How KERN IoP can support your Industry 4.0 implementation

Applying Industry 4.0 requires the key data for a process to be acquired, digitised and structured. At the start of an industrial monitoring project, it is useful to record the number of parts produced and, where possible, the number of conforming parts. In manufacturing, actual operating hours are also important; spindle running time may be relevant for particular equipment. Together with suitable machine-state records, these data help distinguish setup and adjustment periods from downtime due to errors, repairs or a lack of production orders. Other useful records may include the tooling used, wear, operating hours at first use and the failure mode observed at the end of its service life. ​ These data support efforts to improve productivity and machine utilisation, reduce setup and downtime, and understand equipment incidents. They also help plan repairs and maintenance by establishing the actual operating exposure of critical components. A history covering the production chain supports more realistic cost calculations and can provide a basis for modelling process behaviour against its inputs. Predictions about the effects on manufactured parts require suitable data and validation of the model.

The KERN IoP systems can supply these data to a manufacturing execution system (MES) for production-information management and analysis, reducing the amount of information that personnel collect and manage manually. KERN IoP supports a knowledge-management culture in smart factories by helping teams understand why these records should be retained for analysis and future informed decisions.

​ KERN Monitor displays acquired data in real time so that teams can investigate changes in productivity and quality. ​ Having the data available and understanding how production parameters influence the final result gives teams more flexibility when responding to unexpected changes. This can improve how work is organised around the conditions of each process and help planning respond to actual operations. Reducing routine recording tasks gives operators more scope to apply their analytical skills to problems. As understanding of parameter effects improves, with validated models where prediction is required, operators can make better-informed decisions during production. Their alternative solutions can improve efficiency, build the company’s internal knowledge and add value across the production chain.