Overview

Lean production – key figures are the basis

In the early 1990s, scientists at the Massachusetts Institute of Technology (MIT) caused a stir with a study. Between 1985 and 1991, 54 experts in 15 countries took a close look at the production processes of car manufacturers and evaluated them. The result: Japanese OEMs were far superior to European and American manufacturers – they produced faster and made fewer mistakes.

The reason for the clear lead in efficiency and quality was a completely different production system. This was designed by Taiichi Ohno, production manager at Toyota, in the mid-1950s and then continuously developed further. The MIT researchers established the term “lean production” for the Japanese approach, which was basically unknown in Europe and the USA until the study was published. Since then, lean production has become a guiding principle for manufacturing companies in all sectors.

Creating value, avoiding waste

If you want to implement it for your own production, you should first familiarize yourself with the central idea. The aim is to create value while consistently avoiding all waste (muda in Japanese). This includes material movements (transportation), stocks (inventory), movements (motion), waiting times (waiting), processing (over-processing), overproduction and corrections and defects (defects). All activities that are necessary for value creation should be optimally coordinated. Of course, this cannot be achieved in a one-off effort and then be achieved for all time. Instead, a continuous improvement process (CIP) is used to strive for the perfect state.

This has an important practical implication. If waste is to be avoided and production continuously improved, it must be possible to measure and evaluate the statuses at different points in time. For example, it must be clear how long a machine takes to process a particular workpiece and how much waste is produced in the process. And these key figures don’t just have to be collected once to determine the current situation. They need to be generated regularly in order to know whether the measures taken are working and progress is being made.

Digitize data acquisition and data processing

In principle, this is possible with very simple means – a pen and paper and an employee on the store floor who regularly records all relevant parameters. The data recorded in this way must be handed over to the plant manager at the end of each day or at the end of each shift, who then summarizes it into the defined KPIs. Of course, nobody does this. The effort involved would be immense. It is already easier and quicker if a spreadsheet such as Excel is used instead of pen and paper. Especially if the employee on the store floor enters the data directly into the software on site. This procedure is still common practice in quite a few companies in Germany. However, as the effort involved is also quite high, the analysis times are usually rather far apart – for example, evaluations are only carried out monthly or not regularly, but only sporadically. Monitoring is much closer and requires much less effort if the entire process is digitized. In other words, when sensors on the machines record the data and forward it to a manufacturing execution system that automatically calculates the key figures – in any narrow time frame, at machine and plant level.

This is not only advantageous because the digitalized data collection and processing makes it possible to assess at a very early stage whether the optimization measures taken are having the desired effect. If the values are displayed in real time, the plant manager can also see whether everything is running smoothly on the store floor at the moment or whether there are faults that need to be rectified immediately. However, this condition monitoring is only indirectly related to the development of lean production.

Interpretation and communication

Rather, the key figures are decisive. However, they alone are not enough to establish a lean culture. This is because the figures must be interpreted and viewed in a wider context. For example, it may be clear that a machine has a conspicuously high number of downtimes. However, the pure machine data usually does not tell you what the reason for this is: a defect in the machine, inferior materials being processed on the machine or an unfavorably planned production process. In order to be able to determine this, additional information is required, which an employee must provide from the factory floor – to do this, they must recognize the respective situation, interpret it and then record it accordingly. In addition, all findings must be clearly communicated to the employees on the shop floor. After all, they will only make a lasting contribution to continuous improvement if they can directly experience the impact of their efforts.