How well do K-Factors really predict deliveries?

Written by: Philip Baratz, president of Angus Energy

Most, if not all, fuel marketers today still heavily rely on K-Factors to predict, prioritize and route their deliveries. This method of calculating next delivery based upon a mathematical formula has allowed fuel marketers to operate as efficiently as possible. But are K-factors really the most efficient option?

This is not to say that the K-Factor formula is incorrect, because it isn’t. However, the calculation assumes that each home consumes fuel at the same rate per Heating Degree Day (HDD). It also ignores outside factors that directly impact consumption such as:

  • the homeowner’s awareness of the cost of fuel
  • visitors to the home
  • travel of the homeowners
  • wind
  • humidity
  • alternative heating sources, etc.

During a series of presentations on this topic, 70% of our audience knew that K-Factors were inefficient but didn’t realize how inefficient until now…

Weather and consumption do not match

Generally, when we look at how our deliveries did last heating season, we turn to averages. On average you are doing “okay”…perhaps. However, we don’t deliver to averages – we deliver to individual homes.

It is clear that your customers will consume more fuel in a colder winter than a normal winter – all else being equal.  It also makes sense that a normal January should require more fuel than a normal October – all else being equal.  However, the numbers show us that consumption, EVEN IN THE SAME HOUSE IN THE SAME WINTER, is not formulaic. Weather and consumption are not linear, and we are reminded of that every time there is a run out or a very small delivery for a K-factor customer.


K-Factors do tell a story

They let you know how much was consumed between deliveries.  That time span might be 25 days during the winter and might be 150 days from the summer to the fall.  In either case, it is not tracking consumption as it happens, but using it to report what has happened in the past – usually about 6 times per year.  The math, as mentioned earlier, works – mathematically.  However, it is not capable of accurately predicting forward consumption in a way that you can use to increase your profits.


Deliveries based upon K-Factors are not as efficient as you think

Since K-Factors have been trusted for so long, we know you need a little more than “trust us” to believe that they don’t work as well as you think. We analyzed over 1,000 customers with 200,000 individual deliveries and here’s what we found:

  • 39% of heating oil deliveries are either “Too close for comfort” or “too small to truly be profitable”
  • 56% of propane deliveries are either “Too close for comfort” or “too small to truly be profitable”
  • 20% of heating oil deliveries were more than 50 gallons away from the planned delivery size
  • 50% of propane deliveries were more than 60 gallons away from the planned delivery size

This is the reality of the marketer who is using K-Factor forecasts to make deliveries: Low targeted delivery sizes, wide variances in the actual individual deliveries, and a few run outs. All of which just rationalize the small targeted delivery size.  It’s a cycle that you really can’t break out of.  Yes, we are all aware that “K’s” need to be adjusted and accounts need to be looked at.  On the other side, it can take about 2 winters before finding a “good K-Factor” for a new customer, delaying optimization even more.

The upshot is that K-Factor forecasting causes you to target small deliveries so that you can manage your run-outs.  It does a good job, but so would deliveries to every customer every week – hardly a run-out to be seen there!  K-Factor forecasting, if the only tool available, does a competent job of helping you avoid run-outs. However, when your single biggest operational expense is the cost to deliver fuel, isn’t it time to embrace the technology that is now available?

So, while you may look at your average for the past heating season and think that everything looks “okay” – the reality is that it should be much better!


Alternatives and supplements to K-Factor based deliveries

The best way to gauge actual consumption of an individual home is to know exactly how much fuel is in their tank. Tank monitors offer marketers the ability to know what’s in their customer’s tank with a whole new level of accuracy.

Costs of propane or heating oil tank monitors are often the biggest deterrent for marketers. However, the impact – larger and more predictable deliveries, smoother operational logistics, lowering staffing constraints, fewer run-outs, improved customer engagement – is undeniable.

If you’re unsure of tank monitors, then start small. Similar to GREMLIN tank monitors, most monitoring companies offer an analysis of your accounts to help you decide where you actually need fuel tank monitors. This is a great first step for any fuel marketer considering tank monitors. Get comfortable with the technology and start to see how accurate they are.

K-Factors have done a great job predicting deliveries over the years, but at one point – a sundial was also the best way to tell time.

To find out more about how K-Factors really predict deliveries, you can listen to our On Demand webinar by clicking here.

Interested in getting an analysis on your deliveries? Click here to connect with one of our GREMLIN efficiency experts for a no-obligation delivery analysis.

We will analyze your data to show:

  • Potential saved deliveries
  • Return on investment
  • Best and worst performing tanks

Get started with your delivery analysis today!

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