Abstract
Recently, many novel paradigms, concepts and technologies, which lay the foundation for the new revolution in manufacturing environments, have emerged and make it faster to address critical decisions today in supply chain 4.0 (SC4.0), with flexibility, resilience, sustainability and quality criteria. The current power of computational resources enables intelligent optimisation algorithms to process manufacturing data in such a way, that simulating supply chain (SC) planning performance in real time is now possible, which allows relevant information to be acquired so that SC nodes are digitally interconnected. This paper proposes a conceptual framework based on a digital twin (DT) to model, optimise and prescribe a SC’s master production schedule (MPS) in a zero-defect environment. The proposed production technologies focus on the scientific development and resolution of new models and optimisation algorithms for the MPS problem in SC4.0.
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Acknowledgments
The research leading to these results received funding from the European Union H2020 Program with grant agreement No. 825631 “Zero Defect Manufacturing Platform (ZDMP)” and with grant agreement No. 958205 “Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)” and from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-101344-B-I00 “Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)”.
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Serrano, J.C., Mula, J., Poler, R. (2021). Digital Twin for Supply Chain Master Planning in Zero-Defect Manufacturing. In: Camarinha-Matos, L.M., Ferreira, P., Brito, G. (eds) Technological Innovation for Applied AI Systems. DoCEIS 2021. IFIP Advances in Information and Communication Technology, vol 626. Springer, Cham. https://doi.org/10.1007/978-3-030-78288-7_10
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DOI: https://doi.org/10.1007/978-3-030-78288-7_10
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