Slide 0 of 56 Chapter 3 Forecasting in POM: The Starting Point for All Planning.

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Chapter 3Chapter 3Chapter 3Chapter 3

Forecasting in POM:Forecasting in POM:

The Starting Point for All PlanningThe Starting Point for All Planning

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OverviewOverviewOverviewOverview

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 Start-Up Forecasting in Small Businesses and Start-Up

VenturesVentures Wrap-Up: What World-Class Producers DoWrap-Up: What World-Class Producers Do

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Demand ManagementDemand ManagementDemand ManagementDemand Management

Independent demand items are the only Independent demand items are the only items demand for which needs to be items demand for which needs to be forecastforecast

These items include:These items include: Finished goods andFinished goods and Spare partsSpare parts

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Demand ManagementDemand ManagementDemand ManagementDemand Management

A

Independent Demand(finished goods and spare parts)

B(4) C(2)

D(2) E(1) D(3) F(2)

Dependent Demand(components)

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Introduction Introduction Introduction Introduction

Demand estimatesDemand estimates for independent demand products for independent demand products and services are the starting point for all the other and services are the starting point for all the other forecasts in POM.forecasts in POM.

Management teams develop Management teams develop sales forecastssales forecasts based in based in part on demand estimates.part on demand estimates.

Sales forecasts become inputs to both business Sales forecasts become inputs to both business strategy and strategy and production resource forecastsproduction resource forecasts..

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Forecasting is an Integral PartForecasting is an Integral Part of Business Planning of Business Planning

Forecasting is an Integral PartForecasting is an Integral Part of Business Planning of Business Planning

ForecastForecastMethod(s)Method(s)

DemandDemandEstimatesEstimates

SalesSalesForecastForecast

ManagementManagementTeamTeam

Inputs:Inputs:Market,Market,

Economic,Economic,OtherOther

BusinessBusinessStrategyStrategy

Production ResourceProduction ResourceForecastsForecasts

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Examples of Production Resource ForecastsExamples of Production Resource ForecastsExamples of Production Resource ForecastsExamples of Production Resource Forecasts

Forecast Forecast HorizonHorizon

Time SpanTime Span Item Being ForecastItem Being ForecastUnits of Units of MeasureMeasure

Long-RangeLong-Range YearsYears

Product linesProduct lines Factory capacitiesFactory capacities Planning for new productsPlanning for new products Capital expendituresCapital expenditures Facility location or expansion Facility location or expansion R&DR&D

Dollars, tons, etc.Dollars, tons, etc.

Medium-Medium-RangeRange

MonthsMonths

Product groupsProduct groups Department capacitiesDepartment capacities Sales planningSales planning Production planning and Production planning and budgetingbudgeting

Dollars, tons, etc.Dollars, tons, etc.

Short-RangeShort-Range WeeksWeeks

Specific product quantitiesSpecific product quantities Machine capacities Machine capacities PlanningPlanning PurchasingPurchasing SchedulingScheduling Workforce levelsWorkforce levels Production levelsProduction levels Job assignmentsJob assignments

Physical units of Physical units of productsproducts

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Forecasting MethodsForecasting MethodsForecasting MethodsForecasting Methods

Qualitative ApproachesQualitative Approaches Quantitative ApproachesQuantitative Approaches

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Qualitative Forecasting ApplicationsQualitative Forecasting ApplicationsSmall and Large FirmsSmall and Large Firms

Qualitative Forecasting ApplicationsQualitative Forecasting ApplicationsSmall and Large FirmsSmall and Large Firms

TechniqueTechnique Low Sales Low Sales (less than $100M)(less than $100M)

High SalesHigh Sales(more than $500M)(more than $500M)

Manager’s OpinionManager’s Opinion 40.7%40.7% 39.6%39.6%

Executive’s Executive’s OpinionOpinion

40.7%40.7% 41.6%41.6%

Sales Force Sales Force CompositeComposite

29.6%29.6% 35.4%35.4%

Number of FirmsNumber of Firms 2727 4848

Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp. 92-100.Note: More than one response was permitted.

