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Education Lifestyle Productivity

Creating Systems

“Systematize everything, and find peace”

Brandon Turner (The Book on Rental Property Investing)

Why create systems?

Having goals is a fantastic way to be productive and build a life by design, but the most productive results come from having systems. When we have a goal, there are a series of choices we have to make. Usually, goals are things we haven’t done before, so all the choices we’re going to have to make require a lot of attention and energy. Even if the goals have been accomplished before, having to deal with the challenges that arise time and time again can be exhausting.

Systematizing everything makes these processes a lot easier, especially if there are goals that need to be completed regularly. Creating systems gives us the opportunity to make fewer choices and the freedom to do other things. Despite what many people think, our energy is like our time, limited and nonrenewable. The return on saving energy is virtually infinite. Systems allow us to be more productive as well, in terms of quantity AND quality.

Systems also make processes easier when we have to do them. Having a reliable proven strategy for conquering whatever scenario in front of us cuts the effort down tremendously. It’s much easier to just “start the process” than to try really hard just to discover that it wasn’t going to work anyway.

Focusing on systems, not goals, is the key to long-lasting, reliable, and fruitful results.

In order to focus on systems, we first have to understand what they are and how they work. I wrote two posts on systems so far: Gall’s Law & System Components and Analyzing & Improving Systems. I highly recommended checking those out, I go over the fundamental information about systems and how they work. This post synthesizes some of the information discussed in those two posts and emphasizes the concepts that will make creating systems much easier.

When we understand how systems work and how to create our own, then we can engineer our own systems to fit our unique needs perfectly.

Marks of an Effective System

Human beings need purpose and intentionality in order to have fulfilled lives and content with existentialism. I talk a little bit about that in this post. We need something to aim at and a system is no different.

What is the purpose of a system & how we do know if it’s working well?

According to author of The Personal MBA, Josh Kaufman, an effective system does the following:

  • Fulfills its functionality
  • Has great infrastructure
  • Ready connectivity with other systems
  • Versitile
  • Adaptable
  • Reliable
  • Produces benefits which far exceed the initial investment

Keep in a mind, all of these criteria don’t need to be met to have a functional system. A system can work without all of these, however, these are all qualities that a great system can build towards.

How to Create Systems

“If you want to build a system that works, the best approach is to build a simple system that meets the Environment’s current selection tests first, then improve it over time. Over time, you’ll build a complex system that works.”

Josh Kaufman (The Personal MBA)

Keep Gall’s Law in mind when you first start – work on creating a simple system, then add complexity over time. Don’t try to make a system that does too much at once right off the bat.

Creating a complex system immediately will result in failure.

When it comes to building a system from scratch, just try to figure out how to get the task done in the first place. Once we’ve completed the task, we developed a neural pathway that knows exactly how to get that task done. Every single time we work on that task in the future it gets easier and easier. (Exactly like Active Recall.) Once we’ve gotten to this point, it will be much easier to create a system because we will know the steps intimately. Creating a system will speed that process up even more!

Once we know how to get the task done, we need to identify the key components of the system and improve each step of the process so it becomes more efficient and effective. Just make one change at a time. Creating a good system takes time and patience, like building habits.

Systems are almost never correct right off the bat. It takes a few tests to get all the details right and have a system that runs smoothly. All systems start off terribly, but all the best ones improve over time and the only way to objectively improve a system is through experimentation.

Fans of history can probably name a few times human beings have tried to create something but had to revamp the system because they found out it wasn’t working too well. I remember working with a student on history work and I was stunned to discover that this process is the exact process that the American government evolved from. Today, the American government is a massively complex system with multiple branches and precedence for almost every conceivable situation but it did not start off that way. It started off with just the Articles of Confederation, and that system didn’t even have an executive or judicial branch. Then it grew with the Virginia Plan, then the Constitution, then the Bill of Rights, and finally with the amendments we have today. The point is that this extremely complex system didn’t start off how it is today, it grew over centuries through experimentation.

Humans never get anything right the first try.

When I was a noob at creating systems, I thought that I needed to know each step of the process and it had to work perfectly before I even started, but that prevented me from even starting in the first place. There’s no way to know every single step and account for every single variable without testing a system.

All, if not most, problems can be handled with systems. If there isn’t a system for it, then we can create one. We just have to go into the process expecting the system to fail and be willing to make adjustments or we won’t have any systems at all.

