I was doing a little cleaning around the yard yesterday and ran into this little “creature”. I’m titling this photo, Scorpion amongst the Detritus.
I was doing a little cleaning around the yard yesterday and ran into this little “creature”. I’m titling this photo, Scorpion amongst the Detritus.
It’s funny, but certain approaches and phrases seem to suddenly gather unexpected currency, and return on investment (ROI) is one that I have been hearing all over the place recently. For example, a senior manager recently asked me for the ROI on an energy conservation project, and one of my excellent consultants recently provided a payback and ROI summary for an upcoming project.
It was not clear to me, however, how people were talking about ROI in the context of energy conservation projects. To see why, let’s start with the fundamental question: What is Return on Investment?
A performance measure used to evaluate the efficiency of an investment or to compare the efficiency of a number of different investments. ROI measures the amount of return on an investment relative to the investment’s cost. To calculate ROI, the benefit (or return) of an investment is divided by the cost of the investment, and the result is expressed as a percentage or a ratio.
The return on investment formula:
In the above formula, “Gain from Investment” refers to the proceeds obtained from the sale of the investment of interest. Because ROI is measured as a percentage, it can be easily compared with returns from other investments, allowing one to measure a variety of types of investments against one another.
First, let’s contemplate a simple example of this. Say you buy a house for $100,000 and
An energy conservation project is not like this. While there is an initial capital cost that we can identify, the energy project has the following unique characteristics that we really need to capture:
Let’s take a look at a proposed energy project to see where the ROI approach might take us under differing scenarios.
A recent project delivered the following economics:
Net Capital Cost $215,047
Net Annual Energy and Operational Savings $ 63,439
Project Simple Payback (years) 3.4 [ $215,047 / $63,439 ]
Anticipated Useful Life (years) 10
The consultant then calculated the ROI by dividing the annual saving by the capital cost:
ROI [ $63,439 / $215,047 ] 29%
However, we see that this is in actuality nothing but the inverse of the simple payback. The “Gain from the Investment” is actually the net present value of that discounted annual saving over the ten years of the measure life. This account for both the recurrence of savings on an annual basis, and the fact that the energy project (probably) has a finite useful life that is more or less known.
Here’s a refresher on NPV and discounted cash flows for those who want it:
If we assume a discount rate of 5%, our 10 year NPV factor is 7.27 and the net value of the accrued savings is:
7.27 x $63,439 = $461,202
Not bad. And our ROI is subsequent found to be:
($461,202 – 215,047) / $ 215,047 = 114%
Now, 114% return is going to cause some eyeballs to pop, but if we think this through, it’s clear why an ROI should be very high for any energy project with an acceptable simple payback. Unlike almost any other investment, an energy project doesn’t just deliver a return, it is expected to actually pay for the original capital outlay in a relatively short period of time relative to it’s useful life. This is significantly different than the kind of ROI one expects with a transaction involving solid assets that possess intrinsic value like a house or a factory.
Consider our house example. To compare with our energy project, we would need to buy a house for $100,000 and expect it to sell after ten years for $214,000 to deliver an equivalent ROI. At least, this is the case if we subscribe to the notion of net present value of savings. And while energy projects may be stipulated with a “three year simple payback” constraint, who makes an investment on a house by limiting the selection to houses that can deliver a three year simple payback on investment? No one.
This brings up a perverse aspect of energy project funding. Few organizations or individuals expect traditional investments to completely recover the initial capital cost and provide additional returns on top of it, but the standard “simple payback” limitation that is put on energy projects puts them in exactly this disadvantaged position when in competition for investment capital. When one looks at life cycle costs, energy projects are actually extremely competitive.
We’ve gone a bit sideways, but the point is, when someone carelessly says they’d like to see the ROI on an energy project, ask them to clarify: Do you want the ROI based on the life cycle NPV cost savings of the project? Because that will paint a totally different and more attractive financial picture than simple payback.
