KNOWLEDGE CAPTURE AS A CONTROL PROBLEM IN INDUSTRIAL OPERATIONS: AN ENGINEERING APPROACH TO OPERATIONAL KNOWLEDGE CAPTURE IN DYNAMIC SYSTEMS

Abstract

Operational knowledge capture in connected industrial environments has often been discussed in recent literature through digitalization and knowledge management perspectives, yet without a technical framing that represents its temporal dynamics under real operating conditions. This article aims to propose an engineering approach that treats operational knowledge capture as a dynamic systems and control problem, enabling its description in terms of states, disturbances, and technical actuators. The methodology is theoretical and conceptual, based on an analytical review of studies published over the last five years and on the development of a conceptual model integrating cyber-physical systems foundations, human-in-the-loop architectures, adaptive automation, and cognitive support structures. The results organize the literature around a formalization of a “knowledge system”, defining states related to knowledge externalization, accessibility, and reuse, operational disturbances associated with variability and unexpected events, and technical actuators such as sensors, multimodal records, digital platforms, and AI- based assistance mechanisms. The discussion shows that purely manual processes tend to produce informational instability, low traceability, and recurring loss of operational learning, whereas integrated architectures support knowledge consolidation and reuse as part of production system behavior. It is concluded that formalizing knowledge capture as a technical problem strengthens analytical and design capabilities for self-learning industrial solutions, enhancing the integration between human cognition, automation, and operational performance.