What do managers believe in?

"A fill rate higher than 94%? Impossible! It is not possible to sustain it with an acceptable cost".

It is not the first time and I believe it will not be the last time I hear this type of statement when I talk to general managers. The fact is that they are intelligent and experienced people, which has led them to have certain convictions that allow them to make decisions quickly, without spending their scarce time on fantasies.

But what is it that happened in the past that led him to be convinced that it is not possible to have a fill rate close to 100% and at the same time be profitable?

What is fill rate?

This is a term commonly used in logistics to measure the degree of order fulfillment. In its simplest and most acidic expression, it is the percentage of order lines that have been completely fulfilled.

Of course, if the order has only two items of, say 900 units of the first and 100 units of the second, and we deliver 800 units and 100 respectively, with this definition we could have a fill rate of 50%. Then we can refine our definition by considering the quantities as well. One way is to calculate the fill rate as the units delivered divided by the total. In this case we would have 90%.

But if the 900 units represent 50% in money, we could now make another calculation that gives us 94.4%.

You see, fill rate is a KPI that can mean different things, but even so, that manager considered it impossible to sustain it above 94%.

How are decisions shaped?

The case I am relating is that of a consumer goods manufacturer that sells its production to a supply chain, where there are wholesalers, distributors and retailers.

In that company, as in many others, the managers are highly educated and have certainly learned the most well-known cost optimization techniques for the management of consumer goods companies. Among others, balancing production lines, using MIN/MAX and EOQ, and unit costing with the latest ABC (activity-based costing) techniques.

When one uses these techniques, the inevitable result is that capacity is barely sufficient to meet demand and a lot of inventory is accumulated. The inventory backlog uses two fundamental resources: warehouse space and working capital. When there is too much inventory, both resources are at their limit, so suggesting to increase inventory immediately increases the cost of the operation.

What does that have to do with the fill rate?, you ask.

Let's see, if there is a lot of inventory accumulated, that necessarily means more days of sales. In other words, the production schedule must consider a sales horizon farther into the future, so it is increasingly dependent on the accuracy of the forecast. The only thing we know for sure about the forecast is that it is wrong, so those production plans will end up with some items out of stock, resulting in a lower fill rate. Translated with www.DeepL.com/Translator (free version)

But it is even worse: every time an order is missing an item, there are production reschedules, which wastes capacity and now we have to pay a higher cost to achieve the entire production plan.

And don't managers realize this vicious circle?

It's easier to ask than to answer. How can they know that this is a vicious cycle? Or better, how could they know that they are not optimizing the operation? After all, they are following "best practices" and applying basic principles that are taught to this day in very prestigious universities.

And they are concepts practiced by many others in the industry.

After several years of optimization, this company has achieved 94% as a realistic and sustainable maximum. Every time they tried to improve it, maintaining the productive optimizations, inventories rose so much and so many shrinkages appeared, that the logical conclusion is that trying to improve the fill rate is not profitable, and it is not realistic to suggest it after so much experience that proves the opposite.

Is there a way out?

This is a question asked by a non-conformist. Someone who does not accept the trade off between fill rate and cost. Dr. Goldratt taught me not to accept contradictions; that a scientist must think until he eliminates them. Genrich Altshuller also thought this way, putting as the basis of TRIZ the conviction that an invention arises from eliminating a technical contradiction.

I refer to two previous articles to see how they invalidate some of the basic concepts that managers continue to use. See Refutation of line balancing and MIN/MAX and EOQ fallacy to know why these concepts are wrong.

In general, the major problem in business management today is a lack of awareness of the systemic nature of organizations. These examples presented here are just a sample.

The way out of the suboptimal fill rate problem is to question the concepts that give rise to day-to-day factory and supply chain decisions. By abandoning these "beliefs", another set of policies must be adopted. Fortunately, we have already been down that road, and we know what the new concepts and new policies are. And we have seen hundreds of companies (perhaps thousands) that in the last 30 years have achieved a fill rate close to 100% while reducing costs and inventories. Translated with www.DeepL.com/Translator (free version)

Why is the adoption of systems thinking slow?

