Picture this: a Formula One car drives into the pit lane for a tire change and no one is waiting. The mechanics are all busy with something else. Unthinkable, right? The media would be on top of it and the team would no doubt work tirelessly to make sure something like this never happens again.

In a production environment, we can think of our machines as F1 cars. It is in our interest to keep them "competitive" with the right level of performance. That means maximum value production during most of the available time and minimum downtime and failures.

Unfortunately, in several organizations we have seen situations like the one described above continue for years, despite the fact that with a little attention and a little reorganization, machine uptime and output could be significantly increased.

WHAT IS THE PROBLEM?

Let's illustrate this with a concrete example from one of our partners. In their machines, there are units in which certain parts need to be replaced after a certain number of steps - just like the wheels of a race car. Two step values are set in the machines: reaching the lowest value does not stop the machine, but indicates that replacement is needed, while reaching the highest number does stop the machine. In this way, the system can predict when a machine will stop.

OBSERVATIONS

For a long time, parts were replaced when the machine reached the highest number of steps and stopped (in F1 terms, when the car entered the pit lane). Usually the operator noticed this and called the technical team. After a while, a technician came and asked what had happened. The operator explained that a part needed to be replaced. The technician then went to get the necessary tools, removed the part and brought it to the workshop. In the worst case, if the correct size of the part was not in stock, the technical team had to cut one to size, replace it and then return to reassemble the unit. Only then could the machine be put back into production.

The exact reasons for this will not be discussed in detail here, but here are some examples:

- Most operators thought this was standard procedure.

- Engineers thought so, too.

- Reactive problem solving was the prevailing mentality ("I'll fix it when it stops").

- Performance tracking did not reveal this loss and some level of lost daily output was generally accepted.

- "That's how we've always done it."

In an F1 context, this would look like this: the tires wear out and the car indicates that replacement is needed. The driver continues driving until the tires fail. When the tires are about to fail, the car automatically slows down, forcing the driver into the pits. They drive into the empty pit lane and signal the mechanics. One of them finishes what he was doing and then walks over to the driver to ask what is wrong. The driver indicates that they need to change a tire. The mechanic gets the wheel wrench, removes the tire, takes it to the workshop to change the tire, then brings it back and mounts it on the car.

Isn't it painful to read this example in this context, as unrealistic as it may sound? Yet this is exactly what happens in most manufacturing processes!

RESULT

At our partner organization, the replacement process now follows principles similar to changing F1 tires. Although their record is not two seconds, they can replace parts in the shortest possible time, minimizing machine downtime given their current knowledge.

To understand what the process looks like, the steps the company went through to set it up and the challenges of maintaining the process, attend the Coloplast breakout session at the upcoming 11th International Lean Summit in Budapest.

^Author