Chapter 6
Lecture plan
Meaning of Demand Forecasting
Techniques of Demand Forecasting
Subjective Methods of Demand Forecasting
Survey methods
Expert opinion methods
Quantitative Methods of Demand Forecasting
Trend methods
Smoothing methods Simulation
Statistical methods
Objectives
To introduce the relevance of demand forecasting in
business.
To understand the types of demand forecasting.
To explore qualitative techniques of forecasting
demand.
To understand quantitative and econometric methods
of demand forecasting.
Meaning of Demand Forecasting
“An estimate of sales in dollars or physical units for a
specified future period under a proposed marketing
plan.”
American Marketing Association
Demand forecasting is the scientific and analytical
estimation of demand for a product (service) for a
particular period of time.
It is the process of determining
how much
of
what
products is needed
when
and
where
.
An operations research technique of planning and
Categorization of Demand Forecasting
By Level of Forecasting
Firm (Micro) level: forecasting of demand for its product
by an individual firm.
decisions related to production and marketing.
Industry level: for a product in an industry as a whole.
insight in growth pattern of the industry
in identifying the life cycle stage of the product
relative contribution of the industry in national
income.
Economy (Macro) level: forecasting of aggregate
demand (or output) in the economy as a whole.
helps in various policy formulations at government
Categorization of Demand Forecasting
By nature of goods Capital Goods: Derived demand
demand for capital goods depends upon demand of consumer
goods which they can produce.
Consumer Goods: Direct demand
durable consumer goods: new demand or replacement demand Non durable consumer goods: FMCG
By Time Period
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): may extend up to 10 to 20 years.
for capacity planning, facility location, and strategic planning, long term capital
Choice of a forecasting technique
depends on:
Imminent objectives of forecast, whether it is for a new
product, or to gauge impact of a new advertisement, etc.
Cost involved, cost of forecasting should not be more than its
benefits, here opportunity cost of resources will also be important.
Time perspective, whether the forecast is meant for the short
run or the long run
Complexity of the technique, vis-à-vis availability of expertise;
this would determine whether the firm would look for experts “in house” or outsource it
Nature and quality of available data, i.e. does the time series
Techniques of Demand Forecasting
Subjective (Qualitative) methods: rely on human judgment andopinion.
Buyers’ Opinion
Sales Force Composite Market Simulation
Test Marketing Experts’ Opinion
Group Discussion Delphi Method
Quantitative methods: use mathematical or simulation models
based on historical demand or relationships between variables.
Trend Projection
Smoothing Techniques Barometric techniques Econometric techniques
Subjective Methods of Demand Forecasting
Consumers’ Opinion Survey
Buyers are asked about future buying intentions of products, brand
preferences and quantities of purchase, response to an increase in the price, or an implied comparison with competitor’s products.
Census Method: Involves contacting each and every buyer
Sample Method: Involves only representative sample of buyers
Merits
Simple to administer and comprehend. Suitable when no past data available.
Suitable for short term decisions regarding product and promotion.
Demerits
Expensive both in terms of resources and time. Buyers may give incorrect responses.
Investigators’ bias regarding choice of sample and questions cannot be
Subjective Methods of Demand Forecasting
Sales Force Composite
Salespersons are in direct contact with the customers. Salespersons
are asked about estimated sales targets in their respective sales territories in a given period of time.
Merits
Cost effective as no additional cost is incurred on collection of data. Estimated figures are more reliable, as they are based on the
notions of salespersons in direct contact with their customers.
Demerits
Results may be conditioned by the bias of optimism (or pessimism)
of salespersons.
Salespersons may be unaware of the economic environment of the
business and may make wrong estimates.
This method is ideal for short term and not for long term forecasting
Subjective Methods of Demand Forecasting
Experts’ Opinion Method
i)
Group Discussion
: (developed by Osborn in 1953) Decisions maybe taken with the help of brainstorming sessions or by structured discussions.
ii)
Delphi Technique
: developed by the Rand Corporation at thebeginning of the Cold War, to forecast impact of technology on warfare.
Way of getting repeated opinion of experts without their face to face
interaction.
Consolidated opinions of experts is sent for revised views till conclusions
converge on a point. Merits
Decisions are enriched with the experience of competent experts. Firm need not spend time, resources in collection of data by survey. Very useful when product is absolutely new to all the markets.
Demerits
Experts’ may involve some amount of bias.
With external experts, risk of loss of confidential information to rival firms.
Subjective Methods of Demand Forecasting
Market Simulation
Firms create “artificial market”, consumers are instructed to shop with some
money. “Laboratory experiment” ascertains consumers’ reactions to changes in price, packaging, and even location of the product in the shop.
Grabor-Granger test:
Half of members are shown new product to see whether they would actually buy it at various prices on a random price list and then are shown the existing
product. Other half is shown the existing product first and then the new product to ascertain if a product would be bought at different prices.
