More performance thanks to more data

Work more effectively with software support

The reduction of rod breaks is a powerful example of  the potential offered by Hauni Digital’s solutions in the short- and long-term optimization of any production.

Rod breaks are one of the five main reasons for stoppages in any production environment. Hauni Digital’s software solutions provide operators, technicians and project managers with effective support for eliminating these problems – significantly reducing the team’s workload. The solutions approach the challenge from three angles: short-term problem solving, continuous improvement processes and prevention.

Responding to difficulties at short notice

Solving a problem with rod breaks during ongoing production requires a quick response, targeted measures and a final test to confirm the success of these measures. The software module d.uptime provides the operator and responsible technician with everything they need to identify and understand the cause of the problem, implement solu-tions and verify and document their success. Less experienced employees, in particular, benefit from the step-by-step instructions as well as the parameters and settings provided by the software.

Proactive improvement process

To generate continuous improvements in the area of rod breaks and avoid negative rogue events in the production process in the medium and long term, companies must put a proactive continuous improvement process at the heart of their corporate culture. This includes establishing standards and regular meetings.

The digital process management software d.production helps businesses to prepare and manage these meetings efficiently. It also supports them in finding and adhering to optimal standards as well as incrementally developing and refining these further.

Preventive measures for the future

Artificial intelligence (AI) is gradually finding its way into the tobacco industry’s production processes. The vision is to create the “lights-off factory”. Solutions from Hauni’s d.AI range are already being used in automation, continuous learning processes and prevention-oriented  optimization. For example, d.OEEbooster constantly monitors machine behaviour. Insights gained from high-resolution machine data enable the AI to make clear recommendations that keep machines in optimum condition over the long term and prevent a decline in performance data.