There are deeper concepts behind bottlenecks (aka constraints, limiting factors, or key factors).
First, different factors contribute different amounts to goal success. Second, there’s major variation in the amounts contributed.
E.g. I’m adding new features to my software. My goal is profit. Some new features will contribute way more to my profit than others There are lots of features my (potential) customers don’t care about. There are a few features that tons of customers would pay a bunch for.
A bottleneck is basically just a new feature that matters several orders of magnitude more than most others. So most features are approximately irrelevant if the bottleneck isn’t improved.
Put another way: improving the bottleneck translates fairly directly to more goal success, while improving non-bottlenecks translates poorly, e.g. only at 1/1000th effectiveness, or sometimes 0. (It’s possible, but I think uncommon, to have many factors that contribute similarly effectively to goal success. Designing stuff that way doesn’t work well. It’s the same issue as balanced production lines being bad, which Eli Goldratt explains in The Goal: A Process of Ongoing Improvement. It’s also similar to the Pareto Principle which says 80% of effects come from 20% of the causes – meaning most factors aren’t very important.)
What about the software not crashing, not corrupting saved data, and not phoning home with location tracking data? People want those things but I could have them and easily still make zero profit.
A good, typical model for viewing goal pursuit is:
There are many factors that would help, and just one or a few of them are the most important to focus on. This is because most factors have a significantly smaller impact. This is focusing on the key positives.
There are also many dealbreaker factors that cause failure if screwed up. This is avoiding major negatives.
People care about (1) conditional on (2) not being broken. Avoid anything awful, then optimize in the right places.
When buying a cat, I might try to optimize cuteness and cheapness, while also making sure the cat has 4 paws, a tail, no rabies, is tame, and isn’t too old. I want to do well on a couple key factors and also a bunch of easy factors need to be non-broken. It’s generally not that hard to brainstorm dozens of dealbreakers, many of which are quite easy to avoid in your current situation, even to the point of sounding silly to mention it at all.
(Dealbreakers are also contextual. If there were no cats available meeting all my criteria, I might lower my standards.)
The type (2) factors don’t require much attention. If a factor did need attention, it’d switch categories. (2) is just for failure conditions which are pretty easy to handle. This means most of our attention is available to focus on a few key issues.
I think this model is more effective than e.g. something like “consider all the factors; find out it’s way too complicated; try to approximate what you’d do if you had enough attention for all the factors”.
The model I’m proposing can be thought of as a method of organized, effective approximation from a more complex “take everything fully into account” approach. It tells us how to approximate. Thought of another way, I’m saying don’t distribute your significant figures equally.
You might think “Why not just weight all the factors relevant to my goal, then distribute my attention and significant figures according to the weightings?” The difficulty with that is how to weight things. Having a cat that doesn’t attack me and give me rabies is really important. If I’m just weighting factors normally, I’ll give that high weight because I want to reject any cat purchase which fails at that issue.
So if you just start assigning weights straightforwardly, you’ll give the type (2) factors high weights, e.g. 50,000 each, and if they all pass then the type (1) factors will function as tiebreakers worth e.g. 1-50 points each (minor detail: you can scale the weights so they add up to 1, but it’s easier to do that after you have all the factors with weights assigned – I don’t know what fraction of 1 a big factor should be until I know how many big factors there are). But the high value type (1) factors are actually the best place to put a bunch of significant figures. We don’t need a bunch of precision to address our cat having a tail, 4 legs, and no rabies. So attention allocation shouldn’t correspond to weighting.
In general, when we pursue a goal, there are many important but easy factors, and a few important but hard factors. For goals which are achievable but not easy, it has to be this way. If there were dozens of hard factors, that basically means we’re not ready to do it (though with a huge budget and a big team, sometimes it can be done – that lets you have specialists each working on just one hard factor each, plus some additional people figuring out how to coordinate and combine the results). But the standard progression is: if a project has 10 hard factors, that’s too many for me to focus my attention on at once, so I need to work on some easier sub-projects first – e.g. learning about some of those issues in isolation or doing smaller projects that help build up to the bigger one.
Another way to view the difference is that an increase in the key factors increases our success at the goal. E.g. adding the right new feature will increase profit. Or getting a cuter cat will increase enjoyment. Loosely, the more the better (there’s sometimes an upper limit, at which point it stops helping or is even actively harmful, or it keeps helping but now some other factors matters more). But for type (2) factors, the attitude isn’t anything like “more is better”; it’s just “don’t screw it up”.