Merits
Market experiments provide information on consumer behaviour regarding a
change in any of the determinants of demand.
Experiments are very useful in case of an absolutely new product.
Demerits
People behave differently when they are being observed.
In Grabor-Granger tests consumers may not quote the price they may pay.
Subjective Methods of Demand Forecasting
Test Marketing
Involves real markets in which consumers actually buy a product without
the consciousness of being observed.
product is actually sold in certain segments of the market, regarded as
the “test market”.
Choice and number of test market(s) and duration of test are very crucial
to the success of the results.
Merits
Most reliable among qualitative methods. Very suitable for new products.
Considered less risky than launching the product across a wide region.
Demerits
Very costly as it requires actual production of the product, and in event of
failure of the product the entire cost of test is sunk.
Time consuming to observe the actual buying pattern of consumers..
Extrapolation of figures for calculating demand in widely varying markets
across its geographical regions may not give accurate results.
Quantitative Methods of Demand Forecasting
Trend Projection
Statistical tool to predict future values of a variable on the
basis of time series data.
Time series data are composed of:
Secular trend (T): change occurring consistently over a long time
and is relatively smooth in its path.
Seasonal trend (S): seasonal variations of the data within a year Cyclical trend (C): cyclical movement in the demand for a product
that may have a tendency to recur in a few years
Random events (R): have no trend of occurrence hence they create
random variation in the series.
Additive Form: Y = T + S + C + R………..(1) Multiplicative Form: Y = T.S.C.R………….(2)
Quantitative Methods:
Methods of Trend Projection
Graphical method Past values of the variable on vertical axis and time on horizontal axis
and line is plotted.
Movement of the series is assessed and future values of the variable are
forecasted
simple but provides a general indication and fails to predict future value of
demand 0 20 40 60 80 100 120 140 160 180 200 2001 2002 2003 2004 2005 Ye a r D em an d fo r m ob ile s (in la kh s) Contd…
Quantitative Methods:
Methods of Trend Projection
Least squares method
based on the minimization of squared deviations between the best
fitting line and the original observations given.
Estimates coefficients of a linear function.
Y=a+bX where a =intercept and b =slope
The normal equations:
ΣY=na + bΣX ΣXY= aΣX+ bΣX2
Once the coefficients of the trend equation are estimated, we can
easily project the trend for future periods.
Solving the normal equations:
a= b=
X
b
Y
−
Contd… ∑ − − − Σ 2 ) ( ) )( ( X X X X Y YQuantitative Methods:
Methods of Trend Projection
ARIMA method: also known as Box Jenkins method
considered to be the most sophisticated technique of forecasting as it
combines moving average and auto regressive techniques.
Stage One: trend in the series is removed with help of ‘differencing’,
i.e. the difference between values at adjacent period of time.
Stage Two: Various possible combinations are created on basis of:
i. order of involvement of auto regressive terms; ii. the order of moving average terms
iii. the number of differences of the original series. Combinations are selected which provide an adequate fit to the series.
Stage Three: Parameter estimation is done using Least Squares. Stage Four: ‘Goodness of fit’ is tested and if it is not a good fit then
the whole process is repeated from Stage Two.
Stage Five: Once a ‘good fit’ is attained, its coefficients can be used
to forecast future demand.
Quantitative Methods :
Smoothing Techniques
Moving Average
: forecasts on the basis of demand valuesduring the recent past.
Dn= where Di= demand in the ith period, n= number of periods in the
moving average
Weighted Moving Average
: forecast the future value of saleson the basis of weights given to the most recent observations. The formula for computing weighted moving average is given as:
Dn= where Di= demand in the ith period, w
i= weight for the ith
period, n= number of periods in the moving average.
n D n i i ∑ =1 ∑ = n i i iD w 1
Quantitative Methods :
Smoothing Techniques
Exponential Smoothing
: assign greater weights to the
most recent data, in order to have a more realistic estimate
of the fluctuations. Weights usually lay between zero and
one
F
t+1=aD
t+(1-a)F
twhere Dt+1= forecast for the next period, Dt=actual demand in the present period, Ft=previously determined forecast for the present period, and a=weighting factor, termed as smoothing constant.
New forecast equals old forecast plus an adjustment for the
error that had occurred in the last forecast
Ft+1=aDt+ a(1-a)Dt-1+ a(1-a)2D
t-2+ a(1-a)3Dt-3+...+a(1-a)t-1D1+ a(1-a)2Dt-2+ a(1-a)tF1)
F
t+1is thus a weighted average of all past observations.
The older the data, the smaller the weight
.Quantitative Methods :
Barometric Techniques
Barometric Technique alerts businesses to changes in the
overall economic conditions.
