Optimize production, supporting numerical data with text-based data
In global production, there is a need of supporting insights into unstructured data to give better basis for decisions regarding management, maintenance, preventing incidents and improving efficiency. The technical data, historical logs and streaming data from both legacy SCADA systems and new IoT needs insights that is only possible to get from unstructured data.
Use IntOp Engine to connect to all relevant files, logs and record repositories without moving these.
Design useful contexts for data sorting for the different roles and use case.
IntOp Fetch as “one stop shop” for data for all users.
IntOp Engine may stream relevant, contextual information to existing or new dashboards based on structured/numerical data.
Underpinning knowledge from unstructured data
Lower production cost
Less unwanted incidents and delays
Maintenance and operations become less dependent on people
Staff are enabled to make quick and informed decisions
Boost functionality in existing dashboards and data sources
In different production facilities:
The users have access to sensor data (time series/flowing data), log data and other measurements or historical data from SCADA systems and/or IoT from production facilities and equipment.
We assume there are different data streams being monitored for management, maintenance, efficiency and safe operations. The users analyze data in visual dashboards to see KPIs, performance statistics, trends, prevent incidents and plan maintenance. The users may want to use machine learning on data to further analyze and/or predict incidents and downtime.
Use IntOp Engine to harvest all relevant files, logs and record without moving these. Thanks the IntOp Context Layer the users can design useful contexts for data sorting for the different roles and use case. IntOp Fetch will present all data respecting the Company security policy for all users as “one stop shop”. IntOp Engine may stream relevant, contextual information to existing or new dashboards based on structured/numerical data.
As a Data analyst, I analyze unstructured data, including from the other relevant production facilities, so that I can support the production facility better with the solutions I am making for them. As production manager, I get the needed insights into previously produced unstructured data from all relevant production equipment around the world, as a basis of knowledge, so that I better can run the production as efficiently as possible while capitalizing on all recorded knowledge. As a Maintenance planner, I find quickly relevant information about different types of equipment, connected with different categories of tasks, incidents and vendors, so that I can plan maintenance and avoid unwanted delays and incidents. Lessons learned from other locations, departments regarding the same equipment will also be helpful.