POM - J. Galván 1 PRODUCTION AND OPERATIONS MANAGEMENT Ch. 5: Forecasting.

POM - J. GalvánPOM - J. Galván 11

PRODUCTION AND PRODUCTION AND OPERATIONS OPERATIONS

MANAGEMENTMANAGEMENT

Ch. 5: ForecastingCh. 5: Forecasting


Learning ObjectivesLearning Objectives

Understand techniques to foresee the Understand techniques to foresee the futurefuture


What is Forecasting?What is Forecasting?

¨ Process of predicting a future event

¨ Underlying basis of all business decisions¨ Production¨ Inventory¨ Personnel¨ Facilities

Sales will be $200 Million!


Short-range forecastShort-range forecast• Up to 1 year; usually < 3 monthsUp to 1 year; usually < 3 months• Job scheduling, worker assignmentsJob scheduling, worker assignments

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

Long-range forecastLong-range forecast• 3+ years3+ years• New product planning, facility locationNew product planning, facility location

Types of Forecasts by Time Types of Forecasts by Time HorizonHorizon


Short-term vs. Longer-term Short-term vs. Longer-term ForecastingForecasting

Medium/long rangeMedium/long range forecasts deal with forecasts deal with more comprehensive issues and support more comprehensive issues and support management decisions regarding planning management decisions regarding planning and products, plants and processes.and 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 forecasts.accurate than longer-term forecasts.


Influence of Product Life Influence of Product Life CycleCycle

Stages of introduction & growth Stages of introduction & growth require longer forecasts than require longer forecasts than maturity and declinematurity and decline

Forecasts useful in projectingForecasts useful in projecting• staffing levels,staffing levels,• inventory levels, and inventory levels, and • factory capacityfactory capacity

as product passes through stages as product passes through stages


Types of ForecastsTypes of Forecasts

Economic forecastsEconomic forecasts• Address business cycleAddress business cycle• e.g., inflation rate, money supply etc.e.g., inflation rate, money supply etc.

Technological forecastsTechnological forecasts• Predict technological changePredict technological change• Predict Predict newnew product sales product sales

Demand forecastsDemand forecasts• Predict Predict existingexisting product sales product sales


Seven Steps in ForecastingSeven Steps in Forecasting Determine the use of the forecastDetermine the use of the forecast Select the items to be forecastSelect the items to be forecast 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


Realities of ForecastingRealities of Forecasting

Forecasts are seldom perfectForecasts are seldom perfect Most forecasting methods assume Most forecasting methods assume

that there is some underlying that there is some underlying stability in the systemstability in the system

Both product family and Both product family and aggregated product forecasts are aggregated product forecasts are more accurate than individual more accurate than individual product forecastsproduct forecasts


Forecasting ApproachesForecasting Approaches

¨ Used when situation is ‘stable’ & historical data exist¨ Existing products¨ Current technology

¨ Involves mathematical techniques

¨ e.g., forecasting sales of color televisions

Quantitative Methods¨ Used when situation is

vague & little data exist¨ New products¨ New technology

¨ Involves intuition, experience

¨ e.g., forecasting sales on Internet

Qualitative Methods


Overview of Qualitative MethodsOverview of Qualitative Methods Jury of executive opinionJury of executive opinion

• Pool opinions of high-level executives, sometimes Pool opinions of high-level executives, sometimes augment by statistical modelsaugment by statistical models

Sales force compositeSales force composite• estimates from individual salespersons are estimates from individual salespersons are

reviewed for reasonableness, then aggregatedreviewed for reasonableness, then aggregated Delphi methodDelphi method

• Panel of experts, queried iterativelyPanel of experts, queried iteratively Consumer Market SurveyConsumer Market Survey

• Ask the customerAsk the customer


¨ Involves small group of high-level managers

¨ Group estimates demand by working together

¨ Combines managerial experience with statistical models

¨ Relatively quick¨ ‘Group-think’

disadvantage

© 1995 Corel Corp.

Jury of Executive OpinionJury of Executive Opinion


Sales Force CompositeSales Force Composite

¨ Each salesperson projects their sales

¨ Combined at district & national levels

¨ Sales rep’s know customers’ wants

¨ Tends to be overly optimistic

SalesSales

© 1995 Corel Corp.


Delphi MethodDelphi Method

Iterative group Iterative group processprocess

3 types of people3 types of people• Decision makersDecision makers• StaffStaff• RespondentsRespondents

Reduces ‘group-Reduces ‘group-think’think’ Respondents Respondents

Staff Staff

Decision MakersDecision Makers(Sales?)

(What will sales be? survey)

(Sales will be 45, 50, 55)

(Sales will be 50!)


Consumer Market SurveyConsumer Market Survey

¨ Ask customers about purchasing plans

¨ What consumers say, and what they actually do are often different

¨ Sometimes 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.

