Demand forecasting

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Demand forecasting

  1. 1. Managerial EconomicsDemand Forecasting
  2. 2. Demand ForecastingIt means expectation about future course of themarket demand for a product based on statisticaldata about past behavior and empiricalrelationships of demand determinantsTypes: Short term Long term Passive & Active Forecasts
  3. 3. Short Term ForecastingIt normally relates to a period not exceeding ayearBenefits of Short term forecasting Evolving a Sales Policy Determining Price Policy Fixation of Sales Target
  4. 4. Long Term ForecastingIt refers to the forecasts prepared for longperiod during which the firm’s scale ofoperations or the production capacity may beexpanded or reduced Benefitsof Long term forecasting  Business Planning Manpower Planning Long-Term Financial Planning
  5. 5. Factors involved in Demand ForecastingUndertaken at three levels:a.Macro-levelb.Industry level eg., trade associationsc.Firm levelShould the forecast be general or specific(product-wise)?Problems or methods of forecasting for “new” vis-à-vis “well established” products.Classification of products – producer goods,consumer durables, consumer goods, services. Special factors peculiar to the product and themarket – risk and uncertainty.
  6. 6. 1. Criteria of a good forecasting Accuracy – measured by (a) degree of deviations between forecastsand actuals, and (b) the extent of success in forecasting directionalchanges. method2.Simplicity and ease of comprehension.3.Economy.4.Availability.5.Maintenance of timeliness.
  7. 7. Presentation of a forecast to the Management1.Make the forecast as easy for the managementto understand as possible.2.Avoid using vague generalities.3.Always pin-point the major assumptions andsources.4.Give the possible margin of error.5.Omit details about methodology andcalculations.6.Make use of charts and graphs as much aspossible for easy comprehension.
  8. 8. Various macro parameters found useful for demand forecasting1.National income and per capita income.2.Savings.3.Investment.4.Population growth.5.Government expenditure.6.Taxation.7.Credit policy.
  9. 9. Significance of Demand ForecastingProduction PlanningSales ForecastingControl of BusinessInventory ControlGrowth and Long Term Investment ProgramEconomic Planning and Policy Making
  10. 10. Sources of DataPrimary: which are collected for first time forpurpose of analysisSecondary : are those which are obtained fromsomeone’s else records
  11. 11. Consumer Survey MethodsComplete enumeration Method: All potential users ofproduct are contacted and are asked about their future planof purchasing the product in questionLimitations Very expensive in case of widely dispersed market Consumers may not know their actual demand and may br unable to answer query Their plans may change with a change in factors not included in questionnaire
  12. 12. Contd…Sample Survey: Only a few potentialconsumers and users selected from relevantmarket are surveyedMethod is simpler, less costly and less timeconsuming. Surveys are done to understand marketdemand, tastes ad preferences, Consumerexpectations etc
  13. 13. Opinion Poll MethodAim at collecting opinions of those who aresupposed to possess the knowledge of the markete.g sales representatives, sales executives,consultants and professional marketing expertsThis method includesExpert opinionDelphi method
  14. 14. Expert opinionUnder this method each expert is asked independently toprovide a confidential estimate and results could be averaged.Experts may include executives directly involved in the marketsuch as suppliers, distributors or dealers or marketing consultants,officers of trade association etc.Advantage is that there is no danger that group of expertsdevelop a group- think mentality. Moreover, forecasting is donequickly and easily without need of elaborate need of statistics.
  15. 15. Delphi MethodThis method is an attempt to arrive at a consensus onsome issues by questioning a group of expertsrepeatedly until the responses appear to converge alonga single line or the issues causing disagreement areclearly defined.Generally a panel consisting 9 to 12 expertsA coordinator is required for the process
  16. 16. Market ExperimentationTest marketing A testarea is selected, which should be a representative of the whole market in which the new product is to be launched. A test area may include several cities having similar features i.e. population, income levels, cultural and social background, choice and preferences of consumers Market experiments are carried out by changing prices, advertisement expenditure and other controllable variables influencing demand Aftersuch changes are introduced in the market, consequent changes in demand over a period of time are recorded.
