The Science of Decision Making: Turn Constraints Into Effectiveness & Productivity - Dr. Alan Barnard | Flow Research Collective Radio

I'd always been striving for a disruptive question that can help somebody overcome a limiting assumption. And one of our worst limiting assumptions is if we think something is impossible. And what I realized is the easiest way to help somebody overcome that is you get them to set a target to the extent that it's so ambitious that they say, now it's impossible. And then you say to them, it's impossible unless, And what you'll find is your mind can't help it. It will immediately start thinking about the conditions that could make the impossible possible. What does it take to do the impossible?

What does it take to level up your game like never before? What does it take for individuals, organizations, for even institutions to achieve paradigm shifting? Nothing is ever the same again.

Breakthroughs.

Our mission is to decode the neurobiology of flow and cognitive peak performance. Access the minds of maverick scientists, groundbreaking innovators, and world-leading experts to understand what it takes to achieve ultimate human performance so you can feel your best, perform your best, and accomplish your boldest goals. I'm your host, Rhian Dorris, and together with bestselling author Stephen Kotler, I present to you Flow Research Collective Radio. Dr. Alan Barnard, welcome to Flow Flow Research Collective Radio. Absolutely great to have you here. Excited for this one.

Thank you so much, William.

So we met at an event near LA about a month ago now, I believe, and I was mind-blown by your presentation. It was absolutely phenomenal and caused me to think about things that we teach here at the Flow Research Collective around flow and peak performance through entirely new lenses that are based on your work and your world of research. So before we go into your backstory and we talk about what you do overall, I would actually love to ask you a specific question, which was something you broke down in that presentation, which is why multitasking doesn't work. And I think the phrase you used was something along the lines of the, the faster you go, the slower the output, something like that. Maybe I'm butchering that one, but maybe you can give that breakdown as well.

Sure. So this goes all the way back to the mid-1990s when Dr. Goldratt, who was my mentor, he's the creator of Theory of Constraints, started getting organizations to ask, well, can this Theory of Constraints apply to project management? So if you think about flow, flow really has two dimensions. There's flow rate, so how much I can do, And there's float time, how long it takes me to do stuff. And if you ask most project managers, you know, why are their projects often late and over budget, what they will essentially do is they'll give you a list of reasons that all boils down to the world is not perfect. You know, there's uncertainty, resources are not available when promised, there's priorities that constantly change, etc. And Dr. Goldratt's discovery was that actually it's not the fact that there's uncertainty and variability. Of course you have to always deal with that, But it's a fact that when we buffer for it, we end up wasting it. And one of the biggest ways of wasting our scarcest resource, our limited time and attention, is when we multitask. So the example that I can share with the viewers and the listeners is I want you to imagine that you have 3 projects that you have to complete, or 3 tasks, A, B, and C, and each of these tasks are 10 days long. Now you can do them one by one, which means in total it'll take you 30 days to complete it. 10 days to do A, you know, another 10 days to do B, so you'll complete B by time 20, and another 10 days to do C, so you'll complete C by time 20. But of course what happens in reality is often these tasks or projects are for different customers, so they want us to give their project or task the highest priority and they want us to start it as soon as possible. Now, how do you make that decision? It's, it's a very difficult decision to make. So what we try to do is we have this heuristic that says you, you should be fair, right? So do all of them at the same time to make equal progress. So if you think about that, you do a little bit of A, and then you switch over to B, and then you switch over to C, And then you do a little bit of A again and a little bit of B and C. And so you continue. And if there was no switching losses, right, so if there was no cost to switch from A to B, you should be able to complete all the work in the same amount of time, in 30 days. But there's a profound difference, is that A, B, and C will all now take about 30 days to complete. Whereas previously the highest priority project or task you could have completed in 10 days, the second highest one you could have completed in 20 days. So even if there was no switching losses, which we know there is, then multitasking, trying to do multiple things at the same time, is still a really, really bad idea. And the fundamental assumption that we have to challenge when we switch from multitasking to single tasking is what you had hinted about, is we assume that the sooner I start something, the sooner I will finish something. And this is often the case when we manage complexity. We use a set of rules that work in a simple case, and we assume it will always work, and it causes local optimization. So the sooner I start something, the sooner I finish it is absolutely true if I'm doing one thing. But as soon as I do two or three things or more, then that assumption no longer holds. So I want you to imagine that those three boxes, right, A, B, C, each of them are 10 days long. If I started B later, I don't start it as soon as possible, I only start it once I've completed A, I will start at a time 11 and I will finish at a time 20. In the example where we were multitasking, we are constantly switching. So I'm starting B basically as soon as possible, maybe even after day 1, right? I do a day of A and then I switch over to B. I work on B for a day. So I'm starting B on day 2, but I will end up finishing it on day 28. So what we learned from this example is the later I start, the sooner I finish. And this is a massive swift shift in the way that we think about how do we complete any type of work, you know, whether it's your simple to-do list that you are going through or whether it's a massive portfolio of projects. Now, of course, when you consider the fact that they are wheel-switching cost, right? It's not just that cognitively we have to kind of rewire ourselves to think about, okay, where were we with, with A when we are restarting A? But practically, when you are doing everything at the same time, you're also asked to report your progress, which consumes time, and you are asked to essentially explain why things are taking longer than what it should. So when you add up that, it's not uncommon that those same 3 tasks, and you remember the little exercise that we played, right? That that same 3 tasks would take the equivalent of 60 days to complete rather than 30 days. Essentially what we do with a little exercise that your viewers and listeners can play is imagine Project A is writing characters 1 to 10, right? Just simply writing them down in a column. Project B is writing letters from A down to J, just 10 letters down. And Project C is just repeating 3 symbols 10 times, right? And essentially multitasking is where I'm writing a 1, and then I'm writing an A, and then the first symbol of a square, and then going back to 2 and B and C. And what they will experience is when you are multitasking, even with an activity that involves a really simple task where there shouldn't really be much switching cost, it takes you double the amount of time to complete versus single tasking, which is focusing and finishing, just writing down 1 to 10, A down to J, and then doing the 10 symbols. And that's the essence of multitasking is it's all based on this false premise that the sooner I start something, the sooner I will finish it. Absolutely true if I'm doing one thing, completely untrue if I'm doing two or more. And as most of your viewers and listeners will know, That's our life, right? We are always doing at least 2 or more things at the same time. So multitasking is a really bad idea. Everything takes longer, you get much less done. As a result, because costs are obviously often associated with time, it costs you much more than what you expected. And you also make much more mistakes because if the mind goes into this multitasking mode, it's almost chaotic. And you just end up, you know, making more mistakes.

