Cloud-Native AI Systems for Industry-Specific Decision Optimization
DOI:
https://doi.org/10.5281/zenodo.22173050Keywords:
Cloud-Based Intelligent Decision Systems, AI-Assisted Big-Data Analytics, Scalable Cloud Architectures, Industry-Specific Decision Support, Manufacturing and Supply Chain Optimization, Demand Forecasting Systems, Inventory and Production Scheduling, Logistics and Supply Chain Resilience, Cloud-Native Data Pipelines, Governance of Cloud Decision Platforms, Teleological Decision-System Design, Healthcare Resource Allocation, Clinical Decision Support Systems, Life Sciences Data Integration, Cross-Organizational Analytics, Privacy and Security in Cloud AI, Risk-Aware Decision Management, High-Scaling Industry Applications, Cloud Governance and Integrity Frameworks, Intelligent Enterprise Operations.Abstract
Scalable cloud-based intelligent decision systems are investigated for industry-specific problems in different sectors that can be optimized by AI-assisted big-data analytics. Despite the growing popularity of cloud-based AI and big-data-driven decision systems, evolution in the required cloud infrastructure and the actual data pipelines have not yet been undertaken in detail. In addition, the current research has not gone into sufficient detail on cloud-based systems for specific high-scaling industry problems such as for the healthcare and life sciences sector or those supported by the manufacturing or supply chain domain. Cloud decision systems also have specific requirements for management and governance due to the nature of data belonging to different parties, and these aspects require further consideration. Consequently, the focus is on cloud systems for scalable manufacturing or supply chain operation decision management, with practical industry examples clearly described for evaluation and cross-validation of accuracies.
The study serves teleological domain-focused decision-systems deployment in generic governance-integrity-demand-supporting environments. For exemplary industry-use domain-focused problems, demand forecasting, inventory/supply, production scheduling, logistics, or resiliency-enhancement of supply chains, patient-flow management, clinical-decision-support allocation of resources in healthcare, and integration of multiple-organization datasets for results comparison and research in life-sciences decision support are highlighted. The encoding and timing aspects of teleological risks are also emphasized at the decision-system level, while general privacy and security requirements specify the data-handling and incident-reaction measures needed for protection against unauthorized disclosure or service unavailability.
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Copyright (c) 2026 Katarzyna Nowak (Author)
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