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Demand Forecasting

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Demand Forecasting

  1. 1. Demand forecasting• In modern business forecasting is often made I on anticipation of demand. Anticipation of demand implies demand forecasting• Forecasting means expectations about the future course of development. Future is uncertain but not entirely so.• Demand forecasting is not a speculative exercise into the unknown. It is reasonable judgment of future probabilities of market events based on scientific background. Demand forecasting is an estimate of the future demand. It cannot be cent percent precise.
  2. 2. Levels of forecasting• Micro level:- It refers to demand forecasting by individual business firm for estimating the demand for its product.• Industry level:- It refers to the demand estimate for the product of the industry as whole. It relates to market demand as whole.• Macro level:- It refers to the aggregate demand for the industrial output by nation as whole. It is based on the national income or aggregate expenditure of the country.
  3. 3. Importance of forecasting• Production planning.• Sales forecasting.• Control of business.• Inventory control.• Growth and long-term investment programmes.• Stability.• Economic planning and policy making.
  4. 4. Types of forecasting• Short term forecasting:- is for a short period up to one year. It relates to policies regarding sales, purchases, pricing and finance. In most of firms the information regarding the immediate future is necessary for formulating a suitable production policy.• Medium term forecasting:- it is an intermediate between short- term and long-term forecasting. This is usually followed by a firm which is subjected to the medium term variation in trade cycle.• Long term forecasting:- refers to a period beyond one year. The purpose of long term forecasting are:- 1) planning of a new unit of expansion of the existing unit. A multi-product firm must know not only total demand situation, but also the demand for different items. 2) Planning of man power needs. 3) Planning long term financial requirements is necessary for the firm to make necessary arrangements to secure fresh capital investments.
  5. 5. Factors involved in demand forecasting • Time period. • Levels of forecasting. • Purpose – General or Specific. • Methods of forecasting. • Nature of commodity. • Nature of competition.
  6. 6. Objectives of demand forecasting• Helping continuous production.• Regular supply of commodities.• Formulation of price policy.• To formulate effective sales performance.• Arrangement of finance.• To determine productive capacity• Labour requirements.
  7. 7. Methods of forecasting. Methods of forecastingSurvey method Statistical method1 .Survey of buyers intention. 1. Trend projection method 2.Survey of experts opinion. 2. Method of moving 3. Combined experiments. averages.4. Simulated market situation. 3. Regression method. 4. Barometric method. 5. economic indicators.
  8. 8. Survey method.• Forecast are done both for established products and new products. Demand forecasting for the established products can be done in routine manner with information drawn from existing markets and past behavouir of sales.• Forecasts for new products are necessarily custom built jobs that involve more ingenuity and expense. Since the product has not been sold before it is difficult to get any clue for demand forecasting.
  9. 9. Survey of buyers intentions or consumer’s survey.• Least sophisticated method and most direct method of estimating sales in the near future.• In this method customers are directly contacted in order to find out their intention to buy commodities for future. This method is opinion survey method.• Intention’s are recorded through personal interview, mail or post surveys and telephone interviews.• There are two types of survey• Complete enumeration method: It covers all potential consumers in the market and interviews conducted to find out probable demand.• Sample survey method: It covers only few customers selected from total potential consumers interviewed and then the average demand is calculated on the basis of the consumer’s interviewed.
  10. 10. Survey or expert opinion.• There are people who are experts in the field of selling goods like wholesalers, and retailers.• They will be in position to tell what consumers would buy. Many companies get their basic forecast directly from their salesman who have most intimate feel of the market.• The wholesalers and retailers by their experience are in the position to feel about the probable sales in the coming year.
  11. 11. CONTROLLED EXPERIMENTS• Under this method different determinants of demand are varied and price and quantity relationships are established at different points of time in the same market or different markets.• Only one determinant is varied others are kept constant and controlled. This method is relatively new.
  12. 12. SIMULATED MARKET SITUATION• Under this method an artificial market situation is created and participants are selected.• These are called consumers clinics• Those participants are given some money and asked to spend the same in artificial departmental stores. Different prices are set up for different groups of buyers. The responses to price changes are observed and accordingly necessary decisions about price and promotional efforts are undertaken.
  13. 13. STATISTICAL METHODS• Demand forecasting uses statistical methods to predict future demand. This method is useful for long run forecasting for the existing products.• There are several ways of using statistical or mathematical data. They are:• 1. Trend projection method or Time Series• 2. Method of moving averages• 3. Regression method• 4. Barometric methods.• 5. Other methods
  14. 14. 1. Trend projection Method• This method is based on analysis of past sales. A firm which has existence for quite long time will have accumulated considerable data regarding sales for a number of years. Such data is arranged chronologically with intervals of time. This is called Time series.• It has 4 types of components namely: – 1. Secular trends – 2. Seasonal variation – 3. Cyclical variation – 4. Random variations.