It's a phenomenal breakdown. I appreciate that. I just want to really quickly actually share my screen and just show, if it's okay, briefly one of the visuals you showed with us just to really drive this point home for folks. So this is, this is what you were referring to there verbally, which is that when you single task, the actual total time to completion is drastically lower than when you multitask. With, again, the key point being, and this was the profound insight that you elicited in me, that based on decision science, the later you start, the sooner you finish truly is the case, even though it is radically counterintuitive.

And now that you have the visual on there, I'm sorry if you can just bring it back, just another thing from a business perspective, you know, so it not only negatively impacts the individual's productivity, because everything that, that we do takes longer and we get much less done. We feel more frustrated, we make more mistakes, but it also impacts the team's productivity and ultimately the company's productivity in terms of cash flow. As you can see with multitasking, I have to, I have to invest all the dollars to start A, B, and C right up front, and then I have to wait all the way until the end, which in this case is about by day 40 before I can start getting the cash back from completing those projects. Whereas if I was doing it in single tasking, I only need the investment to fund A, I can use the cash generated from A to fund B and then C doing the same way. So also from a cash flow perspective, it is a much, much better way of executing any type of work.

Thank you for that clarification. And what I did straight after listening to your presentation, and what I suggest everyone who's listening to this too, is look at all all the projects that they have present professionally within their business or their job and personally, and see what ones they can do sequentially one after another rather than in parallel. And per that visual and per your point, they're gonna find that on aggregate, the total time to completion of the final project is gonna be much sooner than if they run them in parallel as they're currently doing.

And again, there, it feels like common sense that if I could do things in parallel, I should be able to do them faster, right? It feels like if I do things in series, it will take longer. And yet, this is a very simple proof that the opposite is true when it comes to us as human beings doing work.

Yeah, the counterintuitive nature is that, you know, you would assume if the things are actively moving forward, all of them are, that they're going to all be completed faster. But in reality, it's actually more effective to leave things completely static and move one thing forward actively, and then to just repeat that process.

Just the implications of this is we recently worked with a construction company that were doing condos, and they basically had one team working on two condos at the same time. And it was generally taking them about 120 days to complete these two condos. We got them to just stop multitasking, just work on one condo at a time, make sure that they have a full kit. So when they start, everything is there and they were able to cut the lead times down by almost 50%. They were doing now 2 condos in about 70 days compared to 120 days. So that is the massive impact you can have. And the effect is cumulative. You know, if you just stop multitasking yourself, you don't need anybody's permission for that, right? Because as you can see from the example, both A, B, A, B, and C will benefit if you just stop multitasking. It's less important on how you prioritize A, B, and C. It's more important that you do, right? It's when you don't have a priority and you try to do them all at the same time that it causes damage. So when one person can stop multitasking, there will be a benefit. But if everybody can do it and you synchronize their priorities, everybody's now working on A, then we go over to B, then C, then the business impact is massive. Yeah, thank you.

