Demand Forecasting

Purpose of Demand ForecastingTo reduce risk or uncertainty that the firm faces in its short run operational decision making and in planning for its long term growth

Techniques of ForecastingQualitative forecastsTime-series analysisSmoothing TechniquesBarometric MethodsEconometric Models

Qualitative ForecastsSurvey TechniquesOpinion PollsMarket Experiment

Survey TechniquesSurveys of business executives plant and equipment expendituresSurveys of plans for inventory changes and sales expenditureSurveys of consumers expenditure plansTwo types of surveyComplete Enumeration Sample Survey

Opinion PollsExecutive Polling Top Management polling based on their experience and knowledge of the firm. Averaging of their subjective forecasts is done. Delphi Technique - Outside market experts opinion could also be done. A feedback is given separately to experts on their polls whereby they are asked to revise their polls. This Iteration is contd. till a general consensus is arrived at.

Opinion PollsSales force Polling Forecast of firms sales in each region and for each product line done by the sales force in the fieldConsumer Intentions Polling Used mainly in Consumer durables. Polling a sample of consumers about their buying intention

Market ExperimentationPricing policies of the firms often depends on this forecasting technique. Price of the product is reduced or increased and sold in the market. By this experiment the change in the demand is forecasted. However these types of price experiments involves risk. Company can lose potential customers.

Econometric MethodsTo identify and measure the relative importance of the various determinants of demand or other variables economic variables to be forecastedSingle Equation ModelSimultaneous Equation Model

Single Equation MethodWhen there is a single dependent variable and many independent variables in the model, then the relationship between the dependent variable and its determinants can be shown in a single equation. To forecast the sales of future period that may depend on past sales, past prices, present prices, national income, this method is used

Simultaneous Equation MethodWhen more than one dependent variable has to be estimated from a system of equations, we use simultaneous equation system. Dependent variables appear as explanatory variables

Regression Analysis

Regression AnalysisRegression Line: Line of Best Fit

Regression Line: Minimizes the sum of the squared vertical deviations (et) of each point from the regression line.

Ordinary Least Squares (OLS) Method

OLS Method Single EquationIn order to forecast the sales of a company we have to use the multiple regression technique

Based on the data on prices, income and sales, we estimate the regression equation and use the equation for forecasting

Ordinary Least Squares (OLS)Model:

Ordinary Least Squares (OLS)Objective: Determine the slope and intercept that minimize the sum of the squared errors.

Ordinary Least Squares (OLS)Estimation Procedure

Ordinary Least Squares (OLS)Estimation Example

Tests of SignificanceStandard Error of the Slope Estimate

Tests of SignificanceExample Calculation

Tests of SignificanceCalculation of the t StatisticDegrees of Freedom = (n-k) = (10-2) = 8Critical Value at 5% level =2.306

Tests of SignificanceDecomposition of Sum of SquaresTotal Variation = Explained Variation + Unexplained Variation

Tests of SignificanceCoefficient of Determination

Tests of SignificanceCoefficient of Correlation

Multiple Regression AnalysisModel:

Multiple Regression AnalysisAdjusted Coefficient of Determination

Multiple Regression AnalysisAnalysis of Variance and F Statistic

Problems in Regression AnalysisMulticollinearity: Two or more explanatory variables are highly correlated.Heteroskedasticity: Variance of error term is not independent of the Y variable.Autocorrelation: Consecutive error terms are correlated.

Durbin-Watson StatisticTest for AutocorrelationIf d = 2, autocorrelation is absent.

Steps in Demand EstimationModel Specification: Identify VariablesCollect DataSpecify Functional FormEstimate FunctionTest the Results

Functional Form SpecificationsLinear Function:Power Function:Estimation Format:

Time Series Analysis- Components Secular Trend - long run increase or decrease in the data seriesCyclical Fluctuations major expansions or contractions in most time series data that seem to recurSeasonal Variation regular recurring fluctuation in economic activityIrregular or random influences variations due to political and social unrest, economic crisis, natural calamities

Trend projectionMain assumption past trends would continue in future Nave ForecastingFitting of Trend lineTrend types linear & non-linearUse of statistical tools Ordinary least squares techniques technique of statistical estimation

Estimating Seasonal variation from trendRatio-to-trend MethodFrom trend values we calculate the ratio of actual to trendThen getting an average of the ratio for each quarterThen multiplying the average ratio with the forecasted trend value of the quarter

Seasonal VariationRatio to Trend MethodAdjusted Forecast=Trend ForecastSeasonal Adjustment

Seasonal VariationRatio to Trend Method: Example Calculation for Quarter 1Trend Forecast for 1996.1 = 11.90 + (0.394)(17) = 18.60Seasonally Adjusted Forecast for 1996.1 = (18.60)(0.8869) = 16.50

Smoothing techniquesWhen time series exhibits little trend or seasonal variation but more cyclical fluctuations then this method is usedForecast of the variable is based on some average of its past values In this process irregular or random fluctuations are smoothed out

Two types of Smoothing TechniquesMoving Averages The forecasted value of a particular time point is the average of the values of the previous years (say 3 years or 5 years)Exponential Smoothing Techniques weighted average of the actual and forecasted values of the previous period Root-mean square errors must be the least when weights are chosen

Moving Average ForecastsForecast is the average of data from w periods prior to the forecast data point.

Exponential SmoothingForecastsForecast is the weighted average of of the forecast and the actual value from the prior period.

Root Mean Square ErrorMeasures the Accuracy of a Forecasting Method

Barometric ForecastsLeading Economic Indicators Changes in these indicators that precede the variable to be forecasted. Coincident Indicators time series that move simultaneously with the time series of the variable to be forecastedLagging indicators those following the changes in the variable to be forecasted

Barometric MethodsNational Bureau of Economic ResearchDepartment of CommerceLeading IndicatorsLagging IndicatorsCoincident IndicatorsComposite IndexDiffusion Index

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Demand Forecasting