Relevance and Reliability – Why the qualitative aspects of benchmarking are just as important as the quantitative.
During my time as a public accountant, I believed that benchmarking was going to be the driving force in consulting moving forward.
How could it not be?
The ability to compare hundreds of thousands of producers across provinces and countries to find the most optimized operations financially. What could go wrong? The answer lies in the story of every individual farm.
I became the Chief Financial Officer at Hebert Grain Ventures in 2019. At the time I was coming from an external consultant role where I ran a book of large primary producers – so how could this new role be any different? Within a month, I realized the large learning curve as I transitioned from external to internal management. The shift highlighted some blindspots I had as an advisor. I didn’t understand the “stories” behind all my clients.
Point 1 – Gross Margin Benchmarking
Whether you look at gross margin as a component of dollars per acre, or as a percentage of revenue, the formulas for benchmarking are relatively the same. But, what happens when you follow Alice down the rabbit hole?
My first taste of qualitative benchmarking was the analysis of high-yielding crops. If you grow double the production, but the price is lower, you might end up with the same gross margin as an alternative crop rotation.
However, with double the yield, comes double the storage, double the equipment requirement, and an increase in labor hours. Even though you may benchmark a gross margin equivalent, the increase in fixed costs to compensate doesn’t compute the same.
And if you dive down even further? Say you spend twice as much on inputs to achieve the same increase in percentage of revenue. Then your gross margin will be identical.
But, what about the risk? By spending more on inputs, we have increased our financing costs, increased our logistics, and ultimately increased our entire risk by allocating the cost of production towards achieving the same gross margin. The numbers would show the same return, but between the risk and the additional costs to cover, are we really comparing oranges to oranges?
Point 2 – Labour, Power, and Machinery
Whether you run your numbers using this terminology, or call them equipment and people fixed costs, the analysis is still the same. This is where most farms see a large variance due to management decision making.
How much equipment do you need?
How new is your equipment?
How many people does it take to screw in a light bulb?
All these factors are encompassed in this area of benchmarking.
In my experience, this is where some of the worst mistakes are made when benchmarking. While we can compare dollars per acre, or percentage of revenue, in the end the story of the farm matters just as much.
Case in point: as a highly progressive farm, we choose labour as an investment. This means we hire good people, then find the work to keep them busy – leading us to truck our own grain, perform our own dirt and land improvement work, and do a lot of the repair work on our own machinery.
If you compare our farm to an operation that does none of these things, should the labour or machinery costs per acre be equivalent? When we own a track hoe, pull dozer, scraper, and other construction equipment, should our amortization and repairs not be higher? Does the additional real estate value we create get added back to the costs?
The answer is no. These qualitative factors skew the benchmarking and make it irrelevant in comparison.
Secondly, we have found hiring and retention are the most important factors on our farm. By performing additional work such as trucking and land improvement, we can hold onto our strong staff all year. Where is the opportunity cost of this added back in our numbers? If you are comparing our 6 full-time and 4 part-time employees against a farm where dad, mom, and son are the main operators, how are wages being compared?
None of this is to say that I don’t believe in benchmarking. When performed correctly with analysis of the variances, I believe it is one of the strongest tools for producers. But without relevance, or reliability, it is just raw data.
The industry needs to be more proactive when consulting using benchmarks, as many of the conclusions I have seen drawn in the past are not accurate. And if we are not using accurate data to make decisions, we’re setting a dangerous precedent.
So, how do we, as professionals, use benchmarking appropriately?
The answer is, we tell the story.
For every one of my consulting clients, I go deeper into the reasons for variances. We can’t stop at a surface level analysis of your numbers being too high or too low. The “why” is more important than the “what”. By making the benchmarking relevant, we can use the strengths of comparison, and eliminate the weaknesses of inaccuracy.