Thank you for clarifying that. One thing I would love you to give us a breakdown on in a few minutes after I ask you this question is some examples of the work you've done. I know you gave an incredible example to us of a huge initiative you did with Microsoft, and that was really fascinating. So I'd love to touch on that in a moment. Before we do that, I want to mention that your bachelor's was in industrial engineering. and then you did a PhD in management of technology and innovation. You told me that there was a big emphasis on decision science within this, and your thesis was titled How to Identify and Unlock Inherent Potential Within Organizations and Individuals. And so the first question is, what was the core point that you were making in your thesis that had that title? Sure.

When I, um, you know, I had this life goal very early on that I mentioned of, of helping others identify and Unlock inherent potential. So that requires a belief or an assumption that there's already potential there. You just need to find it. It's invisible, right? But it's inherent. You need to find it. What I soon discovered was that really what's holding us back is not our starting conditions, but our starting assumptions. You know, whether you believe you can do something or can't, you're right. That's a famous quote of Henry Ford. So I had a really interesting experience very early on in my life where I realized that whether whether you believe you can do something or can't makes a profound difference, because at least it will get you to try. So then I read Dr. Goldratt's book, "The Goal," and already in the foreword of "The Goal," he hooked me because what he had done is he had verbalized it, at least in my understanding, the simplest formulation of the scientific method. He said, if you want to make a breakthrough in any field that you are interested in, It just requires two steps. Step number one is have the courage to face an inconsistency. So inconsistency is simply an expectation gap, right? Our current performance is it takes us 4 weeks to get this thing done, but the actual work time is only 4 days. Like, why is there such a big gap? You know, when I place an order for computer and they quote me 4 weeks, and I know it takes them a couple of hours to assemble assemble the computer, or I order a passport from the government agency and they quote me 3 months when I know it's probably a couple of minutes to get a passport done, right? Or I know my bottleneck is capable of doing 20 a day, but the system is only doing 5 a day. Like, that's an inconsistency. There's an expectation gap. But then he said the second question is that, or the second step is have the courage to challenge basic assumptions. So imagine there's this expectation gap, right? How do we cope as human beings with this expectation gap? We basically do two things, neither of which is very useful. The first one is we find someone or someone to blame for the expectation gap, right? This is kind of a midlife crisis, right? Suddenly you realize midlife, you haven't achieved anywhere towards your goals. You've promised all your friends you're going to be retiring by the age of 40, and you're nowhere close to retiring. And now you have to find someone or something to blame. So you blame your family. If I didn't have a wife and kids or a husband and kids, you know, I could have done so much more. If it wasn't for the government, it kind of relieves the suffering, but it's not that useful. The second way of coping with an expectation gap is we start lowering our expectations. And to some degree, that's even more devastating. So what Goldratt did was he challenged us to challenge the assumptions on which our actions are based to realize that expectation. And as soon as I read that, I thought, okay, finding expectation gaps are pretty easy. You know, you walk into any environment, and I still do that today, If I go into a mining environment, I know nothing about mining, how do I know it's possible to do much more? I ask them, what's your best day? What's your average day? And normally the gap is 50%, right? And the same with time, like what's the fastest you've ever done it? How long does it normally take you? The gap is at least 50%. So that part, step 1 of having the courage to face an inconsistency is not that difficult. Knowing what assumptions to challenge, is the difficult part. And that's what I decided to do my PhD on, is I asked myself, is it possible to come up with a systematic process, step-by-step process, that can take any group or any individual through the process of helping them discover their own limiting assumption or belief and finding practical ways of challenging and overcoming that? And that's what I ended up doing my PhD on. I first tested it in the private sector We had a 5-day workshop. We then tested it in the public sector with for-purpose organizations. And then finally we tested it with individuals. And the outcome of that is I developed a new process. It's kind of similar to the pro/con list that was developed by Benjamin Franklin on how to make decisions, but it's called the Pro/Con Cloud Method, consists of 5 steps. And we've embedded that into an app called the Harmony Decision Maker app. And if you go through those 5 steps, steps related to any major problem that you're facing or any decision conflict you have, you are guaranteed to make a breakthrough because in every step, every step has been designed to help you to uncover and overcome a common decision mistake or limiting assumption or belief. So that's what I ended up doing my PhD on.

Decision-making is something we train directly within High Flow Leadership, which is one of our leadership development trainings. And it is remarkable how little people explicitly train the skill of decision-making, yet how much it makes up the overall effectiveness of the majority of knowledge workers, especially leaders who are allocating resources, whether that resource is a group's attention or capital or time or people or whatever the case may be. So I'm curious if you could break down what those 5 steps are and then what you see as biggest sticking points or mistakes people make when it comes to decision-making?

