6.1. Concept of Forecasting
Many new car
buyers have a thing or two in common. Once they make the decision to buy a new
car, they want it as soon as possible. They usually don’t want to order it and
then have to wait six weeks or more for delivery. If the car dealer they visit
doesn’t have the car they want, they’ll look elsewhere. Hence, it is important
for a dealer to anticipate buyer
wants and to have those models, with the necessary options, in stock. The
dealer who can correctly forecast buyer wants, and have those cars available,
is going to be much more successful than a competitor who guesses instead of
forecasting-and guesses wrong, and gets stuck with cars customers don’t want.
So how does the dealer know how many cars of each type to stock? The answer is,
the dealer doesn’t know for sure, but
based on analysis of previous buying patterns, and perhaps making allowances
for current conditions, the dealer can come up with a reasonable approximation of what buyers will want.
Planning is an
integral part of a manager’s job. If uncertainties cloud the planning horizon,
managers will find it difficult to plan effectively. Forecasts help managers by
reducing some of the uncertainty, thereby enabling them to develop more
meaningful plans.
“A forecast is a statement about the
future value of a variable such as demand”- Stevension
“Forecast is a
prediction of future events used for planning purpose”-Krajewski.
People make and
use forecasts all the time, both in their jobs and in everyday life. In
everyday life, they forecasts answers to questions and then make decision based
on their forecasts. Typical questions they may ask are: “Will I get the job?”,
“When should I leave to make it to class, the station, the bank, the
interviews………., on time?” To make these forecasts, they may take into account
two kinds of information. One is current factors or conditions. The other is
past experience in a similar situation.
Forecasting for
business purpose involves similar approaches. In business, however, more formal
methods are used to make forecasts and to assess forecast accuracy. Forecasts
are the basis for budgeting, planning capacity, sales, production and
inventory, personnel, purchasing, and more. Forecasts play an important role in
the planning process because they enable managers to anticipate the future so
they can plan accordingly.
There are two uses
for forecasts. One is to help mangers plan the system and the other is to
help them plan the use of the system.
Planning the system generally involves long-range plans about the types of
products and services to offer, what facilities and equipment to have, where to
locate, and so on. Planning the use of the system refers to short-range and
intermediate-range planning, which involve tasks such as planning inventory and
workforce levels, planning purchasing and production, budgeting, and
scheduling.
Business
forecasting pertains to more than predicting demand. Forecasts are also used to
predict profits, revenues, costs, productivity changes, prices and availability
of energy and raw materials, interest rates, movements of key economic
indicators (e.g., GDP, inflation, government borrowing), and prices of stocks
and bonds.
Forecasts aid in
determining what resources are needed, in scheduling existing resources, and in
acquiring additional resources. Accurate forecasts allow schedulers to increase
customer satisfaction, reduce customer response times, use capacity
efficiently, and cut inventories.
6.2. Features Common to all Forecasts
A wide variety of
forecasting techniques are in use. Certain features are common to all, and it
is important to recognize them.
·
Forecasting techniques generally assume that the same
underlying casual system that existed in the past will continue to exist in the
future.
·
Forecasts are rarely perfect; actual results usually differ
from predicted values.
·
Forecasts for groups of items tend to be more accurate than
forecasts for individual items because forecasting errors among items in a
group usually have a cancelling effect. Opportunities for grouping may arise if
parts or raw materials are used for multiple products or if a product or
service is demanded by a number of independent sources.
·
Forecast accuracy decreases as the time period covered by
the forecast-the time horizon-increases.
Generally speaking, short-range forecasts must contend with fewer uncertainties
than longer-range forecasts, so they tend to be more accurate.
6.3. Elements of A good Forecast
A properly
prepared forecast should fulfill certain requirements:
i.
The forecast should be timely. Usually, a certain amount of
time is needed to respond to the information contained in a forecast. For
example, capacity cannot be expanded overnight, nor can inventory levels be
changed immediately. Hence, the forecasting horizon must cover the time
necessary to implement possible changes.
ii.