  17. 17. Contd…Experiments in laboratory or consumer clinic method Under this method consumers are given some money to buy in a stipulated store goods with varying prices, packages, displays etc. They are also requested to fill a questionnaire asking reasons for the choices they have made The experiment reveals the consumers responsiveness to the changes made in prices, packages and displays.
  18. 18. Limitations of market experiment methodsVery expensiveBeing costly, carried out on a scale too small to permitgeneralization with a high degree of reliabilityBased on short term and controlled conditions whichmay not exist in an uncontrolled marketTinkering with price increases may cause a permanentloss of customers to competitive brands
  19. 19. Types of data used in Statisticalmethods data refer to data collected over aTime seriesperiod of time recording historical changes in price ,income and other relevant variables influencingdemand for a commodityCross sectional analysis is undertaken to determinethe effects of changes like price, income etc ondemand for a commodity at a point in time
  20. 20. Types of Statistical MethodsConsumption level MethodTime series Analysis (Trend Projection)Smoothing Techniques Moving Averages Least Squares Method Exponential Smoothing TechniqueEconometric MethodBarometric Method
  21. 21. Consumption Level MethodUnder this method consumption level method may beestimated on basis of co-efficient of Income elasticityand price elasticity of DemandD* = D(1+M*.e)D* =Projected per capita demandD= Actual Per capita DemandM*= Percentage change in per capita income/priceE=elasticity of demand
  22. 22. IllustrationSuppose Income elasticity of demand forchocolates is 3. In year 1995 per capita income is$500 and per capita annual demand forchocolates is 10 million in a city. It is expectedthat in year 2000 per capita income will increaseby 20 % . Then projected per capita demand forchocolates in 2000 will be?
  23. 23. Time Series AnalysisIt attempts to forecast future values of time series byexamining past observations of dataThe time series relating to sales represent the past patternof effective demand for a particular product. Such data canbe presented either in a tabular form or graphically forfurther analysis.The most popular method of analysis of the time series isto project the trend of the time series.a trend line can befitted through a series either visually or by means ofstatistical techniques.The analyst chooses a plausible algebraic relation (linear,quadratic, logarithmic, etc.) between sales and theindependent variable, time. The trend line is then projectedinto the future by extrapolation.
  24. 24. Time Series AnalysisPopular because: simple, inexpensive, time seriesdata often exhibit a persistent growth trend.Disadvantage: this technique yields acceptableresults so long as the time series shows apersistent tendency to move in the same direction.Whenever a turning point occurs, however, thetrend projection breaks down.The real challenge of forecasting is in theprediction of turning points rather than in theprojection of trends.
  25. 25. Time Series AnalysisReasons for fluctuations in time series dataSecular Trend : value of a variable tends to increase or decreaseover a period of timeCyclical Fluctuations are major expansions and contractions thatseem to recur every several yearsSeasonal variation refers to regularly recurring fluctuation ineconomic activity during each yearIrregular influences are variations in data series resulting fromwars, natural disasters or other unique eventsFour sets of factors: secular trend (T), seasonalvariation (S), cyclical fluctuations (C ), irregular orrandom forces (I). O (observations) = TSCI
  26. 26. Trend ProjectionSimplest form of time series analysis is projectingtrend based on assumption that factorsresponsible for past trends in variable to beprojected will remain same in future.Trends refer to long term persistent movement ofdata in one direction-increase or decreaseTrend component of time series is the overalldirection of the movement of the variable over along period.
  27. 27. Reasons for studying TrendsStudying secular trends permits us to project pastpatterns, or trends, into the futureIn many situations studying the secular trend of a timeseries allows us to eliminate the trend component fromthe series.Methods for trend Projections: Least squares methodSmoothing TechniquesMoving AverageExponential smoothing
  28. 28. Moving average MethodThis method assumes that demand in future yearequals the average of demand in past yearsUnder this method 3 yearly,4 or 5 yearly etcmoving average is calculated by moving total ofvalues in group of years(3,4,5)is calculated, eachtime by ignoring first entry and incorporating lastoneFor Three period Moving average the forecastedvalue of time series for next period is averagevalue of previous three periods in time series
  29. 29. Moving average MethodIn order to decide which of these moving averagesforecasts is better closer to actual data root-mean-square-error (RMSE) is calculated for eachforecast and using moving average that results insmaller RMSEThe greater the number of periods used in movingaverage the greater is the smoothing effectbecause each new observation receives lessweight. Useful when time series data is moreerratic.
