Dynamic Visualization Frameworks for Smart Factories: Enhancing Decision-Making through Cognitive-Centered Design

Abstract

This article examines the evolution of dynamic visualization frameworks in smart manufacturing environments, addressing the fundamental cognitive challenges that arise when human operators interact with complex industrial data streams. As smart factories increasingly integrate IoT sensors, artificial intelligence, and cyber-physical systems, traditional static dashboards prove inadequate for effective decision-making. The article presents a cognitive-centered design principle for manufacturing visualization, demonstrating how role-based information hierarchies, dynamic content prioritization, and mental model alignment significantly enhance operator performance across diverse industrial settings. The real-time IoT data visualization techniques, such illustrates spatiotemporal representations and predictive analytics, enable more effective anomaly detection and process optimization. Additionally, the integration of sustainability metrics within visualization frameworks is shown to transform environmental considerations from compliance requirements to operational optimization opportunities. The article concludes with an evaluation of organizational implementation strategies and emerging technological integrations, establishing evidence-based guidelines for next-generation industrial visualization systems that harmonize technological capabilities with human cognitive processes.