Macro and micro decisions - part 2/2

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I repeat the question from the previous article: What do companies' results depend on? They depend on decisions: the macro decisions set a north, the micro decisions are the execution.

In the previous article, I stated that one of the macro decisions that should guide companies is to fulfill the promise made to customers.

The two typical promises from manufacturing companies are:

  1. They promise immediate delivery.
  2. They promise a delivery time.

I have already examined how promise 1 is frequently broken. And I also offered the simple Theory of Constraints solution, which has had a long history of success. It is a very simple idea and can be automated (see

Promise 2, to deliver on time within the promised deadline, without sacrificing quality, is also one of those promises that all companies make but few keep.

If you produce to order, you know you must promise a delivery time. And you might deliver on that promise 80% -85% of the time.

This was the case of a company in Chile, which had a turnover of about USD 500 million a year. In 2008 or 2009 I met with the operations manager and production manager of this company, to explore if they would be interested in what we have to offer. After exchanging some data, I asked them if they measured delivery on time, to which they answered yes. They measured OTIF - On Time In Full and, they added, they had 79.4% on that indicator, which they considered good.

Extrañado, les pregunté por qué lo consideraban bueno. Su respuesta me sorprendió más: «¡porque está muy cerca del 85%, que es el límite teórico!

Do you remember the 94% fill rate from the previous article? That practical limit that cannot be improved without high cost, making it less profitable. I already showed how a fill rate close to 100% can be achieved with less inventory and less cost.

This is also the same case. More than 85% of OTIF, they explained to me, is not profitable; it can be done, but at too high a cost.

As I offered to increase the OTIF over 98% with lower costs, they did not believe it possible and we did not continue talking.

I want to pause a bit to reflect on what 90% or less of OTIF means. When you declare that you have an 85% on-time delivery, you are saying that there is a 15% chance of being late.

Most of the time, delays cause great damage to clients. They are so large that a low probability of occurrence represents a moderate to high risk. A risk worth mitigating; In other words, if you eliminate that risk, you are generating great customer value. This is also evident by observing the reaction of clients to delays.

In practice, if the damage is appreciable, 10% or 40% are similar. For the client, both probabilities are too high. Consider the game of Russian roulette. If you have 6 bullets, the chance of survival is 14%. If you have 10 bullets, it goes down to 10%. Is the risk acceptable in the second case? What if you have 20 bullets?

It seems to me that delivery on time is acceptable from 95% -98% upwards.

Why are companies unable to better meet their delivery deadlines? Let's think about this a bit: we have an order queue, and we have installed capacity. Simply put, capacity is measured in units per hour, so the known number of queued orders represents a fairly exact number of hours, assuming we know the capacity, of course. And knowing how many hours a day our company works, we already know what is the next date that we could promise.

Many companies are wrong in that estimate. It must be a mistake, because if they promise the dates knowing that they will not comply, they show total ignorance about what the clients value. Where are they wrong? We have only two factors to do the date calculation: the load already committed and the capacity. The committed load is a known quantity, without any variability. Only the capacity remains.

Why are they so wrong about capacity? Don't planners know their plant? They don't know how many hours a day they work and what days are holidays?

The explanation that remains is that the effective production capacity of a plant does not only depend on the installed capacity. There is another factor that strongly affects capacity: it is the WIP for work in progress.

Voy a apelar a su intuición. La capacidad de una autopista para «procesar» automóviles se puede medir en la cantidad de autos que pasan por un punto al final de la autopista. Y esta cantidad va a depender de la velocidad promedio de los autos. La cantidad de autos en la autopista es el WIP. Si el WIP es muy poco, la producción también (no importa que los autos puedan ir a máxima velocidad; son pocos autos por hora). Cuando va creciendo el WIP, más autos por kilómetro, la producción también va creciendo, porque la velocidad sigue siendo la máxima, pero ahora son más autos. Si el WIP sigue creciendo, como el último día de vacaciones y todos vuelven al mismo tiempo, sabemos por experiencia que la velocidad promedio es mucho menor, por lo que la producción de autos por hora también es menor.

That is, the capacity of a system is determined by the WIP. If I control WIP, I control capacity.

In the example of the highway, if we organized and controlled the number of cars entering the highway, they would all get to their destination faster.

We already have enough information to design the first daily micro decision that will make it possible to fulfill the promise to deliver on time.

Micro decision number 1: decide which production orders should be in process.

This also means that we have decided that the rest of the orders will wait a few days. Even if the first resource goes unoccupied ...

Como en el artículo anterior, las micro decisiones diarias de la mayoría de las empresas se toman sobre una base equivocada. En la mayoría de las empresas intentan mantener a todos los recursos ocupados, bajo la creencia de que todo tiempo ocioso en la línea de producción es un desperdicio. Y también, lanzan a producción todas las órdenes lo antes posible por la creencia de «mientras antes empiezo, antes termino».

Both beliefs are false. I am not going to expand here to demonstrate it, because there is abundant material to learn from this.

Micro decision number 2: decide in what sequence the orders should be processed in each resource.

