This document discusses techniques for demand forecasting. It describes both qualitative (subjective) methods like expert opinion, sales force surveys, and market research as well as quantitative (objective) methods like time series analysis, regression analysis, and econometric models. Qualitative methods rely on human judgment while quantitative methods use mathematical or simulation models based on historical data. The document also notes limitations of demand forecasting like changing consumer preferences, difficulty understanding consumer psychology, high costs for small businesses, and lack of past data or experts.

1. Demand Forecasting

2. Chapter Outline
 Meaning of Demand Forecasting
 Techniques of Demand Forecasting
 Subjective Methods of Demand Forecasting
 Quantitative Methods of Demand Forecasting
 Limitations of Demand Forecasting

3. Demand forecasting
 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.

4. Categories of forecasting
 Macro level
 GDP
 Components of GDP
 Share of manufacturing in total GDP in 2011
 Industry level
 Industry sales- car sales in 2011
 Firm level
 Sales of Tata Indica in 2011

5. Choice of forecasting technique
 Objective of forecast
 New product, impact of advertisement
 Cost effective
 Opportunity cost of resources employed
 Time perspective
 Urgency of forecast- breaking of epidemic
 Long run/short run
 Availability of data
 Quality and quantity

6. Techniques of Demand Forecasting
 Qualitative (subjective) technique
 Rely on human judgment and opinion
 Experts’ Opinion
 Group Discussion
 Delphi Method
 Sales force composite
 Opinion polls
 Market research
 Market simulation
 Test marketing
 Surveys of spending plans
 Barometric technique

7.  Quantitative technique
 Use mathematical or simulation models
 Based on historical demand data or
 Relationship between demand and other
variables
 Naïve techniques
 Trend Projections
 Smoothing Techniques
 Econometric Techniques
Techniques of Demand Forecasting

8. Qualitative Technique
 Experts opinion
 Group Discussion
 Within a corporation (jury of executive opinion)
 Structured discussion on topics/ forums
 Eg., stock market, beauty products

9.  Experts opinion
 Delphi Method (Rand Corporation in 1950)
 Forecast the impact of technology on warfare
 Experts do not meet face to face
 Sequential series of written Q & A
 Consolidated opinions of experts is sent for
revised views till conclusions converge on a
point.
Qualitative Technique

10.  Expert’s opinion- Delphi Method
 Merits
 Decisions are enriched with the experience of
competent experts.
 Very useful when product is absolutely new to all
the markets.
 Demerits
 Experts’ may involve some amount of bias.
 Sometimes difficulty in assessing the degree of
expertise
 With external experts, risk of loss of confidential
information to rival firms.
Qualitative Technique

11.  Sales force composite
 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.
Qualitative Technique

12.  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
Qualitative Technique

13.  Opinion poll (Buyers’ opinion, consumers
opinion survey)
 consumers future buying intentions of
 Products
 Brand preferences
 Quantities purchased
 Response to price increase
 Implied comparison with competitor’s products
 Census Method: Involves contacting each and every
buyer
 Sample Method: Involves only representative sample
of buyers
Qualitative Technique

14.  Opinion Poll
 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
 Investigators’ bias regarding choice of sample
and questions
Qualitative Technique

15.  Market research
 Market simulation
 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 for pricing strategy
Qualitative Technique

16.  Market Research- Market simulation
 Merit
 Provides information on changing consumer
behaviour and impact of determinants of
demand
 Very useful in case of new products
 Demerits
 People behave differently when they are being
observed.
 In Grabor-Granger tests consumers may not
quote the price they may pay
Qualitative Technique

17.  Market Research
 Test marketing
 product is actually sold in certain segments of the
markets
 Location, no. of test markets, duration of test are
very crucial to the success of the results.
 Merits
 Most reliable among qualitative methods.
 Very suitable for new products.
 Less risky than launching the product directly
Qualitative Technique

18.  Market Research- Test marketing
 Demerits
 Costly
 Requires actual production
 Failure means entire cost of test is sunk.
 Time consuming
 Extrapolation may not give accurate results
 Markets are geographically widely distributed
Qualitative Technique

19. Qualitative Techniques
 Surveys of spending plans
 More macro type of study
 Income spending habits of consumers
 NSSO survey is India on consumer expenditure
 Proportion of income spent on various items

20.  Barometric Technique
 Alert economic conditions.
 Helps in predicting future trends on the basis
of index of relevant economic indicators
 Particularly helpful when past data do not show
any trends
 Eg., forecasting the impact of recession of
2008-09
Qualitative Techniques

21. Qualitative Techniques
 Indicators may be
 Leading indicators
 Indicators that move ahead of economic events
 Export-import values, Building permits
 Coincident indicators
 Move up or down simultaneously with economic
activity
 Industrial production
 Lagging indicators
 Move with economic series after a period of time
 Average duration of employment, commercial and
industrial loan outstanding

22. Quantitative Techniques
 Naïve forecasting techniques
 Compound Growth Rate
 Trend Projections
 Smoothing Techniques
 Econometric technique

23. Quantitative Techniques
 Constant compound growth rate
 Appropriate when variable is expected to
increase at a constant percentage
 CGR = (E/B) (1/n)
-1 => E/B = (1+i)n
 E : ending value, B : beginning value, n: no. of
years, i: growth rate
 Demerit
 Does not take the fluctuations into
consideration

24. Quantitative Techniques
 Trend Projections
 Statistical tool to predict future values of a variable
on the basis of time series data
 Secular Trend (T)
 Direction of movement of data over long period of time
 Cyclical trend (C)
 Business cycles
 Seasonal trend (S)
 Seasonal variations within a year
 Random events (R)
 Have no trend of occurrence

25.  Additive Form: Y = T + S + C + R
 Multiplicative Form: Y = T x S x C x R
Log Y= log T + log S + log C + log R
 Methods of trend projection
 Graphical
 Linear regression models
 ARIMA or Box Jenkins Method
Quantitative Techniques

26.  Smoothing Techniques
 Moving Average
 Based on averages of recent past data
 Et+1 = ( Xt +Xt-1+…+Xt-N-1) / N
 E: forecast, X: actual observation
 Eg., 3 month moving average, 5 month moving
average
 Weighted moving average
 Attaching weights to the past data
 Et+1 = ( w1Xt +w2Xt-1+…+wnXt-N-1) / N
Quantitative Techniques

27.  Exponential smoothing
 Greater weight is assigned to most recent data
 Et+1 = wXt + (1-w)Et
 0< W <1
 Larger the W, greater the importance of the
observation
 If series is volatile – less smoothing effect
Quantitative Techniques

28.  Econometric Techniques
 Causal or explanatory forecasting techniques
 Regression analysis
 Single equation technique
 Multiple equation technique
 Simultaneous equation technique
Quantitative Techniques

29. Limitation of Demand forecasting
 Changing fashion changes preferences of
consumers
 Consumers psychology
 Understanding of this is very difficult
 Uneconomical for small firms
 Time required to do the analysis
 Data collection is costly
 Lack of experienced experts
 Lack of past data

30. Limitations of Demand Forecasting
 Change in Fashion:
 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.

31.  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 correctly
available. Typical problem in case for a
new product.