Thus one of the most powerful ways of pinpointing the core problems is to start with an undesirable effect, then to speculate a plausible cause, which is then either verified or disproved by checking for the existence of another type of effect, which must also stem from the same speculated cause.
Using this method also means that after having identified one undesirable effect the search for more information should be put on hold. Rather, we should now immerse ourselves in the speculation of a plausible reason, which can explain the exis- tence of this effect.
Then we should try to logically deduce what other totally different effect must exist in reality if the specu- lated reason is valid. Only then, should we seek It's no wonder that verification, not of the original effect but of the speculated when using this one. It's no wonder that when method in a dialogue, using this method in a dia- we will initially give logue, we will initially give the impression that we are jump- the impression that we ing from one subject to an- are jumping from one other.
Remember, the only subject to another. By explaining the entire process of constructing the Effect-Cause-Effect logical "tree" we have a very powerful way to persuade others.
Let's examine an example which uses this technique of think- ing—in Chapter 4 of The Goal. The assumption that Jonah makes is that Alex, being the plant manager, is not doing some- thing which is an artificial, local optimum. Thus the hypothesis is, that when Alex uses the word productivity, he knows what he is talking about.
Therefore Jonah's next question is not directed to the specific tasks of the robots, their manufacturer or even their purchase price, but rather towards the verification of what should be a straightforward resulting effect. Alex, captured in his world of local optimums, thinks that Jonah is the one who is remote from reality. Jonah now has to develop and present an Effect- Cause-Effect tree using Alex's terminology, in order to show Alex why the hypothesis "Alex knows what he is talking about" must be wrong.
If the plant had actually increased productivity this would mean that either the plant increased Throughput or reduced Inventory or reduced Operating Expense. There aren't any other possibilities. Thus Jonah's next question is, "was youi plant able to ship even one more product a day as a result of what happened in the department where you installed the ro- bots?
Basically Jonah was asking whether or not Throughput was increased. The next question was "did you fire anybody? And the third question was "did your inventories go down? It is quite easy now for Jonah to hypothesis a much better reason, "Alex is playing a numbers game," and productivity for him is just a local measure like efficiencies and cost. The un- avoidable effects that will stem from managing a plant in this way is what Jonah tries to highlight by his next question, "With such high efficiencies you must be running your robots con- stantly?
So without further hesitation he firmly states "Come on, be honest, your inventories are going through the roof, are they not?
Then the reaction is "wait a minute here, how come you know about these things? As a matter of fact, it is the only feasible technique that we know of to identify con- straints, especially if it's a policy constraint that doesn't give rise to permanent physical constraints, but only to temporary or wandering ones. This same method also solves the problem of providing solid proof. It turns out that people are very convinced by this type of analysis when they are introduced, not just to the end result, but to the entire logical flow: hypothesizing reasons, deriving the resulting different effects, checking for their existence and, when not finding them, changing the hypothesis and so on.
If you called The Goal common sense then you have already testi- fied to the extent to which this method is accepted as proof. How to Invent Simple Solutions: Evaporating Clouds Once the core problem is pin- as long as we think pointed then the challenge of that we already know, using the Socratic approach is even bigger.
Now the audience we don't bother to re- has to be induced to derive think the situation. The major obstacle to accomplishing such a task is the fact that people usually already have in their minds, the "accepted" solutions. Remember, we are dealing with core problems and typically core problems. They have usually been in existence within our environment for many months or even years—they do not just pop up. This provides us with the best indication that the perceived solutions are insuffi- cient, otherwise the core problem would have already been solved.
It is clear that the nature of human beings is such, that as long as we think that we already know, we don't bother to re- think the situation. Thus whenever we want to induce people to invent, we must first convince them that the "accepted" solu- tions are false, otherwise they will not think, they will just quote It's not unusual to find that the accepted solutions, which dc not work, are solutions of compromise.
Inducing people to invent simple solutions, requires that we steer them away from the avenues of compromise and towards the avenue of re-examining the foundations of the sys- tem, in order to find the minimum number of changes needed to create an environment in which the problem simply cannot exist.
I call the method which can accomplish this the Evaporating Clouds method. Assuming that a core problem can be described as a big black cloud, then this method strives not to solve the problem compromise but to cause the problem not to exist. The origin of the Evaporating whenever we face a Clouds method stems from the situation which essence of two broadly accepted sentences. The first one, is requires a more theological, "God does not compromise, there is limit us, we are limiting always a simple ourselves" and the second, which is regarded to be more practical, solution that does not "You cannot have your cake involve compromise.
