This document discusses forecasting models and techniques. It begins with an introduction to forecasting, then describes two main types of forecasting - business forecasting to predict customer demand, and macroeconomic forecasting to predict economic trends. Next, it outlines qualitative and quantitative forecasting techniques, including executive opinion, market surveys, time-series models, and associative models. It provides examples of forecasting applications in operations, marketing, finance, economics, and demography. It concludes by noting forecasts can be inaccurate when factors are unknown or data is limited.

1. TUMKUR UNIVERSITY
Department of studies and research in
library and information science
Tumkur University, Tumakuru
Forecasting Models
By
Mahesh V M
Student
Department of studies and research in
library and information science
Tumkur University,Tumakuru

2.  Introduction
 Forecasting
 Forecasting Techniques
 Application of Forecasting
 Conclusion
Content

3. • Business forecasting generally attempts to predict
future customer demand for a firm’s goods or services.
• Macroeconomic forecasting attempts to predict future
behavior of the economy and identify business cycle
turning points.
Introduction

4. • Forecasting is the process of making predictions of the
future based on past and present data and most
commonly by analysis of trends.
Forecasting

5. • 1. Qualitative methods
• 2. Quantitative methods
Forecasting Techniques

6. Qualitative methods
Qualitative methods
Executive
Opinion
Approach in which
a group of
managers meet
and collectively
develop a forecast
Market Survey
Approach that uses
interviews and
surveys to judge
preferences of
customer and to
assess demand
Sales Force
Composite
Approach in
which each
salesperson
estimates sales in
his or her region
Delphi
Method
Approach in
which
consensus
agreement is
reached among
a group of
expert

7. Quantitative methods
Quantitative methods
Time-Series Models
Time series models look at
past patterns of data and
attempt to predict the future
based upon the underlying
patterns contained within
those data.
Associative Models
Associative models (often
called causal models)
assume that the variable
being forecasted is related
to other variables in the
environment. They try to
project based upon those
associations.

8. I. Operations management: forecast of product sales; demand for
services.
II. Marketing: forecast of sales response to advertisement
procedures, new promotions etc.
III. Finance & Risk management: forecast returns from investments.
IV. Economics: forecast of major economic variables, e.g. GDP,
population growth, unemployment rates, inflation; useful for
monetary & fiscal policy; budgeting plans & decisions.
V. Industrial Process Control: forecasts of the quality characteristics
of a production process.
VI. Demography: forecast of population; of demographic events
(deaths, births, migration); useful for policy planning.
Applications of forecasting

9. • When the factors that lead to what is being forecast are not
known or well understood such as in stock and foreign exchange
markets forecasts are often inaccurate or wrong as there is not
enough data about everything that affects these markets for the
forecasts to be reliable, in addition the outcomes of the forecasts
of these markets change the behavior of those involved in the
market further reducing forecast accuracy.
Conclusion