Sure. So the first mistake that we make is we stress about problems that are out of our control or we can't do anything about. So the first step is literally called My Important Problem, and it asks the user, tell me what is this problem that you're facing and explain to me why it is important for you personally to resolve it and why it's important for those that you care about to solve it. So So as I mentioned, as we try to prevent the mistake of worrying about or investing your scarcest resource, your limited attention on something that's not important or you can't do anything about. So essentially what the process and the app does is once you've answered those questions, it highlights those two boxes that you've written of why it's important to you and others. And it says, have a look at that and make sure it's really important to you. If it is, then go to step 2. Step 2 then says, okay, you have a problem. Let's say I'm overweight, or our profits are below our target expectations, right? That immediately puts you into a conflict. So a more precise definition of a problem is actually an unresolved conflict. So if my, my problem is I'm overweight, then I might have a conflict between go on a diet versus don't go on a diet, or go for for extreme operations like a gastric bypass or don't. Now I'm stuck. So what's the mistake that we make here is that we only typically look at the pros and cons of the decision, the option that we are considering, and we all know that we have a major bias in certain directions. So we have a major bias towards not losing anything compared to gaining something. So when we ask for the two options, like say it's quit smoking, continue smoking, right? So there's a change that I have to make and the status quo. We ask the person to look at both the pros and the cons of each of those options. So not just the pros and cons of the change, but also the pros and cons of of not changing. And what we got from that was really fascinating, is that we essentially procrastinate because of an exaggerated fear of loss or effort or risk. So if the pro of continue to smoking is that it gives us a way of coping with stress, the reason why we don't stop is not because we don't know how good it will be if we stop smoking, It's the fact that we are scared of losing the positive that we uniquely associate with the status quo, which is I have a way of coping with stress. The other reason is the negative of the change. So if I'm worried that I will gain weight, that's the con of stopping to smoke, or the effort it will take and what if it fails. So what we realize is that from that simple diagram, you have the two changes, Above each of those, you have their pros. Below each, you have their cons. The pro of not changing and the con of changing, those are, are capturing our exaggerated fears of loss, effort, or risk that could block us from making a good change. And that diagram, which I call the Pro-Con Cloud, simply exposes that it transfers those fears from the subconscious mind into the conscious mind when you're asked to write that question. And then you can challenge them. You can say, okay, what can I do? So I want to stop smoking, but I don't want to give up having a stress coping mechanism. So what can I do there? Well, maybe I, I do meditation. Great. So I call it change plus plus, right? What can I add so I don't have to give up that stress coping mechanism? And what can I add to prevent me from gaining weight because I start snacking? Maybe I I can do exercise or have some discipline in doing that. So that's what step 2 is about, is to verbalize the problem as an unresolved conflict. Look at it from all sides, like Joni Mitchell's song, you know, look at the cloud from all sides now, both the pros and cons of each side. But then look at those exaggerated fears of loss, effort, or risk that can cause you to procrastinate. The reason why we overreact sometimes is, if you think about why do you sometimes lose your temper, Why do you sometimes make a decision that's like short-term optimization, like have a piece of cheesecake when you know you probably shouldn't be? And that's because of an exaggerated frustration with the status quo, the con of the current status quo or no change, and an exaggerated expectation of the future, which is the pro of the change. So from that diagram, again, you can check it once you've completed the answers for each of those boxes. You can— the app will pause you and say, look at what you've written down. Could it be that you're exaggerating the cons of the status quo, or you might be exaggerating the pros of the future, and that could cause you to, to overreact? So that's kind of what this— the second step is. The third step, the mistake that we're trying to prevent there, is when we face a conflict, we are almost trained to give up, to compromise. In fact, the motto is you have to give up something to gain something, right? So we both have to lose something in order to gain something. There's a fundamental belief in science that all conflicts can be resolved as long as there's a common goal. So what we do in step 3 is we guide the user through 4 simple ways of resolving that decision conflict in a way that's a win-win. The first one I've already shared with you, it's called change++. Okay, I wanna do the change. Change is not viable if it means I have to give up the pro of not changing or live with the cons of changing. So what can I add to prevent that from happening? The second option is not change++, right? So think about the conflict between implementing a new IT system versus is keeping my legacy system. While keeping the legacy system is not a viable option, if it forces me to give up on the pro I think is uniquely related to the new IT system, or it forces me to continue to live with the cons that's related to the legacy system. So again, it says, what can I add to get most of the pros from the new IT system and deal with most of the cons from the existing system? Interesting one, and that gives me a new option. And then the third one is when and when not to change. And the last one is another change, kind of thinking out of the box to say that now that I really know what I want, all the pros, what I don't want is any of the cons. Can you come up with a different way, maybe outsource the whole of our IT? So that's what we do in step 3, is the mistake is that people feel that the only way to resolve conflicts is by compromising or maybe settling for a win-win. Win-lose versus there's always ways to find win-wins. And we, we give the user 4 options and we guide, we encourage them to try all 4 and then select which one has the biggest upside if it works and the smallest downside if it doesn't. And then step 4, imagine you've come up with a breakthrough. Why do so many breakthroughs not get implemented? Because of 2 mistakes. We use yes-buts, the kind of reservations that come up in our own mind and in those that we try to convince mind. We use them as excuses not to try it, right? So we present our breakthrough and they go, yes, but, you know, I don't think it will be a win for everybody, or it might have some potential negatives, or there's some implementation obstacles. And immediately we say, You know what, you're right, and we don't try. The other opposite mistake is we hear these yes-buts, but we simply ignore them. So what we do in step 4 is we encourage the user to think of all the yes-buts that they or other stakeholders can raise, but for each yes-but to add a yes-and. How do you overcome that yes-but? And that makes your solution, your breakthrough more robust. So we call it yes-but planning, you know, using your yes-buts and turning them into yes-ands. And then the last one is, imagine you have a breakthrough, it's, you've made it so robust, it's counted all the yes-buts that there could be. Why could it still fail? Is that we're not trained to do good experiments. We suck at doing good experiments. So if you think about going on a diet or using that ab roller that you just bought from the internet, infomercial, right? Is unless you are fully compliant to the new breakthrough, those new rules that you've come up with, you'll never know if the bad outcome that you got, was it because your breakthrough wasn't really a breakthrough, or maybe you neglected one of the major yes-buts you addressed it, or the fact that you didn't actually do what you were supposed to do. So in step 5, we show you how to design a really good experiment to make explicit those assumptions that you are challenging so you can get fast feedback to learn from it. And that's basically what the 5 steps are. We've tested it both in the private sector to make massive breakthroughs in redesigning supply chains, for example, the case study that you mentioned about Microsoft, um, or in the public sector to be able to help government agencies do much more with the, the limited budget that they've got all with individuals dealing with issues all the way from kids, you know, how do I deal with being bullied at school? Or, you know, how do I recover from a traumatic event? Or my grades are below expectation, what should I do? And that's kind of the rigorous testing that we've been doing over the last decade or so.

