Many FMs participate in benchmarking programs to see where they stand in comparison to their peers. To obtain good benchmarking results you need comparisons of similar buildings using filters to select the characteristics of the facility you want to compare.
But there is more to it than simply coming up with the right comparables, seeing how your building performs, and then stopping. After all, the object is to improve. And to improve, there are two more critical components:
- Get a better understanding of which comparables are the “right” ones.
- Determine what it is you can do to improve your building’s performance.
The former involves a heuristic process by which you create scenarios by turning various filters on and off, and seeing which have the most impact. In the second, once we’ve identified the most appropriate filters for our building, we see the impact that various best practices have on improving our building’s performance.
Let’s look at a few examples from FM BENCHMARKING to see the value that filters can bring to the analysis. In this example, our facility is a 1,125,000 gross square foot (GSF) facility, 22 years old, with 2,650 FTEs (full time equivalents) with a primary business function of aerospace products.
The first filter we will turn on is the size of the facility so that we will consider only buildings that are 600,000 GSF or greater. In the FM BENCHMARKING system this produces 449 facilities for comparison. In Chart I our consumption is shown by the yellow bar with 35.6 KWH per GSF, indicating performance near the middle of the third quartile.
However, using just the size filter isn’t a good comparison of our building performance. Electrical usage should be impacted by the number of hours of operation. When we turn on the filters:
- Gross square area: 600,000 and greater Square Feet
- Hours of operation per day: 13 – 18 Hours
our electric consumption compared with this peer group moves closer to the median as shown in Chart II. While the move to the left may not look significant, in reality it is. What it means is that because we are comparing our building performance to only those who have similar hours as we do, we lose all those whose buildings are open only 12 or less hours a day (and presumably consume less electricity). So our relative performance has improved.
Since our facility is 22 years old, built at a time when insulation and R values were not as important then as they are today, we suspect that our building’s relative performance may again improve. So let’s add a filter for age:
- Gross square area: 600,000 and greater Square Feet
- Hours of operation per day: 13 – 18 Hours
- Age: 21-50 years
our electric consumption now appears in the second quartile, performing better than half the companies in the filterset, as shown in Chart III.
As you can see, these are significant changes in our ranking position that are made simply by better peer group comparisons from applying filters.
Table I below summarizes the effect of adding filters to our output report, as done in the three Charts above:
There is quite a difference in the relative rankings. Our facility moved from the bottom 70% to a ranking of 48% just by thoughtful application of filters so that our building was compared with true peers. Of course, our building is still consuming the same amount of electricity, but now we see that while initially, our management may say we are doing quite poorly, we now can explain that by taking into account our building age and hours of operation, we actually are in the top half of all buildings.
Now, let’s consider what we can do to improve our real performance (i.e., reduce our electrical consumption). FM BENCHMARKING provides a very useful tool to integrate best practices responses with the quartile results. In Table II below, we had answered ‘No’ for selected best practices; i.e., we have not implemented those best practices (there actually are over 50 best practices in the utilities area):
This very valuable feature shows the percent responding Yes in our performance quartile, and also in the next better performing quartile. This makes it very easy to see what actions we might take to improve our performance. For example, everyone (100%) in the next better performing quartile indicated they had installed high efficiency filters, while only 60% on our quartile had done it, and we hadn’t done it at all. This may be an item we should consider carefully for our facility.
In conclusion you can see how the application of filters can impact your relative rankings in a very significant way. This allows for comparisons to a more similar peer group and is a much more effective way of using benchmarking tools. After we have established our peer group we are able to identify the Best Practices that would further improve our performance.
Benchmarking is indeed an ongoing process and cannot be looked at in a vacuum. Without properly applying the filters, there will be too many variables to gain any further insight. But once the correct filters are applied, one then can apply information to integrate best practices into the model, and at that point, one can identify ways to truly improve building performance.