A Model for Measuring Productivity
December 16, 2017
Disclaimer: this model is wrong.
At its core, productivity is a measure of an amount of output divided by the number of inputs used to create the output. That is essentially it. Depending on if you’re an economist, a pharmaceutical plant manager, or a software developer, the numerator and denominator change based on context.
Leaving metrics aside, there is a heuristic that we can also use to measure productivity. In this model, if productivity is on one end of the spectrum, danger is on the other. Yes, danger, because the opposite of being productive isn’t to be unproductive, it’s to be dangerously unproductive where you’re “adding” negative value.
Again, this is my model and it is wrong but I really feel it’s only mostly wrong and maybe even partially right. In this model, productivity is defined as:
You could also replace productivity with value and still have the same effect. Let me state some premises about this before you shoot it down.
Skill is something we all use to create output. From low-skilled professions such as laborers to high-skill ones such as doctors, we use our ability to create output. This is classified as skill. We all have it in varying degrees, whether it be through education, experience or sheer intelligence. Skill is an input to anything we do, from making a sandwich to fixing a leaking faucet. Without skill, we’d die.
Laziness is the degree to which one chooses not to apply their skill. This could be a student not trying in a course they’re taken, or a chemical engineer not performing the requisite testing when certifying a drug. You can argue that not being lazy and being industrious is a skill in itself, but hey, this is my model and I get to do what I want with it. I will concede that there is plenty of literature which suggests that grit is a skill that is critical to success in life.
As illustrated by the first part of the right-hand side of the equation, all other things being equal, if one is not lazy, they will apply the maximum amount of skill they have. Conversely, and again, ceteris paribus, if one is lazy, they’ll apply none of their skill.
Now that we have a measure of how much skill there is left to apply, we require a multiplier to get an absolute productivity measure. Here, I’ve chosen responsibility because if we want to measure productivity across domains, we need a relative aspect in our model. Responsibility could be defined as the importance of the work being performed, or the impact of not having the work done.
For example, if you are a police officer directing traffic at a broken stoplight, then you have a lot of responsibility of making sure traffic is flowing or else an accident might happen. If you are a police officer at a generic construction site hanging around because the municipality requires a police officer there for some odd reason, we could probably do without you. The examples are countless.
Using responsibility as a multiplier makes this model arguably unfair because not everyone is given the opportunity to have the responsibility that would maximize their productivity. That is a fair criticism and one that I am willing to accept. Another glaring criticism might be that even if you have very low skill but have a position of high responsibility, then your productivity measure will be higher than someone with more skill but less responsibilities. I am willing to accept this as well because in this model, even a critical task performed at lower quality is better than a non-critical task performed at higher quality. I know, I know, I’m reaching, but hey, it’s my model.
An interesting side-effect of this model is that if you are low-skilled, supremely lazy and hold a position of high responsibility, your productivity/value measure is negative. A good example is a low-skilled forklift operator who leaves the forklift in the neutral gear when going on a lunch break. That’s the danger end of the spectrum.
Again, it’s my model and I’m entitled to making whatever up.
Coming back to day-to-day matters, we always find people who have jobs where they don’t have the requisite skill level, but are responsible for performing duties that are critical to the overall operation. From a technology perspective, you can think of QA analysts who don’t know automation, release managers that don’t understand CI/CD pipelines, or senior leaders who are indecisive. Whether it’s lack of skill or laziness, their productivity/value is being limited.
It goes without saying that this model is wrong, flawed and even subject to ridicule. However, it does give me a different perspective on how to view matters professionally, and helps me understand why the output of some people is what it is.
The most frustrating aspect for me is when we find people in key positions in organizations, where if they upgraded their skill or were more proactive, could make a big impact on operations. It is a simple and classic Theory of Constraints problem which is encountered every day. There is always some part of a structure, whether a person, department, or firm, that if only they had higher skill and greater enthusiasm, they would be primed to make a big impact because they are in a position of great responsibility.
By no means is the takeaway here that you should start applying this line of thinking, just that you consider some of the line of thinking applied.