Introduction to Forecasting:
Predicting
the future
Forecasting is about predicting the future as accurately as possible,
given all the information available including historical data and knowledge of
any future events that might impact the forecasts.
Not an
exact science but instead consists of a set of statistical tools and techniques
that are supported by human judgment and intuition.
Business
forecasting generally attempts to predict future customer demand for a firm’s
goods or services
Macroeconomic
forecasting attempts to predict future behaviour of the economy and identify
business cycle turning points.
Short-term
forecasts are
needed for scheduling of personnel, production and transportation. As part of
the scheduling process, forecasts of demand are often also required.
Medium-term
forecasts are
needed to determine future resource requirements in order to purchase raw
materials, hire personnel, or buy machinery and equipment.
Long-term
forecasts are used
in strategic planning. Such decisions must take account of market
opportunities, environmental factors and internal resources.
The Forecasting Process
1. determine the purpose or objective of the forecast
2. select the relevant theoretical model (identify influences, classify
as internal or external, and identify possible constraints)
3. collect data
4.analyze data (Data analysis often determines model choice.)
5.estimate the original model (You may initially select for comparison
more than one technique.)
6.evaluate the model and revise
7.present initial forecast to decision makers
8.make the final revision
9.distribute the forecast
10.establish
monitoring procedures
Advantages of forecasting:
1.
Effective
planning by providing scientific and reliable basis .
2.
Helps
introducing the area of uncertainty in decision making with respect to cost
production, sales, profits and pricing.
3.
Making
and reviewing of forecasts on a continuous basis helps in finding best possible
decision with a dynamic approach.
4.
necessary
for effective managerial control .
Applications of forecasting:
1.
Operations
management: forecast of product sales; demand for services
2.
Marketing:
forecast of sales response to advertisement procedures, new promotions etc.
3.
Finance
& Risk management: forecast returns from investments
4.
Economics:
forecast of major economic variables, e.g. GDP, population growth, unemployment
rates, inflation; useful for monetary & fiscal policy; budgeting plans
& decisions
5.
Industrial
Process Control: forecasts of the quality characteristics of a production
process
6.
Demography:
forecast of population; of demographic events (deaths, births, migration);
useful for policy planning
Limitations of Forecasting:
1.
A degree
of error ; not 100% accurate
2.
Quantitative
techniques on which forecasts are made are based on certain assumptions.
negligence to examine the
forecast.
Steps in forecasting
1.
Understanding
the problem
2.
Developing
the groundwork
3.
Selecting
and analysing the data
4.
Estimating
future events
Implicit Vs Explicit forecasting:
Implicit: based on past, present experience- not reliable, unsystematic, not
accurate
Explicit: rational analytical evaluation and control process- reliable, accurate
and precise
Techniques of forecasting:
Qualitative
methods are based
on: -judgement -opinion -past experience -best guesses
Qualitative
Techniques
• Delphi
method (technological forecasting)
• Market
research
• Panel
of consensus
•
Visionary forecasts
•
Historical analogies
Choice of method of Forecast:
·
Relevance
and availability of historical data
·
Context
of the forecast
·
desired
degree of accuracy
·
the time
period to be covered
·
cost
benefit of the forecast
time available for making the
analysis
Time Series Analysis:
·
regular
or systematic variation in data
·
cyclic
trends which appear every two or three years
o
Forecaster looks for data patterns as
Data = historic pattern + random variation
o
Historic pattern to be forecasted:
Level (long-term average) – data fluctuates around a constant mean
Trend – data exhibits an increasing or decreasing pattern
Seasonality – any pattern that regularly repeats itself and is of a
constant length
Cycle – patterns created by economic fluctuations
o
Random Variation cannot be predicted
Regression Method:
·
estimating the relationships among variables
Regression
analysis helps one understand how the typical value of the dependent variable
(or 'criterion variable') changes when any one of the independent variables is
varied, while the other independent variables are held fixed
Econometric models:
·
Mathematical
economic theory and statistical procedures.
·
Set of
simultaneous equations.
Complex and should require
knowledge in statistics, economic and mathematics
Extrapolation:
extrapolation is the process
of estimating, beyond the original observation range, the value of a variable
on the basis of its relationship with another variable.
simplest method.
Historical perspective:
also called business barometer
future predicted from happenings in present.
reduce chances of wrong forecasting
reliable
Panel Consensus Method
Group of experts have a face to face discussion.
better than one expert working alone.
Delphi Method:
structured communication technique or method,
originally developed as a systematic, interactive forecasting method which relies on a panel of experts
First applications of the Delphi method were in the
field of science and technology forecasting. The objective of the method was to
combine expert opinions on likelihood and expected development time, of the
particular technology, in a single indicator.
applied successfully and with high accuracy
in business forecasting
Morphological Research method:
find out technological alternatives.
compare and analyse structure of data.
Relevance Tree:
feasibility of future objective is judged first.
working backwards technological alternatives are found.
