Service level: is it just a KPI?

What is your company in business for - to make money? I know that was the answer in the book The Goal, however, I have seen many mission statements and none of them say that. Money is a result of fulfilling the company's mission. In the sequel to the book The Goal, Dr. Goldratt tells us that a good strategy has three necessary conditions. In the last chapters of It's not luck, Alex's discussion with the directors leads him to conclude that the three pillars of a good strategy are: to generate a good environment for employees now and in the future; to provide excellent service to customers now and in the future; and to generate good returns for shareholders now and in the future.

Any mission should be consistent with these necessary conditions.

Where does it all start?

It is easy to realize that without sales there is no business. And that, with competition, we must build a competitive advantage that makes us stand out and makes sales flow based on value exchange.

All companies that are selling something have managed to build an offer composed of a product that has an attractive price/quality ratio. Otherwise they would not sell.

Designing or finding those products, then producing or buying them, making good packaging, producing attractive marketing materials, investing in advertising, deploying the sales force; all this is a big effort in time and money.

And the promise made to the market consists of the product, the price and a delivery condition. Delivery may be by promising a date, or it may be by promising availability at the point of sale.

Everything starts by making an attractive promise and customers accept it, generating sales.

What happens when the promise is broken?

Sales do not stop immediately. There is customer frustration that results in customers looking for alternatives. But let's look at which part of the promise is most frequently broken.

Neither the product is frequently degraded nor the price is frequently altered. These two aspects are very much taken care of by the companies.

It is common for delivery to be missed, either by failing to meet the promised date or simply by generating a stock out at the point of sale.

Competitors are not much better at delivering, but this only generates more frustration.

Perhaps the most damaging effect is within companies. Failure to deliver immediately generates complaints from the market, which translates into emergencies, rescheduling, cost overruns, and a lot of stress.

How do you feel when you miss a delivery deadline, or when more than 10% of your products are out of stock in the stores? These facts generate a ripple of pressure throughout the organization. At least I'm sure they are not a source of satisfaction for anyone.

What if a competitor had a much higher level of service? Sales would likely drop and margins would erode. No one is happy now.

The cause of failure to comply

The consequences of not fulfilling the promise are many and negative, as we already sensed.

And it is enough to have a little experience to know that failures to deliver are very frequent.

Why is it that, knowing how bad it is for everyone when they fail to deliver, companies keep making promises that they break?

One explanation could be that some managers don't mind lying and promise things to get more sales. But we already know that breaking promises has too many negative consequences, so this explanation cannot be the majority explanation. There are many companies that renege on their delivery promises and there must be very few or none that base their sales strategy on lying.

Therefore, since the facts show a massive non-compliance, the explanation must be that deadlines or stock are promised without any certainty of compliance, although the intention is not to fail, which is manifested in all the actions to solve emergencies and the frustration felt by those responsible.

That is, the cause of the breach is simply that knowledge is being applied to make the promise highly likely to be breached.

Is there any solution?

If the majority of companies are non-compliant, it seems that there is no good solution, because if there were, everyone would be using it!

I don't know what you call that argumentative fallacy, but it is clearly a fallacy. If something exists and is very good, everyone should be using it. (I think it is ad populum fallacy).

I remembered a book that Jeff Cox (co-author of The Goal) wrote, Selling the wheel. He starts with the invention of the wheel and goes to offer it to pyramid builders to increase productivity, and they answer something like this: "who is using this, if it were so good, many would already use it, right?

The knowledge to calculate a reasonable delivery date that is highly probable to be met exists, is the Load Control of Theory of Constraints.

The knowledge to calculate and maintain adequate inventories throughout the supply chain exists, it is the Dynamic Buffer Management of Theory of Constraints.

These two methods are effective, I have never seen a case of failure and they are simple to implement. But they have a "catch". To implement them you have to abandon several of the beliefs that we take for granted without question and that are the basis of the methodologies used today to promise dates or to calculate inventories. And today's improvement efforts do not question the basic beliefs, so the prevailing results are still the ones we already know. Translated with www.DeepL.com/Translator (free version)

Conclusion

Service level, especially on-time delivery (OTIF) or availability (FILL RATE), are not just KPIs to measure management. In companies with physical products, it is a necessary condition for a good strategy. Without this level of excellent performance, the life of customers is not so good, and the internal experience in companies is much worse, often the cause of the "burnout syndrome" (see https://blog.goldfish.cl/consultoria/mundo-vuca-empresa-vuca/). And, as a result, profitability is limited.

It is possible, and should be indispensable, to achieve excellent service to build a company that is worth working for, and that you want to buy from.

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Refutation of line balancing

On June 16, 2019, an industrial engineering portal published Balanceo de Líneas, where Dr. Eliyahu Goldratt is quoted as saying "An hour lost in the bottleneck is an hour lost in the whole system", but the article lacked analyzing the lines as systems.

