A visualization concept for production data and simulation results : Development and implementation of an adjustable visualization tool using SimAssist and d3.js at BMW AG

Sammanfattning: The human’s visual system is one of the most powerful tools for discovering information and patterns in a given dataset. Increased possibilities for data collection and storage, together with today’s visualization software possibilities, help to facilitate visual analytics. Based on previous research within human perception, visualization techniques and a current situation analysis at BMW AG, a case study to develop and implement a visualization concept for production data and simulation results has been performed. The research question is formulated as how production and simulation data shall be presented in order to add value to the input data and material flow simulation results in the automotive industry. Production and simulation data are stored in databases that can be connected to SimAssist, a software tool developed for the assistance of simulation projects. In the 2view module of SimAssist, the plug-in SimVis offers visualization of selected data based on the front-end programming language JavaScript and the D3 library. D3 binds data to visual objects and manipulates their attributes. The case study has aimed to further develop the SimVis plug-in with regard to visual analytics. The visualization concept closes the observed gap between today’s visual analytic possibilities and the currently used software (often Excel and PowerPoint) at the material flow simulation group at BMW. Defining development and evaluation criteria, two concepts are generated and implemented using an agile method, continuously involving the future users. Two visualizations have been developed. The cluster visualization is a powerful tool that enables hierarchical clustering and visualization of data defined by the user via the user interface. The user interacts with the dataset, exploring relations by defining color ranges, hiding and showing selected nodes and calculating node values with different calculation methods (sum, median or average). Additionally, it includes a bar chart to facilitate a second overview of the dataset. The second concept is the multiline visualization, showing one scale with x-values and several lines with corresponding y-values. When the user moves the cursor over the visualization, the current x-data point, its corresponding y-values and the difference between the y-values are shown, in order to allow the user to interact with the dataset.The results show that the visualization concept is highly flexible, allowing different types and amount of data to be visualized and analyzed. By including the dataset in the SimAssist framework, a suitable visualization can easily be chosen and data can easily be displayed and visually analyzed in a visual analytics context. Interaction with the data via the mouse cursor helps into finding patterns and relations in and between the data and different datasets. The visualization concept saves several intermediate steps in comparison to today’s visualizations.