Guiding Questions

If you’ve read some of my Importance of Questions post, then you know that I believe questions hold the key to everything we want in life. They can allow us access to other people’s minds as well as guide our thinking to solve problems that we haven’t seen before.

Here are some questions that could be useful when creating systems:

  • How do I make this process easier next time?
  • How can I make this process more reliable next time?
  • How can I prevent ____ from happening again?
  • In a perfect world, how would I want to deal with this situation?
  • How can I make _____ happen again?
  • What is the end result? What am I producing?
  • What are we starting with? What do I need to get this going?

Tips for a Great Checklist

Systems can take many different forms. One of the most simple forms, checklists, have also been proven to be one of the most effective. I talk a little about checklists in my post Analyzing & Improving Systems and in that post, I mention how checklists are fantastic for creating processes that haven’t been articulated yet. Checklists are my go-to method for ordering the chaos and are fantastic simple systems to build upon later.

In Dr. Atul Gawande’s fantastic book, The Checklist Manifesto, Gawande lays out the proof that checklists are more reliable than the instincts of even the highest trained professionals. Pilots, especially when confronted with an emergency, are more than willing to turn to their checklists.

The first tip for making a great checklist is to have normal and non-normal checklists. Normal checklists are for normal everyday situations, things that come up often. Pilots have normal checklists for things like take-off and landing. Non-normal checklists are less often used but are for emergencies or other scenarios that wouldn’t occur on a regular basis. Pilots have more non-normal checklists than normal checklists. They have checklists for all conceivable emergency situations.

There are two types of checklists: do-confirm and read-do. Do-confirm lists are when team members perform their jobs first, then pause and run the checklist to make sure that everything was done correctly. Read-do lists are when people do the tasks as they read them on the list. Do-confirm lists act more like a double-check while read-do lists are more like a recipe. Know which type of checklist you are creating and when to use it.

Great checklists are specific and precise. If a checklist is too vague and inaccurate is becomes hard to use and impractical.

They also need to be short and to the point, if a checklist is too long it becomes another hurdle. Try to keep the length of the list within the bounds of our working memory, 7+2 items (5-9). They can be longer, but keep in mind that if checklists take longer than 60-90 seconds then people start shortcutting and they become more hassle than they’re worth. Focus on the critical few.

They also have to be easy to use in the most difficult situations, keeping it short and sweet helps a lot with this.

Contrary to popular belief, a great checklist should not spell everything out. They just simply provide a reminder of the crucial steps. Operators of the checklist should be trained and know what they are doing. The checklist is just an aid, not an instruction manual.

Checklists must, above all, be practical.

Checklists cannot solve problems for us, they simply help us manage complex processes and clarify priorities. They are not comprehensive guides, just reminders of the critical steps. If we include every little step, then the checklist just adds friction to the system. Checklists are designed to remove friction and add ease and clarity to a scenario.

When creating a checklist, I highly recommend clearly defining a point WHEN the checklist will be used.

Like all systems, checklists have to be battle-tested in real life. Experiment with the checklist and pay attention to what works and what doesn’t. The first drafts never stick and revising is always required.

A Few Systematic Principles

A few principles that I’ve noticed systems follow. It’s helpful to keep these in the back of our minds when planning and executing systems.

The Matthew Effect

“For to every one who has will more be given, and he will have abundance; but from him who has not, even what he has will be taken away.”

Gospel of Matthew (25:29 Revised Standard Version)

I talk about this in my post The Myth of Motivation, when I talk about aiming for success spirals.

The Matthew Effect is pretty simple — success breeds success and failure breeds failure. The best part is that this relationship is not linearly, it’s exponential. If we do something like writing a book and some people like it, then we’re more likely to write another book which makes it more likely that more people are willing to like it.

We can see spirals like this is so many things. If we get an A on an exam, it’s easier for us to get an A on the next exam. We know the concepts and are more likely to sacrifice because our sacrifice led to a positive outcome last time. Likewise, if we fail an exam, it’s easier for us to fail the next one. We don’t know the concepts, so we’d have to learn those ones on top of the new stuff. The previous failure adds friction to the next challenge. Our actions compound on each other and it can stack up quickly. This is definitely one of the reasons for the massive wealth inequality in the modern world. It’s much easier to make money when you’re already making money.

When building a system, try to keep in mind that the actions of the system will affect the actions of the system later. The Matthew Effect also provides a fantastic foundation for another systemic phenomenon, the Pareto Principle.