Last week I gave a talk on energy benchmarking, and this caused me to collect some data that demonstrates why industry standard practices can fall short for organizations attempting to quantify facility energy performance. Let’s explore this a little bit.
The data I uncovered identified the Energy Utilization Index (EUI) for a cohort of inpatient hospitals in Boston. With the aid of EUI, the organization hoped to identify best practices, best and worst performers, etc.
The EUI reports the annual energy use per square foot for a given building or facility. Below is the EUI summary that I was able to retrieve (performance is color coded from best [light yellow] to worst [orange].) Lower numbers are obviously better from an energy use standpoint.
From this data, senior managers might draw some or all of the following conclusions:
And surely these seem like logical conclusions. In fact, I have seen billion dollar companies make important decisions based upon data like these.
However, it is important to keep in mind that square footage and patient care delivery are not synonymous. In fact, the amount of health care delivered by a hospital is going to be much more closely associated with patient care proxies such as staffed beds, total discharges, and other measures. It is these activities that consume staff and resources, including energy. If we explore these other proxies and employ them as benchmarks, do the performance findings persist?
Fortunately, the American Hospital Directory provides quite a few metrics pertaining to the amount of health care delivered by a hospital. For the six hospitals in our survey, these include number of staffed beds, total patient discharges, annual patient days, and total patient care revenue.
The expanded table below shows these values for each hospital on the left hand side, shaded in light tan. On the right side of the table, I have created a new energy benchmark for each proxy. Specifically, in addition to kBtu/SF/Yr, we also look at kBtu/Staffed_Bed/Yr, kBtu/Discharge/Yr and so on. I believe you can click on this graphic if you want to blow it up to examine. Back arrow on your browser to return.
It immediately catches the eye that the colors associated with relative performance are not consistent across rows. In fact, if you look at the kBtu/SF/Yr metrics and the kBty/Gross_Revenue/Yr metrics, the two best & worst hospitals literally reverse rankings! Now, what does this mean?
First of all, it is painfully obvious that square footage and gross revenues are not “compatible” benchmarking indices in this instance. The sort of inconsistency we see here proves that the two benchmarks are not measuring the same thing.
Our next question then needs to be, from an energy performance perspective, which benchmark (if any) more accurately identifies desirable behaviors?
An energy engineer will almost automatically turn to the EUI benchmark as the standard for a handful of reasons:
However, if we think like a CFO, we see that the EUI is missing something essential as a benchmark. Let’s see why this might be.
Fundamentally, the viability of a hospital is tied directly to it’s cost of operations and it’s income, not it’s square feet. Therefore, it is desirable to the CFO from an operational level to minimize the expenses required to generate a unit of revenue.
From this vantage point, the CFO is going to view a hospital that requires 140 kBtu of energy per annual dollar of revenue as more desirable than a hospital that requires 200 kBtu of energy per annual dollar of revenue, even if the kBtu per square foot is lower.
It becomes glaring obvious that this is because the EUI fails to take into account the variable density of activity that takes place in each facility. What also becomes obvious is that a CFO should have a very particular and well defined idea of what a benchmark is:
A Benchmark quantifies the ratio of some relevant input to some commercial output in order to allow normalized comparisons between peers. Where “relevant input” is specifically a cost driver such as energy use, FTEs, supplies, rents, tax rates, and where “commercial output” is the goods, services or other output that the organization delivers and depends upon for profitability.
Beyond this, take a look back at the extended table. Hospital B has the worst EUI ranking at 321 kBtu/SF/Year but the best “Energy User per Unit Revenue” (EUUR) ranking at 140 kBtu/$GR/Yr. Conversely, Hospital G had the best EUI ranking at 228 kBtu/SF/Yr and the worst EUUR ranking at 246 kBtu/$GR/Yr. Now consider:
Hospital B is already using 43% less energy per unit of revenue than hospital G, so it is clearly more profitable and efficient from an energy use standpoint. Yet if the EUI prevails as the metric of choice, Hospital B will be put under pressure to reduce it’s EUUR even more in order to match the EUI benchmark of a peer such as Hospital G. This is not only inequitable, it misdirects managerial attention to a well performing facility, and it gives a pass to institutions that may be using far more energy per unit of delivered services.