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Qualitative ApproachesQualitative ApproachesQualitative ApproachesQualitative Approaches

Usually based on judgments about causal factors that Usually 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 or Do 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 scientifically Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future conducted surveys to intuitive hunches about future eventsevents

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Qualitative Methods Qualitative Methods Qualitative Methods Qualitative Methods

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

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Quantitative Forecasting ApproachesQuantitative Forecasting ApproachesQuantitative Forecasting ApproachesQuantitative Forecasting Approaches

Based on the assumption that the “forces” that Based on the assumption that the “forces” that generated the past demand will generate the future generated the past demand will generate the future demand, i.e., history will tend to repeat itselfdemand, i.e., history will tend to repeat itself

Analysis of the past demand pattern provides a good Analysis of the past demand pattern provides a good basis for forecasting future demandbasis for forecasting future demand

Majority of quantitative approaches fall in the Majority of quantitative approaches fall in the category of time series analysiscategory of time series analysis

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Quantitative Forecasting ApplicationsQuantitative Forecasting ApplicationsSmall and Large FirmsSmall and Large Firms

Quantitative Forecasting ApplicationsQuantitative Forecasting ApplicationsSmall and Large FirmsSmall and Large Firms

TechniqueTechnique Low Sales Low Sales (less than $100M)(less than $100M)

High SalesHigh Sales(more than $500M)(more than $500M)

Moving AverageMoving Average 29.6%29.6% 29.229.2

Simple Linear Simple Linear RegressionRegression

14.8%14.8% 14.614.6

NaiveNaive 18.5%18.5% 14.614.6

Single Exponential Single Exponential SmoothingSmoothing

14.8%14.8% 20.820.8

Multiple RegressionMultiple Regression 22.2%22.2% 27.127.1

SimulationSimulation 3.7%3.7% 10.410.4

Classical DecompositionClassical Decomposition 3.7%3.7% 8.38.3

Box-JenkinsBox-Jenkins 3.7%3.7% 6.36.3

Number of FirmsNumber of Firms 2727 4848

Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp. 92-100.Note: More than one response was permitted.

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A A time seriestime series is a set of numbers where the order or is a set of numbers where the order or sequence of the numbers is important, e.g., historical sequence of the numbers is important, e.g., historical demanddemand

Analysis of the time series identifies patternsAnalysis of the time series identifies patterns Once the patterns are identified, they can be used to Once the patterns are identified, they can be used to

develop a forecastdevelop a forecast

Time Series AnalysisTime Series AnalysisTime Series AnalysisTime Series Analysis

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Components of Time SeriesComponents of Time SeriesComponents of Time SeriesComponents of Time Series

1 2 3 4

x

x xx

xx

x xx

xx x x x

xxxxxx x x

xx

x x xx

xx

xx

x

xx

xx

xx

xx

xx

xx

x

x

Year

Sal

es

What’s going on here?

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Components of Time Series Components of Time Series Components of Time Series Components of Time Series

TrendsTrends are noted by an upward or downward sloping are noted by an upward or downward sloping lineline

SeasonalitySeasonality is a data pattern that repeats itself over is a data pattern that repeats itself over the period of one year or less the period of one year or less

CycleCycle is a data pattern that repeats itself... may take is a data pattern that repeats itself... may take yearsyears

Irregular variationsIrregular variations are jumps in the level of the series are jumps in the level of the series due to extraordinary eventsdue to extraordinary events

Random fluctuationRandom fluctuation from random variation or from random variation or unexplained causesunexplained causes

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Actual Data & the Regression LineActual Data & the Regression LineActual Data & the Regression LineActual Data & the Regression Line

40

60

80

100

120

140

160

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Year

Pow

er D

eman

d

Actual Data

Linear (Actual Data)

l

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SeasonalitySeasonalitySeasonalitySeasonality

Length of TimeLength of Time Number ofNumber of

Before PatternBefore Pattern Length ofLength of SeasonsSeasons

Is RepeatedIs Repeated SeasonSeason in Patternin Pattern

YearYear QuarterQuarter 4 4

YearYear Month Month 1212

YearYear Week Week 5252

MonthMonth Week Week 4 4

MonthMonth Day Day 28-31 28-31

WeekWeek Day Day 7 7

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Eight Steps to ForecastingEight Steps to ForecastingEight Steps to ForecastingEight Steps to Forecasting

Determining the use of the forecast--what are the Determining the use of the forecast--what are the objectives?objectives?