Pareto Principle (The 80/20 Rule)

The Pareto Principle definitely needs it’s own post because it’s such a pervasive idea and we can dive into it for hours. Famous entrepreneur and investor, Richard Koch was able to write 4 books on the topic! 4 books!! While the principle itself isn’t complex nor long, Koch focused more on applying the principle to every aspect of our lives. I’ll go more into that later.

While there is a lot to say about the Pareto Principle and its accommodations, it can be summed up relatively quickly:

80% of the output of any system is from 20% of the input.

This principle can apply to all domains of human creation. This shouldn’t be a surprise though. For most of us who have worked on a group project, we know that most of the work ends up getting done by a small portion of the group. This also happens in companies too – 80% of the work is done by 20% of the employees.

There are many names given to this idea because it shows up in so many places. The Pareto Principle is also known as the 80/20 Rule or the Square Root Rule: being that 50% of the work is done by the square root of the number of employees. The numbers aren’t perfect, but the idea is the same:

The majority of the results come from a critical minority of effort.

This principle is game-changing when it comes to creating systems. Knowing that the majority of the results comes from a critical few, we can focus our energy on optimizing for those critical few inputs rather than wasting energy on processes or steps that yield a lower rate of return.

I talk a bit about how the Pareto Principle came to be in my post Analyzing & Improving Systems.

Vilfredo Pareto, the 19th-century economist and sociologist, discovered an interesting pattern when analyzing data regarding land ownership and wealth distribution. He discovered that 80% of the land was owned by 20% of the population. Pareto didn’t just find this pattern in wealth and land distribution, he also saw it in his garden. 20% of his pea pods produced 80% of peas.

We can see this in book sales and album sales too, pretty much any domain of creative production. 80% of the book sales are from 20% of the authors. 80% of music streams are from 20% of the artists.

Richard Koch also noticed a similar pattern when studying for his final exams at Oxford. At Oxford, the students are graded by their performance on a final exam which is a collection of essays on a wide variety of topics. Richard determined that in order to prepare for every possible question they could ask him he would have to memorize somewhere around 550 essays. Obviously, this wasn’t reasonable so Richard found another way around it. He analyzed all the past exams and discovered that every exam asked questions on similar topics. 20% of the topics accounted for 80% of the questions. Upon realizing this, Richard figured that all he had to do was prepare for 20% of the topics. This significantly cut down the work he needed to do to prepare for the exam.

Spoiler Alert: he did great on the test.

The best part about the Pareto Principle is that it can apply to all systems, in business and our personal lives. We can a Pareto analysis of our own lives, as well as any systems that we’d like to optimize.

I love doing the 80/20 analysis of everything. I’m constantly trying to figure out what I really need to do. Trimming the fat automatically maximizes our time and effort. It’s much easier to just focus on what is important than trying to find ways to do everything better.

It can apply to happiness – What 20% of things give me 80% of my happiness?

It can also be applied in reverse – What 20% of things are giving me 80% of my unhappiness?

It can be applied to anxiety, relationship satisfaction, costs, food, anything our hearts desire.

This principle helps us optimize.

Whenever I’m creating a system I try to keep in mind that principles are more valuable than knowledge. If we can understand the principles behind something, then we have the ability to predict and manipulate the system across many different scenarios.

Richard Koch likes to ask people:

“What would happen if we spend all of our time on the critical aspects?”

Parkinson’s Law

I first talked about Parkinson’s Law in my post 5 More Tips for Better Scheduling because I first learned it as a scheduling principle but now I see how it can be applied in a broader sense.

Parkinson’s Law states that work expands to fill the time allotted for it.

So if we have a week to get a project done, it will take us a week. If we have a day to get a project done, then it will take a day. Parkinson’s Law helps explain how people are able to finish massive projects the day before it’s due or how an unmotivated student finds the strength within them to get something turned in. It’s not that we aren’t capable of doing the work, it’s that we know we still have time and we’ll use all of it if we can.

What I do to try to account for this is give myself less time than I think I need to complete a task or I set finite deadlines to work on a project. Either way, they both prevent me from spending extra unnecessary time on something.

There’s something about having our backs up against the wall that makes us perform. I’m not sure what it is, but it’s definitely there and we can use it to our advantage if we want.

However, Parkinson’s Law is more than just a cool scheduling hack. It can also apply to budgets and other resources too.