But the mere fact that they are so widely accepted indicates that both are valid. The second sentence is just a vivid description of the existence of compromising solutions. The first one probably indicates that, whenever we face a situation which requires a compromise, there is always a simple solution that does not involve compromise.
We just have to find it. How can we systematically find such solutions? Maybe the best place to start is by utilizing a third sentence, which is also very widely accepted: "define a problem precisely and you are halfway to a solution. Nevertheless there is one small difficulty, when do we usually realize the validity of the above sentence, only when we've al- ready found the solution. But how can we be sure that we have defined a problem precisely before having reached the solution?
Let's first examine what is the meaning of a problem. Intu- itively we understand that a problem exists whenever there is something that prevents, or limits us, from reaching a desired objective. Therefore, defining a problem precisely must start with a declaration of the desired objective. What should we do next?
Let's remind ourselves that what we are dealing with are the type of problems that involve compromise. A compromise between at least two bodies. In other words, we have to pacify, or satisfy, at least two different things if we want to achieve our desired objective. From this analysis we can "You can't have your immediately conclude that cake and eat it too. In other words to reach the objective there are at least two necessary conditions which must be met.
Thus, the next step in precisely defining a problem is to define the requirements that must be fulfilled. But the definition of the problem cannot stop here. We should realize that whenever a compromise exists, there must be at least one thing that is shared by the require- ments and it is in this sharing that the problem, between the requirements, exists. Either we simply don't have enough to share or, in order to satisfy the requirements, we must do con- flicting things, "you can't have your cake and eat it too.
Let's start by calling the desired objective "A. Maybe the best way is to use the Effect-Cause-Effect method. The effect that we started with is "state a problem precisely and you're half way to solving it. In order to verify this hypothesis, we must be able to explain, with the same hy- pothesis, an entirely different type of effect. Even though there are many such types of effects, I will bring into play here the effect that I originally used to verify this method. At the time that the Evaporating Clouds method began to be formulated, I was deeply immersed in the field of scheduling and materials management.
One would expect that the articles published in profes- sional magazines would deal with the problems that trouble the community of its readers. Therefore, one would expect that the bigger and more important the problem, the more articles there would be trying to address and solve that problem. Skimming the professional magazines in the field of materials management revealed a very awkward phenomena.
In the last fifty years ac- tually from the thirties the problem that attracted, by far the largest number of articles, is the problem of Economic Batch Quantity EBQ. At the same time, talk to any practitioner and you'll find out that batch sizes are determined almost off the cuff and nobody in the plants is overly concerned about it.
I don't think that it is an exaggeration to estimate that at least 10, articles have already been published on this subject. Why is this? What caused such a flood of articles into such a relatively unimportant problem?
Maybe we can explain this What caused such a phenomena, if what we find is flood of articles into that this particular problem had some unique feature.
A feature such a relatively that will attract the interests of unimportant problem? In such a case, people will certainly be more attracted to deal with a problem which is clearly defined, rather than with the more important problems which are vaguely stated.
As it turns out this is exactly the case. The batch size prob- lem is precisely defined, according to the above diagrams. Let's review it in more detail, not to see what the batch size should be, but in order to acquire a much better understanding of the Evaporating Clouds method. The major avenues through which the size of the batch will impact the cost per unit are as follows.
But, if after the one hour setup, we produce ten units of a given item, then each unit will have to carry only one-tenth of the cost of one hour of setup. Thus if we want to reduce the setup cost per unit, we should strive to produce in as large a batch as possible. Graphically the cost per unit as a function of batch size, when setup cost is considered, is shown in Figure 3. We are all aware that as we enlarge the size of the batch we will enlarge the amount of time that we will hold the batch in our possession and thus we increase the carrying cost of inventory.
Most articles indicate a linear rela- tionship; doubling the size of the batch roughly doubles the car- rying cost. When considering the carrying cost per unit, we should strive to produce in the smallest batches possible. Graph- ically the cost per unit as a function of batch size when carrying cost is considered is shown in Figure 4.
We are all aware that as we enlarge the batch we enlarge the time we hold it in our possession. It is quite easy to see that the problem of batch size determi- nation is actually a compromising problem which is precisely defined. But maybe it will behove us to first examine how such problems are treated con- ventionally. The conventional way is to accept the problem as a given and to search for a solution within the framework estab- lished by the problem.
