process models

Due to the advancement of robotics in industrial production, the strict separation of workspaces is gradually dissolving (HRC - human-robot collaboration). HRC combines the strength and efficiency of robots with the skills and cognitive abilities of humans through seamless cooperation [1]. Manufacturing companies, in which HRC scenarios are to be implemented, require a transparent and reflective selection of technology with regard to safeguarding the worker against hazards posed by the robot. To address this challenge, this paper proposes a process model that helps technology developers and systems integrators select appropriate technologies and solutions for HRC scenarios.

Data Science is an established tool for knowledge discovery, in particular from economic data. The progressing digitization of products and production systems enables the broader application of Data Science in technical systems. However, the requirements and constraints, e.g. for control and optimization of production processes, differ significantly from established Data Science applications. Industrial Data Science addresses the issues of applying machine learning to technical systems in industrial setups. This article characterizes challenges of Industrial Data Science, gives application examples of and general indicators for Industrial Data Science.

Due to the complexity of the interdependencies in enterprise logistics, manufacturing companies often have difficulties in meeting their logistic objectives. They struggle to take measures that effectively influence their logistic performance. The Institute of Production Systems and Logistics (IFA) is developing illustrative models that describe and quantify the logistic interdependencies. The models form a basis for strategies and procedures, with which manufacturing controls can control the interdependencies.