Russell Ackoff answered this question several years ago in a short article. And he gave two reasons, one general and one specific.

The general reason has to do with the prevailing education, where mistakes are punished, from school, through college, and into the workplace. And the safest way to minimize the number of mistakes is to minimize the number of opportunities to make them. At least that's one of the strategies. Therefore, the survival instinct and the little urgency to do something new leads most people to avoid profound changes. And adopting systems thinking, also in the words of Dr. Ackoff, is a change of era: the paradigms to be changed are so profound and numerous that it is equivalent to changing the set of shared beliefs of a large group of people; it is a change in their worldview.

Why "take a chance" on something that contradicts the mainstream? To some extent this position is defensible.

The specific reason is related to systems thinking itself, where experts gather at conferences to present their research and cases in a jargon that is almost hermetic to the rest.

I agree more with the former than the latter, although it is true that sometimes the technical jargon is scary, but it cannot be the main reason.

Blocking fears

Before his departure, Dr. Goldratt wrote a preface to the book he was unable to write on the science of management. In that preface he talks about three fears that provoke behaviors in many managers. It is up to the manager to what degree each one affects him or her.

The first is the fear of complexity. The consequence is that the manager divides the system into parts thinking that it is simpler to manage each one separately.

The second is the fear of uncertainty, so the manager seeks to have control at a higher level of detail, thinking that he can better deal with variability.

The third is the fear of conflict, where the manager seeks an amicable solution to the numerous conflicts that arise in the company, which in practice translates into compromise.


With a very complex work experience, where he has never experienced what it means to eliminate conflicts and manage a complex system in a simple way, where uncertainty only grows and increases the complexity of the system, the manager clings to the few certainties he has, those he acquired in his studies, as if they were dogmas.

I invite all readers to review their own beliefs, at least in business management, and trust more in their reasoning ability. You will be pleasantly surprised.

Each company needs its adaptation

Photo by SinAbrochar Photo on Unsplash

A couple of weeks ago we started talking with a furniture factory. I will not give more details yet because we do not have permission to release more information.

However, with a single conversation we knew immediately that this factory is suffering from late delivery of its orders, and from there we made a series of guesses.

The most frequent case is that these factories do not use the time buffer concept to control work in process (WIP), and that is why their capacity fluctuates, making it impossible to estimate delivery dates with sufficient precision. (More details about this concept in https://otif100.com/the-3-keys).

However, in a second conversation, now with the production manager, we learned very valuable things. It was no surprise that the constraint of the process, which consists of four manufacturing steps, was not identified. And from all that conversation, our team deduced where the constraint should be, which in this case turned out to be the second resource.

But that was the first step. The analysis also told us that the first resource could become a bottleneck, but it is very expensive to increase its capacity, because it requires another cutting machine.

Recalling the previous article, about buffers as synchronizers, we designed the operating model for this factory. It turned out to be a combination of time buffer with stock buffer to ensure capacity buffer.

The inventory buffer I'm referring to is not just traditional raw material inventory. In this case it is an inventory of pieces already cut for a set of pieces that are repeated in several models. The supplier charges very little more for sizing, so this inventory is the buffer that builds the capacity buffer in cutting.

The system will look like this: customer orders consume parts already cut from inventory (and replenished with the TOC method), and custom parts (by color or size or shape) that need to be cut on site. The second resource processes both types of pieces, and the last two resources, with extra capacity, finish the furniture, maintaining a low WIP at all times.

Los conceptos son siempre los mismos, pero deben adaptarse a cada empresa como lo hace un sastre con un traje a la medida.

This week we will start with this new design. I hope to report the good results in another article shortly. For now it's just theory ... but it's a good theory!