In this analysis, I’ve basically assumed that type (1) factors and goal success come in matters of degree (can have more or less of them), but type (2) factors have a binary, pass/fail evaluation. The analysis needs extending for how to deal with binary goals, binary type 1 factors, and matters of degree for type (2). Those issues will come up and we need some way to think about them. I’ll leave that extension for a future post.
That extension is part of a broader issue of how binary and degree issues come up in life, how to think about them, how to convert from one to the other (and when that’s possible or not), when one type is preferable to the other, and so on. They’re both important tools to know how to think about and work with.
Factory Example
Now let’s go through an extended example to clarify how some of these issues work.
In my factory, I’m combining foos and bars to make foobars, which I sell. I have more bars than foos. So foos are the bottleneck. Getting even more bars won’t result in producing additional foobars. I already have an excess capacity of bars.
I also have excess capacity for assembly and QA. My current work area and team could produce many more foobars without hiring new people, getting more space, or getting new tools. And they could already check more foobars for defects.
And I have excess capacity in the market: I could sell more foobars if only I could produce them.
I also have excess capacity on foobar quality. I could redesign them to be nicer, but they’re good enough. Customers are satisfied. They do the job.
And I have excess capacity on price. Cheaper would be nicer, sure, but there isn’t much competition and my customers are people with a good reason to get a foobar. They get benefits from the foobar which are well above the price I’m charging.
Excess capacity means non-bottleneck.
Supply of foos is the bottleneck and the other issues are non-bottlenecks.
Using bars is limited by the availability of foos. That’s a traditional, standard bottleneck.
I call niceness a non-bottleneck because, as with foos, there is excess capacity. It won’t make much difference to achieving more of my goal (profit via foobar sales).
Key factor and secondary factor may be better terminology. It has some advantages, mostly because 1) foo supply isn’t blocking niceness from mattering in the way it’s blocking more supply of bars from mattering 2) niceness would help a little (a few orders of magnitude less than getting more foos, but not zero), which contrasts with bars – getting more bars wouldn’t help at all (in current circumstances).
Bottlenecks can be changed. E.g. I find a new supplier who can deliver far more foos than I need. Foos are no longer a bottleneck. Now what’s the bottleneck?What limits my profit? Maybe I’ll start running out of bars now. Maybe I won’t have enough customers and I’ll need better marketing. Maybe I’ll need to hire more workers. Maybe price will become the crucial issue: if I could lower the price, it’d get me a million new customers. Maybe price is key to breaking into the hobbyist market whereas price isn’t so important for the business market I currently serve.
To break into the hobbyist market, I might need to expand production capacity and lower the price and do a new marketing campaign. There could be several key factors. Doing three things at once is realistic (though not ideal), but we can’t split our focus too much. It’d be nice to find a way to improve things more incrementally. Maybe I’ll figure out how to produce foobars more cheaply first while leaving my price the same, and I’ll get some immediate benefit from higher profit margins. Then once I have the price low enough I’ll try to start selling some to hobbyists as a test (sell in some small stores instead of the big chains, or I could try online sales), and only if that works will I try to ramp up production and hobbyist marketing together.
I can also view the new project (selling to hobbyists, via expanding production, producing more cheaply, and a new marketing campaign) as a whole and then look at what the bottleneck(s) and excess capacity are. They might be quite unequal between the different parts of the new project.
(This is just a toy example. I didn’t worry about new distribution for hobbyists nor about designing a different version of the product for them which better meets the needs of a different market, nor did I worry about market segmentation and how to maintain my higher prices for business customers (a separate production version is one way to do that, using different regions is another, e.g. I could do my hobbyist sales in a different country than my existing business sales.))
Category (1) above (key positives) is the bottlenecks, the things that are valuable to pay attention to and optimize. Category (2) above (avoiding major negatives) is the non-bottlenecks, the things with excess capacity, which I can view as either “good enough” or “failure”. Relevant non-bottlenecks are important. I can’t just ignore them. They need to work. They’re in a position to potentially cause failure. But I’m not very worried about getting them to work and I don’t need to optimize them.