Parkinson’s Law applied to budgets – spending expands to fit the budget allotted for it.

Now, the spending doesn’t have to expand to fit the budget but it usually will.

The resources we allocate for something determines the fundamental perspective of our approach.

If we are pressed for time or if we don’t have a lot of money, then we’ll be on the lookout for creative ways to solve problems that could otherwise we solved with time or money.

Parkinson’s Law really just highlights another quality of human nature which is that humans are creatures of necessity and we will always try to solve problems with the least amount of effort possible. Keeping this in mind when building systems is extremely valuable and perhaps we can go further with what we already have.

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Education Productivity

Analyzing & Improving Systems

“If you are unhappy, your system is broken.”

Brandon Turner (The Book on Rental Property Investing)

In my last post, I talked about Gall’s Law and the different components of systems. In this post, I’m going to discuss ideas surrounding analyzing and improving systems. There are going to be some terms I reference from my last post, so I highly recommend checking that out first.

Josh Kaufman beautifully and simply explains how to do this in his book, The Personal MBA, and I also highly recommend checking that out too. The ideas in this post are mostly from that book as well as my own personal insights.

How to Analyze Systems

When analyzing a system is difficult to know what to look for, especially when dealing with complex systems. It’s also crucial to keep in mind that we all have a bias which could get in the way of analyzing systems. Whenever possible, we need to take steps to mitigate our bias.

Here are some other things to look out for when analyzing systems:

Deconstruction

This is an excellent first move when it comes to analyzing systems. As discussed in Gall’s Law, all complex systems arise from simpler systems, therefore every complex system is capable of being deconstructed into simpler systems. Deconstruction is simply breaking up complex systems into their interdependent parts and understanding how each of those parts works. It’s also helpful to try to identify triggers (what kicks off another system) and endpoints (what makes a system stop). Diagrams and flowcharts are also lifesavers when it comes to deconstructing systems. Recognizing if-then and when-then relationships are also invaluable.

After breaking the complex system up into it’s smaller parts, we can then break down those simple systems even further into their components. Identify inflows and outflows, stocks, interdependencies, and so on. I talk about what those components are in my last post.

Deconstruction makes understanding systems possible. Without deconstruction, we are sure to experience confusion.

Measurement

How does the system collect data? What kind of data is it collecting? Measurement describes the process of data collection in a system. If we can understand the information related to the system, then we get an insight into the system itself.

Paying attention to the measurement is fantastic for dealing with absence blindness — the idea that we have a hard time seeing things that aren’t there.

Let me give an example, measuring someone’s blood glucose levels tells us if someone has too little or too much blood sugar. This number gives us tremendous insight into what is going on inside the body even though we wouldn’t be able to visibly see changes in someone’s blood sugar.

Measuring something is the first step in improvement. I wrote a post, Tracking vs. Loss Aversion, that talks about the importance of measuring ourselves and our progress.

Don’t sleep on measurements. Trust me.

KPI (Key Performance Indicator)

I know I just emphasized how important it is to measure things, but there is such a thing as too much data. If we measure too many things, we end up having a bunch of junk data that weighs us down and doesn’t show us anything. In order to prevent this, we try to keep our measurements to only KPIs or Key Performance Indicators.

KPIs are measurements of the essential parts of a system.

Identifying KPIs can be tricky, but try to limit them to only 3-5 KPIs per system otherwise, we risk measuring too many things.

Garbage In, Garbage Out

This is one of the more straightforward ideas – what we put in is what we get out. The quality of the output is only as good as the quality of the input. You get what you give. There are so many cliche phrases that express this idea.

I found this to be especially true when I started cooking. I used to watch Gordon Ramsey cooking videos to try to learn how to cook (I even watched his Masterclasses), but I could never make my food taste good until I spent the extra money on fantastic ingredients. Now, I try to only cook with fantastic ingredients. It really makes all the difference.

One of the best ways to improve the quality of a system is to pay attention to what we start with.

Tolerance

This can be thought of as the range in which the system is working normally. If the system is performing within that range, then it’s within tolerance.

Tolerance can either be loose or tight. A loose tolerance is when there is a considerable amount of leeway and small mistakes don’t make a huge difference while a tight tolerance is when there’s little room for error or change, this is usually the case for essential components of a system.

