Demand Forcasting
- 1. DEMAND FORECASTING DEMAND FORECASTING MEANS PREDICTING OR ESTIMATING THE FUTURE DEMAND FOR A PRODUCT . IT IS UNDERTAKEN FOR THE PURPOSE OF PLANNING AND MAKING LONGTERM DECISIONS
- 2. Business Decision Making –Use of Demand Forecasting Forward Planning and Scheduling Acquiring Inputs Making provision for finance Formulating pricing strategy Planning advertisement
- 3. Demand Forecasting General considerations: 2. Factors involved in demand forecasting 3. Purposes of forecasting 4. Determinants of demand 5. Length of forecasts 6. Forecasting demand for new products 7. Criteria of a good forecasting method 8. Presentation of a forecast to the management Methods of demand forecasting Approach to forecasting
- 4. Steps in Demand Forecasting Specifying the Objective Determining the time Perspective and type of good Selecting a proper method of forecasting Collection of data Interpretation of results
- 5. Forecasting Horizons. 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, facility location, and strategic planning.
- 6. Presentation of a forecast to the Management In presenting a forecast to the management, a managerial economist should: 2. Make the forecast as easy for the management to understand as possible. 3. Avoid using vague generalities. 4. Always pin-point the major assumptions and sources. 5. Give the possible margin of error. 6. Avoid making undue qualifications. 7. Omit details about methodology and calculations. 8. Make use of charts and graphs as much as possible for easy comprehension.
- 7. Factors involved in Demand Forecasting 2. Undertaken at three levels: b. Macro-level c. Industry level eg., trade associations d. Firm level 3. Should the forecast be general or specific (product- wise)? 4. Problems or methods of forecasting for “new” vis-à-vis “well established” products. 5. Classification of products – producer goods, consumer durables, consumer goods, services. 6. Special factors peculiar to the product and the market – risk and uncertainty. (eg., ladies’ dresses)
- 8. Criteria of a good forecasting method 1 . Simplicity and ease of comprehension. 2. Accuracy – measured by (a) degree of deviations between forecasts and actuals, and (b) the extent of success in forecasting directional changes. 3. Economy. 4. Availability. 5. Maintenance of timeliness.
- 10. SURVEY METHODS SURVEY METHOS CONSUMER OPINION METHODS SURVEY METHODS COMPLETE SAMPLE END USE EXPERTS OPINION TEST MARKETING ENUMERATION SURVEY METHOD METHOD METHOD METHOD METHOD DELPHI METHOD
- 11. STATISTICAL METHODS BAROMETRIC REGRESSION METHOD RENDPROJECTION METHODS
- 12. Techniques of Demand Forecasting-Survey Methods Though statistical techniques are essential in clarifying relationships and providing techniques of analysis, they are not substitutes for judgement. What is needed is some common sense mean between pure guessing and too much mathematics. Consumer Survey
- 13. Delphi Method Delphi method: it consists of an effort to arrive at a consensus in an uncertain area by questioning a group of experts repeatedly until the results appear to converge along a single line of the issues causing disagreement are clearly defined. Developed by Rand Corporation of the U.S.A in 1940s by Olaf Helmer, Dalkey and Gordon. Useful in technological forecasting (non- economic variables).
- 14. Delphi method Advantages 2. Facilitates the maintenance of anonymity of the respondent’s identity throughout the course. 3. Saves time and other resources in approaching a large number of experts for their views. Limitations/presumptions: Panelists must be rich in their expertise, possess wide knowledge and experience of the subject . Presupposes that its conductors are objective in their job, possess ample abilities to conceptualize the problems for discussion, generate considerable thinking, stimulate dialogue among panelists and make inferential analysis of the multitudinal views of the participants.
- 15. Statistical Methods Statistical methods are considered to be superior due to the following reasons : The element of subjectivity is minimum Method of estimation is Scientific. Estimates are more reliable. It is very economical method.