Since we want to meet the deadline, the primary criterion is the delivery date. However, there are several considerations that advise processing one order before another, even if it must be delivered later. Therefore, we need a simple and robust system that allows each person in the plant to decide what to do.

Micro Decision # 3: With WIP in control, you can decide which dates to promise to always comply.

Without WIP control, you cannot do load control to predict dates. But already established, it is simple to calculate the most probable dates that can be promised. These three daily micro decisions determine the OTIF of companies. You can learn more about the details at

Don't settle for an on-time delivery of less than 95% - it's possible and it's achieved at a lower cost.

Macro and micro decisions - part 1/2

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What do the results of companies depend on? Results depend on decisions: macro decisions set a north, micro decisions are the execution.

If you work in a company that manufactures or markets something, I am sure you agree with me on a macro decision: maintaining a high level of customer service is the highest priority.

That's a high-level, macro decision. An alternative would be: "Our company's highest priority is to reduce costs, even if it means serving some customers poorly." I assume that you already have enough experience to know that this decision has higher costs in the medium and long term, so I will stay with the first macro decision: fulfilling the promise to customers is the first priority.

Now let's look at the potential promises that a large proportion of companies make. They are basically two possible promises:

  1. They promise immediate delivery.
  2. They promise a delivery time.

Promise 1 is made by companies that produce standardized products, where customers have no tolerance to wait for the delivery time (production and / or transport) because there are other suppliers that have immediate delivery. And to fulfill that promise of immediate delivery, you need to maintain inventory.

Anytime a customer places an order and you have no inventory, this is a lost sale. We call that stock out or inventory shortage or missing. Suppose you maintain a portfolio of 150 items, and 35 of them are out of stock. That gives 23% of stock outs. How many lost sales are you going to have because of this? To answer that question, which is the relevant one, we must understand that not all items are sold in the same volume. Pareto described this phenomenon as an 80/20 asymmetry; 80% of sales are made with 20% of the items. We know that 80/20 is just an indication of skewness. Sometimes it is 70/30 and other times it is 90/10.

The important thing is that if within our 23% there are several high-turnover items, the lost sales are more than 23%, sometimes much more. I knew a case years ago, where a company with about 250 SKUs (items) had a 5% of stock outs. Each week they were different items, but the missing items remained at 5%. They decided something really daring: they decided to eliminate the stock outs by having excess inventory. And they succeeded. After eight months of holding that stock, sales increased 40%. This is an experiment that I do not recommend, but it does demonstrate what I am saying. Usually the mistake was to underestimate the sale of items that are out of stock, so their turnover is higher than originally estimated.

The story of that company does not end well. The first year they increased sales by 40%, but the excess inventory caused problems from the second year:

  • It ran out of storage space and it became very expensive to keep growing inventory, especially if you need to pay for additional space. Note that the problem is that the items that were produced and not sold, accumulate. And you keep producing faster than you take up space.
  • The money to keep growing in inventory is also limited, and afterwards they could not keep the stock outs at zero either.
  • Many of the excess products were perishable and suffered a large loss from waste.

In this case we see that the macro decision is the same that I would have made: top priority to fulfill the promise of immediate delivery.

And each day, what micro decisions did that company make to align with the macro decision? In this case it was manufacturing in excess, what matters is not losing sales. It is with this decision that I do not agree.

The results observed in stock outs and inventory are the product of micro decisions, of those decisions every day. Specifically, for each item it is necessary to decide each day whether or not it is produced, and in what quantity. The same applies to deciding whether to dispatch to other nodes in the supply chain.

It is commonplace to say that it doesn't matter how good a strategy is if it fails to execute. That is, macro decisions are usually very good. It is the micro decisions that lead to a different result than expected. Many people think that it is not possible to achieve a fill rate of more than 94% permanently, because the cost makes it unfeasible, and they settle for an "acceptable" level of stock outs.

I learned directly from Dr. Goldratt not to settle for a compromise result. And in this matter, the Theory of Constraints solution has managed to reduce inventories and costs while availability approaches 100% in thousands of companies, of all sizes and from the most diverse industries, throughout decades.

Why do the micro decisions of most companies produce such suboptimal results? It is because they are based on wrong assumptions. It all comes from the fact that the time between replenishments (production orders, purchase orders, or dispatch orders) is variable and is longer than necessary. That time is the main factor in the amount of inventory required to have availability, resulting in a larger quantity than companies can afford, due to their space and / or capital limitations.

Why is the time between replenishments long and variable? Because it results from calculating EOQ (economic order quantity) and using MIN / MAX techniques or reorder point. Both concepts considered the basis of optimal inventory management. And they are based on the wrong assumption that operating costs are absorbed uniformly by each unit of product when it is produced, purchased, or shipped.

If one sets a fixed frequency and makes that time makes shorter, the amount of inventory to guarantee availability is less, to the extent that neither space nor capital are active constraints, therefore the double objective of reducing inventory and raising fill rate up to close to 100% is achieved.

The next time you are told that it is impossible to achieve a fill rate of more than 94-96%, think again and more deeply about the causal relationships in your system.

Promise 2 will be examined in the following article.