Ryazan, Orel, Russian Federation
graduate student
Ryazan, Orel, Russian Federation
UDC 338.22.021.2
complex of the penal system is facing the need for radical technological reconstruction. Traditional hierarchical management structures are proven to be ineffective, as they are unable to respond adequately to the pressing issues of the present day. The combination of interrelated issues highlights the need for a fundamentally new management architecture focused on achieving adaptability, sustainability, and a high degree of intellectual maturity in a highly regulated environment. Purpose. The research aims at identifying and analysing growing risks, understanding the underlying contradictions that impede the development of the manufacturing sector in the penal system, and justifying the network-centric paradigm as a mechanism for structural and functional transformation. The study illustrates the potential of such a management model and its ability to address challenges in the context of countering systemic threats. Methods. The research is based on a comprehensive analysis of the current challenges facing the manufacturing sector of the penal system. The methodology involves reviewing and synthesising scientific research on cybersecurity, staff shortages, environmental issues, logistics and the integration of artificial intelligence (hereinafter – AI). The study uses the method of logical modelling. The results of the research provide a theoretical substantiation of the potential of the network-centric model to become the main driver of structural changes. Its effectiveness is ensured by the formation of a unified information and communication framework organising horizontal interaction. The study demonstrates the key effects of implementing the model. The distributed system principle implements a coordinated response to cyber threats based on collective analysis and incident localisation. The staff shortage is mitigated by creating a constantly updated knowledge base, where the experience of each employee becomes the common property of all network participants. The formation of a comprehensive information field makes it possible to predict and minimise the impact of environmental and logistical risks. The difficulties of implementing AI are overcome by creating a secure environment with local processing of confidential data and only anonymised analytical conclusions entering the system. The ultimate result of implementing this paradigm is the formation of a qualitatively new operating environment.
network-centric resource management, penal system, penal system manufacturing sector, cybersecurity, staff shortage, environmental risks, supply chain disruption, artificial intelligence (AI), systemic transformation
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