naive method and cumulative mean

1. Supply Chain Planning- part 1
Mcenroe ng

2. The Naive Forecast
• It's naive because all
we're doing is,
• we're saying what we
sold yesterday, that's
how much we're going
to sell today.
• What we sell today is
the number we're
going to project for
tomorrow.

3. The Naive
Forecast
The naive method can be represented
mathematically as well. So our forecast, we denote
that again as F. And we're going to forecast into the
current time period, so it would be F sub t.
And we set that equal to our demand at t- 1. And
that means the previous time interval. If we look at
it on a timeframe then we are trying to forecast
here and we are going to use this level of data
demand.
At minus 1 as our forecast at period t. We can
make several adaptations to the naive method.

4. The Naive Forecast
• Our demand at the previous time period, but
we do know that we are growing every month,
and we know for example every month we sell T
units more, and therefore we add that T into our
forecast.

5. The Naive Forecast
• The naive method is clearly simple, but in situations where other more complex
forecasts are not doing very well, it may be equally good and remember what we
said earlier. If you can use a simpler method and get the same level of accuracy,
it's better than using a more complex one.
• Now the naive forecast is very noisy because it doesn't filter out any noise
whatsoever. That makes it very nervous but also responsive to changes in
demand. Nervous means we have a very volatile forecast.
• That may not be in our best interest when we're trying to plan for a smooth
production. Therefore, we have to understand that typically, only the last period
is used for our forecast. That is a big assumption, and it may not be very realistic.
• However, the naive method can be used as a benchmark to more complex
methods and it tells you whether a more complex method is clearly superior.

6. The Cumulative Mean
• the naive method, we assumed that only the last piece of
information is useful in predicting the future, but what if we think
that all prior data is useful in our forecast?
• That is the idea behind the cumulative mean.
• We take all of the data that we have, average it, and hat is going to
represent our forecast.

7. The
Cumulative
Mean
The cumulative mean is, again, denoted by F sub
t, which is what we're trying to forecast. And
that is made up of the sum of demand, and that
sum is going from the very first period we have,
I=1, all the way to T-1, which is the very latest
time period we have available.
And we are going to divide that by how many
periods we had summed together and that
would be t-1, and that gives us an average over
all periods. So what this means on time frame is,
if we are right here at t, then we are going to
average all the demand from the prior periods
and make that our forecast right here, a time t.

8. The
Cumulative
Mean
So, how good is the cumulative mean? Well, it may work
well in some situations and it's really the antithesis to the
naive method. Where's the naive method?
Was it very responsive, but also picked up a lot of noise?
This forecast is very stable and averages out all the noise,
so it really filters it out.
And advantage is, as I said, it's stable. But it also may not
recognize all the pattern, and you need to be careful how
to use it.
The big assumption in the cumulative mean is that all prior
data is equally useful. That may not be very robust, but it is
the assumption. And there are other methods that may
take into account that flaw in the cumulative mean.

9. Reference
Rutgers the State University of New Jersey