Analytical Honesty

In order to properly analyze a system, we must acknowledge our propensity to make things look better than they are. We have to be able to apply objective judgment to our data which means that the best analysis of a system will come from someone who isn’t personally invested in it.

As I mentioned in my post, Our Unconscious Filters, human beings view the world through their bias and it’s hard to shake them, even if we’re aware of them. Having an outsider provide analysis is the only way to completely prevent our bias from contaminating our observations.

Context

Most measurements are meaningless without context. Context is all of the information we use to understand if measurements are favorable or not. Setting goals for arbitrary numbers like a 20% increase or 3 new deals are meaningless if we don’t know the performance of the system in the past and it’s projected performance in the future.

Trying to oversimplify how a system operates by judging it off one measurement will blind us to other important changes as well. Context is crucial for an accurate understanding.

Sampling

Sampling is what we do when we try things. We’re simply taking a small part of the output and using it as an example for the entire system. Sampling is great for catching errors without needing to check the entire system.

Just like all other methods of analysis, we have to consider our bias, and sampling is prone to bias. A way to control for it is to make sure the sample is random.

Margin of Error

Of course, not all samples will be perfect representations of the entire system. The margin of error is how much the sample deviates from the whole. The higher the margin, the more inaccurate the sample. The more samples we have, the smaller our margin of error.

Ratio

Ratios are fractions. Somethings divided by something else. It’s a simple way of measuring multiple variables at once. Ratios are also great for letting us know how a particular measurement changes.

For example, ROI is a percentage ((Returns/Investment)*100%) or comparing MPG (miles/gallons) or unit price of groceries. You know a ratio is involved in you hear the words per.

Typicality

In order to analyze a system properly, we need to know how it would operate normally. Kaufman suggests that we can measure typicality through calculating mean, median, mode, and midrange or various measurements.

Correlation & Causation

Causation comes from the idea of cause and effect. One part of a system is causing another part of a system to act. Correlation, on the other hand, is not always causation. Sometimes variables may seem the act like one causes the other, but that won’t necessarily be the case. For example, 100% of people who drink water die. Does the water cause the death in this case? Probably not. Water and death are simply correlational.

So how can we determine is something is correlational or causational?

By adjusting for known variables. If we control for as many variables as possible, we can see the relationship between each more clearly. As systems grow more complex, this becomes more and more difficult. The more we can isolate a variable, the more confidence we have that the changes are causational.

Proxy

Proxies are measurements of something by measuring another thing. A proxy is useful when we cannot measure something directly. The closer the proxy is to the original, the more accurate the measurement. We have to be mindful about correlation and causation when measuring a proxy.

Segmentation

Segmentation is grouping data into separate subgroups to get a more comprehensive context. I do this with all of my blog posts! That’s why I have titles and headings and subheadings. It gives all the (seemingly) random information I’m spewing a more detailed context.

Segmentation plays a huge part in how we understand complex and large amounts of information.

Humanization

When looking at data is easy to see it as an inanimate object, but when analyzing systems we have to keep in mind that the data tell us information about human beings. They are insights into real people — their behaviors, experiences, and thoughts. It’s easy to disconnect from data about a system because it seems so abstract and inanimate, but it’s quite the opposite. If we pay enough attention, the data lets us understand people on a deeper level.

When I worked at Kohls, they always emphasized selling to “her.” Her being a personified collection of the average data on their customers. They used average household income, gender, family size, and other variables to create their typical customer and found ways to satisfy that person.

Our data tells us what’s up with other people if we look hard enough.

Other Things to Look Out For

“If something in your business is causing you stress, most likely, you either don’t have a system for that issue, or you are not following your system.”

Brandon Turner (The Book on Rental Property Investing)

Pay attention to environmental changes and selection tests. I talk a little bit about these in my last post. These changes give smaller players a chance to outperform larger players. Identifying selection tests gives us a competitive edge.

Some questions to ask while looking out for these things could be: How is the environment changing? Who is unable to adapt to these changes? What can I do differently from those who cannot adapt? Who is taking advantage of these changes? What can I do similar to those who are doing well?

Always keep an eye out for the “black swan.” I first heard about this idea from Chris Voss, an ex-FBI terrorist negotiator. The “black swan” is any information that if discovered would change everything. Back in the day, people would say swans are white and if anyone said otherwise they would be crazy because swans are white. Eventually, someone discovered a black swan and everyone had to change how they saw the situation. Systems are the same way, try to keep an eye out for the information that would change everything. There’s always a piece of information that, if known, would change everything This is excellent for accurately identifying and balancing risk and uncertainty.

