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  1. 1. Forecasting Market Size Sudarshan Kumar Patel(1320)
  2. 2. What is a Forecast? • A guess about what is going to happen in the future. • An integral part of almost all business enterprise • Logical and rational, but still a guess. • Objective is to minimize error (as you will always be wrong!) • Could be a complicated or simple process
  3. 3. Market Size • The number of buyers and sellers in a particular market. This is especially important for companies that wish to launch a new product or service, since small markets are less likely to be able to support a high volume of goods. Large markets could bring in more competition. • The number of individuals in a certain market who are potential buyers and/or sellers of a product or service. Companies are interested in knowing the market size before launching a new product or service in an area.
  4. 4. Forecasting Models Forecasting Techniques Qualitative Models Time Series Methods Delphi Method Jury of Executive Opinion Sales Force Composite Consumer Market Survey Naive Moving Average Weighted Moving Average Exponential Smoothing Trend Analysis Causal Methods Simple Regression Analysis Multiple Regression Analysis Seasonality Analysis Multiplicative Decomposition
  5. 5. Qualitative & Quantitative Forecasting Methods QualitativeA.Executive Judgement B. Sales Forse Composite C.Market Research/Survey D.Delphi Method QuantitativeA. Time Series Models a.Naïve b.Moving Average 1.Simple 2.Weighted c.Exponential Smoothing- 1.Level 2.Trend 3. Seasonality B.Regression Models
  6. 6. Jury of Executive Opinion  Involves small group of high-level experts and managers  Group estimates demand by working together  Combines managerial experience with statistical models  Relatively quick  ‘Group-think’
  7. 7. Sales Force Composite • Each salesperson projects his or her sales • Combined at district and national levels • Sales reps know customers’ wants • Tends to be overly optimistic
  8. 8. Delphi Method • The Delphi Method is a group decision process about the likelihood that certain events will occur. • Today it is also used for environmental, marketing and sales forecasting. • The Delphi Method uses a panel of experts. • Expert responses to a series of questionnaires are anonymous. • Each round of questionnaires results in a median answer. • The process guides the group towards a consensus.
  9. 9. Consumer Market Survey • Ask customers about purchasing plans. • What consumers say, and what they actually do are often different. • Sometimes difficult to answer.
  10. 10. Demand Patterns in Time Series Model • Time Series: The repeated observations of demand for a service or product in their order of occurrence. • There are five basic patterns of most time series• Horizontal- The fluctuation of data around a constant mean. • Trend- The systematic increase or decrease in the mean of the series over time. • Seasonal- A repeatable pattern of increases or decreases in demand, depending on the time of day, week, month, or season. • Cyclical-The less predictable gradual increases or decreases over longer periods of time (years or decades). • Random- The unforecastable variation in demand
  11. 11. Demand Patterns
  12. 12. Naive Approach • Demand in next period is the same as demand in most recent period • Assumes demand in next period is the same as demand in most recent period • e.g.- If May sales were 48, then June sales will be around 48. • Sometimes it is effective & cost efficient • e.g.- when the demand is steady or changes slowly • when inventory cost is low • when unmet demand will not lose
  13. 13. Moving Average Method • • • • • MA is a series of arithmetic means Used if little or no trend, seasonal, and cyclical patterns Used often for smoothing Provides overall impression of data over time Equation MA Demand in Previous n Periods n
  14. 14. Moving Average Example • S.K. Patel is manager of a museum store that sells historical replicas. You want to forecast sales of item (123) for 2000 using a 3-period moving average. 1995 4 1996 6 1997 5 1998 3 1999 7
  15. 15. Moving Average Solution
  16. 16. Cont…
  17. 17. Cont…
  18. 18. Exponential Smoothing Method • Form of weighted moving average • Weights decline exponentially • Most recent data weighted most • Requires smoothing constant ( ) • Ranges from 0 to 1 • Subjectively chosen • Involves little record keeping of past data
  19. 19. Exponential Smoothing Equations  Ft = Ft-1 + (At-1 - Ft-1) = At-1 + (1 - ) Ft-1 F = Forecast value  At = Actual value  = Smoothing constant  t  Ft = At - 1 + (1- )At - 2 + (1- )2·At - 3 + (1- )3At - 4 + ... + (1- )t-1·A0  Use for computing forecast
  20. 20. Regression Analysis as a Method for Forecasting • Regression analysis takes advantage of the relationship between two variables. Demand is then forecasted based on the knowledge of this relationship and for the given value of the related variable. • Ex: Sale of Tires (Y), Sale of Autos (X) are obviously related • If we analyze the past data of these two variables and establish a relationship between them, we may use that relationship to forecast the sales of tires given the sales of automobiles. • The simplest form of the relationship is, of course, linear, hence it is referred to as a regression line
  21. 21. Formulas y=a+bx where,