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. 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. 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. 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. Factors involved in demand forecasting
• Time period.
• Levels of forecasting.
• Purpose – General or Specific.
• Methods of forecasting.
• Nature of commodity.
• Nature of competition.
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. Methods of forecasting.
Methods of forecasting
Survey method Statistical method
1 .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. 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. 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. 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. 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. 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. 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. 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. • 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. 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. 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)
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. • 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. 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. • 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. • 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.
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. 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. 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.
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. 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.
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. 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. 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. 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. • 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. Difficulties in forecasting
• Changes in size and characteristics of
population
• Saturation limit of the market
• Existing stock of goods
• Constraints of the firm
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. 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. • 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.