It’s also helpful to keep in mind that we process the unknown the same way that we process threats. We literally see and respond to what we don’t know as a threat. Expect to encounter threats, but instead of responding to it, we can respond with curiosity to learn more.

The last thing I want to mention about analyzing systems is to analyze close-calls when they happen to minimize accidents. Sometimes shit happens, but most of the time we can prevent it from happening. If we can notice when things almost go wrong, then we can take the steps to make sure that it doesn’t happen again or prevent the conditions that allowed it to happen in the first place without having to deal with the fallout of the accident.

How to Improve Systems

“Anyone who understands systems will know immediately that optimizing parts is not a good route to system excellence. For example, let’s build the world’s greatest car by assembling the world’s greatest car parts. We connect the engine of a Ferrari, the brakes of a Porsche, the suspension of a BMW, the body of a Volvo. What we get, of course, is nothing close to a great car; we get a pile of very expensive junk.”

Donald Berwick (1946 – )

Now that we’ve discussed analyzing systems and have a base framework for understanding systems, let’s talk about some of the ideas useful for improving systems. Most of these ideas are also included in Kaufman’s The Personal MBA.

Intervention Bias

This is the idea that human beings tend to add changes to a system just to feel like we have more control. So when we set out to improve a system, we have to entertain the thought that we might be implementing a new change just to feel in control. If we don’t, we risk adding unnecessary complexity to the system.

The best way to account for intervention bias is to analyze through a null hypothesiswhat would happen if we did nothing? What if the situation was simply an error?

If the null hypothesis experiment determines that we’re better off doing something than nothing, then we will have minimized our chances for intervention bias to take hold. Examining the null hypothesis isn’t our natural reaction, especially since human beings have a proclivity to doing something rather than nothing, but it’s crucial for actually improving systems.

When improving systems, first think about what would happen if we did nothing.

Optimization

Optimization is what people usually think of when it comes to improving systems. This typically involves maximizing output or minimizing an input. Optimization is usually focused around the KPIs.

Kaufman suggests when optimizing a system to focus on one variable at a time. Optimizing a system across multiple variables will almost always lead to disaster. System interdependencies and second-order effects make it challenging to change more than one thing at any given time.

Refactoring

This refers to changing a system’s process so that it can perform the exact same result but in a more efficient way. This is most obvious in coding. Some programmers will pride themselves on performing the same actions in fewer lines of code.

To the average person, refactoring may seem insignificant, but more efficient systems run faster and require fewer resources which could be redirected elsewhere.

Some questions to ask when refactoring a system could be: What are the essential processes to achieve the desired objective? Do these processes have to be completed in a certain order? What are the constraints of the system?

Critical Few

If you’ve heard of the Pareto Principle (a.k.a. The 80/20 Rule), then you understand the concept of the critical few. Essentially, 19th-century economist and sociologist, Vilfredo Pareto, discovered an interesting pattern when analyzing data regarding land ownership and wealth distribution.

He discovered that 80% of the land was owned by 20% of the population. Pareto didn’t just find this pattern in wealth and land distribution, he also saw it in his garden. 20% of his pea pods produced 80% of the peas.

Today, we can see his 80/20 split in almost everything. In systems is useful to know that 80% of the output is from 20% of the input. In businesses, typically 80% of revenue usually comes from 20% of customers. 80% of the work is done by 20% of the people. 80% of our time communicating is with 20% of people we know.

Focus on the 20%. That is where the biggest changes will happen. Identify which parts are critical and give it attention or starve it of attention, whichever is required. The idea is to not try to focus on the whole thing, but the smaller parts that matter.

I do this with my students. 80% of my income comes from 20% of my clients and my attention and efforts are split accordingly. I give the clients who matter more attention and I starve the ones who don’t. After practicing these methods, I’ve eliminated a lot of headache clients and I’ve strengthened my relationships with the ones I do like. 80% of the problems came from 20% of the clients.

Focus on the critical few.

Diminishing Returns

This is the idea that after a certain point, adding more starts to cause more harm than good. This is common when optimizing high performing systems, people tend to try to push the system even more to the point where the system breaks.

“The last 10 percent of performance generates one-third of the cost and two-thirds of the problems.”

