How can innius help machine users?
Using innius will help you to better understand the state of your machines, how efficiently they are operating, better predict when problems are likely to occur, respond faster when problems do arise, enable collaboration easily with different players, including machine manufacturers, and allow better informed decision making.
How can I use innius with my machines?
If your machines generate a data stream and can be connected to the internet, normally via a PLC, then they should be innius capable.
If the PLC uses the OPC UA or DA protocol, then it can be easily connected to the internet. If your PLC uses a protocol other than OPC, it can normally be connected to the internet using an eWON Flexy modem, running the innius machine integration software.
If you’d like to talk to us about the specifics of your machines, please don’t hesitate to get in touch with us.
My production line is a mix of both hi-tech and older machinery. Isn’t that a problem?
No, you don’t need to connect everything in a production line. innius can deal with individual machines.
Do I need any other hardware, once my machines are connected?
innius is a cloud computing based service, so there is no requirement for on-premises servers. Just a mobile devices or a desktop computer to work with innius.
Can innius data be shared?
Yes, through the security groups mechanism, it is possible to share selected data with other entities. For example: a user decides to share sensor data with a machine maintenance company, but keeps the documents and media hidden.
What about security?
The main thing to consider is that innius only reads your machine data, it can’t send anything back to your PLC or machines, which obviously has huge security advantages.
In addition to that a number of industry standard security steps have been taken. These are fully detailed in the Knowledge Base. Or if you want to know more talk to us.
What is the pricing model?
innius operates on a monthly subscription pricing model, giving you access to both the desktop admin app and mobile app. The cost depends mainly on the data usage in addition to a number of other factors. Please get in touch to talk about your specific needs.
Faster responses with real-time KPI monitoring
To operate production facilities economically you need to know what’s happening on the shop-floor and be able to respond quickly. Efficiency is achieved by minimizing machinery downtown and optimizing output. There are a number of powerful Key Performance Indicators (KPIs) which provide insights into the efficiency achieved, such as the industry standard, Overall Equipment Effectiveness (OEE); and also, Mean Time Between Failures (MTBF), Mean Time To Repair (MTTR) and Energy Efficiency (EE). But calculating KPIs a day later means that you’re often too late to deal with issues that caused problems, and it becomes a process of trying to make sure it doesn’t happen again next time. But connecting to innius allows you to monitor and receive alerts on machine production metrics in real-time, cutting response times and enabling you to take prompt action as a situation begins to emerge. Operators on the shop-floor can view data on a customized dashboard, desk based staff can view data on the desktop application and those on the move can receive automatic alerts and view data in the innius mobile application. This way machinery can be maintained, repaired and re-calibrated long before productivity is disrupted or quality is unacceptable.
Collaboration and support
One of the great strengths of innius is that it provides a direct, reliable and secure means to communicate and share documents and media. This helps to facilitate a collaborative environment. Throughout the mobile application, a great emphasis is placed on the ability to have conversations with other users. The centralized storage of machine documentation and media is another useful innius feature. This allows operators and technicians to carry all machine manuals in their pocket, information can easily be kept up-to-date and training material such as videos can be disseminated. But this doesn’t just have to occur within the confines of your organization, as it is also possible to offer access to third parties. Using the security groups functionality, you are completely in control of what you share and with whom.
Customer Testimonial
“If we fall short on an OEE target, we receive notifications from innius, likely before it happens”
Erik Hjertaas, Head of Packaging Technology at Tronrud
Business decisions based on evidence
innius provides historical and real-time performance information based on individual sensors and KPIs, such as Overall Equipment Effectiveness (OEE), Mean Time Between Failures (MTBF), Mean Time To Repair (MTTR) and Energy Efficiency (EE). This wealth of empirical data, puts business managers in a far stronger position when making critical decisions, such as: When should maintenance be planned to avoided unnecessary downtown? Which replacement parts should I keep in stock? When has a machine become too unreliable to keep maintaining? When is it economical to purchase a new machine? What is the best specification for the new machine I need? What type of maintenance and service strategy is most suitable for my machinery?
Optimize machine and labor performance
In the short term, innius’ real-time KPI and sensor monitoring allows for quick responses to emerging situations. A longer term approach is of course to learn from what’s happened and to refine processes and strategies so that problems are avoided in the first place. innius can be used in a number of ways to help with this. By examining historical sensor and KPI data, it should be possible to identify the earliest warning signs of a particular issue; such as a part starting to heat up before it actually breaks down. Preventative measures can then be defined in innius for the occurrence of those particular sensor conditions. The two preventative responses available are automatic notifications and also automatic tasks. Automatic notifications simply warn users via the mobile application that particular sensor conditions have occurred. An automatically triggered task can be sent to a machine operator, with a pre-defined description of what to do; for instance: inspect, maintain or replace the part in question. It is then important to continue to keep the rules which trigger these alerts and tasks refined so that they detect the issues early enough but don’t cause false alarms.
Another area of learning and improvement can be found by comparing the performance data between different shifts. In this way, the achievements of different teams can be recognized and best practices can be shared.
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