How to model manufacturing shift data with irregular production times? Problem Setting:

Let's say there are three shifts a day in a manufacturing plant. The plant suffers from irregular power supply thereby affecting in how it is functioning in time and the shift's production. Therefore, you have the manufacturing process going on for a while and then pauses due to power outage(or lunch break) to be resumed when the power is back on. This on/off pattern of power is pretty much inconsistent and unpredictable.

Objective:

By mining data of the past history, I want to come up with a model that given a point in a day of a shift I want it to be able to forecast the production for the rest of the shift(possibly accounting for the likelihood of experiencing power outages).

I was hoping for some perspective and idea in how to go about making this problem a machine learning problem and some advise in picking techniques amenable to the problem.
From the information at hand, you could break this down into two problems - 


Predicting the production for the shift, and 
Finding the probability of breakdown during the shift


For (1) you could either go down the time-series route (ARIMA, Box-Jenkins, Exponential smoothening) or the regression route (provided you have good features)

For (2) you could build a logistic regression model that gives you a probability score

Possible features you could look into are 


Time of day, week, month and year
Time between breakdowns
Available labor
Electricity Board data?