Helps in predicting future trends on the basis of index of
relevant economic indicators especially when the past data
do not show a clear tendency of movement in a particular
direction.
Indicators may be
Leading indicators
: economic series that typically go up or downahead of other series
Coincident indicators
: move up or down simultaneously with thelevel of economic activities
Lagging series
: which moves with economic series after a timelag.
Quantitative Methods
Simple (or Bivariate) Regression Analysis:
deals with a single independent variable that determines the value
of a dependent variable.
Demand Function: D = a+bP, where b is negative.
If we assume there is a linear relation between D and P, there
may also be some random variation in this relation.
Sum of Squared Errors (SSE) : a measure of the predictive accuracy Smaller the value of SSE, the more accurate is the regression equation.
Nonlinear Regression Analysis
Log linear function log D =A + B log P + e
where A and B are the parameters to be estimated and e represents errors or disturbances.
Linear form of log linear function D* = a + b P* + e
where D*= log D and P*=log P
Quantitative Methods
Multiple Regression Analysis:
D = a
1+a
2.P+a
3.A+e
(where A = advertising expenditure incurred).
D^ = a^
1+ a^
2P + a^
3A,
(where a1, a2 and a3 are the parameters and e is the random error term (or disturbance), having zero mean).
Similar to simple regression analysis, multiple regression
analysis would aim at estimation of the parameters a1, a2
and a3.
Choose such values of the coefficients that would
minimize the sum of squares of the deviations.
Quantitative Methods
Problems Associated with Regression Analysis
Multicollinearity: when two or more explanatory variables in the
regression model are found to be highly correlated the estimated coefficients may not be accurately determined.
Heteroscedasticity: Classical regression models assume that the
variance of error terms is constant for all values of the independent variables in the model; i.e. variables are homoscedastic.
Specification errors: Omission of one or more of the independent
variables, or when the functional form itself is wrongly constructed or estimate a demand function in linear form, though the function should have been nonlinear.
Identification problem: where the equations have common
variables, like a demand supply model.
Quantitative Methods
Simultaneous Equations Method
Based on the fact that in any economic decision every
variable influences every other variable.
Incorporates mutual dependence among variables.
It is a simultaneous and two way relationships,
A typical simultaneous equation model may comprise of:
Endogenous variables: included in the model as dependent
variables
Exogenous variables: given from outside the model
Structural equations: which seek to explain the relation between
a particular endogenous variable and other variables
Definitional equations: which specify relationships that are
Limitations of Demand Forecasting
Change in Fashion: Is an inevitable consequence of advancement
of civilization. Results of demand forecasting have short lasting impacts especially in a dynamic business environment.
Consumers’ Psychology: Results of forecasting depend largely on
consumers’ psychology, understanding which itself is difficult.
Uneconomical: Requires collection of data in huge volumes and
their analysis, which may be too expensive for small firms to afford. Estimation process may take a lot of time, which may not be
affordable.
Lack of Experienced Experts: Accurate forecasting necessitates
experienced experts, who may not be easily available. Forecasting by less experienced individuals may lead to erroneous estimates.
Lack of Past Data: Requires past sales data, which may not be
Summary
Forecasting is an operations research technique of planning and decision
making; demand forecasting is the scientific and analytical estimation of demand for a product (service) for a particular period of time.
Demand forecasting can be categorized on basis of: i. the level of forecasting, i.e.
firm, industry and economy; ii. time period, i.e. short run and long run iii. nature of goods, i.e. capital and consumer goods.
Techniques of demand forecasting depend upon information on three questions:
a. What do people say? b. What do people do? c. What have people done?
In consumers’ opinion survey buyers are asked about their future buying
intentions of products, their brand preferences and quantities of purchase.
Future demand level may also be ascertained by experts with the help of
brainstorming or by structured discussions or even by discussing without face to face interaction.
Demand forecasting may also be done by market experiments conducted under
controlled or simulated conditions or in real markets in which consumers actually buy a product without the awareness of being observed.
Summary
Trend projection is a powerful statistical tool frequently used to predict future
values of a variable on the basis of time series data. Most time series data have components like seasonal trend, cyclical trend, secular trend and random events. Trend projection can be done by graphical method, least square method and ARIMA (Box Jenkins) method
Smoothing techniques are used when the time series data exhibit little trend
or seasonal variations, but a great deal of irregular or random variation. The most popular smoothing methods include moving average, weighted moving average and exponential smoothing.
In barometric forecasting we construct an index of relevant economic
indicators and forecast future trends on the basis of these indicators.
Econometric methods apply statistical tools on economic theories to estimate
economic variables.
Regression analysis relates a dependant variable to one or more
independent variables in the form of a linear equation. Regression can be linear, nonlinear and multiple.
Simultaneous equations method incorporates mutual dependence among
