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What’s ‘shrink,’ you ask? Microwave me a S’more, and I’ll explain.

By Taylor Hudson

We like data and believe there is never enough of it. (Read more on the power of data here.) Most of the time we’re talking about price data, but in liquid refined fuels we are also talking about volume and temperature data.

To understand why, you need to use your imagination. Have you ever made S’mores by putting your graham cracker, chocolate and marshmallow in the microwave? (Before sending hate mail, understand we are not against the traditional S’mores over a campfire; we are just skilled at adapting when the weather doesn’t cooperate.) If so, you will understand the field of liquid refined product molecular expansion and contraction (congratulations). You may have heard it by a more common shorthand – “shrink”.

When the marshmallow gets hot in the microwave it quickly begins to look like a softball, right? The molecules get all hot and sweaty (temperature) and expand, growing in size (volume). And when you stop the microwave the marshmallow shrinks in size as it cools down. Now imagine a molecule of diesel fuel, heating oil, gasoline, or other liquid refined product. Guess what? When they get hot, they expand too, spinning the meter more. And when they get cold they “shrink,” get smaller in volume, and spin the meter less.

You heard it here first: One of the most important concepts related to liquid refined product pricing is a variation on S’mores in the microwave. “Shrink” or “temperature adjustment” generally refers to the process we go through to compare price offers when the temperature of each fuel offered is different. And you may be surprised to learn that fuel temperature is changing all the time — even at the same terminal fed by the same storage tank. Outside temperatures, piping configuration between storage tanks and truck loading racks, time of the day, new barrel arrivals into the terminal — each of these factors can swing fuel temperature throughout the day and over time, sometimes dramatically. Knowing fuel temperature is critical to evaluating prices. The good news? This data is readily available on each and every terminal Bill of Lading (BOL). The BOL shows the fuel’s temperature, gravity, and the load’s “Gross” as well as “Net” volumes. Yes, add these variables to the growing list of “data points” worth capturing in order to improve your team’s procurement process.

“Gross” volume is the metered amount of fuel loaded, and it ignores the fuel’s temperature. The Gross volume doesn’t care if the molecules spinning the meter were hot and plumpy or cold and shriveled.

“Net” volume does care about fuel temperature and is used in the supply chain to compensate for its impact on volume. Why? Well, think about the marshmallow again. If you paid for it based on size right after the microwave treatment, but didn’t eat it until later tonight, wouldn’t you be upset seeing your dessert being much smaller later on? Of course you would! Same with fuel. So a long time ago a bunch of smart people we don’t know got together and settled on 60 degrees Fahrenheit as the temperature standard for a lot of the liquid refined fuels we buy and sell each day. They figured out the math and equations necessary to calculate a fuel’s “Net” gallon amount (if it were 60 degrees), no matter what its’ current temperature (shown on your terminal BOL). Those equations are what generates the “Net” gallons shown on your terminal BOL. Thanks to their smarts, buyers and sellers could negotiate price and value inventory no matter what the fuel’s temperature. Genius!

So, who cares?

Well, if I offered to sell you a gallon of hot fuel at $3.00 or a gallon of cold fuel at $3.05, which one is the sharper price? You don't know unless you calculate the amount of volume offered at the same 60 degree temperature, or the price per "Net" gallon! In this example you may find out the cold fuel at $3.05 per gallon is actually cheaper than the hot fuel. If you know the fuel's Gross and Net gallon amount, it is easy to convert each price to a Net gallon price. Our next blog will run through some examples.

Our point here is to demonstrate why some data readily available on BOLs should be a part of your procurement data set. We will eventually use it to eliminate the marshmallow "fluff" factor from pricing.

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