Thus, conventionally we concentrate on finding an "optimum" solution. Since we cannot satisfy both requirements, "B" and "C," all the efforts are aimed at finding out how much we can jeopardize each one, so that the damage to the objective "A" will be minimized.
Actually, finding a solu- tion is restricted by the question: what compromise should we make? In the batch size problem, we consider the total cost, which is the summation of the setup and carrying cost contributions see Figure 5. And then, we mathematically or numerically find the minimum cost possible, which indicates the "best" batch size. Actually, finding a solution is restricted by the question: what compromise should we make? This type of approach, with a whole variety of small corrective considerations, is what appears in the vast number of articles mentioned above.
Most articles also point out that the curve is very flat near the minimum and they claim that it's not too terribly important which batch is chosen, as long as it is within the range marked by the two circles in Figure 5. The intuitive, off the cuff choice for a batch size that we make in reality, is usually well within this range. This same point, about falling within this wide range, is what made everyone wonder about the practicality of all these academic articles, that while mentioning it, are concentrating on small corrective factors that do not change the picture in any significant way.
The Evaporating Clouds method does not strive to reach a compromise solution, rather it concentrates on invalidating the problem itself. The first attack is made on the objective itself asking, "Do we really want it?
This comparison is achieved by simply trying to re- state the problem using the terminology of the global objective rather than the local terminology. Are we really trying to achieve a minimum cost per unit? Maybe, but what we are really trying to achieve is, of course, the making of more money. Since most readers have not yet devel- oped their intuition regarding Throughput, Inventory and Oper- ating Expense, we'll use, instead, the slightly more cumbersome global terminology of the relationships of making money; Net Profit and Return on Investment.
Rather than using cost per unit, we should use profit per unit. Since, the problem assumes a fixed selling price; more cost less profit, less cost more profit, we can just replace cost by profit.
This results in a mirror image of the previous graph Figure 5. How do we bring investment into the picture? We should just remind ourselves of the reason for the linear relationship straight line between carrying cost and the batch size. Dou- bling the batch size means doubling the carrying cost. But this implies doubling the investment in the work in process and fin- ished goods material that we hold. In other words, there is also a linear relationship between the batch size and investment.
Thus, we can simply replace the horizontal axis batch size with in- vestment in WIP and FG's and we get a graph which is profit per unit versus investment, as shown in Figure 6. But what about return on investment? The same profit means the same return, but the investment in that interval has more than doubled. If we want to make more money, then we shouldn't aim for the top of the curve but at some point substantially to the left of it.
And what about that brutal, necessary condition called cash? Suppose that the plant has an amount of cash which resides somewhere between the two points as indicated by the bar on the investment axis.
Yes, they are equivalent from the point of view of the net profit, but in this case one means bankruptcy and the other survival. This "optimal" solution has Almost no one bothers been taught for more than 50 to check the local years in almost every university around the globe. Almost no objectives versus the one bothers to check the local global goal.
Let's not fool ourselves, this phenomena is not restricted to just academic problems but is widespread in real life.
How many times has your company worked so hard to win a bid and once it was won, it turned out to be a disaster? How many times have you seen a foreman forced to break setups, go to overtime, in order to expedite some pieces, just to find them two weeks later gathering dust in a warehouse? How many times have you almost climbed the walls to meet tolerances that shouldn't have been there in the first place? Prob- lems that arise whenever we try to satisfy local objectives that do not match, at all, the global goal.
Coming back to the method of behind any logical Evaporating Clouds, let's assume connection there is an for now that the objective has been checked and verified.
Yes, assumption. In our we do want to achieve this case, most probably it specific objective. Is the only way open to turn to the avenue of is a hidden compromise? The answer is assumption. What we have to remind ourselves of, is that the arrows in the Evaporating Clouds diagram, the arrows connecting the requirements to the objective, the pre-requisite to the requirements and the arrow of the conflict, all these arrows are just logical connections. In our case, most probably it is a hidden assumption.
Let's clarify it with an example taken from the Theory of Con- straints Journal. It doesn't matter, "because it's there. The assumption that we intend to reach the top of Mount Everest by climbing.
It is enough just to verbalize this assumption and pic- tures of parachutes and helicopters start to flash in our minds. The Evaporating Clouds technique is based on verbalizing the assumptions hidden behind the arrows, forcing them out and challenging them.
It's enough to invalidate even one of these assumptions, no matter which one, and the problem collapses, disappears. The previous Mount Everest example probably left you with a sour taste in your mouth, as it is too simplistic, unfair.
