It’s been a few weeks since Alex, the CMMS administrator, attended a three day training workshop. Since he came back, he’s been experimenting with various kinds of reports. Now he’s come to you, the maintenance manager, with one that definitely gets your attention.

The report includes a plot of equipment failure dates versus equipment PM dates. You would expect a normal distribution to consist of failures clustered close to impending PM dates with almost no failure occurring within a month of a PM date. Instead, there’s a big cluster of failures within a month of the PM date and it accounts for 30% of total failures. Alex plotted the curve of actual distribution then overlaid the curve of expected distribution. Rather than roughly coinciding, they look almost like mirror images of each other.

If this information gets out, the maintenance department will almost certainly undergo severe personnel cuts. Most likely including you. But if you fix this problem and get the curves to align properly, the resulting improvement to the plant’s profitability will be huge. And that will help your career, too. Where should you begin?

Answer to Quiz

You need to get a more complete picture so you can identify what to address first.

Have Alex generate these reports for equipment that has failed within 30 days of its last PM and graph them as Pareto charts:

• Type of equipment. For example, motors fall within the top five then you need to look at potential defects in motor maintenance.
• Failure mode. For example, if “bearing failure” is among the top five failure modes, you likely have a problem with motor lubrication.
• Who did each PM after which a failure occurred within 30 days. If 80% of the failures are due to three of the electrical maintenance techs, then you probably (but not necessarily) have a competence issue. The solution could range from training to termination. If no clear pattern emerges, you likely have defects in your procedures, test equipment, and/or general work practices.

Next, plot similar reports by number of incidences rather than as Pareto charts. This changes the view from looking at failures relative to each other to looking at absolute numbers of failures by category:

• Type of equipment. For each type, determine the percentage of the whole. If you have 100 motors and 10 failures, that’s a 10% failure rate for motors. Sort these from highest to lowest, left to right.
• Failure mode. In this case, you want the failure mode by type of equipment. Again, sort these from highest to lowest, left to right.
• Technician PM failure rate. This is simply the number of “failed within 30 days” PMs versus total PMs, on a per technician basis.

A pattern will emerge. And it will tell you where the weaknesses are. But it won’t tell you what the weaknesses are. This, however, is where you start.

As you proceed, remember to “consider, not conclude.” It could be that Dan has the highest technician PM failure rate because the equipment Dan does maintenance on has the hardest service and the maintenance interval is too short. By the time Dan does the PM, the failure is already well along the way to happening and the PM is not enough to stop it.

Or there could be other problems that cause these failures and the PM is simply not relevant. For example, Dan can change the motor grease but that won’t save bearings that have already begun to spall due to reasons that have nothing to do with Dan. For example, bonding deficiencies allow undesired current to flow through, and burn out, the bearings.

You are looking to determine causes, not assign blame. In this particular case, the bearings might be conducting undesirable current due to improper bonding.