Integrating Systems

When traditional system integration is no longer enough, it must be advanced to »evolutionary system integration«. In today’s complex production contexts, manufacturing companies cannot escape the need to connect and integrate all systems involved in the process chain. It is by no means enough for humans, machines and information technology to simply communicate with each other. The value chain is no longer limited to centrally planned processes, but decentralized decisions are often made based on assessments of the current situation – both in interactions between purely technical systems and between people and machines. This requires completely new ways to link production methods and technologies with information technology and infrastructure.

The development of system integration in industry is closely linked to technological advances and changing paradigms in production. System integration has evolved from describing the mechanical, rigidly coupled organization of production processes to an increasingly networked and automated structure.

In the second industrial revolution, shaped by Taylor and Ford, the division of labor was systematically realized in production lines. This phase of system integration was primarily mechanical in nature: Machines and workstations were rigidly linked together in order to achieve high efficiency in standardized production processes. When information technology and automation made further advances in the third industrial revolution in the 1970s, the idea of system integration changed. A new vision of Computer Integrated Manufacturing (CIM) represented a major step forward: For the first time, different systems within a company were designed to communicate with each other, significantly improving the planning, control and execution of production processes. Nevertheless, flexibility remained limited here too, as this type of system integration was still based on predefined, often rigid structures.

In today’s era of Industry 4.0, Cyber-Physical Systems (CPS), the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI) are driving integration by enabling deep connections between physical and digital systems. These technologies strive for a completely flexible, self-organizing production in which machines, products and IT systems communicate with each other in real time and can adapt dynamically to changing conditions.

However, mechanical and digital integration is still often limited in its flexibility and, if present at all, restricted to individual ecosystems. Many production systems continue to be rigidly structured, which makes it difficult to respond to changes in the market or demand with agile production processes. Companies can only remain competitive through truly flexible, networked and adaptive system integration. This requires not only technological innovations, but also a new way of thinking in the design of production systems.

Humans will continue to play a central role in system integration, especially in times of demographic change and a shortage of skilled employees. While machines and computer-aided systems become increasingly autonomous, humans are still essential for their design, monitoring and continuous adaptation. Humans can navigate complex production processes and integrate systems in ways that ensure they remain flexible and adaptable. This human intelligence is crucial for interpreting data meaningfully, making strategic decisions and solving unforeseen problems. Especially at a time when machines are taking over more and more tasks, it is crucial that humans manage the integration of these systems responsibly so that technological advances remain in line with social values.

The demographic shift presents a growing challenge in this context: With an ageing workforce and a decline in available skilled labor, it is becoming more and more difficult to find qualified employees with the necessary skills to manage and control highly complex processes. This development increases the pressure to design systems in such a way that they can be operated efficiently even with less qualified personnel, supported by digital assistance systems and artificial intelligence.

Artificial intelligence and machine learning will play a key role in evolutionary system integration. These technologies make it possible to gain valuable insights from the large amounts of data collected along the value chain, which can contribute to continuous optimization processes. AI-controlled algorithms will not only be able to monitor ongoing production, but also proactively suggest improvements that lead to more efficient processes and better quality. The result will be an adaptive production that responds dynamically to changes in demand or unexpected disruptions.

Evolutionary system integration will be shaped by the principles of sustainability. Digital and mechanical integration along the value chain must be designed in ways that enable resource-efficient, waste-minimizing production. Circular economy concepts will ensure that materials and energy are managed in closed loops, allowing raw materials to be used more efficiently. The environment will benefit from a more economical use of resources on the one hand and avoiding waste on the other. 

Digital twins, virtual representations of physical objects and processes, form the basis for monitoring and optimizing the entire life cycle of products based on real data. To ensure the seamless integration of all ecosystems involved in value creation, standards must be created that are binding for all stakeholders. Competitive thinking and the deliberate drawing of system boundaries must be replaced by the pursuit of higher, no longer purely economic goals. Current approaches such as the efforts to establish an international standard for digital twins by the International Digital Twin Foundation (IDTA), the Asset Administration Shell and Gaia-X are welcome but can only be successful, if they are supported consistently and comprehensively by the production industry.