Abstract:

This research explores the transformative potential of Intelligent Collaborative Supply Chain Management (iCSCM) in driving the success of a University Holding Company. Emphasizing the intersection of artificial intelligence and collaborative strategies, the paper delves into how iCSCM optimizes planning, sourcing, manufacturing, delivery, and returns within the university’s commercial ventures. By harnessing the power of predictive analytics, realtime communication, and adaptive decision-making, iCSCM establishes an agile and efficient supply chain ecosystem. The integration of intelligent technologies enhances collaboration with suppliers, improves manufacturing processes, and streamlines logistics, ensuring the University Holding Company effectively commercializes intellectual assets. This synthesis of advanced technologies not only propels operational efficiency but also positions the university as a dynamic player in the commercial landscape, bridging the gap between academia and industry innovation.

S. Phuengrod, P. Wannapiroon and P. Nilsook, “Intelligent Collaborative Supply Chain Management (iCSCM),” 2024 IEEE International Conference on Cybernetics and Innovations (ICCI), Chonburi, Thailand, 2024, pp. 1-6, doi: 10.1109/ICCI60780.2024.10532433.
https://doi.org/10.1109/ICCI60780.2024.10532433

keywords: {Industries;Technological innovation;Supply chain management;Supply chains;Collaboration;Companies;Propulsion;Supply Chain Management;Collaborative Supply Chain;Artificial Intelligence},

Abstract:

This article presents two main objectives: (1) To synthesize the digital asset management process using AI TRiSM. (2) To study the results of the digital asset management process using AI TRiSM. Consequently, the administration of digital assets will bring about an increase in the organization’s overall efficiency through the implementation of technology that utilizes artificial intelligence to drive the management system. On the other hand, having a vast volume of information within an organization may result in management issues and a lack of transparency. A multitude of organizations are making preparations to put AI TRiSM ideas into practice. The analysis revealed that the mean value is 4.91, while the standard deviation is 0.14. A digital asset management platform that can be used to track usage inside an organization can be developed with the help of the AI TRiSM model. This will help establish trust, decrease risk, and guarantee workplace security.

P. Tasatanattakool, P. Wannapiroon and P. Nilsook, “Digital Asset Management Process using AI TRiSM,” 2024 IEEE International Conference on Cybernetics and Innovations (ICCI), Chonburi, Thailand, 2024, pp. 1-6, doi: 10.1109/ICCI60780.2024.10532376.
https://doi.org/10.1109/ICCI60780.2024.10532376

keywords: {Technological innovation;Standards organizations;Employment;Organizations;Asset management;Security;Artificial intelligence;Digital Asset Management;Artificial Intelligence;AI TRiSM},