> For the complete documentation index, see [llms.txt](https://docs.enlyze.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.enlyze.com/en/concepts/understanding-oee/performance/defining-maximum-performance.md).
# Defining maximum performance
## **Why the Right Performance Reference is Crucial**
In continuous manufacturing, it's common to use equipment-based maximum performance as a reference for evaluating performance. However, this approach does not differentiate between different products—all are evaluated against the same standard.
But everyone with practical experience knows: different products can—and must—be produced at different speeds on the same equipment. An equipment-based view therefore leads to apparent performance losses that do not actually exist. The result: low trust in the KPIs and the overall data foundation.
## **Maximum Performance at ENLYZE**
To address this challenge, we at ENLYZE have developed the concept of [Maximum Demonstrated Speed (MDS)](/en/concepts/understanding-oee/performance/maximum-demonstrated-speed.md)—inspired by the principle of **Ideal Cycle Time** from discrete manufacturing.
Here, we analyze all production runs of the same product based on production history and identify the maximum performance that was stably achieved for the product. These stable, maximum performances are called **MDS (Maximum Demonstrated Speed)** and are stored as **product-specific performance references**.
The MDS is determined **continuously** and **automatically** in the background. As a result, it is entirely possible for the Maximum Demonstrated Speed to increase over time due to optimization efforts. Through this ongoing adjustment, we ensure that all performance factors and performance losses are always calculated based on the latest MDS. However, these adjustments also change the calculation basis.
This ensures that performance losses are represented accurately and transparently. It strengthens trust in the OEE metrics and provides a reliable basis for improvements.
***
#### **Example: MDS in Practice**
A production run with **Product A** was completed in January at a rate of **250 kg/h**.\
At that time, the MDS was **263 kg/h**, so the performance factor was:
$$
\text{Performance Factor} = \frac{\text{250}}{\text{263}} = 95%
$$
Later in the year, production was optimized, and the new MDS is now **275 kg/h**.\
If the January run is reviewed today, the updated performance factor would be:
$$
\text{Performance Factor} = \frac{\text{250}}{\text{275}} = 91%
$$
Although the actual performance hasn’t changed, the performance factor has decreased due to the new reference value—a consequence of the dynamic calculation.
***
**What Does This Mean for Goal Setting?**\
Because the MDS adjusts continuously, all performance KPIs are **snapshots**. This directly affects how goals are formulated and progress is evaluated.
❌ **Poor Goal Setting**\
“We want to reach a performance factor of 95% by the end of the year!”\
→ This number can change continuously due to MDS updates.
✅ **Good Goal Setting**\
“We want to improve our performance factor by 5 percentage points over the next 6 months.”\
→ Focus on relative improvement over time—independent of shifts in reference values.
{% hint style="success" %}
With ENLYZE’s MDS approach, you get a fair, product-specific, and dynamically updated foundation for performance evaluation. This enables realistic analysis of performance losses and a solid basis for identifying optimization potential.
{% endhint %}
---
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