Data-Driven Determination and Plausibility Check of Requirement Profiles in Logistics

Author(s):  
Markus Kohl ◽  
Sandra Häring ◽  
Jens Lopitzsch ◽  
Johannes Fottner
Author(s):  
Sebastian Bickel ◽  
Tobias C. Spruegel ◽  
Benjamin Schleich ◽  
Sandro Wartzack

AbstractCurrent trends in product development are digital engineering, the increasing use of assistance tools based on artificial intelligence and in general shorter product lifecycles. These trends and new tools strongly rely on available data and will irreversibly change established product development processes. One example for such a new data driven tool is the plausibility check of linear finite element simulations with Convolutional Neural Networks (CNN). This tool is capable of determining whether new simulation results are plausible or non-plausible according to numeric input data. The digitalization and the increased use of data driven tools employing algorithms known from Artificial Intelligence also shifts the roles of many involved engineers. This paper describes and highlights this transition from current product development processes to a data driven / simulation driven product development process. Particularly, the shifts and changes of different roles and domains are illustrated and an example for changing roles in the design and simulation department is described. Furthermore, required adjustments in the design process are derived and compared to the current status.


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