  30. 30. Three-quarter Moving Average forecasts
  31. 31. Five Quarter Moving Average forecasts
  32. 32. Three & Five year Moving AverageComparisonRMSE= {(A-F)2 / n}1/2RMSE = 78.3534/9 = 2.95RMSE = 62.48/7 = 2.99Thus Three Year Moving Average is marginally better thancorresponding Five year
  33. 33. Exponential SmoothingA serous criticism of using moving averages in forecasting is that they giveequal weight to all observations in computing the average even thoughmore recent observations are more importantIt uses a weighted average of past data as basis for a forecast by givingheaviest weight to more recent information and smaller weights toobservations in more distant past on assumption that future is moredependent on recent past than on distant pastThe value of time series at period t (At) is assigned a weight (w) between 0and 1 both inclusive, and forecast for period t (Ft) is assigned 1-w . Thebasic Equation : Ft+1 = wAt + (1-w)Ft Where Ft+1 = forecast for next period At = Actual value of time t (most recent actual data) Ft = forecast for present period w = weight ie smoothing constant
  34. 34. Contd..Rules of Thumb:When magnitude of random variations is large, w istaken as lower value so as to even out the effects ofrandom variation quicklyWhen magnitude of random variations is moderate, alarge value is assigned to wIt has been found appropriate to have w between 0.1and 0.2 in many systemsTo identify best forecast amongst many arrived fromdifferent values of W,RMSE is used and forecasthaving least RMSE is considered as best
  35. 35. Illustration : Exponential Smoothing
  36. 36. Contd..Forecast sales of time period 8,9and 10Take a smoothing constant w= 0.2
  37. 37. Econometric MethodsCombine statistical tools with economic theories to estimate economicvariables and to forecast intended economic variablesAn econometric model may be a single equation regression modelTypes of Econometric MethodRegression Method
  38. 38. Regression MethodIt attempts to find out relationship between dependent and independentvariablesIt is a statistical technique for obtaining the line that best fits data pointsIt is obtained by minimizing sum of squared vertical deviations of each pointfrom regression line and method used is called Ordinary Least Squares method(OLS)
  39. 39. Contd…Linear EquationY= a +bX Where X and Y are averagesObjective of regression analysis is to estimatelinear relationship ie a and ba = Y-bXb = N∑XY – (∑X) (∑Y) N ∑X2 - (∑X)2
  40. 40. Estimating Linear equationb = 10(10254) – (144)(656) 10(2448) – (144)2b = 2.15a = Y – bX where Y & X are averagesY = 34.54 + 2.15XIt means that an increase of Rs 1 million in ad expenditure will bring anincrease of 2.15 thousand units in sales ie 2,15000 units
  41. 41. When a time series data reveals risingtrend for e.g. in sales then equation is:S= a +bT where a and b are estimatedusing following two equations∑S= na + b∑TEstimating Linear Trend-Least Squares∑ST = a ∑T + b ∑T2Method
  42. 42. Illustration: Suppose that a local bread manufacturer company wants to assessdemand for its product for years 2002,2003 and 2004. for this purpose it usestime series data of its sales over past 10 years.
  43. 43. Estimation of Trend Equation
  44. 44. Contd….164 = 10a + 55b1024 = 55a + 385bS = 8.26 + 1.48TFor 2002, S2 = 8.26 + 1.48(11) = 24,540 tonnes
  45. 45. Problems: Demand Forecasting1. Using method of leastsquares, fit straight linetrend and estimate theannual sales of 1997.
  46. 46. Contd.. 2. Suppose number ofrefrigerators sold in past 7years in a city is given intable. Forecast demand forrefrigerator for year 2002and 2003 by calculating 3-yearly moving average
  47. 47. Contd.. 3. Estimate demandfor sugar in 2003-04 ifpopulation in 2003-04is projected to be 70million by usingmethod of leastsquares to estimateregression equation ofform: Y= a+ bXData on Consumptionof Sugar:
  48. 48. Thank You

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