Norman R. Augustine (Aerospace Executive & Former U.S. Under Secretary of the Army)

A way to control for diminishing returns is to apply Ramit Sethi’s infamous “85% Solution” from his fantastic book I Will Teach You To Be Rich. Simply get 85% of the problem right and move on. Yeah, we can really hone in on getting that extra 15%, but then we risk diminishing returns.

Is it worth doubling the effort just to squeeze out that extra 10-15%?

It might be, but not every time. It’s better to spend our energy getting the big wins, than trying to squeeze out every little bit.

Friction

Friction is something I pay a lot of attention to. I spend a lot of time dedicated to removing friction from my life because it stops me from doing so much. Friction is any force or process that removes energy from a system. Remove the friction, increase efficiency.

Amazon Prime is a perfect example of a company removing friction to make a system more efficient. If you have amazon prime, then you know how easy it is to purchase things. That’s intentional. The ease of use creates more cash flow for the business.

If a system has a lot of friction, it can still perform but it will require much more energy. If we don’t add more energy, then the system will eventually slow and stop.

For me personally, when I encounter friction while doing an activity that I don’t enjoy, then I won’t do it at all. So if I can help it, I try to remove as much friction as possible whenever I’m doing something difficult or something I don’t want to do.

Sometimes introducing friction is what’s needed to improve a system. When I want to prevent myself from performing certain actions, I introduce friction because I know it will stop me. Some business makes it cumbersome for a customer to return their product so they are less likely to return it.

Automation

The gold standard of no friction. Automated systems operate without human intervention. Automation is best for repetitive tasks.

Be mindful that automating a system tends to magnify the efficiencies and inefficiencies. If the system is already efficient, then automation will make it faster. If the system is not efficient, then automation will slow it down.

When it comes to understanding automation, we want to be familiar with the Paradox of Automation: the more efficient an automated system is, the more critical the human inputs are. While automate reduces the need for human intervention, the small amount of human intervention that occurs becomes increasingly significant.

Automation makes our actions count more, not less.

On that note, I also want to mention the Irony of Automation: the more reliable a system is, the less attention humans pay to it. Reliable systems train absentminded operators. This is dangerous because if something goes wrong, we aren’t likely to notice and the automation will propagate that error.

The best way to avoid automation errors is to perform consistent sampling and testing.

Standard Operating Procedure

SOPs are predetermined processes for completing certain tasks or solving common problems. We save cognitive load and cut down the number of decisions we have to make in a day if we have a preselected method that’s known to work.

Using SOP helps us spend our energy on improving a system, rather than solving repetitive problems over and over.

Kaufman recommends reviewing the SOPs every two to three months to keep it running effectively.

SOPs can look many different ways. For example, I have a set price for certain students, and certain times I will tutor. But it can go further than that, I have predetermined phrases that I say when talking to clients to make communication easier when it comes to scheduling or other common tasks. I also have predetermined methods for dealing with certain kinds of students so we have a simple system for us to start with and build upon.

Focus on creating go-to methods for things you encounter often. You’ll find that it can seem like a lot of work upfront, but it will streamline the process in the long run and it’s so worth it.

Checklists

I can’t talk about checklists without referencing Dr. Atul Gawande’s The Checklist Manifesto. That book beautifully describes the power of checklists. Checklists have been a vital part of pilots take off routines and are the reason for their high success rate. Checklists have also played their role in minimizing infection rates in hospitals all over the world. The secret to repeatedly completing complex tasks perfectly is writing it down as a checklist.

Checklists are simplified SOPs for specific tasks. They’re fantastic because they create systems for processes that haven’t been articulated and minimize our chances of skipping critical steps.

I always make a checklist for my students who are struggling to “manage the chaos.” Transforming the glob of craziness that is school work into a list that can help us narrow our focus works for every single student I have ever worked with. Seriously, I haven’t come across any academic situation that a checklist could not solve.

Checklists are so critical for entropy management that I use them whenever I’m feeling overwhelmed. Whenever I’m feeling stressed and swamped with work, I ask myself “What’s the 80/20 I need to tackle here?” then I made a checklist to conquer the critical few.

I’ll probably write another post on checklists because they’re so damn powerful, important, and useful.

Checklists are also great because once we have a good one, we can delegate or automate it — which frees us up from doing the work! Checklists tend to be the first step to freedom.