  15. 15. • The real problem in forecasting is to separate and measure each of this 4 factors. When a forecast is made the seasonal, cyclical, random factors are eliminated from the data and only the secular trend is used.• The trend in Time series can be estimated by using any one of the following of methods – 1. Least square method – 2. Free Hand method – 3. Moving averages method – 4. Method of semi averages.
  16. 16. TREND PROJECTION• A Time series analysis of sales data over a period of time is considered to serve as a good guide for sales or demand forecasting.• For long term demand forecasting trend is computed from the time base demand function data.• Trends refer the long term persistent movement of data in one direction upward or downward. There are 2 important methods for trend projection. – 1. Method of moving averages. – 2. Least square method.
  17. 17. LEAST SQUARE METHOD• The trend line if fitted by developing an equation giving the nature and magnitude of the trend. The common technique used in constructing the line of best fits is by the method of least squares.• The trend is assumed to be linear. The equation for straight line trend is y=a+bx• Where “a” is the intersect and “b” shows the impact of independent variable. Sales are dependant on variable “y” since sales vary with time periods which will be the independent variable “x” Thus “y” intercept and the slope of line are formed by making appropriate substitutions in the following normal equations• ΣY = na+bΣx --------------(1)• ΣXY = aΣx + bΣx2----------------- (2)
  18. 18. • YEAR SALES X X2 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 ΣY=260 ΣX=15 ΣX2=55 ΣXY=813
  19. 19. • SUBSITITUTING THE ABOVE VALUES IN THE TWO NORMAL EQUATIONS WE GET THE FOLLOWING:-• 260=5a+15b----------------• 813=15a+55b-----------------• Solving both equation we get b=3.3• 260=5a +15• 260=5a+49.5• A=42.1• Therefore the equation for the line of best fit is equal to:• Y=42.1+3.3X.
  20. 20. • Using this equation trend values for previous years and estimates of sales for 2001. The trend values and estimates are as follows:-• 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)= 52.2• 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. Based on the trend projection equation illustrated above, the forecast sales for the year 2001 is Rs 61.9 Lakhs.
  21. 21. Method of moving averages.• The trend Projection method is very popular in business circles on account of simplicity and lesser cost. The basic idea in this method is that past data serves a guide for future sales.• This method is inadequate for prediction whenever there are turning points in the trend itself. While irregular factors such as storms and strikes can be averaged out and contained into the equation it is desirable to know how valuable such an exercise could be.
  22. 22. • The calculation depends upon whether the period should be odd or even.• In the case of odd periods like (5, 7, 9) the average observations is calculated for a given period and the value calculated value is written in front of central valuable of the period, say 5 years. The average of values of five years is calculated and recorded against the third year. In the case of five yearly moving averages the first two years and last two years of data will not have any average value.• If the period is even say four years then average of four yearly observations is written between second year and third year values. After this centering is done by finding average of paired values. Let us take up the following illustration:-
  23. 23. • The following are the annual sales of dresses during the period of 1993-2003. We have to find out trend of the sales using a) 3 yearly moving averages, b)4 yearly moving averages.• 3 yearly moving averages will be• a+b+c/3, b+c+d/3,c+d+e/3 ,d+e+f/3-------• The value of 1993+1994+1995/3• 12+15+14/3 = 41/3=13.7.
  24. 24. • YEAR SALES 3 YEARLY 3 YEARLY MOVING• MOVING TOTAL AVERAGE trend values• 1993 12 (-) -• 1994 15 41 41/3=13.7• 1995 14 45 45/3=15• 1996 16 48 48/3=16• 1997 18 51 51/3=17• 1998 17 54 54/3=18• 1999 19 56 56/3=18.7• 2000 20 61 61/3=20.2• 2001 22 67 67/3=22.3• 2002 25 71 71/3=23.7• 2003 24 - -
  25. 25. Advantages and disadvantages• This method is simple and can be applied easily.• It is based on mathematical calculations and finally this is more accurate.• The disadvantage of this method of moving average is that it gives equal weight age to the data related to different periods in the past. It cannot be applied it if some observations are missing.
  26. 26. Regression method• The sales of any commodity depends on time.• It may be associated with competitors, advertising ones own advertising change in population, income and size of familyand environmental factors.• The nature of relationship can be used and future sales can be forecast.• Regression analysis denotes methods by which the relationships between quantity demanded and one or more independent variable can be estimated. It includes measurement of errors that are inherent in the estimation process. Simple regression is used when the quantity demanded is estimated as a function of single independent variable. Multiple regression analysis can be used to estimate demand as function of two or more independent variables.
  27. 27. Trend projection by regression method• This is a mathematical tool, with this adapting “Method of least squares” a trend line can be fixed to know the relationship between time and demand/sales. Based on this trend line sales /demand can be projected for future years.• This is an inexpensive method of forecasting. The data will be available with the organization and based on this data demand or sales, can be projected for future years.
