Maintenance, as a function, is an area in the FDA-ruled manufacturing world in which the task of measuring performance at all, let alone Key Performance Indicators, has too often been misunderstood or ignored. In the evolving world of Reliability and Predictive Maintenance in the FDA-regulated environment, performance indicators have become crucial tools to bringing maintenance organizations in line with the rest of the GMP statistics-dominated organization. Today, it is crucial that an organization knows what its maintenance organization is doing, how well it is doing, and how much it’s costing the company.
There are many reasons why using Performance Indicators (PIs) are important to quantifying your maintenance program. For instance, how much progress you’ve made toward achieving specific objectives in your department or for a particular operation. By observing the rate of progress as indicated by your PIs, you can spot trends and problems with your equipment or processes. PIs can help you determine whether or not changes you have made are helping resolve the issues you’re tracking..
Objectives Need Targets
The implication here is that your objectives need targets. Those targets include acceptable and unacceptable levels of performance. The Key Performance Indicators (KPIs) measure the critical success factors that define a desired outcome. PI numbers, if they are applied correctly, tell the story as it unfolds.
Decreasing or eliminating the risk of equipment or system failure is typically one of our key objectives. Maintenance-related PIs indicate the progress in meeting that objective. One of these standard PIs is Mean Time Between Failures or MTBF. We define failure as an unplanned stoppage for any reason. You arrive at MTBF by logging details for each unplanned downtime incident. You can then calculate the amount of time Between Failures or uptime. You then calculate the mean or average, by dividing the total amount of uptime (time between failures) by the number of unplanned failures. Typically, we calculate the MTBF for any piece of plant equipment during a set time period, usually a year.
The MTBF is nothing more than a single number–hours, days, weeks, or months–that helps you anticipate the typical time period between unplanned stoppages for that piece of equipment. By graphing the actual downtime events (which you logged in your CMMS or elsewhere) you can easily generate a physical representation of failures over time. Why would this be of use?
- You might find that your unplanned stoppage on a particular machine always occurs around the same time of year. Is there a temperature change in your plant?
- Where do the stoppages fall in relation to your planned maintenance? If they are almost always a week before your planned maintenance, maybe you need to shorten the maintenance cycle for that equipment and add an additional maintenance operation each year.
- Does the stoppage always occurs during a particular shift, when the equipment is being operated by a particular individual? Could be a training issue.
Plenty More Techniques and Theories Where That Came From
Just from this simple example, you can probably see how useful these types of performance indicators and key performance indicators can be, when used together. For one thing, you and your maintenance staff will become much more familiar with how the system of equipment works together, and fails together. It’s not unusual for an engineer or technician to become intimately familiar with the quirks of a specific piece of equipment over time.
PIs and KPIs, tracked and graphed, can give these same team members a broader view of how pieces of equipment interact and impact one another as a system. It’s not just corporate mumbo-jumbo to try to achieve some synergy in maintenance operations. If you can determine the likelihood of two pieces of equipment needing a pump overhaul, despite performing completely different functions, you might schedule the same expert technician to overhaul both pumps during the same planned maintenance.
There are many concepts, techniques, formulas, and theories that can help us do a better job of anticipating and resolving problems. Some of these include predictive maintenance, preventive maintenance, reliability-centered maintenance, value-driven maintenance, and condition-based maintenance, just to name a few. We’ll take a look at certain aspects of these concepts, many of which overlap, but all of which aim to provide a way to improve quality and decrease downtime.
Take a look at our recent post on Calculating MTBF. We’ll take a look at some other useful measures and performance indicators over the next few weeks.