Controling or piloting, a nuance that makes the difference.

Whether in terms of quality, performance or safety, control is often the solution chosen to feed the indicators. But what is controling? Controling is the action of checking something, someone, to verify their state or their situation with respect to a standard, so it is a question of checking if our results once we have arrived, we have reached the objective. Example with quality control :

At the end of the production line, the part is inserted into a gauge to ensure that the dimensions are correct. In the event that this product does not meet expectations, it is simply discarded for recycling.

In this case, quality control fulfills its role, with the vast majority of conforming parts delivered to the client. We speak of the vast majority, because human error remains possible it is imaginable that a non-compliant part passes through the net.

For performance, the same thing is happening in companies today. The production managers define production objectives on a shift or daily basis, then the next day the result is observed. We control : What are the results? Was the objective achieved? What are the reasons for the deviation from the target? Here are the questions producers will need to answer.

Controling is watching after the action. When a non-conformity is observed in quality control, it is already too late. The piece is produced, it is thrown away. When the non-performance of the previous day is highlighted by the indicators, it is already too late. But what is the difference with piloting?

What’s the difference with piloting?

Piloting is quite similar to controling. We always talk about indicators. However, it brings to light two very important notions. Time intervals and levers.

The levers are the means to act on the indicators. Let’s put ourselves in a car: If the indicator is the speedometer, the lever is the accelerator pedal. When my speed is too low or too high, I act on my lever to be precisely at the desired speed. In industrial piloting, it is the same principle. Levers must be used in the workshop to achieve the objective. It is therefore necessary to choose indicators on which your producers can have an impact.

Here an example: The customer satisfaction rate is frequently found in the workshops. Try to imagine the vision of your collaborators on this indicator… They see it as a consequence, but above all as an indicator on which they have only a minor impact, or no impact at all. Now replace this indicator by workshop and target it more. If you choose: “Percentage of product delivered on time”, each player in production would have an impact on this objective. In this way, everyone in the workshop feels involved.

Now that we know how to involve every employee in the company, let’s talk about the time interval. Let’s get back in our car to manage our gas pedal. When we drive, we monitor our speed regularly. For the piloting, it is once again identical. We monitor our indicators in much closer time intervals and adjust the levers more frequently to be on target in real time. If we spend the day or the team being at the goal in real time, we are sure to reach the goal at the end of the day.

How to pilote?

How to orient our workshop towards a dynamic of piloting? The differences between piloting and control are more explicit, we will be able to take the right direction. First, let’s talk about quality. The final objective of quality management is to reduce as much as possible all control operations that do not bring any added value to the client. ALL quality control is time and money wasted. Of course, this avoids sending non-conformities to the clients, but today there are ways to control quality very finely in order to produce “right the first time”.

Statistical process control is one of these methods. It allows real-time monitoring of variables to ensure the level of quality. But what variables can tell us our level of quality? By studying the scraps caused by your machine, you will see that the causes follow Pareto’s law. 80% of waste comes from 20% of the causes.

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The 20% of causes are “easy” to monitor. Usually, these are sensitive dimensions, more difficult to hold. Analysis of these variables shows that if the setting is constant, the distribution follows a reduced centered normal distribution. That is, the distribution curve of the variable follows a bell shape, with two important characteristics : The mean and the dispersion. Once these two characteristics have been mastered on your scrap causes, you will enter the world of quality management.

This is the simplified principle of statistical quality control. It may seem far from the workshop in the process, but the relationship between the normal law and the setting of the machine is no longer to be proven today.

In the same sense, there are performance management techniques to get out of the control habit. One of them has been popularized by Lean management, it is the short interval animation, also called SIA.

To apply the SIA, it is necessary to use indicators that producers will be able to control. Once these indicators are in place, you can expect more proactive problem solving from the workshop. Thanks to this type of animation, solutions will appear as close as possible to their source. In addition, by applying this method you offer the possibility to the operators to act on your indicators and thus the possibility for them to reach the objectives. A large part of this method is based on the notion of interval, the closer they are, the more accurate your piloting will be.

Are you ready ?

Here are some keys to help you understand the concepts of control and piloting and help you take the right direction. The implementation of these management systems requires time and resources, but their effectiveness has been proven. They will allow you to make the best use of your machines for a better productivity.

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