The conclusion of that article is that balancing manufacturing or assembly lines reduces unit costs, and further says that “The balance or line balancing it is one of the most important tools for production management, since a balanced manufacturing line depends on the optimization of certain variables that affect the productivity of a process, (…) "

In this article I will start the exposition precisely from the phrase of Dr. Goldratt, who did various experiments to show that balancing the capacities in a line reduces the productivity of the system, increasing the cost of production.

Manufacturing or assembly lines as systems

A system is a set of interdependent elements with a purpose. A manufacturing or assembly line conforms to this definition: each workstation is dependent on another and together they have the purpose of creating a product from raw material.

One of the main characteristics of a system is that it requires the synchronization of all the parts for the result to be produced. In this sense, the production of a product is an emergent result of the system as a whole. None of the parts is capable of producing it by itself, not even a subset of them. This is easy to demonstrate. If the above were true, that subset is our system and the rest is left over.

In this sense, we need all parties to generate the product. This was obvious, however what is not so obvious is understanding how we achieve maximum productivity from a system.

In reality there is variability

To make the demonstration required to refute the aforementioned article, I will begin by establishing a fact of reality. The processing time of a unit on a workstation is a time within a range, it is not a specific number of minutes.

For example, when in a station we say that a product takes 2 minutes, we know that that is an average, but that it could be 1 minute or 5 minutes.

Regarding the process times, we know that they have a marked asymmetry to the right. See the following graphic:

By making our process time measurements on a workstation, considering the process of identical parts, after a large number of cases we obtain a table of results with a large dispersion.

It could never be processed in 0 seconds or less, which was obvious. In a few cases the process was achieved in 50-70 seconds, most of the cases are between 70 and 120 seconds, but not a few cases are in the range between 120 and 250 seconds. Actually, we see that half of the cases are in the last range.

In my experience of more than fifteen years, this graph represents the reality of the vast majority of processes in all types of factories.

Although I know that there is a difference between the median and the mean (or average), I will use the average for simplicity. And we can say that a process has a 50% probability of being running at its average or faster. This I will use next in the next demo.

Effect of process dependency

Variability affects all resources. We are going to distinguish the variability due to common causes from that which has special causes. Special causes are all those that are easily identifiable, for example, a power outage.

Common causes are many and varied, and for all practical purposes, the causes that stop one process do not necessarily affect other processes. Therefore, the productivity of one process in one instant may be above its average while that of another is below it.

Let us now consider a generic line (manufacturing or assembly):

We have a flow direction and we know that a resource cannot process anything if it has not received material from the previous one.

Let's design our process to produce 10 units per hour. After a while, the process is up and running and all resources are processing what they can.

Let's see what happens if we balance the line, that is, all resources have an average capacity of 10 u / h (or an average time of 6 minutes per unit).

We already know that the probability of producing 10 u / h or more is 50%. Let's look at what happens in the first two resources in the first three hours:

Periods

Resource 1

Recurso 2

Total production

First hour

7 u/h

15 u/h

7 u/h

Second hour

14 u/h

6 u/h

6 u/h

Third hour

9 u/h

9 u/h

9 u/h

Despite the fact that on average each one of the resources is capable of making 10 u / h, when combining them in each period, as the capacities are not synchronized, what Dr. Goldratt said is fulfilled: the system moves at the rhythm of the slowest.

Didn't we already know this? Sure you do, but capacity balancing, which is one of the techniques taught in many college courses, ignores the systemic effect of the combination.

By extending this effect to the rest of the resources, we can easily see that the probability that a balanced line will produce at the average design speed is approximately 0.5n, where n is the number of resources chained on the line. In this case, with 7 resources, the probability of achieving 10 u / h of finished product is ~ 0.8%, that is, in a year of 300 work days, only 2 would reach the design productivity of the line.

The better the balance, the worse the performance.

What happens to the cost when balancing the lines?

From the above conclusion, we now know that we will have ~ 20% fewer finished products compared to the original plan (or worse), so all the production cost associated with operating the line (discounted raw material) will be divided into less products, which will increase the real unit cost by 25% (or more).

So, to reduce the total unit cost (the only one that is relevant) it is necessary to ensure that the system maximizes its productivity as a whole, and not the productivity of each of the resources.

What if it is an assembly line?

Normally one sees factories where resources are isolated from each other and material (WIP for work in progress) has to be moved from one center to another. But with the idea of ​​speeding up the process, and following the model attributed to Ford, some lines are arranged in a way where there is no room to accumulate WIP and the entire line advances at the same time.

Now that you know what happens when balancing a line, take a look at what happens with an assembly line, even if it is not balanced!

Unable to accumulate WIP between resources, the entire line advances at the slowest pace. But which one is the slowest? Let's look at the graph again:

Towards the right side we have "the tail" of the distribution, and we have already seen that it is not at all improbable that a resource is in that productive cycle.

Unlike the general case, where the little WIP that can be accumulated does allow some resources to cushion somewhat the effect of dependency, in the case of the assembly line this is not possible. In this case, the entire line moves at the rate of the resource that is operating in its tail.

If one has 7 resources coupled, we already know that the probability that at least one is in the tail is 99%. If the line has a few dozen stations (such as assemblers for bulky products such as automobiles), it is certain that they are operating well below their averages.