Amazing, Alan. That's a fantastic breakdown. One thing I want to highlight for people in what you said there is the skill of problem definition. Because decisions are really about just selecting solutions to problems. But it is unbelievable in my experience, the degree to which people underemphasize the actual definition of the problem for which they are trying to make a decision, which again is ultimately just selecting a solution to the problem. And a simple example is trying to decide on which car to buy buy without even having to find the fact that the problem is not which car to buy, it's how to get to work every morning. And, you know, as soon as you effectively and accurately define the problem, a whole swatch of potential solutions open up that go way beyond the solutions you're assuming are options.

So yeah, it's a great analogy. It's, um, one of the biggest lessons I've learned from my mentor was If you're evaluating two options and they're pretty close, it's guaranteed that you've missed something. Reality is not close. They're always far apart. So you've probably missed some key criteria. Like for me, buying a car really boils down to one thing, is how I feel when I get into that car and I drive it. That's the only thing that matters to me. When that feeling is not there anymore, anymore, it's time for a new car. And once I'd verbalized that, you know, it made the selection much, much easier. Because that was the thing that my mind could focus on is how do you feel when you get into the car? That's it.

Right, right. Yeah, no, that makes total sense. I love that. I want to just touch on your mentor, Eli, what your journey with him, and then also I'd love for you to break down breakdown for folks who he is, what was so profound and seismic about the impact of his book, The Goal. I read The Goal, by the way, before you and I met, probably about a year and a half ago. Absolutely loved it. I found it just utterly fascinating to learn about the theory of constraints and the dynamics that can contribute to throughput and flow and productivity. So if you could give us a breakdown on a few things here. Number one, who Eli was, what was so impactful about his book, The Goal, and his research in general. And then let's come after that to the, to the Microsoft case study.