  28. 28. YEAR:- 1998 1999 2000 2001 2002 SALES:- 240 280 240 300 340YEAR SALES TIME TD PRODUCT DEVIATION SQUARED TIME DEVIATION1998 24O -2 4 -4801999 280 -1 1 -280 -7602000 240 0 0 02001 300 +1 1 3002002 340 +2 4 +680 +980X=5 Σy=1400 Σx=0 Σx2=10 Σxy=220
  29. 29. • The equation is y=a+bx.• In this equation “a” and “b”.• a=Σy/n=1400/5=280.• b=Σxy/Σx2=220/10=22.• Now applying values to regression equation the equation will be y=280+22x• From this we can ascertain sales projection from 2003, 2004, 2005.• For the year 2003=280+22(3)=Rs. 346 crores.• For the year 2004=280+22(4)=Rs. 368 crores.• For the year 2005=280+22(5)=Rs. 390 crores.
  30. 30. Simple linear equation• In case of linear trend in the dependent variable a straight line to data can be fit in whose general form would be sales=a+b price.• The straight line equation can be fit in either graphically or least square method. In the graphical method the sets of data of two variables on a graph are plotted and a scatter diagram can be obtained.
  31. 31. YPRICES X O UNITS OF DRESSES SOLD
  32. 32. • The regression in line can be approximated by sketching it free hand in such a manner than the line passes through the middle of the scatter.• In the least square method of estimating the regression line, S=a+bp, the value of the constants, a and b can be with the help of a following formula:-• b=nΣSiPi –(ΣSi )(ΣPi )/nΣPi2 -(ΣPi)2 and• a=ΣS-bΣPi /n.
  33. 33. Barometric method• Barometric method is an improvement over trend projection method.• In the trend projection method, the future is some past extension of past while in the barometric events, of the present are used to predict the future.• This is done by using certain economic and statically indicators. The barometric techniques use time series to predict variables.• The barometric techniques using time series, which when combined certain ways provide direction of change in the economy or in indicators. These are called barometers of market change.
  34. 34. Simulation Method• Every day life experience can not be mathematically explained the model may become complicated and its solution will become difficult in such a situation simulation method will be helpful .this method is associated with the name of monte carla• This method is used to solve the problem by trial and error approach it is a device for studying an artificial model of a physical or mathematical process ,this method combines probability and sampling method to solve complicated problem.
  35. 35. Forecasting demand for new products• Evolutionary approach:- project the demand for new product as an outgrowth and evolution of existing old product. It may be assumed color T.V. picks up where black and white T.V. sets are off. This approach is useful only when the new product is very close to the old product.• Substitute approach:- According to this approach the new product is to be considered as substitute for the old product. For example the new Foto setter substitutes photographic composition for established type setting equipment as a linotype, polythene bags as substitute for cloth bags or ball pens, or for fountain pens.• Growth curve approach:- The rate of growth and ultimate level of demand for new products can be estimated on the basis of pattern of growth for old products. For example, analyze the growth curve of the all household appliances and establish an empirical law of market development applicable to new appliances.
  36. 36. • Opinion polling approach:- Estimate the demand by direct inquiry of the ultimate purchasers then blow up the sample to full scale.• Sending an engineer with drawing and specifications for new industrial products to a sample company is an example of opinion polling which is widely used to explore the demand for new products.• Sales experience approach:- The new product is offered for sale in a sample market and then the demand for new product is estimated in fully developed market. The sample of market has to be identified.• Vicarious approach:- the consumer’s reactions are indirectly studied in this approach. Specialized dealers are contacted because they have intimate feel of the customers. Dealers opinion are very much solicited regarding the demand for new products. This approach is easy but difficult to quantify.
  37. 37. Difficulties in forecasting• Changes in size and characteristics of population• Saturation limit of the market• Existing stock of goods• Constraints of the firm
  38. 38. Importance of demand forecasting• Useful for planning of production• Sales forecasting depends upon demand forecasting• Useful for controlling inventories• Helps in achieving targets of firm• To stabilize production and employment• Useful for policy making regarding long term investment programmes
  39. 39. Criteria for good forecasting.• Joel Dean lays down the following criteria of good forecasting method:-• Accuracy :- forecast must be accurate as far as possible. Its accuracy must be judged by examining the past forecast with present situation.• Plausibility :- it implies management’s understanding of method used for forecasting. It is essential for a correct interpretation of the results.• Simplicity :- a simpler method is always more comprehensive than a complicated one.
  40. 40. • Economy :- it should yield quick results. A time consuming method may delay the decision making process.• Quickness :- it should yield quick results. A time consuming method may delay the decision making process.• Flexibility : - not only the forecast is to be maintained up to date there should be possibility of changes to be incorporated in the relationships entailed in forecast procedure, time to time.

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