Thanks to an integrated data interface, important filling process parameters can be recorded, pooled and analyzed via remote access — from any workstation. This data foundation can do more than just increase filling efficiency. It also enables remote service and predictive maintenance, which ensure that downtime is minimized and operating costs are reduced.

Target weight, weighings per hour, times for coarse and fine dosing — all machine parameters in the filling process are accurately recorded with time stamps, collected in a cloud and linked with the customer’s process management/process control system via an IoT gateway. Users have a convenient, clear overview of the data in a dashboard. Secure remote access via VPN allows users to get an overview of system efficiency at any time and from any location, or even influence the production process.

Filling Process Optimization through Data Networking

The system draws on the data pool to create exportable reports, for instance on bagging performance per hour or the mean and standard deviation of net weights. Fault notifications are likewise recorded. By linking the individual components of the system and using an intelligent data processing algorithm, we can optimize the filling process and boost production process reliability. For instance, target and gross weight are constantly compared via the production control system; if the gross weight deviates, the dosing unit reacts in real time and adjusts the filling quantity accordingly. This ensures bags are optimally filled at all times no matter the conditions.

Complaint management is likewise simplified, because the cause of the problem can be quickly identified and sustainably fixed by analyzing the data of a specific batch over a specific production period. In the long term, the corrections entered into the system teach it how a certain product is optimally filled in a certain quantity.

Data Analysis for the Further Development of Machines

If it is agreed that process data is also transferred to Greif-Velox, this can be used for the further development of the machine based on customer-specific requirements. Greif-Velox would gain valuable insight into the process technology of customers and would be able to analyze which factors could play a role in improving the filling process.

Increasing Machine Performance through Predictive Maintenance

The same goes for machine maintenance: the processing of data in real time enables prognoses which form a basis for requirements-based maintenance, known as “predictive maintenance”. Machine downtime can thus be reduced and costs for unplanned production stops cut by around 20%. The evaluated data can also be used to determine optimal maintenance times which can best be integrated into the production process. The permanent analysis of process parameters increases the availability of the machine in the long term.

And if failures do occur, more than 95% of fault notifications can be resolved remotely thanks to remote servicing.