To support human decision making, Area 3 defines two objectives: (1) Combine data‐driven approaches with configuration management methods and simulation environments in order to provide a reliable, trustworthy (data) basis for decision making. (2) Provide this objective basis for decision making to humans in such a way that it takes into account their cognitive capabilities (e.g., information filtering in stress situations) as well as the situation/context in which the decision has to be made (e.g., within production process versus design process) in order to ensure timely and optimal decisions. This strategic project fosters these Area objectives by strategic research activities considering the following context: Industry 4.0 is considered as the “fourth industrial revolution” that either fully automatizes the production in the manufacturing industry or optimizes the collaboration of workers and machines. This is only possible when using different helping operators that facilitate the entire product life cycle, such as the decision support assistance systems. The power of the decision support systems lies on providing immediate assistance in situations where human judgment disregards the reactions times. This is considered as highly important particularly in risky and uncertain conditions where making a poor selection might cause catastrophic consequences for humans and the operating environment. What makes these systems cognitive is that they apply methods that simulate the estimation and the thinking process of the humans to choose one option from a set of possibilities (WP2 Definition of decisionmaking methods & computational decision making). In order to enable companies to utilize such assistance systems it is paramount that data collected at the production site and sent to an analytics entity is sufficiently secured. This can be achieved by providing a secure connection (WP3 Secure Data Transmission). Another concern when working with cognitive decision support systems is the transparency. The outputs of the decision‐making processes are often too complex even for the experts, to understand. Yet, this lack of transparency can be a key problem in many applications. In order to tackle this issue, there is a need on tools that can be used for e.g., to explain/explore predictions/decisions made by the applied model(s) (WP4 Visual analytics). - A secure data transmission module will be applied and extended that allows to transmit data from production site to assistive system. -To ensure that end‐user understand why the system made a particular decision, this project further focuses on state‐of‐the‐art visualization tools (2D, 3D) and visual analytics methods that are used, e.g., to explain/explore decisions made by system, the applied model(s) respectively. - The visual analytics tool can further be applied to support scheduling, performance monitoring, and anomaly detection for the manufacturing systems that might help the end‐user in her decision‐making process. - The simulation of scheduling and re‐scheduling after expected (predictive maintenance) and unexpected changes (e.g. down‐time of machines due to failures) allows for a better resiliency of manufacturing processes. The (further) development of algorithms can therefore help in optimizing the design of production systems and schedules for shop floors in cases of stochastic failures.
|Effective start/end date||1/04/18 → 31/03/21|
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