Automatic propane delivery scheduling works by converting weather into gallons. The system tracks how much fuel a customer burned per heating degree day in the past, accrues degree days daily from local weather, and estimates the tank level every morning. When the estimate crosses a reserve trigger, the account drops onto a delivery ticket.
Every automatic-delivery program in the country runs on the same underlying bet: that a house burns propane in proportion to how cold it is outside, and that last winter's burn rate predicts this winter's. Get the burn rate right and a dispatcher can put a truck in a driveway two weeks before the customer would have thought to call. Get it wrong and the same truck rolls 40 miles for a 90-gallon drop, or worse, arrives after the pilot light is out.
The mechanics are not complicated. The discipline around them is where dealers separate.
What is a K-factor, and how is it calculated?
A K-factor is the number of heating degree days a customer consumes per gallon of propane. It is calculated by dividing the degree days accumulated between two deliveries by the gallons pumped on the second one.
Illustration: a house takes a 300-gallon fill, and between that fill and the previous one the local weather station accumulated 1,500 heating degree days. 1,500 ÷ 300 = a K-factor of 5. That customer burns a gallon for every five degree days. On a 40-degree-day January morning, the same math says the tank gave up eight gallons overnight.
Run that forward day by day and the tank level becomes a running subtraction problem. A 500-gallon tank holding roughly 400 gallons at a standard fill, burning eight gallons a day, reaches the 30 percent mark in about a month. That projected date is what the dispatcher schedules against.
Two habits make the number trustworthy. Recalculate the K-factor on every delivery rather than setting it once at signup. And exclude summer degree days for accounts where propane also runs a water heater, range, or dryer — base load that has nothing to do with the weather will drag a heating K-factor in a direction that costs gallons in January.
Where do the degree days come from?
Heating degree days are a standard weather measurement, published by federal agencies and by every commercial weather feed, computed against a base temperature of 65 degrees Fahrenheit for U.S. residential energy analysis. The U.S. Energy Information Administration uses that 65-degree base in its residential consumption survey, and it is the base nearly all delivery software defaults to.
The arithmetic per day: average the high and the low, subtract that from 65, and floor the result at zero. A day averaging 25 degrees produces 40 heating degree days. A day averaging 70 produces none.
What matters operationally is which station feeds the number. A dealer serving a valley floor and a ridge 1,800 feet above it is running one weather assumption across two climates, and the ridge accounts will run short first every season. Dealers with wide elevation spread or a long north-south footprint typically split their book across multiple degree-day zones and assign each account to the station that actually describes its winter.
What happens when a customer's K-factor is wrong?
A wrong K-factor produces one of two failures: a run-out, or a short fill that burns a stop and a delivery charge without moving meaningful gallons. Both trace to a change the software could not see.
The usual culprits are familiar to anyone who has dispatched a winter. A new account with no delivery history is running on a default. A house sold, and the new owners keep it at 72 instead of 65. A wood stove came out, or went in. A tenant moved into the apartment over the garage. A pool heater got plumbed into the same tank in June and nobody told the office. A second home went from four weekends a year to full-time remote work.
The guard rails against all of that are the same handful of settings, and they are worth auditing before every heating season:
1. A reserve trigger, not an empty trigger. Most dealers schedule against a percentage floor with real margin under it, commonly in the 25 to 30 percent range, so a bad estimate still leaves days of fuel. 2. Maximum days between deliveries. A hard ceiling catches accounts whose K-factor drifted so far the model thinks they are barely burning. 3. Minimum drop size. No account should generate a ticket that cannot pay for the stop. 4. A new-customer probation period. Run the first two or three deliveries tight and manual, then let the calculated K-factor take over. 5. An exception queue somebody actually works. Accounts whose K-factor moves more than a set percentage between fills should surface for a human, every time.
How does the system decide which day to deliver?
The system produces a projected date each account will hit its reserve trigger, then the dispatcher fits those dates to the map. The forecast sets the deadline; geography sets the day.
That second half is where automatic delivery earns its money. An account projected to hit reserve on the 14th does not have to be delivered on the 14th — it has to be delivered before it, on a day the truck is already in that township. Good dispatch pulls accounts forward into an existing route day whenever the tank has room to take the gallons. Compare that with a bobtail criss-crossing a county chasing dates and the fuel-and-labor gap over a season is substantial.
Most systems express this as a delivery window: the earliest date the tank can accept a profitable drop, and the latest date before the reserve trigger. Anything inside that window is a legitimate delivery day, and route density decides which one.
Where do tank monitors fit into degree-day scheduling?
Tank monitors replace an estimate with a measurement on the accounts where the estimate is least reliable. They correct the degree-day model rather than replacing it, and they tell a dealer which accounts were wrong.
The highest-return placements are the accounts a K-factor cannot describe: irregular-occupancy homes, new accounts with no history, commercial accounts with process load, long-haul customers where a wasted trip costs the most, and anyone with a documented run-out. A monitor on a predictable, well-behaved residential heating account mostly confirms what the model already said.
Used well, monitor data also feeds back into the model. Measured consumption on monitored accounts in a given degree-day zone is a live check on whether that zone's assumptions still hold for the unmonitored accounts around it.
What should a dealer check before heating season?
Audit the inputs, not the output. Four checks, in order, catch most of what goes wrong in January: degree-day zone assignments against actual customer geography; K-factors that have not recalculated in more than a year; accounts still sitting on the default K-factor assigned at signup; and the reserve, minimum-drop, and maximum-days settings, which tend to get loosened during a busy winter and never get tightened back.
Dealers who run automatic delivery well tend to describe it the same way — as a forecasting operation with trucks attached. The trucks are the easy part.