- 16. TREND ANALYSIS METHOD THISMETHOD IS USED WHEN A DETAILED ESTIMATE HAS TO BE MADE TIME PLAYS AN IMPORTANT ROLE IN THIS METHOD
- 17. TIME SERIES PREDICTS This method uses historical and cross – sectional data for estimating demand Finding a Trend value for a specific year FINDING SEASONAL FLUCTUATIONS IN THE VARIABLE PREDICTING TURNING POINTS IN FUTURE MOVEMENTS OF THE VARIABLE
- 18. Analysis of time series and trend projections Four sets of factors: secular trend (T), seasonal variation (S), cyclical fluctuations (C ), irregular or random forces (I). O (observations) = TSCI Assumptions: The analysis of movements would be in the order of trend, seasonal variations and cyclical changes. Effects of each component are independent of each other.
- 19. There are three techniques of trend projection Graphical Fitting Trend Equation Box-Jenkins method The above method can be used by long standing firms by using the data from sales department and books of account . New firms can use older firms data belonging to the same industry .
- 20. Linear Trend It is represented: Y= a + b x (I) Y=Demand X= Time Period a & b are constants . For calculation of Y for any value of X requires the values of a & b These are : ∑Y=na+b∑X ∑XY=a∑X+b∑X²
- 21. Problem & Solution The data relate to the sale of generator sets of a company over the last five years Year : 2003 2004 2005 2006 2007 sets : 120 130 150 140 160 Estimate the demand for generator sets in the year 2012 if the present trend continues
- 22. Year X Y x² Y² XY 2003 1 120 1 14400 120 2004 2 130 4 16900 260 2005 3 150 9 22500 450 2006 4 140 16 19600 560 2007 5 160 25 25600 800 Total 15 700 55 99000 2190 Substituting table values in equation ii & iii we get ∑Y=na+b∑X 700 = 5a +15b ∑XY=a∑X+b∑X² 2190 = 15a +55b By multiplying equation iv by 3 and subtracting it from equation v we get 10b =90 b =9
- 23. Solution Substitute this value in equation iv we have 700 =5a +15 b 700 = 5a +15 (9) 5a =565 a = 113 Trend equation Y=113 + 9x For 2012 ,x will be 10 Y2012 = 113+9 x 10 =203 sets
- 24. Simple Linear Regression Linear regression analysis establishes a relationship between a dependent variable and one or more independent variables. In simple linear regression analysis there is only one independent variable. If the data is a time series, the independent variable is the time period. The dependent variable is whatever we wish to forecast.
- 25. Simple Linear Regression Regression Equation This model is of the form: Y = a + bX Y = dependent variable X = independent variable a = y-axis intercept b = slope of regression line
- 26. Simple Linear Regression Once the a and b values are computed, a future value of X can be entered into the regression equation and a corresponding value of Y (the forecast) can be calculated.
- 27. Problem : The data of a firm relating to sales and advertisement is given below .If the manager decides to spend Rs 30 mill in the year 2005 what will be the prediction for sales
- 28. YEAR AD.EX SALES0 X2 XY mill 000 units 1995 5 45 25 225 1996 8 50 64 400 1997 10 55 100 550 1998 12 58 144 696 1999 10 58 100 580 2000 15 72 225 1080 2001 18 70 324 1260 2002 20 85 400 1700 2003 21 78 441 1638 2004 25 85 625 2125 N=10 ∑X=144 ∑Y=656 ∑X2=2448 ∑XY=10254
- 29. Solution • a = (∑X²) ( ∑Y) - (∑X )( ∑X Y) N∑X² - (∑X )² b= N∑X Y - (∑X )( ∑ Y) N∑X² - (∑X )²
- 30. a =(2448) (656)- (144)(10254) 10 (2448) - (144)2 = 1605888 - 1476576 = 129312 = 34.54 24480 - 20736 3744
- 31. b= Value • b= 10(10254)-(144)(656) 10 (2448) -(144)2 = 102540 -94464 24480 -20736 = 8076 = 2.15 THERE FOR : Y =a + b x 3744 Y=34.54 +2.15 x , x =30