Demand Forecast

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Demand Forecast

Demand Forecast

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  • 1. DEMAND FORECASTING By [email_address]
  • 2. DEMAND FORECASTING
    • Demand forecasting refers to the prediction or estimation of a future situation under given constraints.
    • TYPES OF FORECASTING:
    • Short Term
    • Medium Term
    • Long Term
  • 3. OBJECTIVES OF DEMAND FORECASTING
    • Helping for continuous production
    • Regular supply of commodities
    • Formulation of price policy
    • Arrangement of finance
    • 5. Labor requirement
  • 4. FACTORS INVOLVED IN DEMAND FORECASTING
    • Time period
    • Levels of forecasting
    • -- International level
    • -- Macro level
    • -- Industry level
    • -- Firm level
    • 3. Purpose - General or Specific
    • 4. Methods Of Forecasting
    • 5. Nature Of Commodity
    • 6. Nature Of Competition
  • 5. DETERMINANTS FOR DEMAND FORECASTING
    • 1. Capital goods – goods required for further production of goods
    • Demand for capital goods is derived demand
    • - Replacement demand
    • - New demand
    • Durable consumer goods — goods used continuously for a period of time
    • - Replacement demand
    • - New demand
    • Non-durable consumer goods — commodities which are used in a single act of consumption
    • Demand for these goods is influenced by
    • - Disposable income of people
    • - Price of the commodity
    • - Size and characteristics of population
  • 6. CRITERIA FOR GOOD DEMAND FORECASTING
    • Accuracy
    • Plausibility
    • Durability
    • Availability
    • Economy
  • 7. METHODS OF FORECASTING SURVEY METHOD STATISTICAL METHOD 1.Survey of buyer’s intentions 1.Trend projection method 2.Expert opinion method or Delphi Method 2.Moving averages method 3.Controlled Experiments 3.Regression analysis 4.Simulated market situations 4.Barometric method
  • 8. SURVEY OF BUYER’S INTENTIONS
    • Least sophisticated method
    • Customers are directly contacted to find out their intentions to buy commodities in the near future
    • Intentions recorded through personal interviews, mail or post service,telephone interviews and questionnaires.
    • Two types of Consumer Survey
      • Complete enumeration Method
      • Sample survey Method
  • 9. DELPHI METHOD
    • The forecasters are given the forecasts and assumptions of other experts, and a final report is compiled with the combined consensus of the experts.
  • 10. MARKET SURVEY METHOD
    • C ONTROLLED E XPERIMENTS
    • Different determinants of demand are varied and price quantity relationships are established at different points of time in the same market or different markets.
    • Only one determinant varied ; others kept constant.
    • S IMULATED M ARKET S ITUATION
    • An artificial market situation is created and “consumer clinics” selected. Consumers are asked to spend time in an artificial departmental store and different prices are set for different buyer groups.
    • The responses to the price changes are observed and necessary decisions taken.
  • 11. TREND PROJECTION METHOD
    • Based on analysis of past sales patterns
    • Shows effective demand for the product for a specified time period
    • The trend can be estimated by using the Least Square Method
  • 12. A producer of soaps decides to forecast the next years sales of his product. The data for the last five years is as follows: YEARS SALES IN Rs.LAKHS 1996 45 1997 52 1998 48 1999 55 2000 60
  • 13. The data is plotted on a graph:
  • 14.
    • The equation for the straight line trend is
    • Y = a + bx
    • a-intercept
    • b-shows impact of independent variable
    • The Y intercept and the slope of the line are found by making substitutions in the following normal equations:
    • ∑ Y = na + b ∑ x
    • ∑ XY = a ∑x + b ∑x 2
  • 15. Substituting the above values in the normal equations: 260=5a +15b (Eq.3) 813=15a + 55b (Eq.4) solving the two equations, a = 42.1 , b = 3.3 YEARS SALES Rs. LAKHS (Y) X X 2 XY 1996 45 1 1 45 1997 52 2 4 104 1998 48 3 9 144 1999 55 4 16 220 2000 60 5 25 300 N=5 ∑ Y=260 ∑ X=15 ∑ X 2 =55 ∑ XY=813
  • 16. Therefore, the equation for the straight line trend is Y=42.1 + 3.3X
    • Using this equation we can find the trend values for the previous years and estimate the sales for the year 2001 as follows:
    • Thus, the forecast sales for year 2001 is Rs.61.9 lakhs.
    Y 1996 = 42.1+3.3(1) = 45.4 Y 1997 = 42.1+3.3(2) = 48.7 Y 1998 = 42.1+3.3(3) = 52.0 Y 1999 = 42.1+3.3(4) = 55.3 Y 2000 = 42.1+3.3(5) = 58.6 Y 2001 = 42.1+3.3(6) = 61.9
