• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content


Flash Player 9 (or above) is needed to view presentations.
We have detected that you do not have it on your computer. To install it, go here.

Like this presentation? Why not share!

Demand forecasting






Total Views
Views on SlideShare
Embed Views



0 Embeds 0

No embeds



Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

    Demand forecasting Demand forecasting Presentation Transcript

    • Demand Forecasting
    • Meaning of Demand Forecasting
      • “ An estimate of sales in dollars or physical units for a specified future period under a proposed marketing plan.”
      • American Marketing Association
      • Demand forecasting is the scientific and analytical estimation of demand for a product (service) for a particular period of time.
      • It is the process of determining how much of what products is needed when and where .
    • Categorization of Demand Forecasting
      • By Level of Forecasting
        • Firm (Micro) leve l : forecasting of demand for its product by an individual firm.
          • decisions related to production and marketing.
      • Industry level : for a product in an industry as a whole.
        • insight in growth pattern of the industry
        • in identifying the life cycle stage of the product
        • relative contribution of the industry in national income.
    • Categorization of Demand Forecasting
      • Economy (Macro) level : forecasting of aggregate demand (or output) in the economy as a whole.
      • helps in various policy formulations at government level.
    • Categorization of Demand Forecasting
      • By nature of goods
      • Capital Goods : Derived demand
        • demand for capital goods depends upon demand of consumer goods which they can produce.
      • Consumer Goods : Direct demand
        • durable consumer goods: new demand or replacement demand
        • Non durable consumer goods: FMCG
      • .
    • Categorization of Demand Forecasting
      • By Time Period
      • Short Term ( 0 to 3 months): for inventory management and scheduling.
      • Medium Term (3 months to 2 years): for production planning, purchasing, and distribution.
      • Long Term (2 years and more) for capacity planning, long term capital requirement, and investment decisions
    • Choice of a forecasting technique
      • depends on:
        • Imminent objectives of forecast , whether it is for a new product, or to gauge impact of a new advertisement, etc.
        • Cost involved , cost of forecasting should not be more than its benefits, here opportunity cost of resources will also be important.
        • Time perspective , whether the forecast is meant for the short run or the long run
    • Choice of a forecasting technique
      • Complexity of the technique , vis-à-vis availability of expertise; this would determine whether the firm would look for experts “in house” or outsource it
      • Nature and quality of available dat a , i.e. does the time series show a clear trend or is it highly unstable.
    • Techniques of Demand Forecasting
      • Subjective (Qualitative) methods : rely on human judgment and opinion.
        • Buyers’ Opinion
        • Sales Force Composite
        • Market Simulation
        • Test Marketing
        • Experts’ Opinion
          • Group Discussion
          • Delphi Method
    • Techniques of Demand Forecasting
      • Quantitative methods : use mathematical or simulation models based on historical demand or relationships between variables.
      • Trend Projection
      • Smoothing Techniques
      • Barometric techniques
      • Econometric techniques
    • Subjective Methods of Demand Forecasting
      • Consumers’ Opinion Survey
      • Buyers are asked about future buying intentions of products, brand preferences and quantities of purchase, response to an increase in the price, or an implied comparison with competitor’s products.
        • Census Method : Involves contacting each and every buyer
        • Sample Method : Involves only representative sample of buyers
    • Subjective Methods of Demand Forecasting
      • Merits
      • Simple to administer and comprehend.
      • Suitable when no past data available.
      • Suitable for short term decisions regarding product and promotion.
      • Demerits
      • Expensive both in terms of resources and time.
      • Buyers may give incorrect responses.
      • Investigators’ bias regarding choice of sample and questions cannot be fully eliminated.
    • Subjective Methods of Demand Forecasting
      • Sales Force Composite / Openion Survey
      • Salespersons are in direct contact with the customers. Salespersons are asked about estimated sales targets in their respective sales territories in a given period of time.
    • Subjective Methods of Demand Forecasting
      • Merits
      • Cost effective as no additional cost is incurred on collection of data.
      • Estimated figures are more reliable, as they are based on the notions of salespersons in direct contact with their customers.
      • Demerits
      • Results may be conditioned by the bias of optimism (or pessimism) of salespersons.
      • Salespersons may be unaware of the economic environment of the business and may make wrong estimates.
    • Subjective Methods of Demand Forecasting
      • Experts’ Opinion Method
      • i ) Group Discussion : (developed by Osborn in 1953) Decisions may be taken with the help of brainstorming sessions or by structured discussions.
      • ii) Delphi Technique : d eveloped by the Rand Corporation at the beginning of the Cold War, to forecast impact of technology on warfare.
        • Way of getting repeated opinion of experts without their face to face interaction.
        • Consolidated opinions of experts is sent for revised views till conclusions converge on a point.
    • Subjective Methods of Demand Forecasting
      • Merits
      • Decisions are enriched with the experience of competent experts.
      • Firm need not spend time, resources in collection of data by survey.
