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Measurable OEE Improvement

Use Case: Data-based efficiency enhancement in production with no drop in quality

The example of the customer Münzing Chemie GmbH shows how production optimization using BCAP (Bilfinger Connected Asset Performance) can be carried out solely on the basis of sensor data while maintaining full plant availability. The project started at the beginning of 2019 and demonstrates how batch output in the powder additive plant can be increased using the Recommender Model and the Virtual Sensor. Bilfinger is working together with Münzing Chemie GmbH as a full-service provider.


Name:Münzing Chemie GmbH
Industry:Specialty chemicals
Region:Germany, Heilbronn
Focus:Mixing plant for construction additives


  • Increasing the Overall Equipment Effectiveness (OEE) of a powder additive plant. Optimization was to be carried out exclusively on a data-driven basis with no physical modifications to the plant.
  • Measuring of the optimization success was to be conducted by increasing the batch output as a target value.


  • The Recommender Model calculates a reduction in mixing time without a drop in quality, thus increasing the batch output of the powder additive plant.
  • The Recommender Model works with sensor data collected during the mixing process and transferred to BCAP.
  • With the help of the Virtual Sensor it is possible to monitor the quality of production.


  • Using the Recommender Model, production was successfully optimized while maintaining the high level of quality: there was an increase of one additional batch per day.
  • The Virtual Sensor also enabled reliable life predictions on product quality. Tedious and time-consuming laboratory tests are no longer necessary.

Challenge: Increasing OEE without modifying the plant

Münzing Chemie GmbH wanted to increase production in its powder additive plant. In the plant, the chemical raw material is applied in liquid form to a silicate and thus solidified into a powder. It was important to avoid any interruption of production that could become necessary due to a physical adjustment of the powder additive plant. The optimization of the production process was therefore based solely on data obtained. Achievement of the optimization goals was to be measured by the increase in the batch output of the powder additive plant.

The optimization of the production process, however, was not only intended to reduce mixing times. Other objectives included improved preparation times, such as earlier possible preparation of the next batch production, together with optimized cleaning of the plant.

Solution: Mixing time reduction calculated by Recommender Model

For the data-based optimization, data was collected on an ongoing basis in the powder additive plant during the production process. The sensors already installed in the plant were used for this purpose. A database was also already in place due to parallel projects. For fast processing of the production parameters, the data transfer rate was increased to one-minute intervals and all data was fed into BCAP. The Recommender Model then calculated production optimization by reducing mixing time, and thus increasing batch output without reducing product quality. BCAP's Virtual Sensor was used to monitor the quality of the production.

Recommender Model

„The Recommender Model fully met our expectations: It was possible to achieve the optimization solely on the basis of sensor data and without any intervention in the existing plant. The increase in production capacity with identical product quality is a clear benefit for Münzing Chemie GmbH.“

Sven Scholz , Plant Manager, Münzing Chemie GmbH

Benefit: Additional daily batch output with the same level of quality

Münzing Chemie GmbH succeeded in optimizing production while maintaining quality using the Recommender Model. The sum of the shortened mixing times results in an additional daily batch of output. The time gained can also be used for maintenance or cleaning processes. This means that a significant increase in production was achieved without the need for physical modifications to the powder additive plant. Plant availability was also ensured during the optimization phase. The Virtual Sensor made it possible to generate reliable predictions of product quality in real time. This eliminates the need for time-consuming sampling and analysis in the laboratory, and quality can be monitored directly.

The user benefits from other advantages, such as the visual display of all process-relevant information in a dashboard. With the dashboard, the operator has a tool at his disposal that allows him to optimize mixing times. Feedback from the plant operators via the dashboard also supports the optimization processes.

As a further benefit, the vibration sensor provides data that can be used for smart alerting, condition monitoring and predictive maintenance.

Main benefit for customers:

  • Data-based production improvement: The Recommender Model can optimize production without physical intervention in the plant and without downtimes - plant availability is maintained.
  • The time saved in this way can also be used for maintenance or cleaning.
  • Increase in output with consistent quality.
  • The Virtual Sensor monitors product quality in real time without laboratory testing.
  • The operator has a dashboard available that enables the visual display of all process-relevant information.
  • The dashboard can be used to control the optimization measures.