Sure. So, uh, Dr. Eliyahu Goldratt was a— he started off as a scientist like many of these top, you know, Deming was a scientist, etc. Um, and he had done some research in terms of the, the flow of, of, um, molecules and systems, um, how heat is transferred, and he found out this concept of a singularity, that there was one thing that actually limited or governed the flow of energy through every system. He then got an opportunity to apply that to a manufacturing environment and found out that there was this— that same concept was there, that the biggest mistakes that you can make when you're trying to make commitments or you're trying to plan is to consider every possible resource. It makes it an almost unsolvable mathematical problem when these resources, because they are all interacting with each other, there's variability, etc. You can dramatically simplify the problem if you focus on what's the bottleneck. What is that one resource that limits the throughput rate, essentially governs the upper potential of what that system is capable of? The analogy is is the weakest link, right? How many weakest links are there in a chain? One. Does it matter if there's 3 links or 300 links? No. There's always only one weakest link in the chain. And I think that insight to help managers focus on where to improve is profound, because suddenly I don't have to look at everything. I can try to find out where my bottleneck is. Unfortunately, what happens in real-life systems is over time or by design, they design systems that are, that have balanced capacities. So they say, look, we want to be able to cope with 10 customers a day, make sure that each of the processes can do 10 per day. But essentially what happens is you then create a system that is chaotic. The bottleneck is moving all the time and it's It's really, really hard to make reliable predictions about what will come out, and it's extremely difficult to know how to manage that system. So a very counterintuitive way of doing it is to actually pick a resource and make that the bottleneck, slow it down so that upstream there's enough capacity to build up inventory in front of your bottleneck to make sure your bottleneck is never starved. And downstream, there's some protective capacity to consume everything that the bottleneck can produce so that the bottleneck is never blocked. And if the bottleneck is never starved or blocked, the system will be able to do what the bottleneck is doing. And that insight was really profound. So the goal was written about a real case study where, you know, this Alex Rogo guy arrives at the plant, his executive is there, He gets hammered because another customer order was late. And he's troubled with this. Why is it that they can't get work to flow through the system? Why is it always taking them longer to complete orders than what they expect? And why does it end up costing them more? And through the help of a kind of a guru or mentor in the book called Jonah, Jonah is asking him all the right questions. And through that, he discovers the importance of knowing where your bottleneck is, making sure that you are fully exploiting that bottleneck so it's never starved, it's never blocked, it's never waiting for, you know, planned or unplanned downtime or for setups. And that's the way to dramatically improve the performance of the system. So it's a massive simplification that you can apply to a system that has just to 3 resources or to a system that has thousands of resources. I love that.

And a really practical application of that for leaders and for knowledge workers in general is to constantly ask themselves, what is the bottleneck in my organization, in my team, in my workflow? And then to focus disproportionately on that bottleneck until it's alleviated due to the point you made, which is that the system can't exceed the bottleneck.

There's kind of two things that I think is a little bit counterintuitive about Theory of Constraints. The one thing is that people often confuse it with debottlenecking. So imagine debottlenecking means I've got a system, you know, the first process can do 11, and then the next one can do 9, and the next one can do 7, and then 6, and, and 10, and 8, and we've got a demand of, of 10, right? So I find the first one and I move its performance from 5 to 10, and then I take the second one and I move the performance from 7 to 10, etc. That ends up creating a balanced system that everything can do 10, but of course there's variability. So if, if the first one has a bad day and it can only do 5, the whole system can only do 5. So these balanced systems often underperform. So fear of constraints is all about unbalancing capacities in order to balance the flow. So what I want is I want the work to flow through the system. It should never wait for resource, whereas what we do is we don't want resources to wait for work, so we overload them. And that's a profound difference in terms of how I'm thinking about my operation. Am I trying to maximize deutilization of my workers, or am I trying to maximize flow? If I want to maximize flow, I do not want work traveling through the system to stop and start. And that to me is very similar to what we have when cognitively we are multitasking, right? Everything is stopping and starting. Things are not flowing through. And, and And that to me is a profound insight. It's not about balancing capacities, it's about balancing flows. And the only way to have balanced flows is to have a single bottleneck with protective capacity up and downstream to ensure that the work just flows through the system.

It's amazing how cleanly the use of flow in the context of an operation or a plant like this analogizes to a psychological or physiological flow state like we talk about. That was one of the things when we met that I just found fascinating.

Absolutely.

Both types of flow are so similar even though they're completely distinct at the same time.

