- About LiSEC
Software for Shopfloor Management
With the help of Analytics, it is possible to gain a precise insight into production, identify potential scope for improvement and derive appropriate measures. Analytics makes data from production available in an appropriate format and facilitates analysis. Key figures such as overall equipment effectiveness (or OEE) are automatically calculated for both machines and lines and on the basis of the shift schedule.
The data is collected on the shop floor by the LiSEC IoT platform and transferred to the Microsoft Azure Cloud using an Edge Device. The Edge Device is an industrial computer developed by an information security service provider explicitly for LiSEC's requirements. The data is prepared in the Azure Cloud and accordingly pre-calculated, so that Analytics can display it quickly and reliably.
Data is generated at different locations in production. One of these sources is the machine itself, where data regarding the machine status, the manufactured product or even alarms that have occurred is generated. This data is used by different systems - for example to generate completion notifications. The LiSEC IoT platform transfers this data to the Microsoft Azure Cloud for further processing.
The data is filtered accordingly and aggregated in the cloud environment, and the KPIs are pre-calculated. Analytics visualises this data and enables filtering so that potentials can be identified and appropriate measures derived.
In order to use Analytics, the machines must already generate and provide the necessary data. Get in touch with your LiSEC representative to find out if your machines already do this.
The increased transparency that Analytics offers simplifies the management of production, because it is possible to determine exactly where there is potential scope for improvement and how great its impact is on current production. The primary basis for this is alarms that lead to downtimes, although also waiting times that occur with machines, right up to a precise analysis of the product mix.
Greater transparency in production
Displaying this data allows employees on the shop floor right through to the production manager to gain a much clearer insight into the production and the current states of the systems.
Identification of improvement potentials
The information gained from the data is used to identify potential scope for improvement. For example, downtimes can be directly assigned to an organisational or technical cause and the focus can be accordingly placed on staff training or maintenance.
Analysis of downtimes
There are various reasons why system downtimes can arise. In Analytics, downtimes are automatically assigned to causes, which can be subsequently reduced through appropriate measures. The essential basis for this is the machine status and the alarms that have occurred.
Breakdown according to products and shapes
Different product dimensions or even shapes affect the cycle time. The output indicator, in conjunction with the product mix in the same time period, is meaningful in terms of performance. With Analytics, both items of information can be mutually considered and measures can be derived if necessary.
Feel free to contact me if you have any further questions.
Matthias Schoebl ,