oee.ai supports the OEE increase of assets. The technology is built around the idea of maximum deployment flexibility. In addition to technology, it still requires people to implement the identified measures. But how do people and technology play together to increase OEE? This user report is an example of how it can be done.
OEE Improvement by KAIZEN/CIP Workshops
In many companies, the KAIZEN or CIP workshop has been proven to increase productivity. This format was also chosen for this commitment. A process engineer from the company leads a group of employees through the workshop. In our example, it is a company in the fast moving consumer goods industry.
The workshop will take place in the area where the product is bottled and packaged in one of the last stages of production – a bottling plant with a chain of equipment such as a bottler, a capper, a cartoner, a celophanizer and a final package.
The workshop with the staff took 4 weeks and was divided into 3 phases:
- Analysis of the initial situation
- Implementation of the optimizations
- Validation of optimizations
The activities of the individual phases are shown in the following graph.
The length of the phases is fundamentally dependent on the scope of the task. However, for classic KAIZEN workshops on OEE enhancement, the above format has proven its worth.
OEE Analysis with oee.ai
The data situation at the end of the analysis phase was as follows:
Within a working week of 5 days, the analysis was carried out in 2-shift operation. During this time over 110,000 products were manufactured on the plant.
OEE was 46.6% over the analysis period – a relatively low value. The availability factor (downtime greater than 5 minutes) was over 70%. Although the value is not very good, but better than the performance factor of about 65% – this was a possible focus of optimizations on the performance losses, ie the speed reductions and short stoppages.
A sentence on the quality factor: Although a separate Q-sensor was connected to the system, the losses were in the range of the second decimal place – no action required.
Detailed Analysis Micro Stop Losses
First, let’s take a look at the availability factor in detail: All shutdowns longer than 5 minutes were assigned to malfunction reasons using tablets installed temporarily on the system. The following overview shows the number and duration of standstills less than 5 minutes, the so-called microstops. The number per shift varied a lot, but this may also have something to do with the production program. That’s not worrying.
The average shift has about 10 microstops lasting less than 20 minutes. That is about 4% of the production time per shift – which is not the focus of this loss at the moment.
In summary, it can be said that the selected 5 minute limit for the current situation at the plant represents a sufficient accuracy of the analysis. In the future, there is the possibility to lower the border further. For the moment, however, it was skilfully chosen.
Detailed Analysis OEE Availability Factor
Let us now turn to the availability factor: The loss of availability with a duration of 5 minutes or greater was deposited via a tablet on the system with disturbance reasons. For the week in question, there were about 17 hours of interference in this category.
Of the 17 hours for 2:50 hours, or 16% of the time, no disturbance was assigned. The smaller this proportion of time, the more meaningful the analysis. In the period considered here, this value is still in order, a conversation with the plant staff on the importance of the complete detection of the causes could still be conducted.
The biggest loss, both in terms of frequency and duration, came from the “setup or format change” category. About once per shift was prepared and it took on average 28 minutes. Here was obviously a great lever for optimization approaches.
The next 3 units were approximately equal in terms of their interference components. Here it will be difficult to quickly achieve a measurable improvement. That does not mean that one should neglect these disturbances. However, with the same effort elsewhere, you can probably provide more improvement. As a possible approach in this group of aggregates, the data identified the labeler. With 11 individual disorders within 10 layers, the badly glued labels accounted for a relatively large proportion of the total number of disorders. Here it is certainly worth a detailed investigation regarding the causes.
One last note on organizational disruptions: Often, this category of disturbances accounts for a large proportion of OEE losses. Not in our operational environment described here. The scheduling and logistics around the plant worked well and offered little room for improvement.
Detailed Analysis OEE Performance Factor
The performance factor in the analysis showed the higher optimization potential than the availability factor. Let’s take a look at the details:
The detection limit was chosen very generously. For the configuration used here, a fault reason was only queried if the system ran for more than 15 minutes at less than 70% of the nominal performance. At a rate of 60 fillings per minute, the system thus lost at least 270 (= 60 pcs./min. * (1-0.7) * 15 minutes) of fillings before a loss reason was requested.
Despite this generous configuration, over 28 hours accumulated at the end of the week, at which less than 70% of the speed was driven for more than 15 minutes. If the detection limit were to be tightened, this number would, according to experience, increase significantly again. For the start of the optimization, it was therefore okay to choose this configuration.
The bottler had experienced the most intense disruptions during the analysis period both in absolute terms and in terms of duration. A clear starting point for the optimization of the performance factor was the clamping pumps at the bottler. If this problem could be optimized by a different feeder, a different placement concept, the procurement of other pumps or similar measures, the biggest obstacle to a better OEE of the plant was attacked.
All other units have a minor importance for the optimization with regard to performance losses. If you still have capacity for optimization projects available, the placement of the spray heads could be edited on the star machine. This is a single disorder that reduces the filling speed on average for 30 minutes.
Implementation of the First Optimization Round
Based on the analysis, the above-mentioned KAIZEN workshop was carried out. Within 2 weeks, the two top OEE loss causes were approached:
- Setup or Format Change: A classic theme for a Single Minute Exchange of Dies (SMED) Workshop. The workshop team analyzed the setup process in detail, identified optimization potential, practiced the new procedure with the employees and documented it as a standard.
- Pump jams at the bottler: Two maintenance engineers at the same time designed, implemented and tested a new pump feed.
The results of the two sub-teams were presented to management and employees during workshop presentations and subsequently implemented.
After 2 weeks of experience with the changes, the validation phase of the KAIZEN workshop was started. The oee.ai sensors and tablets were reinstalled in the system and the measurement repeated under identical conditions as in the analysis phase. In the course of a final presentation, the results were presented: The OEE was increased from 46.6% to 52.2% – that is 5.6 percentage points or 12% in relation to the initial situation. With 10 shifts per week, this increase corresponds to the production volume of more than one shift. This is a result that can be seen.
We have sparked your interest. Contact us at firstname.lastname@example.org or call us: +49 (0) 241/401 842 75. We will gladly provide you with oee.ai sensors and access to the analysis cockpit for a trial period. Only then you decide. Also here we are easy!