Forecasting is a necessary part of planning. The Future cannot be predicted with certainty, but the use of statistical data analysis helps prepare for what lies ahead. Since telecommunications is a supporting department in many organizations generally its forecasting depends on the entire organizational planning and forecasting. It is important for telecommunications managers to understand the organization forecast, or if possible, to be a part of the forecasting team for the organization. Understanding the organization plan and the direction it wants to move helps telecommunications mangers plan efficiently. While some equipment is easy to get on a short term notice, many components and systems take time (e.g.: a new PBX, trunks, increased voicemail ports). It is better to predict these increases and arrange to get the equipment quickly when the needed.
Telecommunications
managers spend most of their time solving problems or “fighting fires”. Managers
can plan efficiently to project future demand and eliminate many of these daily
fire drills by:
·
Making sure
that the number of circuits and trunks meets demand; controlling the cost of
connect vs. reconnect.
·
Arranging
the fastest way to get extra hardware capacity when needed
·
Anticipating
staffing demands such as the number of call center agents and PBX administrator
that will be needed
·
Predicting
physical space, which is often the hardest commodity to obtain
·
Calculating
telecom budgets
Forecasting is very similar to any other business project in an organization. The three fundamental steps to completing a forecast for telecommunications are determining a pattern, fining a source to gather data and using the best method to manipulating the data for the accurate outcomes.
Collecting
statistical data about your organization’s telecommunications services is the
best way to predict the future needs for the organization. You can often denote
patterns in the data from which you can predict the needs in the time to come.
The four important patterns that underline forecasts are:
·
Trends (e.g.
an organization closed on the weekends might see an influx of calls on Mondays)
·
Seasonality
(e.g. weather, Christmas and the New Year)
·
Cyclically
(e.g. rise or fall of economy over several years)
·
Randomness
(e.g. riot and flood)
There
are many ways within telecommunications systems and services to find statistical
reports and data for forecasting. Most equipment (such as the PBX) is computerized
and can easily statistics. Sources of telecommunications data includes:
·
ACD (Automatic
Call Distributor): Management information systems, which produce reports that
range
from quarter hours to weeks
·
Switching
System (PBX): Collect usage information on number of hour and call seconds for
circuit groups
·
Call Accounting
System (part of the PBX)
·
Data can be
sorted and combined in variety of ways
·
Collects data
a month at a time
·
Data doesn’t
show uncompleted calls
Toll
Statement:
·
The bill –
Paper is hard to deal with and it can be incomplete
·
It does not
show the total load on the system
·
It negates
uncompleted calls, local calls, incoming calls, or call setup time
·
Routers and
Other Network Management Systems: Provides transmission and bandwidth statistics
·
Budgeting
Information: Current expenses for various types of telecommunications service
may be useful to predict future demand
There are several methods to forecasting using the data collected from one of the sources described above. The method used depends on the type of information that you have and the type of information that you are searching for. The method also depends on the degree of accuracy that is required. Much of the data can be collected in a spreadsheet, such as Excel, and can be analyzed by one of the following forecasting methods:
· Time Series: Fits a trend to plotted data to show upward or downward movement. The disadvantage is that it predicts the future on history.
· Moving Averages: Smoothing out the variations by averaging the data before and after a given point. The disadvantage of this method is that it gives the same value to all observations. In an effective analysis recent observations should have more value than the older ones.
· Exponential Smoothing: Much like moving averages, but creating an exponential equation to find the average from less evenly weighted data
· Regression Analysis:
·
Uses one truth
to predict other variables
·
Fact or forecast
A is used to predict B
·
The correlation
coefficient is the denotation of how closely data is related (0.0 – 1.0)
·
Another part
of the organization must prepare a forecast for telecommunications manager to
use that forecast for telecommunications needs
· Judgmental Forecasting: Changing the forecast as you go along to keep from getting too far off track
·
The Delphi Method:
This forecasting method is better than simple guess but it is very risky. Data
is gathered through following steps:
1.
Asking the
opinion of the various department leaders about the direction that the organization
going
2.
Weighing their
answers considering knowledge, optimism or pessimism
3.
Averaging
those weighted answers