So maybe we should try to use this technique on the batch size problem. Let's remember that this problem is one in which more than 10, bright people have invested so much time trying to solve to the extent that they have published articles about it. Evaporating this problem certainly serves, in more than one way, as a good illustration of the validity of the Evapo- rating Clouds method.
Examine, for example, the arrow connecting requirement "B" to the objective. The influence of setup cost on cost per unit is the unstated assumption that was taken when we drew the batch size problem. It doesn't take long to realize that we have taken setup as a given. In other words, we assumed that the setup cost is fixed and cannot be reduced. What do we call the method that so viciously attacks this assumption? We call it JIT. Sometimes from many hours to just a few minutes. But there are many ways to have our cake and eat it to.
So, let's try to find out if there is another assumption hiding behind the same arrow. Just thinking about it probably sends flickers through your mind: "does setup really cost us money?
Remember, the Theory of Constraints shies away from the word cost, like it was fire. The word cost belongs to the But the word cost is most dangerous and confusing also used in a third category of words—the multi- meaning words. We use this word way, that of "product as a synonym for purchase price, cost," which is just an like in the sentence, "the cost of a artificial, machine. You might become rich by prudent investments but certainly not by spending your money.
But the word cost is also used in a third way, that of "product cost," which is just an artificial, mathematical phantom Theory of Constraints Journal, Volume 1, Number 4, Article 1. After this long remark on the multiple meanings of the word cost, let's try to rephrase the question "does setup really cost us money? The lightbulb just went on.
The equivalent is "will an additional setup increase the Operating Expense of the organization? Suppose that all the people who have tried to solve the batch size problem would have dealt with a situation, where at least one of the resources involved in the setup was a bottleneck. So let's assume that the situation they have dealt with, is one in which none of the resources involved in the setup is a bottleneck.
In such a case the impact of doing an additional setup on Operating Expense is basically zero. What we see is that exposing the hidden assumption is suffi- cient for us to understand that the whole problem revolved around a distortion in terminology.
What is our answer to the batch size now? Where should we have large batches? On the bottlenecks and everywhere else? Let's have smaller batches. Small, to the extent that we can afford the additional setups, without turning the other resources into bottlenecks. What I would like to demonstrate is that every arrow can be challenged. But since I don't want to turn this into the 10, book on batch sizes, let me demonstrate it by concentrating on what is perceived to be the most solid arrow in the diagram— the arrow of the conflict itself.
What is the assumption behind "large batch is the opposite of small batch"? That large is the opposite of small? To challenge this means to challenge mathe- matics itself. So the only avenue left open is to challenge the assumption, that the word batch does not belong to that cate- gory of words having multiple meanings. Here, it seems that we are at a loss, where the only way out is to ask ourselves if we know of any environment, in which the concept of batch does not fit.
Yes, we all know of such environments—flow lines, con- tinuous production, assembly lines. It seems to reason that batch sizing is not applicable in such environments, because in those environments the distinction between the two meanings of the word batch is so big that we cannot possibly group them to- gether. What is the batch size in a dedicated assembly line, dedicated to the assembly of one type of product?
Of course it's one; we are moving the products along the assembly line in batches of one. A very large number, we don't ever reset a dedicated line. What are we going to do now? It seems as if we have two correct answers to the same question, where the first answer is one and the second is infinite. Rather then putting the whole thing aside, by saying that the batch size concept is not applica- ble to such situations, let's try to verbalize the lessons that we can extract from it.
We reached the answer one, when we looked on this situation from the point of view of the product. The unverbalized question was actually, "how many units do we batch together for the purpose of transferring them, from one resource to another along the line?
On the other hand, we reached the answer of infinite from the point of view of the resources in the line. The question here was "how many units do we batch together for the purpose of processing them, one after the other"?
The answer infinite was thus given to describe the size of the batch used for the purpose of processing—we call it the process batch. In every flow environment, we find very strong indications that the process batch and the transfer batch are totally differ- ent entities that can and do co-exist, even when we consider the same items, on the same resource, at the same time. We move batches of one through a machine on the line, while at the same time the process batch, in which these parts are processed by the machine, is infinite.
Now let's return to our problem: why did we have the pre- requisite of a large batch? To save setup. In other words, the batch that we wanted to be large, was the process batch. Why did we have the pre-requisite of a small batch? Because we wanted to reduce the carrying cost of inventory—the time that we hold the inventory in our possession. In other words, we wanted a small transfer batch.