The second thing that has a great analogy with, with us getting into a flow state of mind is imagine cognitively we have the capacity to say do 10 tasks a day and we are currently only doing 3 tasks a day. So there's a huge gap cap there, right? Now the question is, how are we losing capacity? How are we losing flow potential? The same is true in a factory, right? I, I often go into an environment and I say, okay, where's the bottleneck? The bottleneck— the design capacity is it can do 10, but your system is only doing 3 or 4. Like, where is the rest being lost? Yes, there are cases when we when we lose capacity, when we are starved of work or when we are blocked or when there's planned or unplanned downtime. But by far the biggest loss of capacity is when we produce stuff or work on stuff that's not needed now or may never be needed. So almost always the first job when we are implementing Theory of Constraints with a person or with a big company is to identify what they can stop doing to release capacity. And I'll give you a practical example. I was working a couple of years back with one of the biggest high-tech companies in the world. And they asked me to help them, you know, find out where the bottleneck is. And it's normally pretty simple. Just look for the backlog, right? Which resource has the biggest inbox in front of them, the biggest backlog of stuff waiting for them? So it turned out to be their top developer. These are the people that have to both develop the new fancy functionality for the next version, but they also have to solve the really complicated bugs of the previous version. So I said, well, how can you get them to do more? They said, well, these guys are already working 10, 12 hours, and we suspect they're probably working 6 days a week, never mind 5 days. So there's nothing. The only way to get more is to add more resources. And by the way, it takes years to create these resources to give them the experience and the training. So I asked him a question. I said, how much of the functionality in your software is actually used by users, your most advanced users? What percentage of functionality that exists do they use? For, for the viewers and listeners out there, just imagine software that you know really well maybe Excel or PowerPoint or whatever, right? Think about of all the functions and features that they've got, what percentage do you think you're using? Their answer was about 20 to 30%. So that means that there's 70% of the capacity of their scarcest resources being wasted working on stuff that's not needed or at least not needed now. And that's by far the biggest opportunity to get more out of your existing resources is simply to get them to stop doing the stuff that's not adding value. And the same in a manufacturing environment, right? You have one process that says, you know, my minimum batch is 1,000 tons. Even though the market only needs 200, it makes me efficient when I'm doing 1,000. What they don't realize is that 800 additional tons consumes raw material and it consumes capacity of every other process to produce it to get to the end. So everything is losing 80% of its capacity because one resource is trying to be efficient by saying, oh, I'm gonna do too many setups if I do two batches of 200 tons at the same time. So that to me is by far the biggest, insights of Theory of Constraints is it's not debottlenecking, it's the opposite of debottlenecking. It's designing a bottleneck and making sure upstream and downstream you have enough protective capacity to ensure that the work is just flowing through that bottleneck. And secondly, you know, like I said, it's not about just squeezing more by adding more resources. It's first of all deciding what I can stop doing.

Could you give an example of one of the engagements you've done? I know you've worked with Microsoft and Tata Steel and Nike and Cisco and Intel and all sorts of enormous organizations, Fujitsu. Could you give an example of one of those engagements, what it was that you were able to help one of those organizations with, and then one or two key insights from that? And then after that, we'll close with a final question.

Sure. So I think partly as to think about flow as having 3 parts to the flow, right? Every organization, and even us as human beings, you have 3 types of flow. You have material flow, or the product or service that you provide is flowing through the system. So at the same time, there's a flow time, the total time that they spend inside the system that you want to reduce. There's a flow rate that you want to be able to increase. There's also cash flow, and then there's information flow. Every time I can reduce the time for any of those three, the organization benefits. So the type of projects that we do, some of them focuses on the flow time, some of it focuses on the flow rate. For example, in Microsoft, they asked us to help with the redesigning their overall supply chain. They wanted a supply chain that was capable of putting any product through with the highest throughput, the shortest lead time, the fastest responsiveness, and the lowest levels of cost and inventory. How did we know that it was possible to improve? We measured the level of shortages and surpluses at the retail level of the products that they were selling, like their smartphones, the tablets, keyboards, et cetera. And we then designed a solution that were quite counterintuitive, that we're using some simple heuristics of getting feedback of what's actually selling. So not trying to solve the problem with more sophisticated or advanced forecast, but rather by getting the demand signal of what's actually selling faster back to everybody. And in the same financial year, we were able to reduce total inventories by over quarter of a billion dollars and got sales up by a few hundred million dollars by reducing surpluses that would then be sold as, you know, at a discount or would occupy space that could have been used to serve others. So that's one example. When it comes to cash flow, for example, we worked with one of the largest book publishers in the world, and they had this very ambitious target of doubling their net profit. So their sales in the US was about $1.5 billion, that had profit of about 150, and they, the CEO had committed to doubling the net profit from 150 to 300. And we looked at, well, what are the opportunities to do that in the simplest, fastest, lowest cost, lowest risk way? They assumed that to double net profit, they would have to double volume, which means that they would have to publish more books and getting into more markets. Both of those very costly and risky. People kind of ignore often leverage. So I asked them, do you think that customers care, really care, whether they pay $20 or $22 for a book? And they said, probably not. So I said, okay, if you could just increase the average selling price of a book by 10%, your revenue will go up by 150 million. For 1.5 billion, add 150 million. Your variable costs won't go up, your fixed costs won't go up, that whole 150 will drop to the bottom line. Now you think about which of those two options that you have to double your net profit is the simplest, the fastest, the lowest cost, the lowest risk. Finding a way of increasing just the average selling price by 10%, and that doesn't mean actually increasing increasing the price to customers. That's the worst case scenario. It just means reducing the discounts that you offer or maybe selling more direct or changing the product mix. So those are the type of examples that all is about is what are you trying to do? Are you trying to get more? You trying to do it in less time or with less resources? And based on that, the approach is always the same. Find that one thing that if you could just get that done, you will have the biggest impact. So in a production environment, it's often stopping overproduction, cutting dramatically the batches. That's another thing that we did for, for the, the book publisher company is the rule at that stage was if a retailer orders books, they typically order 3 months of books because they want to get good discount on that big batch. We asked the book publisher Why do you force them to order 3 months? Because 3 months isn't 3 months, right? If it's a bestseller, that 3 months can sell out in 3 days. If it's a bad seller, they'll never sell it out. And that turned out to be the key part, is that 86% of all books that are ordered by retailers never sell their first order quantity. Can you imagine that? The first order that's placed, they never sell out that in the history of the book. So it doesn't matter if you have very fancy algorithms that are working on replenishment. So what we did for them is to say, look, you can ship books to almost every retailer in the US on a daily basis, or at least a couple of times a week. Why on earth would you ask them to order 3 months in advance? Just get them to say, The most I think I will sell for next week is this. That's my first order. So if it turns out they didn't sell that, they only stuck with one week of inventory. If they do sell it already on day 2 or 3, you can see that they're ordering more, they're selling more than they expected, you can quickly replenish. And now you have the inventory sitting in your distribution system to replenish them. But what happens if you push out all the inventory because everybody's ordering 3 months? There is nothing to replenish with. So those are just a few examples of the order of magnitude of improvement that you can get done. And if I go into an environment, my automatic assumption is if they want to do more, probably with the same resources, it's possible to do somewhere between 20 and 25, 25 and 50% more with the same resources, because I know what to look for. The mistakes that they make is they overproduce, They don't have enough buffers. They cause starvation of the bottleneck. They don't pay enough attention to planned and unplanned maintenance, et cetera. From a flow time perspective, I would be surprised if we can't reduce the flow time anywhere between 25% in the most efficient industries all the way to up to 75% in the ones that's not that efficient. And the same with cost, there's somewhere between 10% to 30% reduction of avoidable cost is always possible.