This is a very rough first draft, but I thought I’d spend a few minutes at least planting the flag for benchmarks that go beyond the often used (misused?) EUI.
We all need a break, right?
So some time ago, my friend Jeff turned me onto the pleasure of home made salsa. The freshness simply overwhelms store bought brands, and it’s easy to make. How easy? Let’s take a look.
First, ingredients. I’m not a big fan of measuring things, but this is more or less what you need:
A few (five?) pounds of good tomatoes. Take a look below for a rough idea.
Habanero or other hot pepper
Jalepaneo or other medium hot pepper
1 Tsp Salt
2 Tsp Suger
Few ounces of Beer
Juice of One Lime
So away we go. Here’s a picture of my whole kit. Let’s start by mincing up a bunch of cilantro using a mincing knife (before and after pictures below.) A mincing knife is incredibly handy, especially when it comes to dicing up the peppers later.
Okay, next the habanero. You need to be a little careful with these for two reasons. First, some people find them to be too hot, so you don’t want to inadvertently add too much. Second, the hotness of these peppers seems to vary widely, so you need to taste a sliver before you start to see what you’re working with. For my family, I usually use about 1/4 of a minced pepper. For just myself I would use more. It needs to be cut into very small pieces, almost like a paste, so that it spreads smoothly throughout the salsa and doesn’t leave little “hot bombs” for the unsuspecting. Here the pepper has been sliced but not yet minced:
Jalepeno or long hot or whatever else you use follows the same rule:
After dicing it looks like this, almost paste:
Okay, now let’s cut up a red onion:
At this point, you can throw the cilantro, the onion, the peppers, the juice of one lime, a few ounces of beer, a teaspoon of salt and a couple of teaspoons of sugar into a bowl to let the flavors start mixing:
Ahh, that’s good stuff. Now, unlike my friend Jeff who is a really good cook who hand prepares everything, I’ll use a food processor to chop up my tomatoes for two reasons. One, I’m lazy. And two, it makes it quick to make a large pot of Salsa that will last the week. I confess Jeff’s Salsa always seems to taste better than mine, but even my “mass produced” stuff is head and shoulders above the supermarket jars surrounding the chip aisle.
I quarter the tomatoes and cut out the top stem part, then throw them in the processor. You just need to pulse for a second or two to get great texture:
Simply add to your other ingredients as you process:
At the end, the whole thing is mixed thoroughly until you get this awesome looking (and tasting) final product:
At this point, I’ll taste the salsa and make judgements. Sometimes a little more salt is needed. Sometimes a little more sugar. Sometimes you may want a little more heat via additional hot peppers. It all depends on many factors that weigh into the particular batch.
At this point, cover and put in fridge for a few hours. The flavors will begin to really blend, delivering a fantastic fresh salsa that tastes nothing like the stuff you get in jars or most restaurants. Enjoy!
One more thing. Heirloom Tomatoes come in an array of radical colors (purple, yellow, green!) and make for a colorful as well as delicious final product. My past few batches have been 100% Heirloom.
UPDATE: I made a batch of Salsa yesterday using Heirloom tomatoes. The picture below is not great, but it at least shows the color variations that can happen. This looks almost like a salsa verde. I went heavy on the yellow Habanero on this one – Excellent!
I haven’t posted in a long time, so I thought we should start by having some summer fun.
