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Currently, a digital transformation is taking place in logistics and manufacturing environments of the Robert Bosch GmbH. This digital transformation consists of several central initiatives, resulting in big impacts on the organizations of the business units. This paper aims to provide an overview of these organizational changes with regard to the role of change management. After identifying the organizational setting at Bosch, the ongoing digital transformation at the business unit Powertrain Solutions is analyzed by focusing on the future collaboration strategy between logistics and manufacturing as well as the organizational transformation along the Value Stream Integrated Processes and IT program. The complexity of the ongoing transformation is narrowed down to the changes in processes and IT-landscapes before elaborating the future interaction between the landscape documentation tools “scout.it” and “Lean IX”. The readiness of plants in preparation for the Bosch Manufacturing and Logistics Platform is determined by creating a dashboard using Microsoft Power BI.
Ahead of the analytical work, the paper considers theoretical insights about the current state of research in terms of the digital development and change management to ensure successful planning, implementation and preservation of future organizational changes at Bosch.
Distributed machine learning algorithms that employ Deep Neural Networks (DNNs) are widely used in Industry 4.0 applications, such as smart manufacturing. The layers of a DNN can be mapped onto different nodes located in the cloud, edge and shop floor for preserving privacy. The quality of the data that is fed into and processed through the DNN is of utmost importance for critical tasks, such as inspection and quality control. Distributed Data Validation Networks (DDVNs) are used to validate the quality of the data. However, they are prone to single points of failure when an attack occurs. This paper proposes QUDOS, an approach that enhances the security of a distributed DNN that is supported by DDVNs using quorums. The proposed approach allows individual nodes that are corrupted due to an attack to be detected or excluded when the DNN produces an output. Metrics such as corruption factor and success probability of an attack are considered for evaluating the security aspects of DNNs. A simulation study demonstrates that if the number of corrupted nodes is less than a given threshold for decision-making in a quorum, the QUDOS approach always prevents attacks. Furthermore, the study shows that increasing the size of the quorum has a better impact on security than increasing the number of layers. One merit of QUDOS is that it enhances the security of DNNs without requiring any modifications to the algorithm and can therefore be applied to other classes of problems.