Evaluation of stationary demand models


November 20, 2015


November 20, 2015


Select a suitable model among the given choices.


TrainMax is a manufacturer of high‐end specialty engine equipment for high speed trains. They produce parts that are sent to the original equipment manufacturers (OEMs) for manufacturing new engines. They face a continuing challenge of trying to forecast demand for their products. The demand for one part in particular, XC‐288, was highlighted as needing to be examined.

Visualization of all the models

Check for stationary demand

One way of doing is to determine the coefficient of variation (CV)

\[ CV=\frac{STDEV(data)}{AVERAGE(data)} \]

We get

STDEV 62.6899312
AVERAGE 1103.78571
CV 0.05679538

CV is very low & hence the demand is quite stationary & stable in nature.

Calculations among the models

Prediction for period 15 MAPE(%)
Period 14 value 1169
Naïve 1169 7.08446274
Cumulative 1103.78571 5.13422811
2MA 1145 6.46420824
4MA 1113.25 4.79251584

Here we see that the moving average forecasts need not always be between the naïve & cumulative forecasts.

Selection of the model in the presence of a trend

If we assume there is a positive trend in the data then none of these models are appropriate for demand with a trend pattern. The Cumulative, Naive, and Moving Average forecasts all assume stationary demand. That means that you only assume a Level pattern to the demand with some random noise.