POM - J. GalvánPOM - J. Galván 16165-22

Overview of Quantitative Overview of Quantitative ApproachesApproaches

Naïve approachNaïve approach Moving averagesMoving averages Exponential Exponential

smoothingsmoothing Trend projectionTrend projection

Linear regressionLinear regression

Time-series Models

Causal models


Quantitative Forecasting Methods Quantitative Forecasting Methods (Non-Naive)(Non-Naive)

QuantitativeForecasting

LinearRegression

CausalModels

ExponentialSmoothing

MovingAverage

Time SeriesModels

TrendProjection


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 valuesForecast based only on past values

• Assumes that factors influencing past, Assumes that factors influencing past, present, & future will continue present, & future will continue

ExampleExampleYear:Year: 19931993 19941994 19951995 19961996 19971997

Sales:Sales: 78.778.7 63.563.5 89.789.7 93.293.2 92.192.1

What is a Time Series?What is a Time Series?


TrendTrend

SeasonalSeasonal

CyclicalCyclical

RandomRandom

Time Series ComponentsTime Series Components


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

Due to population, technology etc.Due to population, technology etc. Several years duration Several years duration

Mo., Qtr., Yr.

Response

© 1984-1994 T/Maker Co.

Trend ComponentTrend Component


Repeating up & down movementsRepeating up & down movements Due to interactions of factors Due to interactions of factors

influencing economyinfluencing economy Usually 2-10 years duration Usually 2-10 years duration

Mo., Qtr., Yr.Mo., Qtr., Yr.

ResponseResponseCycle

BB

Cyclical ComponentCyclical Component


Regular pattern of up & down Regular pattern of up & down fluctuationsfluctuations

Due to weather, customs etc.Due to weather, customs etc. Occurs within 1 year Occurs within 1 year

Mo., Qtr.

Response

Summer

© 1984-1994 T/Maker Co.

Seasonal ComponentSeasonal Component


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

Due to random variation or Due to random variation or unforeseen eventsunforeseen events• Union strikeUnion strike• TornadoTornado

Short duration & Short duration & nonrepeating nonrepeating

Random ComponentRandom Component


Any observed value in a time series is the Any observed value in a time series is the product (or sum) of time series componentsproduct (or sum) of time series components

Multiplicative modelMultiplicative model• YYii = = TTii · · SSii · · CCii · · RRii (if quarterly or mo. data) (if quarterly or mo. data)

Additive modelAdditive model• YYii = = TTii + + SSii + + CCii + + RRii (if quarterly or mo. (if quarterly or mo.

data)data)

General Time Series ModelsGeneral Time Series Models


Naive ApproachNaive Approach

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

¨ e.g., If May sales were 48, then June sales will be 48

¨ Sometimes cost effective & efficient

© 1995 Corel Corp.


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 oftenUsed often for smoothingfor smoothing

• Provides overall impression of data over timeProvides overall impression of data over time EquationEquation

MAMAnn

nn Demand in Demand in Previous Previous PeriodsPeriods

Moving Average MethodMoving Average Method


Year

Sales

0

2

4

6

8

93 94 95 96 97 98

Actual

Forecast

Moving Average GraphMoving Average Graph


Increasing Increasing nn makes forecast makes forecast less sensitive to changesless sensitive to changes

Do not forecast trend wellDo not forecast trend well Require much historical Require much historical

datadata© 1984-1994 T/Maker Co.

Disadvantages ofDisadvantages of Moving Average Method Moving Average Method


Used for forecasting linear trend lineUsed for forecasting linear trend line Assumes relationship between Assumes relationship between

response variable, response variable, Y, Y, and time, and time, X, X, is is a linear functiona linear function

Estimated by least squares methodEstimated by least squares method• Minimizes sum of squared errorsMinimizes sum of squared errors

iY a bX i

Linear Trend ProjectionLinear Trend Projection


Time

SalesSales

00

1122

33

44

92 93 94 95 96

Sales vs. Time

Scatter DiagramScatter Diagram


Least Squares EquationsLeast Squares Equations

Equation: ii bxaY

Slope:

xnx

yxnyxb

i

n

i

ii

n

i

Y-Intercept: xbya


Multiplicative Seasonal ModelMultiplicative Seasonal Model Find Find average historical demandaverage historical demand for each “season” by for each “season” by

summing the demand for that season in each year, and summing the demand for that season in each year, and dividing by the number of years for which you have dividing by the number of years for which you have data.data.

Compute the Compute the average demand over all seasonsaverage demand over all seasons by by dividing the total average annual demand by the dividing the total average annual demand by the number of seasons.number of seasons.

Compute a Compute a seasonal indexseasonal index by dividing that season’s by dividing that season’s historical demand (from step 1) by the average demand historical demand (from step 1) by the average demand over all seasons.over all seasons.

