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ForecastingForecasting

Chapter 11Chapter 11

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OutlineOutline

A Forecasting FrameworkA Forecasting Framework

Qualitative Forecasting MethodsQualitative Forecasting Methods

Time-Series ForecastingTime-Series Forecasting

Moving AverageMoving Average

Exponential SmoothingExponential Smoothing

Forecast ErrorsForecast Errors

Advanced Time-Series ForecastingAdvanced Time-Series Forecasting

Causal Forecasting MethodsCausal Forecasting Methods

Selecting a Forecasting MethodSelecting a Forecasting Method

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A Forecasting FrameworkA Forecasting Framework

Focus of the chapterFocus of the chapter

Difference between forecasting and planningDifference between forecasting and planning

Forecasting application in various decision areas Forecasting application in various decision areas of operations (capacity planning, inventory of operations (capacity planning, inventory management, others)management, others)

Forecasting uses and methods (See Table 11.1)Forecasting uses and methods (See Table 11.1)

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Use of Forecasting (Table 11.1)Use of Forecasting (Table 11.1)Operations DecisionsOperations Decisions

TimeHorizon

AccuracyRequired

Number ofForecasts

ManagementLevel

ForecastingMethod

Processdesign Long Medium Single or few Top

Qualitativeor causal

Capacityplanning,facilities

Long Medium Single or few TopQualitativeand causal

Aggregateplanning Medium High Few Middle

Causal andtime series

Scheduling Short Highest Many Lower Time series

Inventorymanagement Short Highest Many Lower Time series

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Use of Forecasting (Table 11.1)Use of Forecasting (Table 11.1)Marketing, Finance, HRMMarketing, Finance, HRM

TimeHorizon

AccuracyRequired

Number ofForecasts

ManagementLevel

ForecastingMethod

Long-rangemarketingprograms

Long Medium Single or few Top Qualitative

Pricingdecisions Short High Many Middle Time series

New productintroduction Medium Medium Single Top

Qualitativeand causal

Costestimating

Short High Many Lower Time series

Capitalbudgeting Medium Highest Few Top

Causal andtime series

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Qualitative Forecasting MethodsQualitative Forecasting Methods

Major methods:Major methods:– Delphi TechniqueDelphi Technique– Market SurveysMarket Surveys– Life-cycles AnalogyLife-cycles Analogy– Informed JudgementInformed Judgement

Characteristics of the methods (see Table Characteristics of the methods (see Table 11.2)11.2)

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Time-Series ForecastingTime-Series Forecasting

Common components in time-series (see Figure Common components in time-series (see Figure 11.1):11.1):– AverageAverage– SeasonalitySeasonality– CycleCycle– TrendTrend– Error (random component)Error (random component)

““Decomposition” of time-seriesDecomposition” of time-series

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Simple Moving Average:Simple Moving Average:

Weighted Moving Average:Weighted Moving Average:

Moving AverageMoving Average

N

DDDA Nttt

t11 ......

tt AF 1

11211 ...... NtNtttt DWDWDWAF

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Simple Exponential Smoothing:Simple Exponential Smoothing:

Smoothing Coefficient (alpha) determinationSmoothing Coefficient (alpha) determination

Determination of the initial forecastDetermination of the initial forecast

Exponential SmoothingExponential Smoothing

1 ( )t t t tF F D F

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Time-Series Data Plot (Figure Time-Series Data Plot (Figure 11.2)11.2)

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Exponential SmoothingExponential Smoothing

Basic logic:Basic logic:

The forecastThe forecast

11 ttt ADA

tt AF 1

tttt FDFF 1

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Forecast ErrorsForecast Errors

Cumulative Sum of Forecast Error (CFE)Cumulative Sum of Forecast Error (CFE)

Mean Square Error (MSE)Mean Square Error (MSE)

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

Mean Absolute Percentage Error (MAPE)Mean Absolute Percentage Error (MAPE)

Tracking Signal (TS)Tracking Signal (TS)

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Forecast Errors: FormulasForecast Errors: Formulas

t

n

=1i

e = CFE Cumulative sum ofForecast Errors

n

t

n

=1i

e = MSE

2Mean Square Error

n

|e| = MAD

t

n

=1iMean Absolute

Deviation

n

|D

e|

= MAPE t

tn

=1i

100Mean AbsolutePercentage Error

MAD

e = TS

t

n

=1iTracking Signal

n

t

n

=1i

e = MEMean Error

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Advanced Time-Series ForecastingAdvanced Time-Series Forecasting

Adaptive exponential smoothingAdaptive exponential smoothing

Comparison of time-series forecasting Comparison of time-series forecasting methods (see Table 11.5)methods (see Table 11.5)

Box-Jenkins methodBox-Jenkins method

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Causal Forecasting ModelsCausal Forecasting Models

The general model:The general model:

Other forms of causal model (see Table Other forms of causal model (see Table 11.7):11.7):– EconometricEconometric– Input-outputInput-output– Simulation modelsSimulation models– OthersOthers

xbay ˆ

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Example of Causal MethodExample of Causal Method

t Dt Ft1 120 119.522 124 121.183 119 122.844 124 124.55 125 126.156 130 127.817 129.47

Intercept (a) 117.8667Slope (b) 1.657143

Yt = a + b(t)

F7 = 117.87 + 1.66 (7) = 129.47

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Selecting a Forecasting MethodSelecting a Forecasting Method

User and system sophisticationUser and system sophistication

Time and resource availableTime and resource available

Use or decision characteristicsUse or decision characteristics

Data availabilityData availability

Data patternData pattern

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Graphical ComparisonGraphical ComparisonMoving average method with various Moving average method with various nn

Mean Error

-8.83

-34.75

-17.10

-26.32

n = 3

n = 6

n = 10

n = 12

ME

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Graphical ComparisonGraphical ComparisonMoving average method with various Moving average method with various nn

Mean Absolute Deviation

256.53

234.17

215.62

223.30

n = 3

n = 6

n = 10

n = 12

MAD

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Graphical ComparisonGraphical ComparisonMoving average method with various Moving average method with various nn

Mean Squared Error (MSE)

85,999

84,281

72,664

75,475

n = 3

n = 6

n = 10

n = 12

MSE

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Graphical ComparisonGraphical ComparisonMoving average method with various Moving average method with various nn

MAPE

0.1182

0.1098

0.0932

0.1034

n = 3

n = 6

n = 10

n = 12

MAPE