Many FMs are often seeking benchmarking data to measure how their organization is performing. When asked about what they want to learn, they will often reply with a table or chart showing their building’s performance compared to those of others.
If it were only as easy as that! Usually, once an FM is provided a chart, the next question becomes something like, “Do you have that broken down for just: industry type older facilities in my city that operate two shifts maintained with union labor with around 1500 employees etc.
What is really happening here is that the FM is realizing that general numbers may be a good starting point, but to really make informed decisions a more detailed breakdown by criteria that affect operating costs is necessary. That is the only way one can compare the benchmarked facility to one that is best-in-class. And the most meaningful breakdown for one building is likely not the best for another building (we show that below).
We define that “breakdown” as using a set of filters. Each of the above items becomes a filter:
- Industry type
- Age of the facility
- City or region
- Hours of operation
- Union or non-union labor
- Number of employees
In actuality, there are nearly 40-50 potentially useful filters for looking at maintenance metrics, and more that are germane to other operating costs, such as utilities, janitorial, security, landscaping, etc.
For FMs there isn’t any single table that contains all the critical data needed for a detailed benchmarking analysis. For maintenance, there may be 30-40 critical dimensions that could be applied and the importance probably varies by the situation. For example, most FMs would probably expect higher costs at an older facility. But if you have recently replaced many of the key systems and are gaining the benefits from an extensive upgrade age would not be a significant factor. The conclusion, then, is that the filter set for any one building will likely be different from the one for any other building, so there is no standard table that can work for everyone.
Benchmarking of operating costs is popular among FMs, as these are what the FM can measure and control. More than 95 percent of operational expenses are incurred by:
- Utilities
- Maintenance
- Janitorial
- Security
Defining the Correct Basis of Comparison: An Example
Let’s look at how the FM can benchmark maintenance costs, given the above conclusions. We will illustrate an example for maintenance using tools provided courtesy of FM BENCHMARKING, the online benchmarking service. For this example, we will benchmark a 827,000 gross square feet (GSF) office building that is 35 years old, operating 18 hours per day. Some of the input fields are shown in Figure 1 below.
The first filter we will turn on is the size of the facility so that we will only consider buildings that are 600,000 GSF or greater and a ‘campus setting.’ In the FM BENCHMARKING system this produces 448 facilities for comparison. In Figure 2, our consumption is shown by the yellow bar with $2.50 per GSF which indicates a performance just above the median value at $2.45 per GSF.
What other factors might impact our maintenance expenses? Since our building is a manufacturing facility, let’s look only at other manufacturing facilities. We’ll apply the filter for hours of operation to see how we will compare with this peer group. As shown in Figure 3, the median cost increases to $2.60 per square foot, so even though our cost, of course, remains the same at $2.45 per GSH, we now have appeared to move to the left and are in the second quartile. Comparing our facility to other manufacturing facilities is valid comparison that would make sense to senior management when you are presenting your performance results.
One might expect that a manufacturing facility that is open 19 hours a day may have higher maintenance expenses than one that is open for less time. We will apply that filter in Figure 4. When we run the analysis as shown in Figure 4, our relative performance looks even better. We are now in the mid-range of the second quartile.
This seems like a reasonable peer group for comparison purposes and there is quite a difference in the relative ranking. By careful application of filters, which is a reflection of our true peer group, our facility has moved from above the median with a ranking of 57% to below the median in the middle of the second quartile with a ranking of 37%, even though our actual cost per square foot hasn’t changed. But by filtering out the better-performing buildings that likely aren’t true comparisons to our building, we have a more accurate basis of comparison.
Of course, when one applies more filters, there will be less buildings in the comparison set, so one must be careful to use trial and error to find the “idealized filter set” for your specific building and the metric being studied (yes there will likely be a different filter set if one were looking at utility costs).
Applying the Benchmarking to Get Improved Performance: An Example
Many FMs don’t go any further with the benchmarking process. But nothing we’ve done so far will help you improve your building’s performance. All we’ve done is find out how we’re doing compared to our peer group (filter set). So let’s consider what could be done to improve our performance—isn’t that the purpose of this exercise? To do this, we will look at the best practices that have been implemented by our peer group.
FM BENCHMARKING provides a very useful tool to integrate best practices responses with the quartile results. As shown in Figure 5, we will compare our building’s best practices to those implemented by other buildings in our quartile and then by those in the next better-performing quartile.
Shown in Figure 5 are just a few of the best practices for maintenance. Where our facility has answered NO and there is a high percentage for our quartile and the next better performing quartile, we should consider implementing the practice. For example, we answered NO for equipment standards for replacement components. Yet 50% of the participants in our quartile and 83% of the next better performing quartile have implemented this best practice. So this may be something we should look at.
These examples are meant to show how you can use benchmarking with filters to narrow your benchmarked comparisons to a valid peer group and then see which best practices others have implemented. By implementing those best practices, your performance should also improve over time.
