One of the major areas of application of Predictive Analytics is in predicting and optimizing the time at which Maintenance of a machine should be performed. If done right, this could potentially save thousands of dollars by predicting the right time at which a high performing machine (machines which literally run round the clock for 365 days a year) could break if maintenance is not done since every working minute of such machines is worth a lot of dollars lost in revenue.
Many metrics could be used while doing such Predictive Modelling to identify exactly when do such kind of machines need maintenance. One such metric that we will be using today is Idle Capacity.
Idle Capacity is the amount/capacity of the machine is the capacity which is not being utilized. The more the idle capacity is for a machine, less is its utilization and worse it is. We will check if the Idle Capacity of a machine is over 10% for at least one 8-hour period, then it requires maintenance.
Idle Capacity = (Actual Tonnage - Nominal Capacity) / Nominal Capacity
We have data for 5 Machines. For every machine we are given the Actual tonnage and the nominal tonnage of the machine. The granularity of the data is at Hour level. For some machines, data for a few days are missing. But it won't make any difference since there is no effect of one machine on another.
Let's get started!
We will have to make a separate datetime file/sheet so that we can join the data of different machines based on datetime. If we joined all the sheets using the data of any one machine, there is a possibility that we lose some data if there are some missing values in the datetime column of the machine we are joining other sheets to.