I want to talk about, the theory from the recent researcher in learning. There is a really famous professor in this literature the [UNKNOWN] professor Angynis, chris Angynis. His name here, Chris Angynis He was a professor at Harvard. And he was, actually a pioneer in action learning theory. One of his well known theories is about single-loop versus double-loop learning. Let me just give you an example. Let's say, this example is from the great professor [INAUDIBLE] was was at Harvard, and he told a story that, let's say, in this room, we have a goal. Our goal is to keep the temperature, keep the temperature at 20, 20 celsius. So our goal is keep the temperature 20, but the problem is that the temperature, Is not staying, steadily. Let's say, when the temperature goes down, was below 20, was below 20, we turn on the heater, we turn the heater, we turn on the boiler. And then the temperature will go up, right? But then soon the temperature will go down again and whenever the temperature goes down, we start the boiler, we start the heater and then the temperature goes up, and so and so forth. So, it's not staying at the [UNKNOWN], it always goes down, goes up, goes down, and goes up for traiting. So, after why, I was a sort of of exhausting, and I try to fix this problem once and for all. So I look around the room, look around the room and then I found out that at the back ,at the back of the room there was a broken window. ha! That's the reason, that's the reason why the temperature does not stay at the desired level. And I decided that, I gotta fix the problem, so I changed the window, the big window, I replaced it with the new one. And then after that, the temperature will temperature stays, at the desired level. So I don't have to go up and down all the time. What does this story tell you, about. The fluctuation, I mean, that I turning the boiler, I turning the heater, that's kind of a symptomatic solution. I deacted to the symptom,what about replacing the broken window with a new one? That sort of, solving a problem at a fundamental level. And now let's think about this, model, there are symptoms, right? And this symptoms causes some consequences, outcomes and usually we have goals. In other words, if I say our goal is on keep this a room at 20 celsius. And uh,the consequence is room temperature,room temperature. And then I compare these two, compare these two and if there is a fit between the consequence in my goal, goal and outcome then it's okay, I then force this all thing. The other hand, If there is some discrepancy or mismatch between my goal and consequence it got [UNKNOWN] the problem, and now let's say i directly treat this symptoms. The symptom is my temperature goes up and own and Corrective measures for symptoms is toning the boiler, right? So I tone the boiler, I turn on the heater, and therefore, I eliminate the symptoms, symptoms in this case going below, going down, below 20. But as we can see in that example, this one goes on and on and on and on, without, stabilizing solutions. So it's always repeat, over and over again,no permanent solutions. That's because, our corrective measures, stops, at symptoms. And symptoms is the room temperature, you know, going below 20 celsius, or the thermometer we observing. So this is a sort of a short term and symptomatic solutions, that cannot solve the problem at a fundamental level. Then what about the depression, the broken window in the new one? Chris said that's double loop learning, that's double loop learning. Double loop learning,which is long term and fundamental. In other words, whenever we see variances of discrepancy between my goal and outcomes, I want to eliminate the good causes, not just the symptoms. So I'll go to the this root causes in this case, broken window. So I change the window, that's my fundamental problem solving. Due causing, due causing and eliminate. And as you can see, single loop learning is short term oriented and double of learning is a long term oriented. And one might say that the double-loop learning is better than single-loop learning, I would just say it the lone that double-loop learning is important, but we may not say that the double-loop learning is more important than single-loop learning, or vice versa. Let's say there is some old town, there is an old town, where all these houses are mingled together, meandering roads, and everything is old style, and you know, old is, fire-safety is compromised, and so on and so forth. And now there is a fire, there is fire there. Then there are two approaches, right? One is, oh okay we gotta, we gotta make a fundamental change there. And therefore we probably need to, you know, build a new, a new facilities or we straighten these goals and will probably the four different electricity, accumulative electricity facilities. Of course, by doing so we may eliminate the due courses of the fire at the [UNKNOWN] and all the time. But you know, there is a fire burning there so you gotta, you gotta extinguish it first right. You gotta extinguish the fire first, even if you worry about, even if you plan for the future, in other words on the one hand, you have to think about the long-term double-loop learning, and at the same time, you've gotta do something you have to do immediately. The point is that, I cannot ignore one at the coastal gather. You gotta apply these two, single of durny, and double of durny at the same time. On the one end we solve the problem right now, fire fighting, and on the other hand, I have to plan for the future, so that I destruct the whole town so that it becomes more, you know, robust against any fire in the future. And I will just say, that's the integrated learning, integrated learning. And this matter, It's not just about singular corrective measures. It's not just about double corrective measure It's both. So,the question is how to allocate, how to allocate this resources into this chart. So the question is not just about taking one major at the cost of the other, but basically the question is about how to balancing. How to balancing these two approaches, these two learning approaches. These two problem solving approaches, in an optimal balance. So only one hand we deal with these a short term, immediate problems, so we do the fire fighting, right now, and at the same time we want to make sure that the same problem does not repeat in the future, or necessarily so frequently. So, you gotta think about the long term fundamental solutions at the same time, I'll record that as integrate learning. And uh,in the topics we learned in the context of supporting management in the, in the you know next chapters on our next lecture. This approach into a learning approach is very important, In supply chain management on one end you should not ignore the immediate the most you know immediate problems. If you cannot survive it today, there is no life in the future, right. So you have to deal with the current problems and at the same time you should not be obsessed with the current problems only. You gotta have some extra resources, you gotta have some people, you gotta have some, you know, time or attention. There must be devoted to the [INAUDIBLE] fundamental problems in other word the double loop learning. How can I balance these two in an optimum and most effective way? That's probably one of the very important goals, the manager must pray. [BLANK_AUDIO]