Principles Behind Bottlenecks
This post follows my Chains, Bottlenecks and Optimization (which has the followups Bottleneck Examples and Comment Replies for Chains, Bottlenecks and Optimization). This post expands on how to think about bottlenecks.
There are deeper concepts behind bottlenecks (aka constraints, limiting factors, or key factors).
First, different factors contribute different amounts to goal success. Second, there’s major variation in the amounts contributed.
E.g. I’m adding new features to my software. My goal is profit. Some new features will contribute way more to my profit than others There are lots of features my (potential) customers don’t care about. There are a few features that tons of customers would pay a bunch for.
A bottleneck is basically just a new feature that matters several orders of magnitude more than most others. So most features are approximately irrelevant if the bottleneck isn’t improved.
Put another way: improving the bottleneck translates fairly directly to more goal success, while improving non-bottlenecks translates poorly, e.g. only at 1/1000th effectiveness, or sometimes 0. (It’s possible, but I think uncommon, to have many factors that contribute similarly effectively to goal success. Designing stuff that way doesn’t work well. It’s the same issue as balanced production lines being bad, which Eli Goldratt explains in The Goal: A Process of Ongoing Improvement. It’s also similar to the Pareto Principle which says 80% of effects come from 20% of the causes – meaning most factors aren’t very important.)
What about the software not crashing, not corrupting saved data, and not phoning home with location tracking data? People want those things but I could have them and easily still make zero profit.
A good, typical model for viewing goal pursuit is:
There are many factors that would help, and just one or a few of them are the most important to focus on. This is because most factors have a significantly smaller impact. This is focusing on the key positives.
There are also many dealbreaker factors that cause failure if screwed up. This is avoiding major negatives.
People care about (1) conditional on (2) not being broken. Avoid anything awful, then optimize in the right places.
When buying a cat, I might try to optimize cuteness and cheapness, while also making sure the cat has 4 paws, a tail, no rabies, is tame, and isn’t too old. I want to do well on a couple key factors and also a bunch of easy factors need to be non-broken. It’s generally not that hard to brainstorm dozens of dealbreakers, many of which are quite easy to avoid in your current situation, even to the point of sounding silly to mention it at all.
(Dealbreakers are also contextual. If there were no cats available meeting all my criteria, I might lower my standards.)
The type (2) factors don’t require much attention. If a factor did need attention, it’d switch categories. (2) is just for failure conditions which are pretty easy to handle. This means most of our attention is available to focus on a few key issues.
I think this model is more effective than e.g. something like “consider all the factors; find out it’s way too complicated; try to approximate what you’d do if you had enough attention for all the factors”.
The model I’m proposing can be thought of as a method of organized, effective approximation from a more complex “take everything fully into account” approach. It tells us how to approximate. Thought of another way, I’m saying don’t distribute your significant figures equally.
You might think “Why not just weight all the factors relevant to my goal, then distribute my attention and significant figures according to the weightings?” The difficulty with that is how to weight things. Having a cat that doesn’t attack me and give me rabies is really important. If I’m just weighting factors normally, I’ll give that high weight because I want to reject any cat purchase which fails at that issue.
So if you just start assigning weights straightforwardly, you’ll give the type (2) factors high weights, e.g. 50,000 each, and if they all pass then the type (1) factors will function as tiebreakers worth e.g. 1-50 points each (minor detail: you can scale the weights so they add up to 1, but it’s easier to do that after you have all the factors with weights assigned – I don’t know what fraction of 1 a big factor should be until I know how many big factors there are). But the high value type (1) factors are actually the best place to put a bunch of significant figures. We don’t need a bunch of precision to address our cat having a tail, 4 legs, and no rabies. So attention allocation shouldn’t correspond to weighting.
In general, when we pursue a goal, there are many important but easy factors, and a few important but hard factors. For goals which are achievable but not easy, it has to be this way. If there were dozens of hard factors, that basically means we’re not ready to do it (though with a huge budget and a big team, sometimes it can be done – that lets you have specialists each working on just one hard factor each, plus some additional people figuring out how to coordinate and combine the results). But the standard progression is: if a project has 10 hard factors, that’s too many for me to focus my attention on at once, so I need to work on some easier sub-projects first – e.g. learning about some of those issues in isolation or doing smaller projects that help build up to the bigger one.