The forecast should be accurate and the degree of accuracy
should be stated. This will enable users to plan for possible errors and will
provide a basis for comparing alternative forecasts.
iii.
The forecast should be reliable, it should work
consistently. A technique that sometimes provides a good forecast and sometimes
a poor one will leave users with the uneasy feeling that they may get burned
every time a new forecast is issued.
iv.
The forecast should be expressed in meaningful units.
Financial planners need to know how many dollars
will be needed, production planners need to know many units will be needed, and schedulers need to know what machines and skills will be required. The choice of units depends on user needs.
v.
The forecast should be in writing. Although this will not guarantee that
all concerned are using the same information, it will at least increase the
likelihood of it. In addition, a written forecast will permit an objective
basis for evaluating the forecast once actual results are in.
vi.
The forecasting technique should be simple to understand and use. Users
often lack confidence in forecasts based on sophisticated techniques; they do
not understand either the circumstances in which the techniques is an obvious
consequence. Not surprisingly, fairly simple forecasting techniques enjoy
widespread popularly because users are more comfortable working with them.
vii.
The forecasts should be cost-effective.
6.4. Steps in the Forecasting Process
There are six
basic steps in the forecasting process:
i.
Determine the purpose of the forecast. How will it be
used and when will it be needed? This will provide an indication of the level
of detail required in the forecast, the amount of resources (personnel, computer
time, dollars) that can be justified, and the level of accuracy necessary.
ii.
Establish a time horizon. The forecast must
indicate a time interval, keeping in mind that accuracy decreases as the time
horizon increases.
iii.
Select o forecast techniques.
iv.
Gather and analyze relevant data. Before a forecast
can be made, data must be gathered and analyzed. Identify any assumptions that
are made in conjunction with preparing and using the forecast.
v.
Make the forecast.
vi.
Monitor the forecast. A forecast has to
be monitored to determine whether it is performing in a satisfactory manner. If
it is not, reexamine the method, assumptions, validity of data, and so on;
modify as needed, and prepare a revised forecast.
6.5. Approaches to Forecasting
Forecasting
methods may be based on mathematical models that use available historical data,
on qualitative methods that draw on managerial experience and customer
judgments, or they may be based on a combination of both. So, we can say that there
are basically two general approaches to forecasting: qualitative and quantitative.
·
Qualitative
methods consist mainly of subjective inputs, which often defy precise
numerical description.
·
Quantitative methods involve
either the projection of historical data or the development of associative
models that attempt to utilize casual (explanatory) variables to make a
forecast.
Qualitative
techniques permit inclusion of soft information (e.g., human factors, personal
opinions) in the forecasting process. Those factors are often omitted or
downplayed when quantitative techniques are used because they are difficult or
impossible to quantify. Quantitative techniques consist mainly of analyzing
objective, or data. They usually avoid personal biases that sometimes
contaminate qualitative methods. In practice, either or both approaches might
be used to develop a forecast.
6.6. Techniques of Forecasting
Techniques of
forecasting are as follows:
·
Judgmental
forecasts rely on analysis of subjective inputs obtained from various sources,
such as consumer surveys, the sales staff, managers and executives, and panels
of experts. Quite frequently, these sources provide insights that are not
otherwise available.
·
Time series
forecasts simply attempt to project past experience into the future. These
techniques use historical data with the assumption that the future will be like
the past. Some models merely attempt to smooth out random variation in
historical data; others attempt to identify specific patterns in the data and
project or extrapolate those patterns into the future, without trying to
identify causes of the patterns.
·
Associative models use equations
that consist of one or more explanatory variables that can be used to predict
future demand. For example, demand for paint might be related to variables such
as the price per gallon and the amount spent on advertising, as well as
specific characteristics of the paint. (e.g., drying time, ease of cleanup).