New era in systems engineering

Synostik, an IT service provider from Oebisfelde, has recognized in its industrial projects that systems engineering processes are the most important basis for the targeted and efficient optimization of production systems.

“Used in a structured manner, systems engineering helps companies design their production and manufacturing more effectively, strengthen their competitiveness and ultimately remain successful in the long term. One tool for this is DiagnosticDesign - a method that can successfully implement systems engineering approaches in production. This method has been used for years in the area of Maintenance Excellence, especially for preventive and predictive maintenance, ensuring the competence of staff as well as for integration and continuous improvement processes,” explains Maik Ekert, Managing Director of Synostik GmbH.

Manufacturing companies today face numerous challenges in an increasingly competitive market environment. Increasing customer demands, a shortage of skilled workers and the need to constantly find innovative ways to differentiate are just a few of them. Companies are therefore increasingly relying on Manufacturing Excellence, an approach that aims to develop and implement first-class production processes in order to achieve the highest levels of efficiency, quality and customer satisfaction. Typical topics in manufacturing excellence include continuous improvement processes (CIP), total quality management (TQM), process optimization and efficiency (operational excellence), employee development and the integration of new technologies and innovations.

Systems engineering for optimized production systems

“When introducing manufacturing excellence, systems engineering approaches should be pursued that include systems thinking, requirements management, life cycle management, risk management, CIP and model-based system development (MBSE),” recommends Johannes Diedrich, Head of Industry at Synostik.

From system analysis to mobile applications

Systems engineering with the DiagnosticDesign method follows a clear pattern: Starting from a detailed system analysis, the events that determine and influence the system state are identified and algorithms are developed to detect and treat these events. These algorithms serve as a basis for structured improvement of the production system, for example through automation and optimization using technologies such as robotics and other artificial intelligence (AI), e.g. machine learning and neural networks. For example, step-by-step instructions for machine operators and maintenance staff can be exported from the algorithms and made available on paper or digitally. Parts of the tasks can be supported or even taken over by AI applications.

Practical example of robotics

For example, an extensive system analysis was created for the robot-guided tools of Glaub Automation & Engineering GmbH from Salzgitter. This includes components, possible events that can occur on these components and algorithms for how these events can be noticed and handled. On this basis, structured options for automatically implementing these algorithms can be determined. These in turn can be implemented in a targeted and efficient manner with the help of requirements and project management, for example. The result is the early and automatic detection and elimination of undesirable operating states and thus increased tool availability.

Companies that use these methods can achieve the following goals: increasing efficiency and productivity, reducing costs, improving quality, gaining competitive advantages, employee satisfaction, retention and development, as well as competence assurance and risk management.