What is over-processing, why you’re doing it and how to avoid it with the Pareto principle
Lean production aims to eliminate as much waste from a process as possible. Lean defines seven common wastes, abbreviated by TIMWOOD. One of the hardest to understand in an office environment is over-processing, which means doing more work than necessary on a particular task. How do you know when too much is too much? The Pareto principle is the key to knowing when too much is too much.
Over-processing is the enemy of good
In software development and data science, over-processing is easy to understand. Here are some examples:
- requirements call for a system latency of 100 ms, but engineers design it for 1 ms;
- SLAs require 99.9999% uptime, but engineers design it for 99.9999999% uptime;
- a model needs false positives to be below 3%, but a data scientist spends extra time to get to below 1%.
Similar situations exist outside of these fields. Whenever a KPI is exceeded by too much, there is a chance of over-processing. The same is true for any process that needs improvement. For example:
- customer service targets mean time to resolution (MTTR) to less than 10 minutes, but extra time is spent to reach < 1 minute;
- HR needs to process performance reviews at a rate of 10 per day, but HR buys a system to process reviews at 1000 per day;
- Onboarding a new employee takes 2 weeks and needs to improve; Operations buys a system to reduce this to 1 day.
Why is over-processing a waste? Shouldn’t we be rewarded for exceeding our targets? In isolation, yes, but the issue is that goals are not tackled in a vacuum. Whenever we spend time on one objective, we take away time from another objective. So over-processing results in opportunity cost, in addition to potentially wasting money on unnecessary people or tools. Voltaire summed up this concept as “the best is the enemy of the good”.
The Pareto Principle
If there is a clear target for a KPI and that target is met, it may seem that there is no risk of over-processing. This is incorrect. It’s difficult to know how much time is required to reach a goal. Suppose the current onboarding process takes 2 weeks per new hire, and the CHRO indicates this needs to be reduced by 50% in 3 months. What if it takes 1 month to get to 49% reduction and 2 months for the last 1%? Is it worth spending an extra 2 months for a 1% reduction? Probably not, as there are likely more important things to work on.
The Pareto principle says that this situation is common and about 80% of an effect is driven by 20% of a cause. If you focus on the right 20%, you can accomplish 80% of a goal in 1/5th the time. Wow! The trick here is to know which 20% of work to focus on. This requires an understanding of the problem and the drivers of an outcome. For example, if you are at risk of lung cancer, would you focus your energy on quitting smoking or getting exercise? In the onboarding example, suppose the process involve 4 steps, and on average the steps take 5 days, 3 days, 1.5 days, and 0.5 days. Would you focus on improving the task that takes 0.5 days or the one that takes 5 days?
Once you’ve prioritized tasks according to their impact, it’s important to monitor how much time you spend and how close you are to your goal after each task. This data-driven approach provides enough transparency to help you quickly decide when to stop. When you finish each task, check to see how close you are to your goal and how much time has been spent. If you are near 80% of your goal, look to see how long it takes you to reach 90%. If that takes more than another 1/5th of the allocated time, then this is a good time to stop. Evaluate whether it makes sense to invest the remaining time for a small marginal improvement.
Once you master this productivity hack, you’ll see that you’re getting more done in less time. Where do you see examples of over-processing in your daily work? Let us know in the comments!
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Brian Lee Yung Rowe is founder and CEO of Pez.AI, a chatbot company automating management one bot at a time. Learn how bots can automate information gathering, knowledge sharing, and coordination in your business processes and projects at www.pez.ai 🤖