Cessation

This refers to the idea of stopping something intentionally. Sometimes a system may be going haywire and the best thing to do is to stop a process. As I mentioned earlier, humans have a proclivity to do something to improve a system, but sometimes the best choice may be to not do anything or stop altogether.

Cessation is not our natural reaction when we want to improve systems and it’s usually an unpopular choice when dealing with a group, but keep in mind that it’s a valid option.

When analyzing and improving systems, I entertain the idea of cessation after I’ve tried the null hypothesis. If both of those options are determined to be ineffective, then I’ll start doing something to improve the system.

Resilience

The resilience of a system is determined by how much change it can withstand. The ability to weather change and adjust plans means the difference between disaster and survival.

Resilience usually comes at the price of optimal performance. A system can increase its resilience through leverage. For example, if a business needed some money to weather the storm it will be more resilient, but that money can’t be used more efficiently. Resilience comes at a cost.

Another way to boost resilience is to prepare for the unexpected. Having plans for different/unexpected scenarios or extra supplies on hand are great ways to make a system more resilient. Fail-safes and backups are great for that also. A fail-safe is a backup system designed to prevent or recover from the original system failure.

Stress Test

This is the process of identifying the boundaries of a system by changing the environment. Testing different extremes on a system can help determine which variables affect which processes.

When I stress test my systems, I try to break them. This is the part when we want to try and test out our “what-if” scenarios. Scenario planning is at the heart of any effective strategy. Rather than trying to predict the future, we can prepare for a handful of imagined scenarios and be ready for what comes next. A proper stress test can really help with a system’s resilience.

Sustainable Growth Cycle

This cycle is a pattern that systems follow when undergoing consistent growth without any major issues and it’s split up into three phases:

The Expansion Phase – this is when the system is focused on growing. This is a creation phase. New components and strategies are implemented and dedicated to growing the system and collecting data.

The Maintenance Phase – this is when the system focuses on executing the strategies and maintaining the functionality of the system. Pressing play on the system, so to speak.

The Consolidation Phase – this is when the system is focused on analysis. All the data that was collected is now put into context. Things that work are given more resources and attention while things that don’t are cut back or reworked.

The Middle Path

This idea comes from the fact that the balance between too much and too little are constantly changing. Balancing what systems need requires constant reevaluation. The best approach usually lies somewhere between too much and too little.

Experimentation

No one has everything figured out and determining the best choice when it comes to improving a system is a difficult task. This is when experimenting comes in handy. Frequent experimentation is the only way to accurately determine what improves and system and what doesn’t.

I like to treat experimenting is play. I love trying to new things, changing stuff up, and seeing what happens. The more we experiment, the more we learn about our systems.

More Methods of Improvement

“A man with a surplus can control circumstances, but a man without a surplus is controlled by them, and often has no opportunity to exercise judgment.”

Harvey S. Firestone (Founder of the Firestone Tire and Rubber Company)

A personal note for improving systems – I like to have stock built up for projects with continuous deadlines like blog posts and beats. Having more stock makes me less anxious about meeting a deadline and I can focus on making good music or writing a good post. To increase stock, simply increase inflows and decrease the outflows. In this case, I increased how many beats I made in a week, but released only 1 (I usually release 2). After a while, I started to build a stock and the beats I made later were of higher quality. My end goal is to make high-quality music while enjoying the process, so I modified my system to make that happen.

In every system, there’s always a limiting reagent. Finding that constraint and removing it will improve a system’s efficiency. Israeli author, Eliyahu Goldratt, suggests using the “Five Focusing Steps” to identify and eliminate constraints:

  • Identification – examining the system to find the limiting factor
  • Exploitation – ensuring that the resources related to the constraint aren’t wasted
  • Subordination – redesigning the entire system to support the constraint
  • Elevation – permanently increasing the capacity of the constraint
  • Reevaluation -after making a change reevaluating the system to see where the constraint is located

When dealing with systems that involved other parties, we introduce counterparty risk. The best way to deal with counterparty risk is to have a plan of action in the event that the other party doesn’t deliver on their end of the deal.


These ideas are foundational for analyzing and improving systems but the methods are endless and I recommend that you go out and find concepts and methods to build upon your knowledge of systems. Remember Gall’s Law, all complex systems evolve from simple systems, and these ideas are the components of creating a simple system to analyze and improve other systems. How meta.

However, the most important concept for analyzing and improving systems is understanding that we can always learn more and education never stops. Systems can be complex and there is always something more to learn about a system or systems in general