Manroland Goss has launched a major update of the AI-assisted maintenance platform MAINTELLISENSE. Since 2019, MAINTELLISENSE has been an integral part of manroland Goss’ service portfolio. The main goal of the data-driven application is to further increase the reliability and productivity of printing systems in order to sustainably reduce costs for customers.
Thanks to the update, monitoring of press and production data is now individualised and automated. If MAINTELLISENSE detects relevant deviations, the customer is notified automatically. Its new look and feel also highlights the ease of use and pioneering approach of the maintenance platform with its clear design language.
Due to the major update, MAINTELLISENSE now automatically notifies users in case of deviations. Based on analysed press parameters, the digital maintenance platform provides information on necessary or sensible measures in print production.
‘MAINTELLISENSE enables us to carry out maintenance in a more targeted and predictive manner. Thanks to intelligently evaluated information, we know when and where we need to start maintenance, and which parts of the system to check. MAINTELLISENSE thus ideally complements our reliable and competent service,’ said Franz Kriechbaum, CEO at manroland Goss, about the digital maintenance platform.
The web application compares individual production data with similar presses to identify potential issues. The database is derived from worldwide installed press historical data and the technical knowledge and expertise of more than 375 employees involved in the development process of the application to continuously enhance MAINTELLISENSE.
Data protection is of particular importance to MAINTELLISENSE. For maximum security, data is stored on German servers in accordance with German data protection laws. MAINTELLISENSE makes it possible to evaluate and classify large volumes of data and to provide indications of potentials and risks.
For 2023 and beyond, the focus is on Big Data for more sustainable and efficient production. Using the specially developed IoT-box, it will be possible to combine various measured values with the production data to allow further relevant perspectives on the machine. By connecting various sensors, the user can decide which resources he/she wants to monitor. This will make it possible to determine the consumption of resources for a single product or the effect of adjusting the production speed on the consumption of operating materials and more. The possibilities are nearly endless.