Coal Terminal Maintenance Analysis

As a part of the Udemy Advanced Tableau course, I found the exercise described in the storyboard below really interesting. The course provided thorough explanation of the real-world example, and really exemplified how we can use data to solve problems that don't even exist yet.

https://public.tableau.com/profile/samuel.l.peoples#!/vizhome/CoalTerminalMaintenance_2/CoalTerminalMaintenanceAnalysis

The scenario is placed at a Coal Terminal in Australia, which has a series of machines processing the coal before it is loaded onto container ships. Since the ships are kept on a tight schedule, it's pretty noticeable why machine downtime would be a costly issue! Here, we're looking at only Stacker-Reclaimers (SR) and Reclaimers (RL).

The challenge was as follows:

You have been hired by a Coal Terminal to assess which of their Coal Reclaimer machine require maintenance in the upcoming month.

These machines run literally round the clock 24/7 for 365 days a year. Every minute of downtime equates to millions of dollars lost revenue, that is why it is crucial to identify exactly when these machines require maintenance (neither less or more frequently is acceptable).

Currently the Coal Terminal follows to following criterion: a reclaimer-type machine requires maintenance when within the previous month there was at least one 8-hour period when the average idle capacity was greater than ten percent. 

Your task is to find out which of the five machines have exceeded this level and create a report for the stakeholders with your recommendations.

So first, we needed to create a few table calculations for the idle capacity, and adjust the parameters to visualize the performance of the five machines for the past month in eight hour buckets. A red 10% threshold line was placed on each graph, clearly letting the viewer know whether the machines exceeded the idle capacity threshold. An orange trend line was also used to indicate whether the performance was constant, better, or worse, throughout the month.

Next each machine was individually analyzed, but first let's make a note of a few things. SR6 and RL2 seem to be in the same path, and it can be seen that SR6 exceeds the idle capacity threshold for quite a long period. This is a false positive because RL2 is operating at 100% of it's capacity during the same period, through having an idle capacity of zero. Another period to note is the missing data for SR1 and SR4A. This is accounted for because it can be assumed that the machines were stacking during this period, and thus would note be relevant to our analysis. This would however be something to verify with the client, as it is definitely has the potential to significantly skew the findings for the two machines.

So in conclusion, we can confidently say that RL1 should be flagged for maintenance, as it has exceeded the 10% idle capacity threshold four times, and has an increasing trend of under utilization.

SR4A should also be flagged for maintenance, where although it has not exceeded the 10% threshold, the high trend of under utilization will be more costly over time, and it will quickly exceed the idle capacity limit.

RL2, SR1 and SR6 are performing within standards, and should continue to be monitored for early signs the necessity for maintenance.