Amazing, Alan. It's a phenomenal example. Thank you for that. So to close here, I would just love you to share where people can learn more about you. And then also what the parent category is for the theory of constraints and this sort of research. Is it management science? Is it decision science? Just so people can learn more. And then finally, what are the resources you would recommend to folks to go deeper on these topics?

So our website is Goldrat Research Labs. We've developed a whole range of apps that each of them have been targeted on specific decision mistakes. So we have the Harmony Decision Maker app that I mentioned to you that helps us to identify and overcome these exaggerated fears of, of, of loss, effort, risk, or exaggerated frustrations or expectations. So it helps us to create breakthroughs in any environment. We also have a range of simulation apps and that deals with a common decision mistake where our minds are not capable of dealing with a lot of complexity and non-linearity. So today we can build digital twins for full supply chains and full project management systems that help us to press the play button to see what will happen. So we've got a whole range of simulation apps that you can use to test hypothesis in a very low-cost low-risk virtual way and, and use it to teach your, your team how the dynamics of the system work. So those they can find on harmonyapps.com. So, so that's the second resource. And then I have my own personal, um, dralanbarnard.com website, and there's also a YouTube channel with hundreds and hundreds of hours of free keynotes and sharing of insights. And then lastly, my podcast series is called Impossible Unless, and that's kind of— I've always been striving for a disruptive question that can help somebody overcome a limiting assumption. And one of our worst limiting assumptions is if we think something is impossible. And what I realized is the easiest way to help somebody overcome that is to, you get them to set a target to the extent that it's so ambitious that they Now it's impossible. And then you say to them, it's impossible unless— and what you'll find is your mind can't help it. It will immediately start thinking about the conditions that could make the impossible possible. So it's a great little hack of over— of finding ways of making the impossible possible. So the podcast series is each episode we pick something that people think is impossible and and we share with you the unless conditions to do it. Your second question is, where does theory of constraints sort of fit in? Probably the part that you'll most find it in is operations research because of its background, that the goal was written in a manufacturing environment. But you'll also see it, it has many elements to it. So you can also find it in business strategy, decision-making, because each of the applications of theory of constraints Constraints is just a set of simple heuristics to improve flow through the system. But even though they're simple, they often are counterintuitive, like don't multitask, or the slower I go, the more I get out. Those are the type of things that you can get. And then in terms of reading up, the goal is still the most common resource that that we recommend for people to read. There's also an amazing movie that's about 50 minutes long that people can get if they don't want to read the book. But if your field is more project management, Goldratt wrote Critical Chain Project Management that shows you how the theory of constraints is applied to project management. If your field is more retail and distribution, there's a book called Isn't It Obvious? that he wrote. And if your field is more high-tech, there's a book called Necessary But Not Sufficient. Each one of those are real classics within those fields, and they're really good at coming up with those simple but very profound insights of how to improve flows through those systems.

Amazing. Thank you so much. Incredible, incredible insight and wisdom shared and phenomenal resources for people to dive deeper. I know lots are going to. So thank you so much for coming on Flow Research Collective Radio. It was an absolute blast.

Thank you so much for the invitation.

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