Cognitive Network Orchestration: Predictive Resilience in Enterprise Supply Chain Systems
- DOI
- 10.2991/978-94-6463-894-3_22How to use a DOI?
- Abstract
This paper introduces a novel framework for achieving adaptive resilience within complex supply networks through advanced analytical and automated control mechanisms. It explores how leveraging real-time data streams and predictive intelligence, integrated within enterprise resource planning (ERP) environments, can transform traditional supply chain management. By focusing on the proactive identification and mitigation of systemic vulnerabilities, the proposed analytics-driven paradigm enables sophisticated response optimization against unforeseen disruptions. The integration of cutting-edge cognitive analytics and data-driven automation within core enterprise platforms facilitates a dynamic, self-optimizing supply network, enhancing both operational performance and inherent resilience, fundamentally reshaping strategic supply chain oversight. The novelty of this research lies in the integration of AI, IoT, blockchain, and digital twins within SAP-centric supply chains, offering the first unified framework for predictive and adaptive resilience in enterprise systems.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Sanjoy Das PY - 2025 DA - 2025/11/10 TI - Cognitive Network Orchestration: Predictive Resilience in Enterprise Supply Chain Systems BT - Proceedings of the International Conference on Policies, Processes and Practices for transforming Underdeveloped Economies into Developed Economies (PPP-UD 2025) PB - Atlantis Press SP - 316 EP - 325 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-894-3_22 DO - 10.2991/978-94-6463-894-3_22 ID - Das2025 ER -