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The sales forecast is a prediction of a business's unit and
dollars sales for some future
period of time, up to several years or more. These forecasts are
generally based primarily
on recent sales trends, competitive developments, and economic
trends in the industry,region, and/or nation in which the
organization conducts business.Sales forecastingis
management's primary tool for predicting the volume of
attainable sales. Therefore, thewhole budget process hinges on an
accurate, timely sales forecast.
These technical projections of likely customer demand for
specific products, goods, orservices for a specific company within
a specific time horizon are made in conjunction with
basic marketing principles. For example, sales forecasts are
often viewed within the contextof total market potential, which can
be understood as a projection of total potential sales for
all companies. Market potential relates to the total capacity of
the market to absorb the
entire output of a specific industry. On the other hand, sales
potential is the ability of themarket to absorb or purchase the
output from a single firm.
APPROACHES TO FORECASTING.
BOTTOM-UP FORECASTING. Analysts using this methodology divide
the market into
segments, and then separately calculate the demand in each
segment. Typically, analysts
use sales force composites, industry surveys, and
intention-to-buy surveys to collect data.They aggregate the
segments to arrive at a total sales forecast. Bottom-up forecasting
may
not be simple because of complications with the accuracy of the
data submitted. Theusefulness of the data is contingent upon honest
and complete answers from customers,and on the importance and
priority given to a survey by the sales staff.
TOP-DOWN FORECASTING. This is the method most widely used for
industrial
applications. Management first estimates the sales potential,
then develops sales quotas,and finally constructs a sales forecast.
Problems arise with this method, however, when theunderlying
assumptions of the past are no longer applicable. The correlation
between
economic variables and quantity demanded may change or weaken
over time.These two forecasting methods encompass a number of
methodologies which can be dividedinto three general
categories:qualitative, times-series analysis andregression, and
causal.
QUALITATIVE METHODS. Qualitative methods rely on non-statistical
methods of derivinga sales forecast. A company solicits the opinion
or judgment of sales executives, a panel ofexperts, the sales
force, the sales division supervisors, and/or outside expert
consultants.Qualitative methods are judgmental composites of
expected sales. These methods are often
preferred in the following instances: 1) when the variables
which influence consumer buyinghabits have changed; 2) when current
data is not available; 3) when none of the qualitativemethods work
well in a specific situation; 4) when the planning horizon is too
far for the
standard quantitative methods; and 5) when the data has not yet
factored in technological
breakthroughs taking place or forthcoming.The Probability
Assessment Method (PAM) forecasts sales volume by utilizing
in-houseexpert opinion that provides probabilities between one and
99 percent, plus and minus, on
certain target volumes. Analysts translate these estimates into
acumulativeprobabilitycurve by plotting the volumes by the
probability assigned to them. They use this curve to
aid in forecasting.
TIME-SERIES ANALYSIS AND PROJECTION. Trend projection techniques
may be most
appropriate in situations where the forecaster is able toinfer,
from the past behavior of a
variable, something about its future impact on sales.
Forecasters look for trends that formidentifiable patterns which
recur with predictive frequency. Seasonal variations and
cyclicalpatterns form more obvious trends, while random variables
make projection more complex.
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While time-series methods do not explicitly account for causal
relationships between a
variable and other factors, analysts find the emergent
historical patterns useful in making
forecasts. Analysts typically use time series for new product
forecasts, particularly in theintermediate and long-term. The data
required varies with each technique. A good rule of
thumb is a minimum of five years' annual data. A complete
history is very helpful.
CAUSAL METHODS. When analysts find a cause-effect relationship
between a variable andsales, a causal model may provide better
forecasts than those generated by other
techniques. Life-cycle analysis forecasts new product growth
rates based on analysts'projections of the phases of product
acceptance by various groupsinnovators, early
adapters, early majority, late majority, and laggards.
Typically, this method is used to
forecast new product sales.. It is often necessary to do market
surveys to establish thecause-effect relationships.
THE PRODUCTION BUDGET. Both small and large businesses construct
their productionbudgets within limitations of
production,warehousing, delivery, and service
capabilities.Subsequently, a company attempts to schedule
production at maximum efficiency. By
anticipating the variations in monthly sales, management can
keep production at levels
sufficient to provide adequate supply. Labor costs generally
comprise the greatest single
production cost. Therefore, management may adjust labor hours to
production schedules.Production levels remain rather constant if
current inventory is sufficient to meet increases
in sales. If management expects an increase, it may build
inventories during the firstquarter of the budget, and sell them
down to planned levels during the remaining threequarters. From the
production budget, a company estimates the mix of materials, labor,
and
production overhead needed to meet planned production levels
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