I am tied up doing my budget for FY 2016, but had to take a moment to post this link from DOE that discusses energy baselining and tracking. AT LAST, a guide from the Feds that goes beyond the per-square-foot method of trying to assess performance. This should really be a motivator for people to rethink how they measure and monitor energy utilization. At least people who think instead of just doing what they are told. And indeed, this could actually be a useful approach for organizations that want to effectively and (pretty) accurately assess and manage energy use. Worth a perusal if nothing else. The link is here:
If there’s any justice in the world, this will be the first nail in the coffin of EPA’s Portfolio Manager…
There is a column in today’s New York Times that is absolutely loaded with fantastic links pertaining to climate science. It’s written by Andrew Revkin, so the column itself is also packed with interesting and useful information as one would expect. Highly recommended and worth investigating.
There is an interesting article on the Atlantic Monthly site about Artificial Intelligence (AI) investigator Douglass Hofstadter. The article itself is located here and is well worth a read:
This column is relevant here for a couple of reasons.
To me, one major takeaway of the column is that Hofstadter’s research is not only qualitatively distinct from what I will call “industrial AI” conceptually, it is intellectually superior. By which I particularly mean, it is (or will be) vastly more relevant in helping humankind understand ourselves and our world – even though it is quantitatively less productive than industrial AI – if his research is successful and understood.
However, industrial AI (let’s abbreviate it IAI) is more effective at delivering results that can be monetized than anything Hofstadter has done. And thus, a brute force computing monster like Google can do amazing things, and make lots of money, without really addressing deep questions about learning and thinking that Hofstadter focuses on.
I think that this column indirectly says something in general about questions that are relevant to energy and energy conservation, though I may be making a stretch. But let me take a shot.
Hofstadter is ultimately interested in figuring out how human beings think (hint, he thinks we are absolutely amazingly evolved analogy machines) whereas IAI is principally concerned with delivering accurate and reliable results. Which, I would add, is a non-trivial and intellectually interesting field of research. Nonetheless, Hofstadter is pursuing something more akin to fundamental natural philosophy that could lead to astounding discoveries about how and why we think. The results-oriented application of his findings would then be layered on top of that conceptual foundation.
Let’s get a little more specific.
I can input an English sentence into Google Translate and it will convert that sentence into serviceable Spanish or French or Chinese or any of a number of foreign languages. But Google Translate does not know what it is translating, nor does it even know that it is translating. It is a dumb system that provides pretty smart results.
Hofstadter is looking for something different. An AI system that would, somehow, “understand” that it was translating, and that would rely upon associations instead of brute force to come up with a workable solution.
Just so. But the relation to energy?
As I think I have written elsewhere, there seems to be a growing trend to treat energy conservation – via energy policy – sort of like IAI. Namely, if you throw enough resources and regulations and credentialed “experts” at it, you will get the results that you desire.
And thus, the policy maker might (and probably would) conclude that if $100,000 on energy conservation saves one million kilowatt-hours, then $200,000 will save you two million.
Alas, this is not automatically true. But why it is not true requires a Hofstadterian investigation into where energy is actually being used, why it is being used, and whether it can be judiciously reduced. After that, maybe you can or maybe you can’t deliver two million kilowatt-hours of electrical energy savings. But most essentially, you will know why you can or why you cannot. It is a deeper and intellectually more profound understanding of the situation.
This is, of course, partially an argument for more control of the framing of technical issues by competent experts, which I have broached elsewhere.
I think, though, that we can broaden this. The sidelining of Hofstadter (he is not in the mainstream of current AI research) parallels the sidelining of deep competence in many fields of endeavor in the United States today. Respect for deep competence has been supplanted with respect for, or deference to, gaudy credentials and smartly packaged deliverables. And so we have semi-competent consultants and policy makers issuing poorly informed advice that is acted upon to the detriment of the enterprise.
This is not a good trend, and it is a difficult one to reverse. Hofstadter at least shows that the qualitatively superior path can be taken. This path will not necessarily deliver great immediate profit, but the potential long-term benefits could be enormous. A smart enterprise, then, should treat consultants with a wary eye, as one option that does not preclude other approaches or ideas.