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demand forecasting

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demand forecasting

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demand forecasting

  1. 1. DEMAND FORECASTINGMEANINGIMPORTANTSOBJECTIVESMETHODS
  2. 2. What is forecasting all about? Demand for Mercedes E We try to predict the Class future by looking back at the past Predicted demand looking Time back six Ja Fe Mar Apr May Jun Jul Aug months n b Actual demand (past sales) Predicted demand
  3. 3. WHY DEMAND FORECASTING• Planning and scheduling production• Acquiring inputs• Making provisions for finances• Formulating pricing strategy• Planning Advertisement
  4. 4. OBJECTIVESShort –term ForecastingTo evolve a suitable production policyTo reduce the cost of purchaseTo determine appropriate price policyTo set sales targets and establish controlTo forecast short-term financial requirements
  5. 5. OBJECTIVESLong –term Forecasting Planning of a new unit or expansion of an existing unit Planning of long-term financial requirements Planning of man-power requirements
  6. 6. Levels of Forecasting• Macro level• Industry level• Micro level
  7. 7. STEPS IN DEMAND FORECASTING• Determination of the objectives• Sub-dividing the task• Identifying of demand determinants• Selection of the method• Collection of Data• Estimation and interpretation of result• Reporting
  8. 8. METHODS OF DEMAND FORECASTINGQUALITATIVE QUANTITATIVE TECHNIQUES TECHNIQUES Survey Method  Barometric Techniques  Direct Interview Method  Time Series Analysis  Collective opinion  Regression Method Delphi Method Controlled Experiments
  9. 9. SURVEY METHOD• Surveys are conducted to collect information about the future plans of the potential consumers• A firm may launch a new product, if the suvey indicates that there is a demand for that particular product in the market.
  10. 10. Direct Interview MethodConsumers are contacted directly to ask them what theyintend to buy in futureCollective opinionThe opinions of those who have the feel of the market, likesalesman, professional experts, market consultants etc.Advantages•Simple•Quick•Low cost•ReliableDisadvantagesPersonal judgements may go wrongUseful only foe short-term forecasting
  11. 11. DELPHI METHOD• Applied to uncertain areas where past data or future data are not of much use• Some expert in an area will be contacted with questionnaires• A co-ordinator collect all the opinions• Each expert will be supplied with responses of other experts without revealing their identity• Expert may revise his opinion, if needed• Process will be repeated so that all experts come to an agreement
  12. 12. CONTROLLED EXPERIMENTS• Studies and experiments in consumers behavior are carried out under actual market conditions• Three or four cities having similarity in population , income level, cultural and social background ,occupational distribution , taste etc.. are chosen• Various demand determinants like price, advertisement , expenditure etc are changed one by one and these changes on demand are observed
  13. 13. QUANTITATIVE TECHNIQUESBASED ON DATA AND ANALYTICAL TECHNIQUESBarometric TechniquesTime series AnalysisRegression Method
  14. 14. BAROMETRIC FORECASTING based on the observed relationships between different economic indicatorsIt can be divided into three groups Leading indicators Coincident indicators Lagging indicators
  15. 15. Leading Indicators• which run in advance of changes in demand for a particularproduct•an increase in the number of building permitsgranted which would lead to an increase in demand forbuilding-related products such as wood, concrete and so onCoincident Indicators•occur alongside changes in demand•an increase in sales would generate an increase in demand forthe manufacturers of the goods concernedLagging Indicators•run behind changes in demandNew industrial investment by firms which will only invest innew production facilities when demand is already firmlyestablished.
  16. 16. TIME SERIES ANALYSIS• Used to predict the future demand for a product based on the past sales and demand. Simple moving average Weighted moving average Exponential smoothing
  17. 17. Time series: simple moving averageIn the simple moving average models the forecast value is At + At-1 + … + At-n Ft+1 = n t is the current period. Ft+1 is the forecast for next period n is the forecasting horizon (how far back we look), A is the actual sales figure from each period.
  18. 18. Example: forecasting sales at KrogerKroger sells (among other stuff) bottled spring water Month Bottles Jan 1,325 Feb 1,353 What will Mar 1,305 the sales Apr 1,275 be for May 1,210 July? Jun 1,195 Jul ?
  19. 19. What if we use a 3-month simple moving average? AJun + AMay + AApr FJul = = 1,227 3What if we use a 5-month simple moving average? AJun + AMay + AApr + AMar + AFeb FJul = = 1,268 5
  20. 20. Time series: weighted moving averageWe may want to give more importance to some of thedata… Ft+1 = wt At + wt-1 At-1 + … + wt-n At-n wt + wt-1 + … + wt-n = 1 t is the current period. Ft+1 is the forecast for next period n is the forecasting horizon (how far back we look), A is the actual sales figure from each period. w is the importance (weight) we give to each period
  21. 21. Time Series: Exponential Smoothing (ES) Main idea: The prediction of the future depends mostly onthe most recent observation, and on the error for the latest forecast. Smoothi ng constan Denotes the t alpha importance of the past α error
  22. 22. Exponential smoothing: the methodAssume that we are currently in period t. We calculated the forecast for the last period (Ft-1) and we know the actualdemand last period (At-1) … Ft = Ft −1 + α ( At −1 − Ft −1 )The smoothing constant α expresses how much ourforecast will react to observed differences…If α is low: there is little reaction to differences.If α is high: there is a lot of reaction to differences.
  23. 23. Linear regression in forecastingLinear regression is based on1. Fitting a straight line to data2. Explaining the change in one variable through changes in other variables. dependent variable = a + b × (independent variable) By using linear regression, we are trying to explore which independent variables affect the dependent variable
  24. 24. Linear Regression Model• Shows linear relationship between dependent & explanatory variables – Example: Diapers & # Babies (not time) Y-intercept Slope ^ Yi = a + b X i Dependent Independent (explanatory) (response) variable variable
  25. 25. Example: do people drink more when it’scold? Alcohol Sales Which line best fits the data? Average Monthly Temperature
  26. 26. The best line is the one that minimizes theerror The predicted line is … Y = a + bX So, the error is … εi = y i - Yi Where: ε is the error y is the observed value Y is the predicted value
  27. 27. Conclusion• Accurate demand forecasting requires – Product knowledge – Knowledge about the customer – Knowledge about the environment

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