Firms are by definition characterized by a high degree of complexity. Indeed, firms are complex systems whose success is strictly related to several different internal and external involved players and contexts. This is particularly true for knowledge-intensive manufacturing firms, which increasingly need huge amount of data and a wide spectrum of information flows spanning the whole organization. Hence the need to explore more deeply the impact of digital technologies on this category of firms. The aim of this research is to investigate whether and how information systems based on internet of things (IoT) and artificial intelligence (AI) may allow managers of knowledge-intensive manufacturing firms to better manage complexity.
Ciampi, F., Rialti, R. (2019). Managing complexity in knowledge-intensive manufacturing firms in big data era. The importance of internet of things and artificial intelligence. In IMCIC 2019 - 10th International Multi-Conference on Complexity, Informatics and Cybernetics, Proceedings (pp.58-61). International Institute of Informatics and Systemics, IIIS.
Managing complexity in knowledge-intensive manufacturing firms in big data era. The importance of internet of things and artificial intelligence
Rialti R.
2019-01-01
Abstract
Firms are by definition characterized by a high degree of complexity. Indeed, firms are complex systems whose success is strictly related to several different internal and external involved players and contexts. This is particularly true for knowledge-intensive manufacturing firms, which increasingly need huge amount of data and a wide spectrum of information flows spanning the whole organization. Hence the need to explore more deeply the impact of digital technologies on this category of firms. The aim of this research is to investigate whether and how information systems based on internet of things (IoT) and artificial intelligence (AI) may allow managers of knowledge-intensive manufacturing firms to better manage complexity.File | Dimensione | Formato | |
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https://hdl.handle.net/11365/1259440