updating ARIMA model

#1
When should one use the different types of ARIMA model as mentioned below:

Estimate the model order in the training data set and use the same order to forecast future values (updating the parameter estimates)
Use a rolling window (e.g. 30 day )to make a new forecast by estimating model order and parameters everytime to make a new forecast.
I tried the above strategies to forecast the daily downtimes of the machine as a percentage of scheduled time in manufacturing. I used the cross-validation methods (as mentioned by Rob http://robjhyndman.com/hyndsight/tscvexample/ http://robjhyndman.com/hyndsight/rolling-forecasts/) and found that the rolling window with the new estimation of model order (p,d,q) and parameters every time a new forecast is a better forecasting model (MAPE for the second model is 3.34% compared to 10.25% for the first model)

I got confused on understanding why the model performs better with the method 2 or Am I doing something wrong? How do we explain this model in this manufacturing case? The breakdown pattern of the machine changes when repair work is done every time as breakdowns also depend on the "quality" of the repair work. So it is hard to fix model order p,d,q for the machine. Could this be a reason for the model 2 to perform better? any thoughts? Also, is it practical and advisable to estimate the model order everytime ?