Why then do we claim that we have a conflict, when these two pre-requisites can be fully satis- fied, at the same time. We 50 years was due to should strive to maximize the the improper use of process batches on bottlenecks, while at the same the terminology.
The efforts to find the "best" batch size, should have been directed towards straightening out the paper work on the shop floor rather than finding some artificial optimum. Otherwise, "work-orders" will arbitrarily force the transfer batch to be equal to the process batch.
Read The Goal, if you have already read it read it again, and whenever you find Alex developing a simple, common sense solution, that's exactly where the Evaporating Clouds method was used. The Race, which is devoted to explaining why inven- tory is even more important than operating expenses, is actually a collection of examples that make extensive use of the Effect- Cause-Effect and Evaporating Clouds methods.
Here, for exam- ple, is an arbitrary page from The Race. Try to reconstruct the Effect-Cause-Effect tree and the Evaporating Clouds diagram that lead to the conclusions outline on these pages. What causes this universal phenomenon? If all companies in an industry are providing delivery of a prod- uct within two months, then customers will not place orders and commit themselves to specific due dates a year in advance.
Even when they place an order for a whole year, they will feel free to change the quantity and ship date two months in advance without risk of jeopardizing deliveries or plac- ing their vendors in an impossible situation. Consequently, the plant's forecast for this product will be quite reliable for the first two months and quite unreliable for the period beyond three months.
If we operate with high inventory relative to our compet- itors, it means that our production lead time is longer than the valid forecast horizon of the industry. The length of the valid horizon will be dictated by our low inventory competitors. As a result, the high inventory company's production plans are based on pure guesses and not on a reliable forecast.
It's no wonder that due-date performance is a problem where we have high inventories. When we operate in a lower inventory mode than our competitors, we enjoy an enviable position that gives us an inherently more accurate forecast.
Now when we start production, we have firm orders or a valid forecast which is much less likely to change. Our due-date performance will certainly be much improved. Our production plans are now driven by more reliable information and we are in a much better position to give reliable requirements to our vendors. Remember, a prime reason that our vendors cannot deliver reliably is because we keep changing our requirements on them, the same way our customers are changing their requirements on us.
How about the last competitive element, shorter quoted lead times? We will again find that inventory plays an unexpected role? In order to demonstrate that both The Goal and The Race cover only a small portion of the applications of the Theory of Constraints, even as far as production itself is concerned, the next example, I would like to present, is from the Theory of Constraints Journal.
But not less important is how these methods merge together to en- able the effective use of the Socratic method. Al has done a lot of work. Tom, one of our plant managers has made some physical changes on the floor that really made a nice, big difference.
More than once we have broken bottlenecks and now we can ship things we were not going to ship before. In my opinion the problem is no longer in manufacturing! I would estimate that we have cut our work-in- process inventory to about one half of its historical level.
I'm afraid that we are once again stagnating. Accurate but not unusual. Al, can't you do something about it? My people do release all material's sched- ule promptly. Overall, I don't think that our systems are much worse than the rest of our industry.
The accuracy of our data is quite good, even though I would like to get a little bit more cooperation from your produc- tion people in this area. You must admit that the timeliness of re- porting transactions on the floor can be substantially improved. The superinten- dents and foremen are already complaining that the hassle to feed the computer is too much. If you want better and faster data from the floor, you need to provide updated feedback reports within a day, not a week.
The computer is so loaded that it's a miracle that you get the response we are currently giving. You know that almost every weekend my people have to stay to guaran- tee that everything will be ready on Monday morning.
If you need faster response, and I agree on that point one hundred percent, we must go to an online system. No, I manufacturing has maintain that the problem is no longer in production. It is on the improved preparation side of the house. In this business everything is made to order and the engineering and paperwork functions needed to design and specify the furniture are a big part of the organization.
We must have more modern technology if we want to change things around here. The first edition of the novel was published in , and was written by Eliyahu M. The book was published in multiple languages including English, consists of pages and is available in Paperback format. The main characters of this business, non fiction story are ,.
The book has been awarded with , and many others. Please note that the tricks or techniques listed in this pdf are either fictional or claimed to work by its creator.
Book report on. Critical Chain by. Written by. Erik Baggerud in the course. Autumn Join for free. Ever since Goldratt introduced critical chain CC in his.. Show what is missing in our Critical Path methodology. Introduce you to Dr. Eliyahu Goldratt. Lots of information and many Free Videos. Many videos on.. Keywords: critical chain; buffer management; resource-constrained project scheduling. Your Rating:.
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