|
|
|
You have been working with the OEE metric for a while, but it does not reflect the reality of your shop floor or your equipment?
Then you are exactly right with this article.
Learn more about the common mistakes made in OEE calculations for continuous manufacturing processes and what to pay attention to for correct implementation.
This article is based on our webinar OEE for Continuous Manufacturing: Common Mistakes & Best Practices.
What is a continuous process?
A continuous manufacturing process refers to the process where performance is measured as flow.
Performance is measured based on flow rates, such as m/min, kg/h, or m3/h. These flow rates with their respective characteristics play a role in the calculation of OEE.
Example processes:
Extrusion (often kg/h)
Converting (often m/min)
Beverage production (often m3/h)
etc.
The 3 causes of incorrect OEE calculations for continuous processes
Cause 1: Incorrect recording of downtimes and no product-based performance references
Downtimes are often recorded manually and entered into the system with a delay. If there is a problem, operators usually take care of troubleshooting the machine first before documenting the downtime.
Thus, the respective BDE/MES system does not accurately reflect when, for how long, and why a machine is down. In our experience, availability losses are misrepresented due to this, leading to an incorrect OEE calculation and wrong decisions to improve OEE. Additionally, the effort for manual recording of downtimes is enormous.
The second source of error in data quality is the maximum performance with which you calculate the OEE. In classic OEE calculation, it is assumed that all products can be produced at maximum machine performance. The fact is: Depending on design or material, the production time of a product varies and consequently the maximum machine performance fluctuates.
If customers work with a single performance reference, regardless of the product, the recorded data does not reflect the reality on the shop floor and with the machines. This usually results in a too low OEE value that indicates problems that do not even exist.
Again, the acceptance of the data decreases - both for the OEE manager and the operators - and trust in the calculated metrics is low. Unfortunately, this also conceals levers for improvement measures.
Here’s how you can fix the problem
The automatic downtime recording by ENLYZE ensures that all downtimes are recorded with consistent quality down to the second. How does it work? We set rules for when a downtime should be documented as such, for example, via a predefined threshold of throughput. This leads to accurate recording of downtimes, and the actual availability losses are correctly reflected in the data. This increases data acceptance and reduces manual effort.
To also accurately represent performance losses in the data, we store product-based performance references (Maximum Demonstrated Speed) in the system for their calculation. This leads to maximum transparency and allows you to derive and implement specific improvement measures for individual products.
Cause 2: No systematic categorization of losses
For the OEE to be meaningful, it is advisable to exclude downtimes caused by company vacations, poor planning, or missing orders when calculating it. The same applies to quality losses (scrap) that occur during machine start-up.
This exclusion is only possible if downtimes and scrap are divided into subcategories, e.g., by Six Big Losses.
Without this exclusion, the OEE would be distorted by planning errors, vacations, etc., and comparisons over longer periods would be difficult.
Here’s how you can fix the problem
Divide your losses into subcategories. If you create a standardized loss catalog, you can record the reasons for downtimes. In case of need, they can be easily assigned and stored. This categorization helps you later in properly analyzing your data.
The same applies to scrap. By using the Pareto principle to find the most common reason for producing scrap, you can identify the biggest problems in your manufacturing.
Cause 3: Use of averages for specific days or shifts
Do you want to calculate the OEE over specific time periods, such as for the night shift or day shift, or for an entire day? The OEE must then be calculated on a machine or site basis.
This requires a complex calculation that self-built Excel tools typically fail to achieve.
Because Excel tools (or many MES systems) rely on simple averaging calculations, which are worthless for the analysis of specific time periods.
Here’s how you can fix the problem
ENLYZE calculates the OEE by forming the integral over the performance parameter for the respective time period. This way, the performance factor reflects reality.
The availability is also allocated proportionally to the time period. Thus, the OEE is calculated correctly and at the push of a button according to your wishes at order, machine, or site level.
This integral calculation is certainly possible in Excel as well. However, it is associated with days of effort that starts anew with each analysis. Employees who do not master this analysis also have no access to the insights.
With tools like ENLYZE, you make these insights available to all employees at the push of a button.
ENLYZE: OEE Software for Continuous Manufacturing
ENLYZE automatically captures your overall equipment effectiveness (OEE) accurately for continuous manufacturing processes. We take care of the entire process for you: from data collection at your machines, through data standardization, to providing OEE tools in the ENLYZE app.
With ENLYZE, you find your most important manufacturing data (machines, ERP, MES) all in one place. Using the app, you can create real-time dashboards or analyze past orders.
With minimal IT effort, the system is implemented in less than a week. The ENLYZE software provides correct metrics that you can trust. You can derive improvements and implement effective measures.
The software outputs data in real-time and calculates overall equipment effectiveness at three detail levels: sites, machines, and orders. This puts an end to tedious and inaccurate manual OEE calculation. Losses are categorized correctly and transparently represented with automated reports. Thus, you are less busy manually collecting data and conducting analyses in Excel, and have more time to focus on optimizing your manufacturing.
Furthermore, ENLYZE can also be used for other applications, such as production controlling or optimizing energy consumption. The ENLYZE Manufacturing Data Platform is open and equipped with modern interfaces. We ensure that even legacy systems and specific peripherals can be connected.
It pays off: The purchase of the ENLYZE software pays for itself according to the experiences of our customers in a short time.
Become an OEE expert with our OEE series
Here you will learn how to calculate and sustainably improve the OEE.
