I

1. Demand Forecasting - Prerequisites of
a Good Forecast
 A good forecast should:
 be consistent with other parts of the business
 be based on knowledge of the relevant past
 consider the economic and political environment
as well as changes
 be timely

2. Forecasting Techniques
 Factors in choosing the right forecasting
technique:
 Item to be forecast
 Interaction of the situation with the characteristics
of available forecasting methods
 Amount of historical data available
 Time allowed to prepare forecast

3. Forecasting Techniques
 Expert opinion
 Opinion polls and market research
 Surveys of spending plans
 Projections
 Econometric models

4. Forecasting Techniques
 Qualitative forecasting is based on judgments of
individuals or groups.
 Quantitative forecasting utilizes significant
amounts of prior data as a basis for prediction.
 Naïve forecasting projects past data without
explaining future trends.
 Causal (or explanatory) forecasting attempts to
explain the functional relationships between the
dependent variable and the independent variables.

5. Forecasting Techniques
 Expert opinion techniques
 Jury of executive opinion: Forecasts generated by a
group of corporate executives assembled together. The
major drawback is that persons with strong personalities
may exercise disproportionate influence.
 The Delphi Method: A form of expert opinion
forecasting that uses a series of questions and answers to
obtain a consensus forecast, where experts do not meet.

6. Forecasting Techniques
 Opinion polls: Sample populations are surveyed to determine
consumption trends.
 may identify changes in trends
 choice of sample is important
 questions must be simple and clear
 Market research is closely related to opinion polling.
 Market research will indicate “not only why the consumer is or is not
buying, but also who the consumer is, how he or she is using the
product, and what characteristics the consumer thinks are most
important in the purchasing decision.”

7. Forecasting Techniques
 Surveys of spending plans: seek information about
“macro-type” data relating to the economy.
 Consumer intentions
 Survey of Consumers, Survey Research Center,
University of Michigan
 Consumer Confidence Survey, The Conference Board
 Inventories and sales expectations

8. Forecasting Techniques
 Trend projections: A form of naïve
forecasting that projects trends from past data
without taking into consideration reasons for
the change.
 Compound growth rate
 Visual time series projections
 Least squares time series projection

9. Forecasting Techniques
 Compound growth rate: Forecasting by projecting
the average growth rate of the past into the future.
 Calculate the constant growth rate using available data,
then project this constant growth rate into the future.
 Provides a relatively simple and timely forecast
 Appropriate when the variable to be predicted increases at
a constant percentage

10. Forecasting Techniques
 Visual Time Series Projections: plotting
observations on a graph and viewing the
shape of the data and any trends.

11. Forecasting Techniques
 Time series analysis: A naïve method of forecasting
from past data by using least squares statistical
methods.
 Data collected of a number of periods usually exhibit
certain characteristics:
 Trends
 Cyclical fluctuations
 Seasonal fluctuations
 Irregular movements

12. Forecasting Techniques
 Time Series Analysis Advantages
 easy to calculate
 does not require much judgment or analytical
skill
 describes the best possible fit for past data
 usually reasonably reliable in the short run

13. Forecasting Techniques
Yt = f(Tt, Ct, St, Rt)
 Yt = Actual value of the data at time t
 Tt = Trend component at t
 Ct = Cyclical component at t
 St = Seasonal component at t
 Rt = Random component at t
 Additive form: Yt = Tt + Ct + St + Rt
 Multiplicative form: Yt = (Tt)(Ct)(St)(Rt)

14. Forecasting Techniques
 Must decompose the time series into its four
components
 Remove seasonality
 Compute trend
 Isolate cycle
 Cannot do anything with random component

15. Barometric Method
 With this method an index of the relevant
economic indicators and movement across the
same is examined for forecasting purpose.