Another way to view the difference is that an increase in the key factors increases our success at the goal. E.g. adding the right new feature will increase profit. Or getting a cuter cat will increase enjoyment. Loosely, the more the better (there’s sometimes an upper limit, at which point it stops helping or is even actively harmful, or it keeps helping but now some other factors matters more). But for type (2) factors, the attitude isn’t anything like “more is better”; it’s just “don’t screw it up”.
In this analysis, I’ve basically assumed that type (1) factors and goal success come in matters of degree (can have more or less of them), but type (2) factors have a binary, pass/fail evaluation. The analysis needs extending for how to deal with binary goals, binary type 1 factors, and matters of degree for type (2). Those issues will come up and we need some way to think about them. I’ll leave that extension for a future post.
That extension is part of a broader issue of how binary and degree issues come up in life, how to think about them, how to convert from one to the other (and when that’s possible or not), when one type is preferable to the other, and so on. They’re both important tools to know how to think about and work with.
Factory Example
Now let’s go through an extended example to clarify how some of these issues work.
In my factory, I’m combining foos and bars to make foobars, which I sell. I have more bars than foos. So foos are the bottleneck. Getting even more bars won’t result in producing additional foobars. I already have an excess capacity of bars.
I also have excess capacity for assembly and QA. My current work area and team could produce many more foobars without hiring new people, getting more space, or getting new tools. And they could already check more foobars for defects.
And I have excess capacity in the market: I could sell more foobars if only I could produce them.
I also have excess capacity on foobar quality. I could redesign them to be nicer, but they’re good enough. Customers are satisfied. They do the job.
And I have excess capacity on price. Cheaper would be nicer, sure, but there isn’t much competition and my customers are people with a good reason to get a foobar. They get benefits from the foobar which are well above the price I’m charging.
Excess capacity means non-bottleneck.
Supply of foos is the bottleneck and the other issues are non-bottlenecks.
Using bars is limited by the availability of foos. That’s a traditional, standard bottleneck.
I call niceness a non-bottleneck because, as with foos, there is excess capacity. It won’t make much difference to achieving more of my goal (profit via foobar sales).
Key factor and secondary factor may be better terminology. It has some advantages, mostly because 1) foo supply isn’t blocking niceness from mattering in the way it’s blocking more supply of bars from mattering 2) niceness would help a little (a few orders of magnitude less than getting more foos, but not zero), which contrasts with bars – getting more bars wouldn’t help at all (in current circumstances).
Bottlenecks can be changed. E.g. I find a new supplier who can deliver far more foos than I need. Foos are no longer a bottleneck. Now what’s the bottleneck?What limits my profit? Maybe I’ll start running out of bars now. Maybe I won’t have enough customers and I’ll need better marketing. Maybe I’ll need to hire more workers. Maybe price will become the crucial issue: if I could lower the price, it’d get me a million new customers. Maybe price is key to breaking into the hobbyist market whereas price isn’t so important for the business market I currently serve.
To break into the hobbyist market, I might need to expand production capacity and lower the price and do a new marketing campaign. There could be several key factors. Doing three things at once is realistic (though not ideal), but we can’t split our focus too much. It’d be nice to find a way to improve things more incrementally. Maybe I’ll figure out how to produce foobars more cheaply first while leaving my price the same, and I’ll get some immediate benefit from higher profit margins. Then once I have the price low enough I’ll try to start selling some to hobbyists as a test (sell in some small stores instead of the big chains, or I could try online sales), and only if that works will I try to ramp up production and hobbyist marketing together.
I can also view the new project (selling to hobbyists, via expanding production, producing more cheaply, and a new marketing campaign) as a whole and then look at what the bottleneck(s) and excess capacity are. They might be quite unequal between the different parts of the new project.
(This is just a toy example. I didn’t worry about new distribution for hobbyists nor about designing a different version of the product for them which better meets the needs of a different market, nor did I worry about market segmentation and how to maintain my higher prices for business customers (a separate production version is one way to do that, using different regions is another, e.g. I could do my hobbyist sales in a different country than my existing business sales.))
Category (1) above (key positives) is the bottlenecks, the things that are valuable to pay attention to and optimize. Category (2) above (avoiding major negatives) is the non-bottlenecks, the things with excess capacity, which I can view as either “good enough” or “failure”. Relevant non-bottlenecks are important. I can’t just ignore them. They need to work. They’re in a position to potentially cause failure. But I’m not very worried about getting them to work and I don’t need to optimize them.