a processing unit is shut down due to a machinery, equipment, or processing issue. Unplanned production losses are especially costly. A great deal of effort is expended to ensure production losses are minimized. Production losses outages are typically reported in, 1) hours or days of downtime, 2) pound of production lost, 3) barrels of production lost, and 4) dollars of production lost, etc. If a production unit has no “make-up” capacity, then the production lost during an outage is lost forever.
Figure 2.8 A trend of machinery outage cost will tell you the magnitude of the production losses over a period of time and if the losses are increasing, decreasing, or staying constant.
Here are a few types of plots that can be used to track process outages:
1 Production losses related to machinery outages (see Figure 2.8)
2 Breakdown of unit outages (see Figure 2.9)
3 Outage downtimes causes (machinery, exchangers, controls, etc.) (see Figure 2.10)
4 Root causes of outages (see Figure 2.11)
Process Outage Related to Machinery Outages
Pareto of Production Losses Across a Site
Figure 2.9 A Pareto of production losses across a site. This Pareto indicates that most (89.4%) of production losses are associated with four process units. The next step would be to drill down into this data to determine the root causes of these outages and address them.
Pareto downtimes causes (machinery, exchangers, controls, etc.)
A Pareto made up of RCFA causes can assist your organization in determining:
1 The best types of training that might benefit your organization.
2 How to best use your predictive maintenance resources.
3 Where design upgrades could be justified.
Note that to generate this type of Pareto chart your organization will need to perform RCFAs to determine the root cause of failures. RCFAs are essential for organizations that wish to have operational reliability.
Figure 2.10 Pareto of causes of production losses over the last 12 months. This Pareto summarizes the reasons for unit outages. At a glance, management can see what is causing production losses. In this hypothetical example, we see that 68.7% of the process losses were caused by operational factors and heat exchanger issues. One conclusion you could draw from these results is that more operator training may be warranted to reduce process losses. Another recommendation that could stem from this Pareto is to review the present heat exchanger inspection program to see if improvements could be made.
Planned Maintenance Percentage (PMP)
Planned maintenance percentage (PMP), which is a reliability metric for both spared and critical machines, is calculated by dividing the total number of planned maintenance hours in a given period by the total number of hours spent on all maintenance in the same period. This number is multiplied by 100 to give the final percentage. The formula for the planned maintenance percentage is as follows:
For example, let’s say the maintenance team of a chemical plant performs 500 hours of maintenance in a month. Of those 500 hours, 400 of them are planned maintenance hours. This makes our equation:
Figure 2.11 Pareto of machinery RCFA findings related to process outages. This chart points out the primary reasons for machinery-related process outages and can assist in optimizing your repair and PM strategies. In this hypothetical example, we see “End-of-Life” and “Design Issue” are the top reasons for machinery failure associated with unit outages. The “End-of-Life” results could suggest that you are either, 1) not identifying machines that are failing early enough to allow outages to be scheduled, or 2) you don’t have the right inspection intervals identified for our critical equipment. “Design Issues” failures should be analyzed to see if economics warrant design upgrades.
Ideally, at least 90% of planned maintenance time is necessary for optimal productivity. You can use the findings of Planned Maintenance Percentage to strengthen planned maintenance and reduce the likelihood of unexpected failures. There are numerous reasons to want to improve your facility’s planned maintenance percentage. Some of these reasons might include wanting to:
Reduce the amount of unplanned downtime at your site by optimizing equipment PMs.
Lower maintenance costs by reducing overtime pay and expediting costs.
Have greater control of facility budget due to planned costs as opposed to reactive costs.
Reliability Analysis Capabilities of your CMMS Software
Most maintenance organizations rely on some types of CMMS (Computerized maintenance management system) software to assist them in storing and analyzing the massive amounts normally generated by sites with sizable equipment populations. When selecting your CMMS software, it is important to clearly define the reliability metrics and reports you wish to create going forward. The software can only create the reports with data that is going to be available in the maintenance database. For example, if you expect to create Pareto graphs of equipment failure modes, the failure modes will need to be predetermined and the capability of entering failure modes needs to be available within your CMMS software. Since it will be costly to incorporate these database design changes after your software purchase, I recommend that these details are finalized early in your software selection and launch.
The right CMMS software allows you to determine:
Are equipment failure rates getting better or worse?
How well is your current preventive maintenance program working?
Which machines are failing the most?
What are the most common failure modes?
A major advantage of using an advance CMMS database is having the ability to drill down into the failure data. Take for example, the Pareto of the Machine Outage Causes. If you wanted to know which area experienced the most lubrication failures, you would drill down into the lubrication failure data to see if the failures