Select the items to be forecastSelect the items to be forecast Determine the time horizon of the forecastDetermine the time horizon of the forecast Select the forecasting model(s)Select the forecasting model(s) Collect the dataCollect the data Validate the forecasting modelValidate the forecasting model Make the forecastMake the forecast Implement the resultsImplement the results

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Quantitative Forecasting ApproachesQuantitative Forecasting ApproachesQuantitative Forecasting ApproachesQuantitative Forecasting Approaches

Linear RegressionLinear Regression Simple Moving AverageSimple Moving Average Weighted Moving AverageWeighted Moving Average Exponential Smoothing (exponentially weighted Exponential Smoothing (exponentially weighted

moving average)moving average) Exponential Smoothing with Trend (double Exponential Smoothing with Trend (double

smoothing)smoothing)

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Simple Linear RegressionSimple Linear RegressionSimple Linear RegressionSimple Linear Regression

Relationship between one independent variable, X, Relationship between one independent variable, X, and a dependent variable, Y.and a dependent variable, Y.

Assumed to be linear (a straight line)Assumed to be linear (a straight line) Form: Y = a + bXForm: Y = a + bX

Y = dependent variableY = dependent variable X = independent variableX = independent variable a = y-axis intercepta = y-axis intercept b = slope of regression lineb = slope of regression line

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Simple Linear Regression ModelSimple Linear Regression ModelSimple Linear Regression ModelSimple Linear Regression Model

b is similar to the slope. However, since it is b is similar to the slope. However, since it is calculated with the variability of the data in mind, calculated with the variability of the data in mind, its formulation is not as straight-forward as our its formulation is not as straight-forward as our usual notion of slope usual notion of slope

Yt = a + bx

0 1 2 3 4 5 x (weeks)

Y

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Calculating a and bCalculating a and bCalculating a and bCalculating a and b

a = y - bx

b =xy - n(y)(x)

x - n(x2 2

)

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Regression Equation ExampleRegression Equation ExampleRegression Equation ExampleRegression Equation Example

Week Sales1 1502 1573 1624 1665 177

Develop a regression equation to predict sales based on these five points.

Week Week*Week Sales Week*Sales1 1 150 1502 4 157 3143 9 162 4864 16 166 6645 25 177 8853 55 162.4 2499

Average Sum Average Sum

b =xy - n(y)(x)

x - n(x=

2499 - 5(162.4)(3)=

a = y - bx = 162.4 - (6.3)(3) =

2 2

) ( )55 5 9

63

106.3

143.5

Regression Equation ExampleRegression Equation Example

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y = 143.5 + 6.3t

135140145150155

160165170175180

1 2 3 4 5 Period

Sal

es

Sales

Forecast

Regression Equation ExampleRegression Equation Example

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Forecast AccuracyForecast AccuracyForecast AccuracyForecast Accuracy

Accuracy is the typical criterion for judging the Accuracy is the typical criterion for judging the performance of a forecasting approachperformance of a forecasting approach

Accuracy is how well the forecasted values match the Accuracy is how well the forecasted values match the actual valuesactual values

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Monitoring Accuracy Monitoring Accuracy Monitoring Accuracy Monitoring Accuracy

Accuracy of a forecasting approach needs to be Accuracy of a forecasting approach needs to be monitored to assess the confidence you can have in monitored to assess the confidence you can have in its forecasts and changes in the market may require its forecasts and changes in the market may require reevaluation of the approachreevaluation of the approach

Accuracy can be measured in several waysAccuracy can be measured in several ways Mean absolute deviation (MAD)Mean absolute deviation (MAD) Mean squared error (MSE)Mean squared error (MSE)

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Mean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)

n

demandForecast -demand Actual=MAD

n

1=i i

n

)F - (An

1

ii iMAD

n

)F - (An

1

ii iMAD

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Mean Squared Error (MSE)Mean Squared Error (MSE)Mean Squared Error (MSE)Mean Squared Error (MSE)

MSE = (SMSE = (Syxyx))22

Small value for SSmall value for Syxyx means data points tightly means data points tightly

grouped around the line and error range is small. grouped around the line and error range is small. The smaller the standard error the more accurate The smaller the standard error the more accurate the forecast.the forecast.