  • 17. MOVING AVERAGES METHOD
    • Moving averages method can be used when the forecast period is either odd or even.
    • These are the annual sales of goods
    • during the period of 1993-2003.
    • We have to find out the trend of the
    • sales using (1) 3 yearly moving averages
    • and (2) 4 yearly moving averages
    • and forecast the value for 2005.
    YEAR SALES IN Rs.LAKHS 1993 12 1994 15 1995 14 1996 16 1997 18 1998 17 1999 19 2000 20 2001 22 2002 25 2003 24
  • 18. 3 yearly period: The value of 1993 + 1994 +1995 12 +15+14 = 41 written at the capital period 1994 of the years 1993, 1994 and 1995 YEAR SALES (Rs. LAKHS) 3 YEARLY MOVING TOTAL 3 YEARLY MOVING AVG. TREND VALUES 1993 12 - - ’ 94 15 41 41/3= 13.7 ’ 95 14 45 45/3= 15 ’ 96 16 48 48/3 =16 ’ 97 18 51 51/3 =17 ’ 98 17 54 54/3 = 18 ’ 99 19 56 56/3 = 18.7 2000 20 61 61/3 = 20.2 ’ 01 22 67 67/3 = 22.3 ’ 02 25 71 71/3 = 23.7 ’ 03 24 - -
  • 19. 4 YEARLY MOVING AVERAGES 57 = ‘93 + ‘94 +’95 + ‘96 = 12 + 15 + 14 + 16 120= 57 +63, 128 = 16 +65 and so on. 120 is total of 8 years and so the avg. is calculated by dividing 120 by 8 57 63 65 70 74 78 86 91 YEAR. SALES (Rs. LAKHS) 4 YEARLY MOVING TOTAL MOVING TOTAL OF PAIRS OF YEARLY TOTAL 4 YEARLY MOVING AVG. TREND VALUES ’ 93 12 - - - ’ 94 15 - - - ’ 95 14 120 120/8 = 15 ’ 96 16 128 128/8 = 16 ’ 97 18 135 135/8 = 16.9 ’ 98 17 144 144/8 = 18 ’ 99 19 152 152/8 = 19 ’ 00 20 164 164/8 = 20.5 ’ 01 22 177 177/8 = 22.1 ’ 02 25 - - ’ 03 24 - - -
  • 20. The trend values from the previous tables can be plotted on a graph as follows:
  • 21. REGRESSION METHOD
    • “ Method of Least Squares”
    • From the above data we can project the sales for ‘03, ‘04, ‘05.
    • First we calculate the required values which are (i) Time Deviation,
    • (ii) Deviation Squares, (iii) Product of time deviation and sales.
    YEAR 1998 1999 2000 2001 2002 SALES (Rs. In crores) 240 280 240 300 340 YEAR (n) SALES (RS. CRORE) (y) TIME DEVIATION FROM MIDDLE YEAR 2000 (x) TD SQUARED (x 2 ) PRODUCT OF TIME DEVIATION & SALES(xy) ’ 98 240 -2 4 -480 ’ 99 280 -1 1 -280 ’ 00 240 0 0 0 ’ 01 300 +1 1 +300 ’ 02 340 +2 4 +680 X = 5 ∑ y = 1400 ∑ x = 0 ∑ x 2 = 10 ∑ xy = 220
  • 22.
    • The equation is
    • Y = a + bx
    • ‘ a’ – independent variable
    • ‘ b’ – exhibits rate of growth
    • a & b can be found out as follows:
    • a = ∑y / n = 1400 / 5 = 280
    • b = ∑xy / ∑ x 2 = 220/10 = 22
    • Now, applying values to the regression equation,
    • Y = 280 + 22x
    • Hence, sales projection from 2003-2005 can be ascertained.
    • 2003 = 280 + 22(3) = Rs.346 crores
    • 2004 = 280 + 22(4) = Rs.368 crores
    • 2005 = 280 + 22 (5) = Rs.390 crores
  • 23. “ Method of Simple linear Regression ” The linear trend can be fitted in the equation Sales = a + b (Price) i.e. S = a + bP where in, a and b are constants. b = n ∑S i P i - (∑S i )(∑P i ) n ∑P i 2 – (∑P i ) 2 a = ∑S i - b ∑ P i n
  • 24. e.g. fit a linear regression line to the following data & estimate the demand at price Rs.30 YEAR ’ 81 ’ 82 ’ 83 ’ 84 ’ 85 ’ 86 ’ 87 ’ 88 ’ 89 ’ 90 ’ 91 ‘ 92 PRICE (P i ) 15 15 12 26 18 12 8 38 26 19 29 22 SALES (S i ) in 1000 units 52 46 38 37 37 37 34 25 22 22 20 14
  • 25. To find the values of a and b the following table is constituted: P i S i P i 2 S i 2 S i P i 15 52 225 2704 780 15 46 225 2116 690 12 38 144 1444 456 26 37 676 1369 962 18 37 324 1369 666 12 37 144 1369 444 8 34 64 1156 272 38 25 1444 625 950 26 22 676 484 572 19 22 361 484 418 29 20 841 400 580 22 14 484 196 308 ∑ P i = 240 ∑ S i = 384 ∑ P i 2 = 5708 ∑ S i 2 = 13716 ∑ S i P i = 7098
  • 26.
    • b = n ∑S i P i - (∑S i )(∑P i ) = 12(7098)-(240)(384) = 0.641 n ∑P i 2 – (∑P i ) 2 12 (5708)-(240) 2
    • a = ∑S i - b ∑ P i = [384-(240)(-0.641)] = 44.82
    • n 12
    • Thus the regression line is S= 44.82 - 0.641P
    • By assigning value 30 to P,
    • The corresponding sales level is
    • S = 44.82 – 0.641 (30) = 25.29 thousand units
  • 27. BAROMETRIC METHOD
    • Improvement over trend projection method
    • Events of the present are used to predict future demand
    • Basic approach- constructing an index of relevant economic indicators
    • Leading indicators
    • Coincident indicators
    • Diffusion indices
  • 28. IMPORTANCE OF DEMAND FORECASTING
    • Planning and scheduling production
    • Budgeting of costs and sales revenue
    • Controlling inventories
    • Making policies for long term investment
    • Helps in achieving targets of the firm
  • 29.