      • Very useful when product is absolutely new to all the markets.
      • Demerits
      • Experts’ may involve some amount of bias.
      • With external experts, risk of loss of confidential information to rival firms.
    • Subjective Methods of Demand Forecasting
      • Market Simulation
      • Firms create “artificial market ”, consumers are instructed to shop with some money. “Laboratory experiment” ascertains consumers’ reactions to changes in price, packaging, and even location of the product in the shop.
        • Grabor-Granger test:
      • Half of members are shown new product to see whether they would actually buy it at various prices on a random price list and then are shown the existing product. Other half is shown the existing product first and then the new product to ascertain if a product would be bought at different prices .
    • Subjective Methods of Demand Forecasting
      • Merits
      • Market experiments provide information on consumer behaviour regarding a change in any of the determinants of demand.
      • Experiments are very useful in case of an absolutely new product.
      • Demerits
      • People behave differently when they are being observed.
      • In Grabor-Granger tests consumers may not quote the price they may pay.
    • Subjective Methods of Demand Forecasting
      • T est Marketing
      • Involves real markets in which consumers actually buy a product without the consciousness of being observed.
      • product is actually sold in certain segments of the market, regarded as the “test market”.
      • Choice and number of test market(s) and duration of test are very crucial to the success of the results.
    • Subjective Methods of Demand Forecasting
      • Merits
      • Most reliable among qualitative methods.
      • Very suitable for new products.
      • Considered less risky than launching the product across a wide region.
      • Demerits
      • Very costly as it requires actual production of the product, and in event of failure of the product the entire cost of test is sunk.
      • Time consuming to observe the actual buying pattern of consumers..
    • Quantitative Methods of Demand Forecasting
      • Trend Projection
      • Statistical tool to predict future values of a variable on the basis of time series data.
      • Time series data are composed of:
        • Secular trend (T): change occurring consistently over a long time and is relatively smooth in its path.
        • Seasonal trend (S): seasonal variations of the data within a year
        • Cyclical trend (C ): cyclical movement in the demand for a product that may have a tendency to recur in a few years
        • Random events (R): have no trend of occurrence hence they create random variation in the series.
    • Quantitative Methods: Methods of Trend Projection
      • Graphical method
        • Past values of the variable on vertical axis and time on horizontal axis and line is plotted.
        • Movement of the series is assessed and future values of the variable are forecasted
        • simple but provides a general indication and fails to predict future value of demand
    • Quantitative Methods: Methods of Trend Projection
      • Least squares method
      • based on the minimization of squared deviations between the best fitting line and the original observations given.
      • Estimates coefficients of a linear function.
      • Y=a+bX where a =intercept
      • and b =slope
      • The normal equations:
      • ΣY=na + bΣX
      • ΣXY= aΣX+ bΣX 2
      • Once the coefficients of the trend equation are estimated, we can easily project the trend for future periods .
    • Quantitative Methods : Barometric Techniques
      • Barometric Technique alerts businesses to changes in the overall economic conditions.
      • Helps in predicting future trends on the basis of index of relevant economic indicators especially when the past data do not show a clear tendency of movement in a particular direction.
    • Quantitative Methods
      • Simple (or Bivariate) Regression Analysis:
        • deals with a single independent variable that determines the value of a dependent variable.
        • Demand Function: D = a+bP, where b is negative.
    • Quantitative Methods
      • Problems Associated with Regression Analysis
      • Multicollinearity : when two or more explanatory variables in the regression model are found to be highly correlated the estimated coefficients may not be accurately determined .
      • Heteroscedasticity : Classical regression models assume that the variance of error terms is constant for all values of the independent variables
    • Specification errors : Omission of one or more of the independent variables, or when the functional form itself is wrongly constructed or estimate a demand function in linear form, though the function should have been nonlinear. Identification problem : where the equations have common variables, like a demand supply model. Problems Associated with Regression Analysis
    • Limitations of Demand Forecasting
      • Change in Fashion : Is an inevitable consequence of advancement of civilization. Results of demand forecasting have short lasting impacts especially in a dynamic business environment.
      • Consumers’ Psychology : Results of forecasting depend largely on consumers’ psychology, understanding which itself is difficult.
    • Lack of Past Data : Requires past sales data, which may not be correctly available. Typical problem in case for a new product. Limitations of Demand Forecasting Limitations of Demand Forecasting Limitations of Demand Forecasting
    • Uneconomica l: Requires collection of data in huge volumes and their analysis, which may be too expensive for small firms to afford. Estimation process may take a lot of time, which may not be affordable. Lack of Experienced Experts : Accurate forecasting necessitates experienced experts, who may not be easily available. Forecasting by less experienced individuals may lead to erroneous estimates. Limitations of Demand Forecasting