If
you run a small business, then you know how important it is to have an idea of
what your monthly profits or sales may be, so you can plan ahead and better
schedule your growth.
Thankfully,
this is possible with tools such as forecasting. A business forecast studies
the behavior of important business indicators like sales and expenses over long
periods of time to arrive at a predictive model for future performance in those
areas.
[Related Article: Modelling Important Business Decisions with Game Theory]
[Related Article: Modelling Important Business Decisions with Game Theory]
The
validity of forecast models is guaranteed by the mathematical nature of
business processes; your business can be modeled as a mathematical system
receiving inputs (factors of production) and producing output (sales and
profits).
Accuracy
of forecast models is guaranteed by the fact that business performance
fluctuates between reasonably fixed high and low points.
These
fluctuations may be seasonal (such as sales highs during holidays or profit
lows in the first quarter of a new year), cyclic (consisting of alternating
highs and lows), or reactive (consisting of nearly-random small changes).
In
any case, these fluctuations can be reasonably predicted over the long term
using certain mathematical systems called forecast models. Software companies
have successfully packaged these models into forecasting software that
accurately predict key business performance indicators.
The
problem is that many of these models are very complicated, and may not be
suitable for relatively simple business processes – like those of small
businesses. As a result of the complex nature of many of these programs, they
are also very expensive.
This
post focuses on a simple and free forecast
model for small businesses.
Introducing
the Mean Adjustment Prediction Model
(MAPM) for sequential business data:
With this model you can predict new monthly
values of data given a substantial set of historical data by performing a
simple calculation on a spreadsheet (e.g. MS Excel)
Read
on to discover the actual equation for the model, as well as the code (formula)
for EXCEL, so you can use this at home or in the office.
This
forecast model is very simple and only requires that you obtain the mean
(average) of the values of data you are interested in, and add an adjustment
factor.
It
is remarkable to note that this model does not sacrifice accuracy on account of
its simplicity. For a detailed background of this model (open only if you are
mathematically inclined), see Mean Adjustment Prediction.
For
a simpler analysis, consider the following:
The MAPM is especially recommended
for slightly-varying non-constant data, such as small business sales and
profits.
Notice
the high accuracy of the model (ranging between 87% and 120% for the example
above). In the experiment shown above, MAPM was used to guess random sales
figures ranging between $7,000 and $9,000 with very impressive results. Accuracy
values in excess of 100% indicate an optimistic prediction (greater than the
actual result).
Tip: I recommend you use MAPM in a spreadsheet to quickly
calculate a prediction for applications such as planning and budgeting.
To
use MAPM with Excel, you can use the following formula:
=AVERAGE(B3:G3) + 0.5*(G3 - B3)*(1 -
1/N)
B3
= first value in sequence
G3
= last value in sequence
N
= number of values in sequence
I
understand it’s a little technical, but I assure you it works great every time –
and of course, it’s free. So by all means get started with MAPM in Excel and
let me know if you need any assistance! Thanks for reading!
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