MSE = 1.25(MAD)MSE = 1.25(MAD)

When the forecast errors are normally distributedWhen the forecast errors are normally distributed

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Example--MADExample--MADExample--MADExample--MAD

Month Sales Forecast1 220 n/a2 250 2553 210 2054 300 3205 325 315

Determine the MAD for the four forecast periodsDetermine the MAD for the four forecast periods

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SolutionSolutionSolutionSolution

MAD = A - F

n=

40

4= 10

t tt=1

n

Month Sales Forecast Abs Error1 220 n/a2 250 255 53 210 205 54 300 320 205 325 315 10

40

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Simple Moving AverageSimple Moving AverageSimple Moving AverageSimple Moving Average

An averaging period (AP) is given or selectedAn averaging period (AP) is given or selected The forecast for the next period is the arithmetic The forecast for the next period is the arithmetic

average of the AP most recent actual demandsaverage of the AP most recent actual demands It is called a “simple” average because each period It is called a “simple” average because each period

used to compute the average is equally weightedused to compute the average is equally weighted . . . more. . . more

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Simple Moving AverageSimple Moving AverageSimple Moving AverageSimple Moving Average

It is called “moving” because as new demand data It is called “moving” because as new demand data becomes available, the oldest data is not usedbecomes available, the oldest data is not used

By increasing the AP, the forecast is less responsive By increasing the AP, the forecast is less responsive to fluctuations in demand (low impulse response)to fluctuations in demand (low impulse response)

By decreasing the AP, the forecast is more responsive By decreasing the AP, the forecast is more responsive to fluctuations in demand (high impulse response)to fluctuations in demand (high impulse response)

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Simple Moving AverageSimple Moving AverageSimple Moving AverageSimple Moving Average

Week Demand1 6502 6783 7204 7855 8596 9207 8508 7589 892

10 92011 78912 844

F = A + A + A +...+A

ntt-1 t-2 t-3 t-n

Let’s develop 3-week and 6-Let’s develop 3-week and 6-week moving average forecasts week moving average forecasts for demand. for demand.

Assume you only have 3 weeks Assume you only have 3 weeks and 6 weeks of actual demand and 6 weeks of actual demand data for the respective forecastsdata for the respective forecasts

Week Demand 3-Week 6-Week1 6502 6783 7204 785 682.675 859 727.676 920 788.007 850 854.67 768.678 758 876.33 802.009 892 842.67 815.33

10 920 833.33 844.0011 789 856.67 866.5012 844 867.00 854.83

Simple Moving AverageSimple Moving AverageSimple Moving AverageSimple Moving Average

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500

600

700

800

900

1000

1 2 3 4 5 6 7 8 9 10 11 12

Week

Dem

and Demand

3-Week

6-Week

Simple Moving AverageSimple Moving AverageSimple Moving AverageSimple Moving Average

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Weighted Moving AverageWeighted Moving AverageWeighted Moving AverageWeighted Moving Average

This is a variation on the simple moving average This is a variation on the simple moving average where instead of the weights used to compute the where instead of the weights used to compute the average being equal, they are not equalaverage being equal, they are not equal

This allows more recent demand data to have a This allows more recent demand data to have a greater effect on the moving average, therefore the greater effect on the moving average, therefore the forecastforecast

. . . more. . . more

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Weighted Moving AverageWeighted Moving AverageWeighted Moving AverageWeighted Moving Average

The weights must add to 1.0 and generally decrease The weights must add to 1.0 and generally decrease in value with the age of the datain value with the age of the data

The distribution of the weights determine impulse The distribution of the weights determine impulse response of the forecastresponse of the forecast

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Weighted Moving AverageWeighted Moving AverageWeighted Moving AverageWeighted Moving Average

F = w A + w A + w A +...+w At 1 t-1 2 t-2 3 t-3 n t-n

w = 1ii=1

n

Determine the 3-period weighted moving average forecast for period 4

Weights (adding up to 1.0): t-1: .5t-2: .3t-3: .2

Week Demand1 6502 6783 7204

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SolutionSolutionSolutionSolution

Week Demand Forecast1 6502 6783 7204 693.4

F= .5(720)+.3(678)+.2(650)4

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Exponential SmoothingExponential SmoothingExponential SmoothingExponential Smoothing

The weights used to compute the forecast (moving The weights used to compute the forecast (moving average) are exponentially distributedaverage) are exponentially distributed

The forecast is the sum of the old forecast and a The forecast is the sum of the old forecast and a portion of the forecast errorportion of the forecast error

FFtt = F = Ft-1t-1 + + (A(At-1t-1--FFt-1t-1)) . . . more. . . more

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Exponential SmoothingExponential SmoothingExponential SmoothingExponential Smoothing

The smoothing constant, The smoothing constant, , must be between 0.0 and , must be between 0.0 and 1.0 (excluding 0.0 and 1.0)1.0 (excluding 0.0 and 1.0)

A large A large provides a high impulse response forecast provides a high impulse response forecast A small A small provides a low impulse response forecast provides a low impulse response forecast

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Exponential Smoothing ExampleExponential Smoothing ExampleExponential Smoothing ExampleExponential Smoothing Example

Week Demand1 8202 7753 6804 6555 7506 8027 7988 6899 775

10

Determine exponential Determine exponential smoothing forecasts for smoothing forecasts for periods 2 through 10 periods 2 through 10 using using =.10 and =.10 and =.60.=.60.

