Abstract
Sector 4.0 technologies, such as cloud computing, big data, the internet of things, and cyber-physical systems, are transforming the manufacturing industry significantly. Companies must concurrently address consumer expectations and production capacities, resulting in an increase in system requirements. This growth in requirements necessitates the ongoing modification of the production characteristics’ structures, culminating in the development of Smart Manufacturing Systems that enhance the client value proposition. As market needs change over time, the system's capabilities must adjust to these changes, which may lead to issues such as an increase in production time, cost, and a decline in the quality of the final product. To tackle this problem and help in the decision-making process, assessment techniques may be used to examine and offer several alternatives depending on the current capabilities and needs. In addition, these models may be adopted and linked with ontological techniques since they can effectively represent and communicate information across multiple systems and domains, hence enhancing the accuracy and efficacy of decision-making. In this context, the primary objective of this study is to build a thorough literature mapping of current research on the Smart Manufacturing Systems decision-making process.
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Canciglieri, M.B. et al. (2023). A Systematic Literature Mapping on the Process Reconfiguration of Smart Manufacturing Systems with the Integration of Multi-criteria Decision Models and Ontology Based Interoperability. In: Kim, KY., Monplaisir, L., Rickli, J. (eds) Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus. FAIM 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-17629-6_68
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