The global supply chains have also become susceptible to disruptions that are inflicted due to the geopolitical tensions, pandemics, cyber threats, climate change, and market volatility. With the advent of the digital era, the development of the supply chain in organizations has been revolutionized in the way they are designing, managing, and recovering their supply chains. There are new opportunities offered by digital technologies in order to improve supply chain resilience (SCR), created by artificial intelligence, blockchain, Internet of Things (IoT), big data analytics, and cloud computing. The current paper is the extensive exploration of the supply chain resilience in the digital era with the focus on the way the digitalization process allows identifying the risks in advance, implementing the adaptive response mechanisms, and providing quick recovery opportunities. The article methodically examines the available literature to formulate the main dimensions of resilience and digital enablers alongside strategic models used. It suggests a systematic approach, which includes both digital maturity assessment and resiliency capability modeling as well as performance evaluation metrics. The empirical evidence draws your attention to the issue of digital technologies and their role in supply chain robustness, agility, and sustainability. Findings show transforming her supply chains with digital capabilities allows having better disruption preparedness, led to lower recovery time, and enhanced decision-making quality. The article has a value to both theory and practice as it summarizes constructs of digital resilience and has practical implications to managers interested in developing resilient supply chains that meet the needs of future consumers.
Marco Bianchi· International Journal of Com...· 0 citations
Industry 4.0 has evolved traditional manufacturing into a highly connected, data-driven, intelligent production environment. Digital twin (DT) and predictive control have been considered as two of the enabling technologies in industrial automation that can greatly enhance the manufacturing efficiency, operational cost reduction, and product quality improvement. Digital Twin — In the context of manufacturing, a digital twin is a virtual model of a manufacturing system that continuously receives live operational data using IoT devices, cloud computing and artificial intelligence (AI). By integrating predictive control strategies, Digital Twins predict system behaviour (particularly Model Predictive Control (MPC)), and provide timely optimised actions before any faults/ inefficiencies happen. This is a survey on the state of art works related to Digital Twin-based Predictive Control for intelligent manufacturing systems, including its architecture, enabling technologies and practical applications. This discusses the role of Digital Twins in real-time monitoring, predictive maintenance, process optimization, Quality assurance and energy-efficient manufacturing. Moreover, the novel integration of machine learning algorithms enables Digital Twins to leverage big data in manufacturing (various plant operational informatics) as well as dynamically update control strategies based on changing operational environments. While researchers have made strides towards realizing Digital Twins, challenges still remain regarding abundance of computational requirements, inherent cybersecurity security risks, interoperability across applications and systems, pricey to implement solutions and absence of standardized Digital Twin frameworks. These research gaps are highlighted in this paper, and future directions towards autonomous self-optimizing manufacturing systems are discussed. The conjoining between Digital Twin technology and predictive control proposed in this article offers a paradigm of smart manufacturing that facilitates increases in production flexibility, reductions in downtime, increases in resource utilization and sustainable development. These results show that Digital Twin-based predictive control can be an important technological basis for next-generation intelligent manufacturing environments.
Marco Bianchi, Laura Conti· International Journal of Int...· 0 citations
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