hft 3431 chapter 9 forecasting methods forecasting how important is forecasting?how important is...
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HFT 3431HFT 3431
Chapter 9Chapter 9
Forecasting MethodsForecasting Methods
ForecastingForecasting
• How Important Is Forecasting?How Important Is Forecasting?
• Is Forecasting Only Financial?Is Forecasting Only Financial?
• How Is It Done?How Is It Done?
• Does Forecasting Help Be Successful?Does Forecasting Help Be Successful?
• What Are Forecasting Limitations?What Are Forecasting Limitations?
• Does Forecasting Differ From Does Forecasting Differ From Planning?Planning?
ForecastingForecasting
• What Is the Difference Between What Is the Difference Between Seasonal and Cyclical Patterns?Seasonal and Cyclical Patterns?
• How Do Quantitative and Qualitative How Do Quantitative and Qualitative Forecasting Methods Differ?Forecasting Methods Differ?
ForecastingForecasting
• How Is a Moving Average How Is a Moving Average Calculated?Calculated?
• When Are Causal Forecasting When Are Causal Forecasting Approaches Useful?Approaches Useful?
Implicit versus Explicit Implicit versus Explicit ForecastsForecasts
• Implicit - (Intuitive)Implicit - (Intuitive)
UnsystematicUnsystematic
ImpreciseImprecise
Difficult to EvaluateDifficult to Evaluate
Implicit versus Explicit Implicit versus Explicit ForecastsForecasts
• Explicit - (Analytical)Explicit - (Analytical)
SystematicSystematic
Reasonably Reliable and AccurateReasonably Reliable and Accurate
Rational EvaluationRational Evaluation
The Nature of ForecastingThe Nature of Forecasting
• Done by All Levels of ManagementDone by All Levels of Management
• Looks at the FutureLooks at the Future
• Involves UncertaintiesInvolves Uncertainties
• Based on Historical DataBased on Historical Data
• Less Accurate Than DesiredLess Accurate Than Desired
• Extensive Use of Naive ModelsExtensive Use of Naive Models
The Nature of ForecastingThe Nature of Forecasting
• Large Properties Need to Use More Large Properties Need to Use More Sophisticated ModelsSophisticated Models
• Trend - Straight Line ProjectionTrend - Straight Line Projection– Long run estimate-several yearsLong run estimate-several years
• Seasonal - Fluctuate Over TimeSeasonal - Fluctuate Over Time• Cyclical - Movements Over a TrendCyclical - Movements Over a Trend– Movements around a trend lineMovements around a trend line
• Random Variations Create UncertaintyRandom Variations Create Uncertainty
Data PatternData Pattern
0
50
100
150
200
250
300
350
400
450
TREND Seasonal Cyclical
Formal Forecasting MethodsFormal Forecasting Methods
• QualitativeQualitative
–Emphasize human judgmentEmphasize human judgment
• QuantitativeQuantitative
–Causal & time period approachesCausal & time period approaches
Qualitative MethodsQualitative Methods
• Market ResearchMarket Research– Gather information from potential Gather information from potential
customerscustomers• Juries of Executive OpinionJuries of Executive Opinion– Top executive jointly prepare forecastsTop executive jointly prepare forecasts
• Sales Force EstimatesSales Force Estimates– Bottom up approach from unit Bottom up approach from unit
managersmanagers• Delphi MethodDelphi Method– Formal process with a group of expertsFormal process with a group of experts
Quantitative MethodsQuantitative Methods
• Time SeriesTime Series
–NaïveNaïve
–SmoothingSmoothing
–DecompositionDecomposition
• Causal methodsCausal methods
–Regression AnalysisRegression Analysis
–EconometricsEconometrics
Time SeriesTime Series
• NaïveNaïve–Simples rulesSimples rules
• SmoothingSmoothing–Uses moving average or recent pst Uses moving average or recent pst
values (exponential smoothing)values (exponential smoothing)• DecompositionDecomposition– Time series broken down into Time series broken down into
cyclical, seasonality randomnesscyclical, seasonality randomness
Forecasting MethodsForecasting Methods• Naïve Method - Multiply current sales Naïve Method - Multiply current sales
level (or sales price) by a percentage level (or sales price) by a percentage increase (or decrease); or add (or increase (or decrease); or add (or subtract) a fixed amount.subtract) a fixed amount.
