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11Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
ForecastingForecasting
Chapter 11Chapter 11
22Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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
33Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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)
44Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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
55Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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
66Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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)
77Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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
88Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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
99Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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
1010Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
Time-Series Data Plot (Figure Time-Series Data Plot (Figure 11.2)11.2)
1111Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
Exponential SmoothingExponential Smoothing
Basic logic:Basic logic:
The forecastThe forecast
11 ttt ADA
tt AF 1
tttt FDFF 1
1212Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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)
1313Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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
1414Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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
1515Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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
1616Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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
1717Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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
1818Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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
1919Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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
2020Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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
2121Irwin/McGraw-Hill The McGraw-Hill Companies,
Inc. 2004
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
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