Types of visualization

Real-time “Dashboard”
  • Use of bar charts, pie charts or heat maps
Graphical representation
  • Schematic representation of the position of antennas, sensors and readers
  • Visualization of the direction of detected objects using arrows
  • Color coding, e.g. for different product types
Table view
  • View in structured form
  • Columns with information such as timestamps, EPCs and directions

 

Progression diagram
  • Displays the number of objects detected over a certain period of time
  • Shows trends and peaks in the flow of goods

 

3D model
  • The detected objects light up green
  • Identification of incorrect differences (missing or incorrectly detected objects) by lighting up red or flashing
  • Possible acoustic signal in the event of incorrect differences

What information do you get from visualizations?

Stock and warehouse information

  • Information on stock changes target/actual
  • Current stock level

Object detection and monitoring

  • Number of objects recognized
  • Display of the electronic product codes read on the parts
  • Monitoring display: Display and analysis of passages and transmitted data

Motion and flow analysis

  • Display of recognized directions and running directions
  • Display of trends and peaks in the flow of goods, e.g: Passages per hour/day/week as well as peak times and seasonal fluctuations
  • Display of real-time statistics

Notifications and warnings

  • Notifications/warnings when certain threshold values are exceeded

Forecasting trends and developments

With the help of visualizations at the RFID gate and various models, you can extract trends from historical data that can serve as a basis for future predictions.

Continuous data collection at the RFID gate gives you real-time insights into the flow of products and materials. Timestamps, product types, quantities and other relevant variables are captured and visualized to give you a comprehensive picture. By visualizing this data, patterns and trends can be identified.  Based on past patterns and an analysis of historical data, it is therefore possible to make predictions about the future.

For example, analysis of past data could reveal increased demand for certain products on certain days or at certain times, as well as peak times of logistical movements. This insight could make it possible to adjust stock levels accordingly in order to avoid bottlenecks or adapt production planning. In addition, this data can also be used to improve capacity planning in the logistics sector.

Such an analysis can be carried out by experienced experts who are able to predict future developments based on their knowledge and intuition. However, machine learning and artificial intelligence (AI) are also able to recognize complex patterns in the data and use them to make predictions.

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Contact person
Viktor Wagner
Viktor WagnerManaging Director