Common mistakes in OEE calculation for continuous processes

You have been working with the OEE metric for quite some time, but it does not reflect the reality of your shop floor or your equipment? 

Then this article is just right for you. 

Learn more about the common mistakes made in OEE calculation for continuous manufacturing processes and what to consider for a 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 is one where performance is measured as flow. 

Performance is measured using flow rates, such as m/min, kg/h, or m3/h. These flow rates, along with their specific 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 faulty OEE calculations for continuous processes

Cause 1: Incorrect recording of downtime and no product-based performance references

Downtime is often recorded manually and input into the system with a delay. When there is a problem, operators typically focus on troubleshooting the machine first before documenting the downtime. 

This means that 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, leading to incorrect OEE calculations and poor decisions for improving OEE. Additionally, the effort required for manual downtime recording is enormous.  

The second source of error in data quality is the maximum performance with which you calculate 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 for a product varies, and accordingly, the maximum machine performance fluctuates. 

When customers work with a single performance reference, regardless of the product, the recorded data does not reflect reality on the shop floor and for the machines. This often results in an artificially low OEE value, indicating problems that do not actually exist. 

Again, the acceptance of the data decreases - both for the OEE manager and for the operators - and trust in the calculated metrics is low. Unfortunately, this also hides 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 that work? We establish rules for when a downtime should be documented as such, for example, based on a predefined throughput threshold. This leads to accurate recording of downtimes, and the actual availability losses are accurately reflected in the data foundation. This increases data acceptance and reduces manual effort.

To also accurately reflect performance losses in the data, we establish product-based performance references (Maximum Demonstrated Speed) in the system for their calculation. This results in maximum transparency and allows you to derive and implement specific improvement measures for individual products.

Cause 2: No systematic categorization of losses

To make OEE meaningful, it is advisable to exclude downtimes caused by operational breaks, poor planning, or missing orders during its calculation. The same applies to quality losses (scrap) that occur during the start-up of the machine.

This exclusion is only possible if downtimes and scrap are categorized into subcategories, e.g., according to Six Big Losses.

Without this exclusion, OEE would be distorted by planning errors, holidays, etc., and comparisons over longer periods would be difficult.

Here’s how you can fix the problem

Categorize your losses into subcategories. By creating a standardized loss catalog, you can document reasons for downtimes. If it comes to it, they can be easily assigned and stored. This categorization helps you analyze your data correctly later on. 

The same applies to scrap. By identifying the most common reason for producing scrap using the Pareto principle, you can recognize the biggest issues in your manufacturing. 

Cause 3: Using averages for specific days or shifts

Do you want to calculate OEE for specific time periods, such as for the night or morning shift, or for an entire day? Then OEE must be calculated on a machine basis or for the entire facility.

This requires a complex calculation, which self-built Excel tools often fail to handle. 

Excel tools (or many MES systems) rely on a simple average calculation, which is worthless for analyzing specific time periods.

Here’s how you can fix the problem

ENLYZE calculates OEE by creating the integral over the performance parameter for the respective time frame. This way, the performance factor accurately reflects reality. 

Availability is also proportionally attributed to the time period. Thus, OEE is calculated correctly and can be generated on demand at the order, machine, or site level with the push of a button. 

This integral calculation can certainly also be performed in Excel. However, it involves a labor-intensive 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 and precisely captures your Overall Equipment Effectiveness (OEE) for continuous manufacturing processes. We handle the entire process for you: from data collection at your machines to standardizing the data, to providing OEE tools in the ENLYZE app.

With ENLYZE, you can finally find your most important manufacturing data (machines, ERP, MES) all in one place. The app allows you to create real-time dashboards or analyze past orders.

With minimal IT effort and in less than a week, the system is implemented at your facility. The ENLYZE software delivers accurate figures that you can trust. You can derive improvements and implement effective measures.

The software outputs data in real-time and calculates the Overall Equipment Effectiveness at three levels of detail: locations, machines, and orders. The tedious and inaccurate manual OEE calculation is thus a thing of the past. Losses are correctly categorized and transparently presented through automated reporting. This allows you to spend less time manually collecting data and performing analyses in Excel, and more time focusing on optimizing your manufacturing.

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 connections to legacy systems and specific peripherals also work. 

This pays off: According to the experience of our customers, the investment in ENLYZE software quickly pays for itself.

Become an OEE Expert with our OEE Series

Here, you will learn how to calculate and continuously improve OEE.

1. The Importance of the OEE Metric

2. How to Calculate OEE (+ Excel Template)

3. Special Considerations for OEE Calculation for Continuous Processes

4. Choosing OEE Software: How to Compare Vendors

5. ROI Calculation for OEE Software

6. Performance Losses: Why Does the Machine Not Always Operate at Maximum Speed?

7. How to Manage Machine Downtime through Categories

8. Using the 6 Big Losses to Optimize the Shop Floor

9. Recording OEE Manually? Why the Future of Productivity Improvement Lies in Automated Data Collection