Visualize data and make informed decisions

Visualisation and Business Intelligence

Data visualization refers to the graphical representation of data in order to make complex information understandable and accessible. With the help of visual elements such as diagrams, graphs, and maps, trends, outliers, and patterns in production data can be quickly and easily identified and understood.

Why is Visualization Important?

In the age of big data in Industry 4.0, tools and methods for data visualization are essential for analyzing large amounts of information, making data-driven decisions, and optimizing processes.

How does data visualization work?

Data from various sources is collected and transferred to a central system (more information on the individual steps can be found here). The data is then processed: it is cleaned, filtered, and structured in a meaningful way so that it is optimally prepared for later presentation.

KRIS can be easily connected to common visualization tools such as Tableau, Power BI, Node-RED, or MES-integrated solutions. In addition, numerous external widgets and widgets created directly in KRIS can be integrated into the workspace for even more flexibility in the presentation of your data.

The next step is the actual visualization. The tools mentioned above can be used to create informative dashboards and graphics that present your data in a clear and understandable way.

Methods of Visualization:

Time Series Charts

For displaying trends (e.g., production volume over time).

Heat Maps

For identifying hotspots such as bottlenecks or high energy consumption.

Pareto Charts

For identifying the main causes of errors.

Sankey

Visualization of where data comes from and where it flows.

Pie Charts

Presentation of percentages of a whole.

Tables

Structured, detailed presentation of raw data or results.

Provision and Use:

Dashboards: Employees and managers access the visualized data via intuitive dashboards.
Real-time display: Live data enables a direct response to problems or changes.
Personalized insights: Reports and visualizations can be tailored to specific roles, e.g., machine operators, production managers, or maintenance teams.

Example of an Implementation

Data collection: An IoT sensor measures the temperature of a motor on a production line.
Data transmission: The data is transmitted to a cloud platform such as AWS IoT Core.
Data processing: The platform analyzes the data, detects a rising temperature, and predicts a potential failure.
Visualization: A dashboard displays the temperature curve in a time chart, highlights critical values, and generates an alert.
Action: The maintenance team responds proactively before a failure occurs.

State-of-the-art technology combined with intuitive displays

Applications of Data Visualization in Production

Data visualization combines state-of-the-art technologies (IoT, big data, AI) with intuitive representations. This promotes data-driven decision-making, which increases productivity and efficiency while reducing costs.