Let FLet F11=D=D1 1

Week Demand 0.1 0.61 820 820.00 820.002 775 820.00 820.003 680 815.50 820.004 655 801.95 817.305 750 787.26 808.096 802 783.53 795.597 798 785.38 788.358 689 786.64 786.579 775 776.88 786.61

10 776.69 780.77

Exponential Smoothing ExampleExponential Smoothing ExampleExponential Smoothing ExampleExponential Smoothing Example

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Effect of Effect of on Forecast on ForecastEffect of Effect of on Forecast on Forecast

500

600

700

800

900

1 2 3 4 5 6 7 8 9 10

Week

Dem

and

Demand

0.1

0.6

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Criteria for SelectingCriteria for Selectinga Forecasting Methoda Forecasting MethodCriteria for SelectingCriteria for Selectinga Forecasting Methoda Forecasting Method

CostCost AccuracyAccuracy Data availableData available Time spanTime span Nature of products and servicesNature of products and services Impulse response and noise dampeningImpulse response and noise dampening

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Reasons for Ineffective ForecastingReasons for Ineffective ForecastingReasons for Ineffective ForecastingReasons for Ineffective Forecasting

Not involving a broad cross section of peopleNot involving a broad cross section of people Not recognizing that forecasting is integral to Not recognizing that forecasting is integral to

business planningbusiness planning Not recognizing that forecasts will always be wrong Not recognizing that forecasts will always be wrong

(think in terms of interval rather than point forecasts)(think in terms of interval rather than point forecasts) Not forecasting the right things Not forecasting the right things

(forecast independent demand only)(forecast independent demand only) Not selecting an appropriate forecasting method Not selecting an appropriate forecasting method

(use MAD to evaluate goodness of fit)(use MAD to evaluate goodness of fit) Not tracking the accuracy of the forecasting modelsNot tracking the accuracy of the forecasting models

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How to Monitor andHow to Monitor andControl a Forecasting ModelControl a Forecasting Model

How to Monitor andHow to Monitor andControl a Forecasting ModelControl a Forecasting Model

Tracking SignalTracking Signal

Tracking signal = Tracking signal =

= =

MAD

demand)Forecast - demand (Actualn

1

ii

MAD

demand)Forecast - demand (Actualn

1

ii

MAD

)F - (An

1

iii

MAD

)F - (An

1

iii

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Tracking Signal: What do you notice?Tracking Signal: What do you notice?Tracking Signal: What do you notice?Tracking Signal: What do you notice?

20

25

30

35

40

0 1 2 3 4 5 6 7 8 9 10 11

Period

Sal

es

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Sources of Forecasting DataSources of Forecasting DataSources of Forecasting DataSources of Forecasting Data

Consumer Confidence IndexConsumer Confidence Index Consumer Price IndexConsumer Price Index Housing StartsHousing Starts Index of Leading Economic IndicatorsIndex of Leading Economic Indicators Personal Income and ConsumptionPersonal Income and Consumption Producer Price IndexProducer Price Index Purchasing Manager’s IndexPurchasing Manager’s Index Retail SalesRetail Sales

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Wrap-Up: World-Class PracticeWrap-Up: World-Class PracticeWrap-Up: World-Class PracticeWrap-Up: World-Class Practice

Predisposed to have effective methods of forecasting Predisposed to have effective methods of forecasting because they have exceptional long-range business because they have exceptional long-range business planningplanning

Formal forecasting effortFormal forecasting effort Develop methods to monitor the performance of their Develop methods to monitor the performance of their

forecasting modelsforecasting models Use forecasting software with automated model Use forecasting software with automated model

fitting features, which is readily available todayfitting features, which is readily available today Do not overlook the short run.... excellent short range Do not overlook the short run.... excellent short range

forecasts as wellforecasts as well