How to Improve Work-Stuff, Scientifically!

One of my favorite tasks to do when I’m at work is to find ways of optimizing workflows, actions, or processes that I’m regularly doing. If you do something multiple times per day or week, it’s worth doing it as well as possible, right? In my experience, most tasks or workflows are created thoughtfully, but then executed relatively automatically, and, over time, thoughtlessly. Sure, if you have a workflow that’s deeply detail oriented or requires a lot of conscious, brain-on-task time, you’re likely to be mentally active while you execute it, but actually thinking about the efficiency of the process itself may not  be on your mind.

Sometimes I set aside time for process improvements, but usually I fit it into a block of time that I don’t have slated for anything else. Depending on what kind of work you do and what kind of improvements you’re seeking to make, making a change to your process may require a lot of paperwork. If making changes to your workflow or process will require a lot of paperwork, it’s still worth at least investigating whether you can make a change, but the bar for what criterion you use to select your change will probably differ, as it makes more sense to fix a ton of small changes or radically re-haul the process entirely.

When optimizing work, take care to not disrupt old dogmas willy nilly. I propose investigating your workflows scientifically, and determining which optimizations to make scientifically as well. This means that the technique for optimizing workflows I’ll be discussing in this article will be suitable for some kinds of work, but not others. Additionally, my scientific way of investigating beneficial changes may not operate properly for every type of work.

How do you select a process for scientific optimization? The following points are a good guide to seeing whether or not your process can be improved scientifically:

  1. Measurable outcomes and rigorous metrics. In order to think about optimizations scientifically, we need to be able to quantify the pieces we’re talking about. A manufacturing process that produces 15 yellow cubes in 1 hour is an easy candidate for scientific optimization because changes to the process will alter the number, color, or time it takes to produce the cubes. A painting technique that is used to produce impressionistic portraits is not a good choice for optimization, though with some time invested into making qualitative rubrics it may be possible.
  2. Empowerment to experiment. Everyone has bosses, and not everyone’s boss is going to be keen on experimentation with company assets. Having supportive co-workers and bosses is essential to experimenting with process improvements. Bosses may be scared away from the scientific optimization process because it’s resource intensive. Others may be scared due to their own insecurity with scientific pursuits, which tend to be perceived as complicated. Aside from clearance to experiment generally, some processes at work may be open for reinterpretation, whereas others will be sacred and untouchable.
  3. Non-catastrophic failure. Experimenting with the workflow that props up an entire business is sometimes necessary, but should be avoided if it can’t be done safely. The last thing an employee should do is destroy an already-functioning process by attempting to improve it. For some workflows, safe experimentation isn’t possible without the potential for massive fallout if things go wrong. In these cases, making a smaller model to play with typically isn’t possible. I suggest you avoid playing around with systems that will have bad consequences if they fail or have null results.
  4. Controls and Variables. If you’re really going to be conducting a scientific evaluation of your workflows, you need to have the ability to create controls and variables for your investigation. This means that it must be possible to keep the majority of your process the same while changing small pieces individually. Additionally, you need to have data for the way that the process behaves under normal, non-experimental conditions. Most workflows have a paperwork component of some kind, so this is a great place to start looking for control data that you can compare your experimental data with after you’ve run your experiment.

Now you know how to evaluate a process for scientific optimization, so let’s dive right into the meat of how to actually run an experiment once you’ve picked a process to change.

  1. First, if you haven’t already, decide what your variables will be. Remember, you should only be investigating one state of one variable for each trial in the experiment. The variables you pick are up to you, but keep in mind that the items you pick as variables are the items which will end up being improved by beneficial changes to the process that you discover after the experiment is over.
  2. Next, decide your controls. The controls are the most important part of getting usable data from the experiment. I suggest having a negative control (the process as executed before the improvements proposed by the experiment). If you want to get fancy and your process permits it, I’d also add in a null control (a control designed to terminate the process from moving forward) and a positive control (a control designed to test your ability to detect positive results and gather data), but these aren’t strictly necessary.
  3. Once you have decided your controls and variables, it’s time to write up an experimental protocol. How will you be isolating your controls from your experimental group? How will you be altering your variables and setting up your controls? How will you be changing your variables? What will your output look like? How will you be measuring the results of the experiment? How will data be presented in raw form? This is the hardest step and also the most risky step, scientifically. Ensure that your protocol is as close to the normal, pre-experiment way of doing things as possible in order to minimize variability. An experiment is only as strong as its protocol!
  4. Run your protocol and gather data. Each run of the protocol counts as a trial in your experiment. Take care to follow your protocol to the letter, and record data about how the output of the process changes based off of the state of the variables. Don’t worry about analyzing data yet, just try to stick to the protocol and pay attention to your controls and variables. It’s best to minimize variability by running protocols at the same time of day.
  5. Repeat step 4 as many times as needed. Gather data until you feel as though you have enough trials to make a decision. If you want to be super scientific, do some statistics and determine the sample size you need in order to make a good decision, but for most workplace experiments, this level of application isn’t necessary.
  6. Analyze the data gathered in steps 4-5. Which changes to which variables created the most beneficial changes to your originally stated metrics? Were there any consequences to optimization?
  7. Implement changes to your workflow. This should be quite easy, with data in hand. Be sure to argue for your changes using the data that you gathered scientifically, if necessary. If there’s no boss to convince, then enjoy the fruits of your labor immediately.
  8. Show off your good results! Be sure to keep a record of the way that your workflow was run beforehand, just in case. It also helps to maintain records of how your metrics were performing before your scientific optimizations, so that you can show off the positive differences you effected later on. If your results were negative, don’t sweat– most experiments have negative results. More experimentation might be useful, but know when it’s time to throw in the towel. There isn’t necessarily room to improve every single process, especially if it’s already been through the ringer a few times over the years.

Hopefully this guide was helpful to you; I know that I’ve more or less run this regimen on every workflow and process that I’ve touched throughout my professional life. The core concept is systematically tracking changes to variables. As long as you can keep track of what you’re changing, you can make a causative connection between your changes and the outcome.

If you liked this piece, follow me on Twitter @cryoshon and be sure to subscribe to the email list on the right!

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