On an assembly line it becomes incredibly relevant to reduce variability, leading the company to a flood of improvement projects that cost a lot of time and money. And it is not possible to eliminate the tails of the distributions either. It seems like a sisífea task to improve productivity.

Even Elon Musk regrets so much automation in the line of Tesla, although I am not sure if he already noticed the effect that I just described or has other reasons, but he sees that his results are below what was planned.

The solution is to find the resource that is the constraint of the entire system (has the smallest average capacity) and isolate it, allowing WIP to accumulate before and after. This will raise the overall productivity of the system quite a bit. And yes, I realize the investment that is required to modify the layout, but with an increase of only 10% in total productivity, I am sure that this project is profitable.

"If we don't balance the line, there is a lot of waste"

In our example, suppose that the third resource is our constraint, the one with an average capacity of 10 u / h, and the rest have 20 u / h or more.

First I clarify that the double is not an exaggeration. The capacity of the line to recover when there are losses, or in other words, to absorb the variability, depends on this extra capacity. If the excess over the restriction is small, we still have a problem with variability. In my experience, this extra capacity, which in Theory of Constraints (TOC) jargon is called protective capacity, must exceed 30-50% and sometimes more.

So we see that if we feed the line with all the material that the first one is capable of processing, in a short time we have an intolerable accumulation of WIP in the corridors of the plant, because the constraint is not capable of draining that WIP. In fact, what happens is that one has the sensation that the bottleneck is moving inside the plant. The latter is one of the symptoms of the opposite, that there is excess capacity. And when there is excess WIP, there are several effects by which capacity is wasted, even in the constraint. And here the phrase "an hour lost in the bottleneck, is an hour lost in the whole system" applies.

We must control the amount of WIP to ensure that the constraint always has work but that it is not so much that it wastes capacity. In another article I will delve into how capacity is wasted with excess WIP.

This WIP control mechanism must release material to process at the rate dictated by the constraint, so all other resources will have idle times. But these idle times are not real wastes of capacity; they are actually waiting times for the system to synchronize to the rhythm of the constraint. In TOC jargon this is a buffer, which is the mechanism for achieving maximum productivity.

That is why I have written that, many times, LEAN implementations, understood as waste reduction, are the enemy of productivity.

In addition, an operator receives the same salary if he operates a machine of greater or lesser capacity. So the salary expense does not change if one has more capacity machines. Look at the prices of the machines and you will see that doubling the capacity does not cost twice the investment.

All the times that are generated like this are not waste, and are excellent for practicing 5S or for doing preventive maintenance.

Now may be a good time to reformulate the productivity measurement. If production orders are what is needed and no more, when “idle” time increases, it is a sign that productivity has increased.

"I don't know, something doesn't add up ..."

To demonstrate the effect of line balancing, I suggest an experiment that you can do at home or with your work team.

Get 100 tokens and 7 dice, and build a production line with 7 stations. Each station is assigned a die, which will be our variability simulation. Note that the die is not asymmetric, because it is uniform between 1 and 6, although it may exaggerate the spread. But it is a good simulator of variable capacity.

If the simulation of a workday is done, each station rolls its die and produces at most the number it rolled. If you roll a 5 and have three chips, you can only pass to the next 3 chips. The tokens that are going to be passed to me in the same turn cannot be used. The first resource "produces" what the die rolled because it has an unlimited supply of tokens.

What is the average capacity of a die? It is the average of all your numbers. The sum is 1 + 2 + 3… + 6 = 21 and that divided by six gives 3.5. So each station has an average capacity of 3.5 units / day. In 20 days they should be able to make 70 units.

To start the experiment on steady state, distribute 4 chips to each one, and now do 20 days of production.

Compare what you got to the expected 70 units.

This experiment saves hours of discussion and mathematical proofs, and is much more fun. Then variations can be made to demonstrate other things, such as that moving people from one place to another only increases the variability but does not get more capacity.

Conclusion

Balancing the production line only reduces the total capacity of the line. Worse still, the actual capacity is considerably less than planned, so it will default on a large proportion of the orders, in addition to the obvious effect on invoiced sales.

Delivering late isn't just bad service - it's selling less

Photo by Nandhu Kumar on Unsplash

When you promise a delivery time, you have to take production capacity into account. That means you have planned to deliver, invoice, and collect a number of orders within the promised timeframes.

When we deliver an order late, that means that the time we had planned to spend on that order was used on another order, so at the end of the month, with several late orders, we have delivered fewer orders than we had planned. This also prevents billing and collection.

This in itself is already a reduction in budgeted sales, so achieving 100% OTIF (On Time in Full), that is, managing to deliver on time and always complete, is the same as meeting the sales plan.

Lost time is never made up again. This is the effect on present sales.

I will also remember here the consequences on future sales. Late deliveries create so many problems for our customers that sooner or later our reputation suffers.

I end this brief insight by repeating a quote I saw today:

Thankful to Jorge Arias Galeas for sharing it on LinkedIn.

Yet another reason to achieve 100% OTIF.

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