• This method does not use any This method does not use any analytical or scientific methodanalytical or scientific method
Forecasting MethodForecasting Method
• Naïve Example– Current year sales level is 1,000 units– Current year sales price is $15.00– Next year levels increase 10%– Next year price decreases $0.50
– Current Year Total Sales Equals
1,000 * $15.00 = $15,000
Forecasting MethodForecasting Method
• Naïve Example - ContinuedNaïve Example - Continued– Next year sales level equalsNext year sales level equals
1,000 * 1.10 = 1,1001,000 * 1.10 = 1,100– Next year price equalsNext year price equals
$15.00 - $0.50 = $14.50$15.00 - $0.50 = $14.50– Next year Total Sales equalsNext year Total Sales equals
1,100 * $14.50 = $15,9501,100 * $14.50 = $15,950
Forecasting MethodsForecasting Methods• Moving Averages - Sum of Activity in Moving Averages - Sum of Activity in
Previous N Periods Divided by Previous N Periods Divided by NN, , Where Where NN Is the Number of Periods Is the Number of Periods
Moving AverageMoving Average
• Page 408; Forecast week 13 using 3 Page 408; Forecast week 13 using 3 week moving averageweek moving average
Thus use data in weeks 10, 11, 12 Thus use data in weeks 10, 11, 12 and divide by 3and divide by 3
(1025 + 1000 + 1050) / 3 = 1025(1025 + 1000 + 1050) / 3 = 1025
Forecasting MethodsForecasting Methods• Exponential Smoothing - Uses a Exponential Smoothing - Uses a
Smoothing Constant and Recent Smoothing Constant and Recent Actual and Forecasted Activity to Actual and Forecasted Activity to Estimate Future ActivityEstimate Future Activity
Exponential SmoothingExponential Smoothing
• Forecast for Period 3:Forecast for Period 3:• Using Data belowUsing Data below• Period 1 Forecast 1,025Period 1 Forecast 1,025• Period 1 Actual 1,000Period 1 Actual 1,000• Period 2 Forecast 1,020Period 2 Forecast 1,020• Period 2 Actual 1,050Period 2 Actual 1,050
Exponential SmoothingExponential Smoothing
• Forecast for Period 3:Forecast for Period 3:• Step 1 - Determine Smoothing ConstantStep 1 - Determine Smoothing Constant
Period 2 Forecast - Period 1 ForecastPeriod 2 Forecast - Period 1 Forecast
Period 1 Actual - Period 1 ForecastPeriod 1 Actual - Period 1 Forecast
(1,020 - 1025 ) / (1,000 – 1,025 ) = 0.20(1,020 - 1025 ) / (1,000 – 1,025 ) = 0.20
Exponential SmoothingExponential Smoothing
• Step 2 Step 2
• Forecast for Week 3Forecast for Week 3• Wk 3 F = Wk 2 F + SC(WK2 Act – Wk2 F)• Wk 3 F = 1,020 + 0.2(1,050 - 1,020)• Wk 3 F = 1,020 + 0.2(30)• Wk 3 F = 1,026
Forecasting MethodsForecasting Methods
• Causal - Regression Analysis Which Causal - Regression Analysis Which Is Estimating an Activity (Dependent Is Estimating an Activity (Dependent Variable) on the Basis of Other Variable) on the Basis of Other Activities (Independent Variables)…Activities (Independent Variables)…
• How Closely Related Is Measured by How Closely Related Is Measured by Coefficient of Correlation and Coefficient of Correlation and Coefficient of DeterminationCoefficient of Determination
Forecasting MethodsForecasting Methods
• Coefficient of Correlation ( r )– is the Coefficient of Correlation ( r )– is the measure of the relationship between the measure of the relationship between the dependent and independent variables. dependent and independent variables. Closer to 1 the stronger the relationship.Closer to 1 the stronger the relationship.
• Coefficient of Determination ( rCoefficient of Determination ( r22 ) – reflects ) – reflects the extent to which the change in the the extent to which the change in the independent variable explains the change independent variable explains the change in the dependent variablein the dependent variable
Regression AnalysisRegression Analysis
• FormulaFormula
Y = A + BXY = A + BX
Y is the dependent variableY is the dependent variable
A is a constantA is a constant
B is a multiplierB is a multiplier
X is the independent variableX is the independent variable
Regression AnalysisRegression Analysis
• Page 412 Page 412
Y = 370 + 1.254xY = 370 + 1.254x
If X = 3,000 roomsIf X = 3,000 rooms
Y = 370 + 1.254(3000)Y = 370 + 1.254(3000)
Y = 6,013 mealsY = 6,013 meals
Forecasting LimitationsForecasting Limitations
• Scarcity of DataScarcity of Data
• Assumes Continuation of TrendsAssumes Continuation of Trends
• Unforseeable OccurrencesUnforseeable Occurrences
Qualitative MethodsQualitative Methods
• Based on human judgmentBased on human judgment
–Market ResearchMarket Research
– Jury of Executive OpinionJury of Executive Opinion
–Sales force estimatesSales force estimates
–Delphi MethodDelphi Method
Consideration In Choosing Consideration In Choosing a Forecasting Methoda Forecasting Method
• Effectiveness in Providing Effectiveness in Providing InformationInformation
• Cost of ImplementationCost of Implementation
• Frequency of Forecast UpdatesFrequency of Forecast Updates
• Turnaround Time of ForecastingTurnaround Time of Forecasting
Consideration In Choosing Consideration In Choosing a Forecasting Methoda Forecasting Method
• Size and Complexity of OperationSize and Complexity of Operation
• Forecasting Skills of PersonnelForecasting Skills of Personnel
• Purpose of Making ForecastPurpose of Making Forecast
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