As part of Industry 4.0, industrial companies aim to achieve the most efficient, flexible and networked manufacturing possible – and are increasingly using artificial intelligence (AI) to do so. Using AI, however, requires high-quality and ideally dynamic machine data.
In Industry 4.0, there are three challenges that must be solved to do so:
- At a typical manufacturing company, the process and plant levels are often characterized by a classic multi-vendor landscape – that is, the machines come from different manufacturers.
- The data of such manufacturing systems is heterogeneous and originates from components that have different technology standards, not the least due to their different ages.
- What is more, industrial systems have different communication interfaces and protocols. They generally generate and store data statically, which is then prepared manually by data scientists.
When artificial intelligence is integrated in these processes, this integration is often performed manually as well, due to the challenges described above.