You are entitled to your own opinion, but you are not entitled to your own facts: Decision-making 2021 - Angus Energy
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by Phil Baratz

As our industry, and the world, ebbs further into the digital age, the distinction between decisions based on opinion and those based on fact, continues to blur as decisions are generally made in consideration of both.  However, at Angus we believe there are compelling reasons to rely less on “opinion” (AKA: experience, gut feelings, understanding of what’s going on) and more on facts, available in the form of “data”.

We stipulate for the reading audience:

  1. Gut feelings are usually pretty good. They are generally based on our experiences and are clearly better than flipping a coin
  2. We recognize that data-based decisions are not perfect (i.e., sometimes wrong)

These two “truths” often leads one to conclude the following “If my opinion is usually right, and the data is sometimes wrong, I might as well stick with what I know”.  And once you’ve convinced yourself that this is perfectly logical, why bother if the other choice (data) is not always right?  The reason is because the data is almost always better overall.  If for example your gut feeling is right 65% of the time and “the data” is right 78% of the time, isn’t a 20% improvement worth considering – even with the imperfections?

Raw data is not smart – certainly not smarter than people – but our experiences and gut feelings are also filtered by personal biases and inconsistency.

  • Do you always make the same credit decisions in the morning as you do in the afternoon?
  • Would you make the same decision after a customer moves out of their home owing you money?
  • Would you make the same delivery decision after a surprise runout as you would if there hadn’t been a runout that week?

Data doesn’t have opinions.  Data doesn’t have a bad day.  It is true that data-based predictions are imperfect as they are limited to an  assessment of the past, they do not have a crystal ball to perfectly predict the future.  Simple data models consider facts, not opinions.  The larger the data set and the broader the set of variables, the more accurate the data set is likely to be.  However, even a customer with a perfect payment history and excellent credit score does not protect against a corporate relocation and a sudden loss of income.  There is a tendency to point out when data-based predictions don’t match up with results, but when our opinions lead to an unexpected poor outcome, there is a lot of “well, I am usually right”.  It’s just human nature.

Focusing on two of the many decisions in your daily business life (granting credit and delivery planning) can help to shed light on the best ways to use data to dramatically improve profits.  Suppose that your entire credit-approval process is an in-person interview with a prospective customer.  You sit down with them, ask questions about their job, their work experience, their house, and perhaps about their education and family.  After that interview, you would likely do a very good job of determining if and how much credit to grant them.  However, what if you chose to simply get their credit score?  Is a decision based on a credit score better than a decision without a credit score?  The answer is overwhelming Yes! even with the understanding that in the future things change, and today’s decision might end up with a bad result.  Credit scores are exhaustive and very complex measures of someone’s creditworthiness.

Delivery planning and efficiency is the lifeblood of a fuel distribution company.  Aside from service and installation the only time you make money is when you are pumping fuel into your customer’s tanks.  The revenue side of that model is very straightforward, but the profitability of those deliveries is fully dependent on the cost of fuel and the cost of deliveries.  Smaller deliveries usually result in smaller profits.  Excess capacity (too many trucks in the summer AND the winter) usually results in smaller profits.  Understating K-factors or overstating baseloads usually results in smaller profits.  Newer customers usually result in smaller profits.  The list goes on and is widely known.  Given the amount of data readily available, you should start to question whether all new accounts need to start with a K-factor lower than you think it really should be (to protect against a possible runout).  You also consider why your long-term customers don’t get deliveries of the size that you plan.  Is it your BOS?  Is it your dispatcher?  Is it you?

If all your deliveries were EXACTLY at the level that you planned, three things are true:

  1. Your dispatchers have a lot of discipline
  2. You don’t need any tank monitors, and
  3. You should target larger deliveries!

The entire reason for delivery planning targeting a 180-gallon delivery into a 275-gallon tank is because you KNOW (back to your gut feelings, not your data) that if you targeted larger delivery sizes you would have an unacceptable number of runouts.  Is that true?  How do you know?  Do you have the data to back that up?

Back-office systems are very good at math.  If you put a K-factor of 4 into a BOS for a new customer, the BOS will calculate 150 gallons of consumption after 600 heating degree-days have passed.  If you have a summer baseload of .4 gallons per day, it will calculate 24 gallons of consumption between June and July.  The BOS will also adjust the K-factor (given a set of rules) over time to match up against the customers’ “real” K (if the assumed K of 4 was not accurate).  If all of that is true, why are average delivery sizes ALWAYS smaller than targeted?  Is it that the deliveries are “pulled ahead” (early)?  Is it because someone turned on their heating equipment in early November instead of mid-October?  The answers to all these questions exist in YOUR data.  You just need to tap into it and listen to what the data is saying – not only to what your gut is saying.

We believe – and have the data to prove – that every company delivering heating fuels can achieve much better results, with very few runouts.  This approach does not change reserves or Ks.  It does not outguess the BOS, which is already doing a good job.  It also doesn’t force your dispatchers to be so aggressive that you will have a ton of “almost runouts”. We believe that your data is as valuable as your customer list and that if you had a realistic recognition of the limitations of your gut feelings as well as the limitations of what data can do for you, there would be no question as to your approach in the future – and no question about the expected improvement in results.  If you are interested in hearing more, let us know.


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