Originally borrowed from the domain of parallel computing, Amdahl's law states that "the overall performance improvement gained by optimizing a single part of a system is limited by the fraction of time that the improved part is actually used."
For example, let's say you have a computer program that needs 20 hours to complete on a single processor. To speed up the process, you decide to add multiple processors to parallelize the work and run the program in less time.
But you realize that a one-hour portion of the program cannot be parallelized.
Therefore, you can only parallelize the remaining 19 hours of execution time. And regardless of how many processors you devote to a parallelized execution of this program, the minimum execution time cannot be less than one hour.
Hence, the theoretical speedup is limited to at most 20 times the performance of a single processor — from 20 hours to approaching an hour.
This is closely related to the Theory of Constraints, which hinges on two fundamental assertions or statements:
1. Every system has one bottleneck tighter than all the others, in the same way a chain has only one weakest link.
2. The performance of the system as a whole is limited by the output of the tightest bottleneck or most limiting constraint.
What is the constraint preventing Rameshwaram Cafe in Bengaluru from serving more customers?
It could be the time it takes for the batter to ferment. It could be the number of gas stoves they have. It could be the number of chefs working at any given point of time. It could be the amount of payments they can accept per hour. It's not easy to say. But there is a constraint, otherwise there would never be a queue of people waiting outside the cafe on a Sunday morning.
Let's say the biggest constraint for Rameshwaram Cafe is the number of gas stoves they have.
The Theory of Constraints states that no matter what the cafe does, unless it increases its number of gas stoves, it won't be able to serve more customers per hour.
In fact, if it tries to improve everything else in the process chain, it will only lead to more traffic at the bottleneck. If it increases the number of chefs on its payroll, they will simply prepare all the raw ingredients and then wait to get access to a stove.
Now, this might not be the perfect representation of how a restaurant process chain works, but you get the point. If the cafe decides to speed up its payments system, it will only result in more customers waiting for their order, leading to a crowd and worsening the experience for everyone.
So, the only way Rameshwaram Cafe can improve the overall performance of the system is to improve the output at the bottleneck: increase the number of stoves. Any other improvements in efficiency made anywhere in the system will only lead to higher stress felt at the bottleneck.
You can also think about the Theory of Constraints when thinking about optimizing your marketing funnel.
If the major bottleneck in your funnel is at the conversion stage, it does not matter how many ads you run for awareness at the top of the funnel. It will only make things worse for your sales team down the funnel as they will now have to handle more prospects, consequently worsening the quality of consultation and service they can offer.
Apply this to any system, including upskilling and career growth.
If the biggest constraint in your system is that of courage and taking action, no amount of additional books you read or courses you take is going to help. All additional consumption will only confuse you more and lead to analysis paralysis.
When it comes to productivity, the things you spend the most time on in your workflow is your major bottleneck or constraint. If it takes you two hours to reach office and it's mandatory to go to office, no matter how hard you try to make your work efficient, you cannot work less than 20 hours a week (4 hours per day x 5 days a week). So the best thing you can do for work-life balance then is reduce the time you spend commuting.
Think about why it's so hard for food and grocery delivery businesses to be profitable as well.
The biggest constraint is needing a person to carry the package, travel to your address, and deliver it to you.
Hence, if the ticket size, i.e., the average order value per order is low, it’s hard for a delivery business to make a profit because there is no way around the fixed delivery cost, even at scale. Every customer still has to be delivered the product individually, which involves fixed fuel and travel costs.
A D2C/Delivery business can only become profitable if the average order value is high, which then allows the business to have enough margins to absorb the cost of delivery. Examples of such D2C businesses are cosmetics, jewelry, and expensive tech. The product size is small, which means it costs less to deliver — while the product price is large, which allows for higher margins to accommodate the fixed delivery cost.
So, unless that fixed delivery cost can be reduced or average order value increased sufficiently to have good margins, there's nothing one can do to increase the profitability of the system.
Conclusion
Identify the biggest constraint. Work on relaxing that constraint first, before anything else. Because any other optimizations in the system will not help increase performance. In fact, they might actually reduce it.
