Forecasting methods by Neeraj Bhandari ( Surkhet.Nepal )
Forecasting is the process of making statements
about events whose actual outcomes (typically)
have not yet been observed.
Prediction is a similar, but more general term.
Risk and uncertainty are central to forecasting and
prediction; it is generally considered good
practice to indicate the degree of uncertainty
attaching to forecasts.
Marketing: Forecasts sales for new and
Production: uses sales forecasts to plan
production and operations; sometimes
involves in generating sales forecasts
Time Horizon for Forecasting
The key factor in choosing a proper forecasting
approach is the time horizon for the decision
requiring forecasting. Forecasts can be made
for various timeframes:
1. Short- term
2. Mid- term
3. Long- term
Short- term Forecasting
Short- term (1 day to 3 months), managers are
interested in forecasts for disaggregated
demand ( for specific product, for specific
Little time to react to errors in demand forecast,
so the forecasts need to be as accurate as
Time series analysis is often used.
In absence of historical data managers use
Medium- term Forecasting
Time horizon for medium term (3 months to
Relates to aggregate planning (sales &
Medium term forecasts is used to build up
Both time-series and causal methods are
Time horizon exceeds two years
Long term forecasts are used for process
selection, capacity planning & location
Judgement models & causal models are used.
Forecasting (Objective &
The various categories of forecasting methods
that are available to businesses:
Forecasting methods can be either objective
(using quantitative approaches) or;
Subjective (using more intuitive or qualitative
approaches), depending on what data is
available and the distance into the future for
which a forecast is desired.
Qualitative forecasting techniques are
subjective, based on the opinion and
judgment of consumers, experts
They are appropriate when past data are not
They are usually applied to intermediate- or
Quantitative forecasting models are used to
forecast future data as a function of past
data; they are appropriate when past data are
These methods are usually applied to short-
or intermediate-range decisions.
1. Sales force composite:
Marketers have sales managers or
representatives at different sales territories
(districts/region) and marketers believe that
sales managers know their territory better than
Managers ask respective sales manager to
forecast expected sales in their own territories.
The total of all these estimates basically gives
company’s sales/demand forecast for next
2. Customer Survey: Marketers ask buyers about
how many units that they would like to purchase
from ABC company’s products for coming period
3. Jury of Executive Opinion Method:
In the Jury of executive opinion method of Sales
Forecasting, appropriate managers within the
organization assemble to discuss their opinions
on what will happen to sales in the future.
Since these discussion sessions usually resolve
around experienced guesses, the resulting
forecast is a blend of informed opinions.
4. Delphi Method also gathers, evaluates, and
summarizes expert opinions as the basis for a forecast,
but the procedure is more formal than that for the jury
of executive opinion method.
The Delphi Method has the following steps:
STEP 1 –Various Experts are asked to answer,
independently and in writing, a series of questions
about the future of sales or whatever other area is being
STEP 2 – A summary of all the answers is then prepared.
No expert knows, how any other expert answered the
STEP 3 – Copies of summary are given to the individual
experts with the request that they modify their original
answers if they think it necessary.
STEP 4 – Another summary is made of these
modifications, and copies again are distributed
to the experts.This time, however, expert
opinions that deviate significantly from the
norm must be justified in writing.
STEP 5 – A third summary is made of the opinions
and justifications, and copies are once again
distributed to the experts. Justification in writing
for all answers is now required.
STEP 6 –The forecast is generated from all of the
opinions and justifications that arise from step 5.
1. Casual Methods:
Some forecasting methods use the assumption
that it is possible to identify the underlying
factors that might influence the variable that is
being forecast. (Cause and Effect)
For example, including information about
climate patterns might improve the ability of a
model to predict umbrella sales.
This is a model of seasonality which shows a
regular pattern of up and down fluctuations.
2.Time SERIES ANALYSIS METHOD:
The time series analysis method predicts the
future sales by analyzing the historical
relationship between sales and time.
Although the actual number of years included in
a time series analysis will vary from company to
company, as a general rule, managers should
include as many years as possible to ensure that
important sales trends do not get undetected.
The three most common historical data used are:
Seasonality: A seasonal pattern (eg., quarter of the
year, month of the year, week of the month, day of
the week) exists when the demand is influenced by
Trend: During the growth and decline stages of the
product-life cycle, a consistent trend pattern in
terms of demand growth or demand decline can be
Level: It is difficult to capture short term patterns
that are not repetitive in nature. In short run,
sometimes there is a swing, which could be in either
direction, upward or downward, and is usually has
momentum that lasts for a few periods.