@article{greis_nogueira_rohde_2021, title={Digital Twin Framework for Machine Learning-Enabled Integrated Production and Logistics Processes}, volume={630}, ISBN={["978-3-030-85873-5"]}, ISSN={["1868-422X"]}, url={https://doi.org/10.1007/978-3-030-85874-2_23}, DOI={10.1007/978-3-030-85874-2_23}, abstractNote={This paper offers an integrated framework bridging production and logistics processes that employs a machine learning-enabled digital twin to ensure adaptive production scheduling and resilient supply chain operations. The digital-twin based architecture will enable manufacturers to proactively manage supply chain risk in an increasingly complex and dynamic environment. This integrated framework enables “sense-and-respond” capabilities, i.e. the ability to sense potential supplier and production risks that affect ultimate delivery to the customer, to update anticipated customer delivery dates, and recommend mitigating steps that minimize any anticipated disruption. In its core functionality this framework senses disruptions at a supplier facility that cascade down the upstream supply chain and employs the predictive capabilities of its machine learning-based engine to trigger and support adaptive changes to the manufacturer’s MES system. Any changes to the production schedule that cannot be accommodated in a revised schedule are propagated across the downstream supply chain alerting end customers to any changes.}, journal={ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, APMS 2021, PT I}, publisher={Springer International Publishing}, author={Greis, Noel P. and Nogueira, Monica L. and Rohde, Wolfgang}, year={2021}, pages={218–227} }
 @article{greis_nogueira_schmitz_dillon_2019, title={MANUFACTURING-UBER: Intelligent Operator Assignment in a Connected Factory}, volume={52}, ISSN={["2405-8963"]}, url={https://www.sciencedirect.com/science/article/pii/S240589631931609X}, DOI={10.1016/j.ifacol.2019.11.621}, abstractNote={This paper introduces the Manufacturing-Uber concept for dynamic assignment of operators in the Connected Factory. In traditional non-IoT machining environments it is common to assign an operator to a (small) number of machines, clustered in close proximity within a cell. In contrast to “fixed” assignment within a cell, the Manufacturing-Uber approach leverages the connectivity of the IoT environment to allow on-demand “floating” operator assignment across cells. An intelligent assignment engine determines and assigns the operator to achieve best system performance. Results show that Manufacturing-Uber outperforms fixed assignment with respect to reduction in required operators, increased machine up-time and more parts completed.}, note={9th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2019}, number={13}, journal={IFAC PAPERSONLINE}, publisher={Elsevier BV}, author={Greis, Noel P. and Nogueira, Monica L. and Schmitz, Tony and Dillon, Michael}, year={2019}, pages={2734–2739} }