Changing demands on companies as well as their engineering and production processes pose ever increasing challenges.
The evaluation of existing production processes and the modernization of existing plants is the basis for future competitiveness. Targeted automation, the optimization of value-added processes, the continuous integration of existing employees and the use of data-based methods lead to a holistic view of engineering, production and organization.
Targeted use of industrial robotics, image processing and automation enables intelligent, changeable and efficient production cells and plants.
The rapid further development of technologies, such as collaborative robot systems, autonomous intralogistics systems or machine learning based image processing, are continuously changing the technical possibilities of production plants. By accompanying feasibility studies, developing implementation concepts or creating functional prototypes, these can be evaluated company-specific and, if necessary, adapted and implemented.
By means of a transparent presentation of production processes as well as a structured survey of automation potentials, optimization possibilities can be identified and decision-making processes can be supported. Operations research methods and simulation-based studies support the analysis of existing production processes. An integrated data analysis forms the basis for a systematic investigation of scenarios and effects. Methods such as combined material and energy flow simulations can also be used to analyze material and energy consumption and design more resource-efficient and sustainable overall processes.
Production optimization in the context of Lean and Six Sigma methods aim at increasing efficiency and quality as well as reducing lead time. In this context, both analytical methods for process optimization can be used and corresponding competencies can be transferred to the company through employee training.
A sustainable transformation is only possible if individual employees as well as the entire organization also support this change. Company-specific challenges are examined in detail, corresponding success factors are developed and the findings are translated into implementation recommendations. A special focus is placed on the continuous integration and further training of existing employees, individual knowledge and competence management, and making the company more attractive for employees.
The latest assistance systems in engineering and production – such as those based on projections or virtual and augmented reality – can provide employees with individual support and thus enhance the qualitative value of their work.
Data-based methods and concepts – such as machine learning in quality assurance, predictive maintenance or AI-supported machine and operating data analysis – offer great potential for innovation in engineering and production processes. Of particular focus is the practical implementation in existing processes and in special machine engineering – e.g., in the development of automated test benches or monitoring systems. Especially in combination with classical methods, e.g., industrial control engineering, industrial measurement technology or industrial image processing, innovative solutions can be created.
Through the structured identification and assessment of use cases and potentials – in combination with the examination and visualization of available data –, project planning of the necessary data infrastructure as well as the development of a corresponding software and system design, short-, medium- and long-term development stages can be defined.
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