Estimate next year’s total demandEstimate next year’s total demand Divide this estimate of total demand by the number of Divide this estimate of total demand by the number of

seasons then multiply it by the seasonal index for that seasons then multiply it by the seasonal index for that season. This provides the season. This provides the seasonal forecastseasonal forecast..


Y Xi i= +a b

Shows linear relationship between Shows linear relationship between dependent & explanatory variablesdependent & explanatory variables• Example: Sales & advertising (Example: Sales & advertising (notnot time) time)

Dependent (response) variable

Independent (explanatory) variable

SlopeY-intercept

^

Linear Regression ModelLinear Regression Model


Linear Regression EquationsLinear Regression Equations

Equation: ii bxaY

Slope:

xnx

yxnyxb

i

n

i

ii

n

i

Y-Intercept: xbya


Slope (Slope (bb))• Estimated Estimated YY changes by changes by bb for each 1 unit for each 1 unit

increase in increase in XX If If bb = 2, then sales ( = 2, then sales (YY) is expected to increase ) is expected to increase

by 2 for each 1 unit increase in advertising (by 2 for each 1 unit increase in advertising (XX))

Y-intercept (Y-intercept (aa))• Average value of Average value of YY when when XX = 0 = 0

If If aa = 4, then average sales ( = 4, then average sales (YY) is expected to ) is expected to be 4 when advertising (be 4 when advertising (XX) is 0) is 0

Interpretation of CoefficientsInterpretation of Coefficients


Answers: ‘Answers: ‘how stronghow strong is the linear is the linear relationship between the variables?’relationship between the variables?’

Coefficient of correlation Sample Coefficient of correlation Sample correlation coefficient denotedcorrelation coefficient denoted rr• Values range from -1 to +1Values range from -1 to +1• Measures degree of associationMeasures degree of association

Used mainly for understandingUsed mainly for understanding

CorrelationCorrelation


r = 1r = 1 r = -1r = -1

r = .89r = .89 r = 0r = 0

YY

XXYYii = = aa + + bb XXii^

YY

XX

YY

XX

YY

XXYYii = = aa + + bb XXii^ YYii = = aa + + bb XXii

^

YYii = = aa + + bb XXii^

Coefficient of Correlation and Coefficient of Correlation and Regression ModelRegression Model


You want to achieve:You want to achieve:• No pattern or direction in forecast errorNo pattern or direction in forecast error

Error = (Error = (YYii - - YYii) = (Actual - Forecast)) = (Actual - Forecast)Seen in plots of errors over timeSeen in plots of errors over time

• Smallest forecast errorSmallest forecast errorMean square error (MSE)Mean square error (MSE)Mean absolute deviation (MAD)Mean absolute deviation (MAD)

^

Guidelines for Selecting Guidelines for Selecting Forecasting ModelForecasting Model


Time (Years)

ErrorError

00

Desired Pattern

Time (Years)

Error

0

Trend Not Fully Accounted for

Pattern of Forecast ErrorPattern of Forecast Error


Measures how well forecast is Measures how well forecast is predicting actual valuespredicting actual values

Ratio of running sum of forecast Ratio of running sum of forecast errors (RSFE) to mean absolute errors (RSFE) to mean absolute deviation (MAD)deviation (MAD)• Good tracking signal has low valuesGood tracking signal has low values

Should be within upper and lower Should be within upper and lower control limitscontrol limits

Tracking SignalTracking Signal


-3-2-10123

1 2 3 4 5 6Time

TS

Tracking Signal PlotTracking Signal Plot


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• issues of holidays and calendarissues of holidays and calendar• unusual eventsunusual events


Forecasting exampleForecasting exampleSALES DURING LAST YEAR

LAST YEAR Real sales

Spring 200

Summer 350

Fall 300

Winter 150

TOTAL ANNUAL SALES 1000

ESTIMATION: Annual increase of sales 10,00%

What are the estimated seasonal sales amount for next year?


Forecasting example (II)Forecasting example (II)

LAST YEARPastsales

Average sales for each season

Seasonal factor

Total past annual sales/

nº of seasons

Past sales/Avg. Sales

Spring 200 250 0,8

Summer 350 250 1,4

Fall 300 250 1,2

Winter 150 250 0,6

TOTAL ANNUAL SALES 1000 1000


Forecasting example (III)Forecasting example (III)

NEXT YEAR SALES 1100 (10% (10% increase)increase)

NEXT YEARAverage sales for

each seasonSeasonal

factor

Next year's seasonal forecast

Total estimated annual sales/nº

of seasons

As calculated

Avg.sales*Factor

Spring ? 275 0,8 220

Summer ? 275 1,4 385

Fall ? 275 1,2 330

Winter ? 275 0,6 165

TOTAL ANNUAL SALES

1100 1100 1100

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POM - J. Galván 1 PRODUCTION AND OPERATIONS MANAGEMENT Ch. 5: Forecasting.

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