Due to globalization, companies in all industries are under an increasing competitive pressure. Widespread production and logistic networks result in various problems that influence the security of supply for their production. Therefore complex structures and processes in production and logistic networks often go alongside with a lack of transparency of supply chain partners about the current status of the entire value chain and lead to inefficiencies of all parties involved. Companies face this challenge by using resources efficiently from an economic and ecological point of view. Auto-ID technologies are often used in order to monitor the processes and adherence to planning in an enhanced way. This trend will be found in companies more often in the future. The increased use of new technologies leads to an increase in the volume of data available that enables to track the system status of the logistic network at all times in a detailed way.
In the future, it will therefore be decisive for the success of the company networks to filter relevant information for planners and decision makers from the large amount of data in order to quickly evaluate the system status, detect critical system states early on and derive possible actions. In addition to the acquisition and filtering of information, the preparation of information is particularly important in order to achieve a transparent overview of the current and future system status. Here it needs to be considered that different target groups for example logistics manager or dispatchers require different key figures with different levels of detail. In addition to a visualization appropriate to the target group, an intuitive presentation is one of the central challenges for reaching quick decisions. For an improved planning and control of logistics processes in complex value creation networks and an improved human-machine interaction, the Visual Logistics Management research project funded by the Federal Ministry of Economics and Technology (BMWi) investigated the question of target group-specific and intuitive visualizations for better control of logistics networks.
Using the automotive industry as an example, the question was examined with a focus on bottleneck management and container management.