+49 231 97 00 340           contact@divis-gmbh.com

All industries at a glance

Individual solutions for your company

The scope of applications of our services and software is manifold. All areas where products and processes need to be optimized are included. We use data from experiments, simulations or directly from the production process. Based on these data we analyze and optimize your product or process. We develop customized solutions specifically for your application, and we integrate them completely into your system environment.


Industries of the automotive sector generate big amounts of data in production processes and in development. Our tools for data analysis and optimization are applicable to a wide range of tasks, including:

Production processes

Forming process

Casting process

Corrosion protection and coating

Joining technology

Quality control

Predictive maintenance


Demand forecasting and stock prediction

Residual value forecasts


Passenger and pedestrian safety

Forming technology

Engine development

Exhaust systems


Driving dynamics


Drive train

Air conditioning technology

Corrosion protection

Asset management

Case studies

BMW, Mr. Ganser

“As the leading expert in the application of evolutionary strategies and meta-models to hard industrial optimization problems, Prof. Bäck has always delivered highest quality results for BMW. We truly appreciate our collaboration and warmly recommend a collaboration with Prof. Bäck to any industrial partner who is in the need of solving tough optimization, modeling, and prediction problems.”

Markus Ganser, Manager Standardization. Design, Innovation, BMW AG, Munich, Germany

Case study (view PDF file)

Honda, Prof. Sendhoff

“Besides researching and developing the most suitable individual software components, successful multi-disciplinary optimization in an industrial context requires a system level analysis of the problem including issues of workflow, interoperability and resource sharing. Professor Bäck is world-wide one of the leading researchers in computational intelligence and his contributions to the development of new algorithms and methods cannot be overestimated. However, from our perspective, it is his unique analytical ability to amalgamate systems engineering with computational intelligence which makes him such a valuable partner for our institute.”

Prof. Dr. Bernhard Sendhoff, Chief Technology Officer, Honda Research Institute, Offenbach, Germany

Ford, Dr. Hochkirchen

“Guaranteeing highest quality standards of Ford vehicles requires a continuous evaluation of quality data from a variety of data sources. With divis GmbH, we have access to a competent partner in the areas of data warehousing, data analysis, and data mining. We would like to express our gratitude to Prof. Bäck and his team.”

Thomas Hochkirchen, PhD Statistical Methods, Central Quality, Ford Werke GmbH, Cologne, Germany

Many different aspects of data analytics, machine learning and optimization can be of importance in these application domains. Examples include the identification of the most influential parameters, optimization, the usage of the model as interactive prototyping tool, process optimization, robustness and sensitivity analysis. Our process analysis provides support for all tasks including the optimization based on simulations or analytical objective functions.

Oil, Gas, Energy

Due to complex processes both upstream and downstream, large data volumes, and tasks ranging from reservoir modeling to asset management, exploration, production, and distribution, to name a few, this industry benefits tremendously from the technology. Artificial intelligence, machine learning, and optimization are at the core when it comes to generating additional value. Sample applications domains include the following, but many more are equally important:

Batch schedule optimization in pipeline systems

Scheduling optimization for exploration equipment

History matching for geological simulations

Analysis of exploration data

Analysis of drilling data and prediction of critical situations

Automatic generation of predictive models for the performance of wells

Anomaly detection of sensor signals in plants

Plant data analytics and plant operations optimization

Consumer goods

In industries like cosmetics, dental care, detergent or food and beverages, experimental formulation optimization as well as process optimization are daily business.

Due to a targeted analysis of the formulation data, relations can be identified and formulation properties can be predicted, such that experiments can be saved. In this way it is possible to create or optimize products with a clear objective in mind. This method is used successfully in applications such as:

Stability prediction and stability optimization of cosmetics

Optimization and prediction of SPF

Prediction of the microbiological performance of formulations

Prediction and optimization of the performance of applicators in combination with the formulation

Prediction of rheology / thixotropy

Modeling and prediction of sensory characteristics as a function of physicochemical characteristics of formulations

Modeling and prediction of consumer liking as a function of sensory characteristics of formulations

Analysis and optimization of production processes

Analysis and correlation of laboratory, clinical and consumer data

Thus, it is possible to obtain valuable predictions concerning the consumer behavior and the success of new products.

Among others, German and American companies are among our customers, as well as suppliers of the food and beverage industry.

Case studies

Beiersdorf AG, Dr. Hillemann

“Our past experience limited our trials to include only familiar ingredients, and the lengthy process of attempting each combination had led us to declare the desired product infeasible after three months of research. However, with ClearVu Analytics, we satisfied all the technical requirements faster than with traditional processes, and now have a new product we will bring to the market.”

Thomas Hillemann, PhD, Head of Study Coordination, Beiersdorf AG, Hamburg, Germany

Case study (View PDF file)

Johnson & Johnson Germany, Dr. Hauser

„We are still implementing and evaluating our new model, but clearly we can now predict micro results for new formulations using known components at this time. We will continue to expand our use of the model, but critical to this process is internal data collection. But even this early in the project, we have a better picture of how our materials interact and the effects to the wipe. And we have a set of working rules for our formulators.“

Matthias Hauser, PhD, Associate Director Scientific Relations, Johnson & Johnson GmbH, Düsseldorf, Germany.

Case study (View PDF file)

Chemical Industry

Production processes in the chemical industry have to be controlled continuously to guarantee a consistent product quality and quantity. With modern data acquisition systems a large volume of data are already collected, however, most often these data are not used to their full potential.

Our methods of process analysis are suitable for generating models which predict process changes early enough to avoid down times or quality deviations.

This provides valuable insights, for example, into:

Identification of critical influencing parameters

Stabilization of quality and quantity of products

Prediction of process status over a given time period

Optimization of the production process

These applications are not limited to the chemical industry, but generally suitable for all production processes. By targeted analysis of process parameters and the corresponding product quality it is possible to identify influencing factors. Using predictive models and optimization, it is possible to control the process in an optimal, robust and stable way.

Synthetic Materials Industry

Plastic extrusion is a universal method which is used globally. The products have many different characteristics like being solid (pipes made of hard plastic), flexible (plastic bottles) or soft (foam). No matter whether it is about window frames, seals, tubes or foam, in every case there are individual customer requirements concerning quality, price and delivery reliability.

Today’s products are subject to increasing requirements, for example, concerning lifetime or surface quality. Besides customer requirements, factors like energy consumption, storage and logistics have to be taken into consideration.

Minimized rejects

Shorter setup time

Continuous quality monitoring

Our methods describe the complex interdependencies between raw material, semi-finished products, machines and process control so that cause-effect correlations become obvious and the final product characteristics can be predicted. If the predicted characteristics of the final product do not meet the expectations, it is possible to determine the best settings to reach the desired quality.

Further Industries

divis provides valuable support for the optimization of products and processes. Besides the above mentioned industries, many other branches can benefit from our methods, for example:

Pharmaceutical industry, for example QSAR, QbD, Scale-up, process optimization

Coatings and finishes, particularly formulation and process optimization

Adhesives, in particular formulation and process optimization

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Contact details

divis intelligent solutions GmbH
Joseph-von-Fraunhofer-Str. 20
44227 Dortmund, Germany
+49 231 97 00 340


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