Modern manufacturing environments are characterized by increasing complexity and reliance on sophisticated digital systems, particularly Manufacturing Execution Systems (MES) [1]. The seamless and secure update of MES software is critical to maintaining operational efficiency, enhancing security, and introducing new functionalities without disrupting production [14]. Traditional update strategies often involve significant downtime or introduce risks to tightly integrated industrial processes [11, 15]. This article proposes an adaptive, hybrid deployment strategy for software updates to the MES layer, leveraging concepts from cloud computing, edge computing, and robust deployment methodologies. The proposed framework aims to minimize disruption, enhance system resilience, and ensure data integrity during update cycles in complex industrial settings.

Background: Modern manufacturing systems increasingly require integration of adaptive control, machine vision, and socio-technical design practices to respond to variability, defects, and evolving market requirements. Drawing on classical systems and cybernetics theories alongside contemporary advances in machine vision, robotics, and MLOps-driven engineering, this article synthesizes a multidisciplinary framework for adaptive cybernetic design in intelligent manufacturing. Methods: We develop a conceptual process model grounded in established theoretical traditions (systems theory, cybernetics, design science) and augment it with process-level mechanisms from product development, organizational learning, and modern data-driven practices. The model is elaborated through text-based methodological constructs—control architectures, feedback-loop specification, knowledge-integration patterns, and human–robot collaboration protocols. Results: The framework yields a detailed set of design heuristics and process prescriptions: (1) explicit layering of feedback loops for perception, decision, and organizational learning derived from cybernetic principles; (2) modular vision–robot integration patterns informed by contemporary object-detection and quality-control research; (3) resource allocation and testing strategies that balance exploration and exploitation in product development; and (4) socio-technical collaboration processes that foster shared understanding and integrate tacit and codified knowledge. Each prescription is linked to measurable metrics—controllability, robustness to defects, speed of learning, and ambiguity reduction in team cognition. Conclusions: The proposed adaptive cybernetic design framework unites time-tested theoretical foundations with modern vision-based automation and MLOps practices to address complexity in small and large manufacturing environments. The framework supports safer, more resilient, and more rapidly learning manufacturing systems but requires careful consideration of interpretability, traceability, and human factors. Implications: Researchers should empirically validate the framework in longitudinal industrial case studies; practitioners may adopt the heuristics to redesign feedback architectures and vision-robot integration pipelines.