© 2006 prentice hall, inc.4 – 1 operations management chapter 4 - forecasting chapter 4 -...

© 2006 Prentice Hall, Inc. 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


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

??


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


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


Product Life CycleProduct Life Cycle

Best period to Best period to increase market increase market shareshare

R&D engineering is R&D engineering is criticalcritical

Practical to change Practical to change price or quality price or quality imageimage

Strengthen nicheStrengthen niche

Poor time to Poor time to change image, change image, price, or qualityprice, or quality

Competitive costs Competitive costs become criticalbecome criticalDefend market Defend market positionposition

Cost control Cost control criticalcritical

Introduction Growth Maturity Decline

Co

mp

an

y S

tra

teg

y/Is

sue

sC

om

pa

ny

Str

ate

gy/

Issu

es

InternetInternet

Flat-screen Flat-screen monitorsmonitors

SalesSales

DVDDVD

CD-ROMCD-ROM

Drive-through Drive-through restaurantsrestaurants

Fax machinesFax machines

3 1/2” 3 1/2” Floppy Floppy disksdisks

Color printersColor printers

Figure 2.5Figure 2.5


Product Life CycleProduct Life Cycle

Product design Product design and and development development criticalcritical

Frequent Frequent product and product and process design process design changeschanges

Short production Short production runsruns

High production High production costscosts

Limited modelsLimited models

Attention to Attention to qualityquality

Introduction Growth Maturity Decline

OM

Str

ate

gy

/Issu

es

OM

Str

ate

gy

/Issu

es

Forecasting Forecasting criticalcritical

Product and Product and process process reliabilityreliability

Competitive Competitive product product improvements improvements and optionsand options

Increase capacityIncrease capacity

Shift toward Shift toward product focusproduct focus

Enhance Enhance distributiondistribution

StandardizationStandardization

Less rapid Less rapid product changes product changes – more minor – more minor changeschanges

Optimum Optimum capacitycapacity

Increasing Increasing stability of stability of processprocess

Long production Long production runsruns

Product Product improvement and improvement and cost cuttingcost cutting

Little product Little product differentiationdifferentiation

Cost Cost minimizationminimization

Overcapacity Overcapacity in the in the industryindustry

Prune line to Prune line to eliminate eliminate items not items not returning returning good margingood margin

Reduce Reduce capacitycapacity



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 productPredict sales of existing product


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 Pool opinions of high-level

executives, sometimes augment by executives, sometimes augment by statistical modelsstatistical models

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


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 Set of evenly spaced numerical datadata Obtained by observing response Obtained by observing response

variable at regular time periodsvariable at regular time periods

Forecast based only on past Forecast based only on past valuesvalues Assumes that factors influencing Assumes that factors influencing

past and present will continue past and present will continue influence in futureinfluence in future

Time Series ForecastingTime Series Forecasting


Trend

Seasonal

Cyclical

Random

Time Series ComponentsTime Series Components


Naive ApproachNaive Approach

Assumes demand in next period is Assumes demand in next period is the same as demand in most the same as demand in most recent periodrecent period 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


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 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


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 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


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


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)


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




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




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


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


Common Measure of ErrorCommon Measure of Error

Mean Absolute Deviation Mean Absolute Deviation ((MADMAD))

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

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))


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







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

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



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^



| | | | | | | | |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^̂


Seasonal Variations In DataSeasonal Variations In Data

The multiplicative seasonal model can The multiplicative seasonal model can modify trend data to accommodate modify trend data to accommodate seasonal variations in demandseasonal variations in demand

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

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


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 20032003 20042004 20052005 2003-20052003-2005 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 2003-2005 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 2006

© 2006 prentice hall, inc.4 – 1 operations management chapter 4 - forecasting chapter 4 -...

Documents

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critical product

individual product forecasts

product focus

product passes

rapid product changes

system product family

years new product planning