585 9090 9595 9090 9494

OctOct 7777 7878 8585 8080 9494

NovNov 7575 7272 8383 8080 9494

DecDec 8282 7878 8080 8080 9494


0.9570.957

Seasonal index = average 2005-2007 monthly demand

average monthly demand

= 90/94 = .957



JanJan 8080 8585 105105 9090 9494 0.9570.957

FebFeb 7070 8585 8585 8080 9494 0.8510.851

MarMar 8080 9393 8282 8585 9494 0.9040.904

AprApr 9090 9595 115115 100100 9494 1.0641.064

MayMay 113113 125125 131131 123123 9494 1.3091.309

JunJun 110110 115115 120120 115115 9494 1.2231.223

JulJul 100100 102102 113113 105105 9494 1.1171.117

AugAug 8888 102102 110110 100100 9494 1.0641.064

SeptSept 8585 9090 9595 9090 9494 0.9570.957

OctOct 7777 7878 8585 8080 9494 0.8510.851

NovNov 7575 7272 8383 8080 9494 0.8510.851

DecDec 8282 7878 8080 8080 9494 0.8510.851




JanJan 8080 8585 105105 9090 9494 0.9570.957

FebFeb 7070 8585 8585 8080 9494 0.8510.851

MarMar 8080 9393 8282 8585 9494 0.9040.904

AprApr 9090 9595 115115 100100 9494 1.0641.064

MayMay 113113 125125 131131 123123 9494 1.3091.309

JunJun 110110 115115 120120 115115 9494 1.2231.223

JulJul 100100 102102 113113 105105 9494 1.1171.117

AugAug 8888 102102 110110 100100 9494 1.0641.064

SeptSept 8585 9090 9595 9090 9494 0.9570.957

OctOct 7777 7878 8585 8080 9494 0.8510.851

NovNov 7575 7272 8383 8080 9494 0.8510.851

DecDec 8282 7878 8080 8080 9494 0.8510.851


Expected annual demand = 1,200

Jan x .957 = 961,200

12

Feb x .851 = 851,200

12

Forecast for 2008



140 140 –

130 130 –

120 120 –

110 110 –

100 100 –

90 90 –

80 80 –

70 70 –| | | | | | | | | | | |


TimeTime

Dem

and

Dem

and

2008 Forecast2008 Forecast

2007 Demand 2007 Demand

2006 Demand2006 Demand

2005 Demand2005 Demand


Associative ForecastingAssociative Forecasting

Used when changes in one or more Used when changes in one or more independent variables can be used to predict independent variables can be used to predict

the changes in the dependent variablethe changes in the dependent variable

Most common technique is linear Most common technique is linear regression analysisregression analysis

We apply this technique just as we did We apply this technique just as we did in the time series examplein the time series example


Associative ForecastingAssociative Forecasting

Forecasting an outcome based on predictor Forecasting an outcome based on predictor variables using the least squares techniquevariables using the least squares technique

y y = = a a + + bxbx^̂

where ywhere y= 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 though = the independent variable though to predict the value of the to predict the value of the dependent variabledependent variable

^̂


Associative Forecasting Associative Forecasting ExampleExample

SalesSales Local PayrollLocal Payroll($ millions), y($ millions), y ($ billions), x($ billions), x

2.02.0 113.03.0 332.52.5 442.02.0 222.02.0 113.53.5 77

4.0 –

3.0 –

2.0 –

1.0 –

| | | | | | |0 1 2 3 4 5 6 7

Sal

es

Area payroll



Sales, y Payroll, x x2 xy

2.0 1 1 2.03.0 3 9 9.02.5 4 16 10.02.0 2 4 4.02.0 1 1 2.03.5 7 49 24.5

∑y = 15.0 ∑x = 18 ∑x2 = 80 ∑xy = 51.5

xx = = ∑∑xx/6 = 18/6 = 3/6 = 18/6 = 3

yy = = ∑∑yy/6 = 15/6 = 2.5/6 = 15/6 = 2.5

bb = = = .25 = = = .25∑∑xy - nxyxy - nxy

∑∑xx22 - nx - nx22

51.5 - (6)(3)(2.5)51.5 - (6)(3)(2.5)

80 - (6)(380 - (6)(322))

aa = = yy - - bbx = 2.5 - (.25)(3) = 1.75x = 2.5 - (.25)(3) = 1.75



4.0 –

3.0 –

2.0 –

1.0 –

| | | | | | |0 1 2 3 4 5 6 7

Sal

es

Area payroll

y y = 1.75 + .25= 1.75 + .25xx^̂ Sales Sales = 1.75 + .25(= 1.75 + .25(payrollpayroll))

If payroll next year If payroll next year is estimated to be is estimated to be $6$6 billion, then: billion, then:

Sales Sales = 1.75 + .25(6)= 1.75 + .25(6)SalesSales = $3,250,000 = $3,250,000

3.25


How strong is the linear How strong is the linear relationship between the relationship between the variables?variables?

Correlation does not necessarily Correlation does not necessarily imply causality!imply causality!

Coefficient of correlation, r, Coefficient of correlation, r, measures degree of associationmeasures degree of association Values range from Values range from -1-1 to to +1+1

CorrelationCorrelation


Correlation CoefficientCorrelation Coefficient

r = r = nnxyxy - - xxy y

[[nnxx22 - ( - (xx))22][][nnyy22 - ( - (yy))22]]


Correlation CoefficientCorrelation Coefficient

r = r = nnxyxy - - xxy y

[[nnxx22 - ( - (xx))22][][nnyy22 - ( - (yy))22]]

y

x(a) Perfect positive correlation: r = +1

y

x(b) Positive correlation: 0 < r < 1

y

x(c) No correlation: r = 0

y

x(d) Perfect negative correlation: r = -1


Coefficient of Determination, rCoefficient of Determination, r22, , measures the percent of change in measures the percent of change in y predicted by the change in xy predicted by the change in x Values range from Values range from 00 to to 11

Easy to interpretEasy to interpret

CorrelationCorrelation

For the Nodel Construction example:For the Nodel Construction example:

r r = .901= .901

rr22 = .81 = .81


Multiple Regression Multiple Regression AnalysisAnalysis

If more than one independent variable is to be If more than one independent variable is to be used in the model, linear regression can be used in the model, linear regression can be

extended to multiple regression to extended to multiple regression to accommodate several independent variablesaccommodate several independent variables

y y = = a a + + bb11xx11 + b + b22xx22 … …^̂

Computationally, this is quite Computationally, this is quite complex and generally done on the complex and generally done on the

computercomputer


Multiple Regression Multiple Regression AnalysisAnalysis

y y = 1.80 + .30= 1.80 + .30xx11 - 5.0 - 5.0xx22^̂

In the Nodel example, including interest rates in In the Nodel example, including interest rates in the model gives the new equation:the model gives the new equation:

An improved correlation coefficient of r An improved correlation coefficient of r = .96= .96 means this model does a better job of predicting means this model does a better job of predicting the change in construction salesthe change in construction sales

Sales Sales = 1.80 + .30(6) - 5.0(.12) = 3.00= 1.80 + .30(6) - 5.0(.12) = 3.00Sales Sales = $3,000,000= $3,000,000


Adaptive ForecastingAdaptive Forecasting

It’s possible to use the computer to It’s possible to use the computer to continually monitor forecast error and continually monitor forecast error and adjust the values of the adjust the values of the and and coefficients used in exponential coefficients used in exponential smoothing to continually minimize smoothing to continually minimize forecast errorforecast error

This technique is called adaptive This technique is called adaptive smoothingsmoothing

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