I have implemented Maximo for many clients over the last 30 years. The ability of Maximo to calculate Mean Time to Repair (MTTR) and Mean Time Between Fixing has been available in the product for over 20 years. Despite this, very few Maximo users I know utilise these calculations.
Recently, during the worst oil prices in 2 years, I helped a large Canadian oil (tar sands) producer implement Maximo. During the project, oil price fell to within $2 of the lifting cost for the production facility. I expected the project to be cancelled along with a number of other projects oil operators cancelled in those troubled times. The project, however, was not cancelled and after a year of operation of its new Maximo system the facility has doubled its production output and reduced its lifting costs by $15 per barrel. I would love to tell you this was all down to Maximo, which it wasn’t, but a huge amount of it was.
The operator argued that unlike conventional oil production, tar sands was a more costly process but with any process it can be made to be as cost efficient as possible. They actually argued that they could improve their maintenance processes to take less time and money, and keep the production equipment operating efficiently. Whilst their competitors floundered and ran their process at losses (to the point where 2 operators have vested their tar sands assets – no prizes for who bought them), this operator produced oil at good healthy profits throughout the oil price slump.
How did they do this? It was very simple. They used their maintenance tool to do the maintenance, and they adopted some of these simple principles:
- Schedule your work to be done
- Carry out your work when it is scheduled
- Monitor your schedule compliance and try to be above 80% compliant each month
- Improve repair times by removing delays in the Supply Chain
It’s nice when you have been working in an industry sector for 20 years and you see it done really well. However, it got me thinking, “why doesn’t everybody do it?” Why don’t they just track how long it takes them to fix things and then try to improve it? I ran some stats on various data sets I had available and the data is compelling.
It always takes longer on average to do a maintenance task if it requires a material, a service, or a spare part. If you improve the time it takes to procure them, you see the average fix for work also fall. Whilst looking at the data, I also noticed that within a similar industry sector the general average for fix times (regardless of whether it included procured items) was vastly different. Why? I could understand why if we were comparing a pharmaceutical maintenance regime with say a power generation maintenance regime, but in the same industry sector? How different can the maintenance be?
In general, maintenance is the same everywhere. A mechanical technician can move between companies relatively painlessly – what changes for them is business process. It is exactly the same with Mean Time to Repair. You can affect the time it takes you to repair everything by applying different business processes. As it was for the operator in the Canadian tar sands, they switched from a firefighting its broken, fix it fast! approach to a preventive don’t let it break, but if it does fix it fast! approach.
The choice to maintain is our own. My house could do with a number of improvements but I can always find something better to do with my time than sort out the poor quality laminate floor boarding! But my house is pretty small beer in terms of the maintenance world. In the maintenance world, doing this stuff can save you millions of dollars.
The hard part with maintenance is knowing whether or not the task I carried out was worthwhile. How do I show the fruits of my labour? Apart from getting a well done tick for fixing the floor, how do I know that it was more beneficial to me than watching another episode of Narcos on Netflix? The answer is with the same statistics that I used to find out MTTR stats. Over time, the process change can be reviewed to see a fall or rise in the average time to repair something. Ideally, we are looking for falls rather than rises but the changes we introduce can be reviewed periodically after the change and the trend should generally be downwards.
How far should I continue trying to improve the repair time? This world is a law of diminishing returns. The number can never go down to zero but it can easily go down. Eventually, somebody in your organisation may ask a much more important question, “can’t you just stop it from breaking in the first place?” Mean time between failures is a much more interesting proposition but for now let’s start with the repairs. Everybody’s data support repairs.