IA2IA - Industrial Automation to Industrial Autonomy
IA2IA is what Yokogawa foresees as the transition from Industrial Automation to Industrial Autonomy.
Yokogawa defines industrial autonomy as "Plant assets and operations have learning and adaptive capabilities that allow responses with minimal human interaction, empowering operators to perform higher-level optimization tasks."
What is "Industrial Autonomy"?
Autonomy means to be independent or to be able to control or govern oneself. It is different from automation, which performs a sequence of highly structured pre-programmed tasks requiring human oversight and intervention. Industrial autonomy is where plant assets and operations have learning and adaptive capabilities that allow responses with minimal human interaction, empowering operators to perform higher-level optimization tasks.
What is "Autonomous Operations"?
Autonomous operations is the final stage of industrial autonomy, representing an ideal state where operations function with complete autonomy throughout a value chain. Autonomous operations can be defined as assets and operations that have human like learning and adaptive capabilities that allow them to respond without operator interaction to situations within a secure bounded domain that are not pre-programmed or anticipated in the design and that are responsible for all safety-critical functions. In a fully autonomous operation (no human intervention required), the cognitive system performs all aspects of the operation.
Details
Levels of Autonomy - a Transition to be Taken in Steps
Autonomy will expand to multiple functional domains including process control and operations, planning & scheduling, supply chain management, field operations, maintenance, and engineering. To jump directly to autonomous operations is very difficult to achieve. Therefore, Yokogawa has developed a maturity model to establish where companies are today and where they need to be in the future.
Level 0-1 MANUAL/SEMI-AUTOMATED
A facility is minimally instrumented and automated. Many operations are performed manually with paper-based instructions and record keeping. The automation system conducts some of the production processes to eliminate error-prone operations and improve productivity.
Level 2 AUTOMATED
Humans are responsible for safe operations, assisted by traditional automation systems. The automation system conducts the majority of production processes and aids in workflow and maintenance tasks but requires human oversight and intervention to properly deal with anything that is outside of normal operations.
Level 3 SEMI-AUTONOMOUS
It is characterized by a mixture of autonomous components and automated assets with human orchestration. An autonomous component is different from automation due to its learning, adaptive, and self-optimization capabilities to situations that are not pre-programmed. Companies at this level deploy a range of selective autonomous components or applications orchestrated by humans.
Level 4 AUTONOMOUS ORCHESTRATION
Most assets operate autonomously and are synchronized to optimize production, safety, and maintenance under certain circumstances or conditions. It brings together autonomous components with appropriate functionality to perform as a system. However, there is still a need for humans to perform many tasks as not all disciplines are integrated at this level. In addition, if the specific circumstances are not met, then the operators must take control of the operation.
Level 5 AUTONOMOUS OPERATIONS
A highly idealized state where facilities operate autonomously and are integrated with multiple disciplines that also operate autonomously and extend to supply chain partners. Operations are completely autonomous and require no human interaction at all.
Enablers and Drivers
From AI, digital twins, and robots, new technologies are revolutionizing the way that plants operate. They are facilitating a shift whereby physical tasks and decision-making processes are being made more autonomous with the aim of improving productivity and worker safety. In today’s business environment, operational resilience has never been more important.
The transition to industrial autonomy requires a variety of technologies across information technology (IT) and operational technology (OT) areas. Yokogawa's strength comes from its combination of both IT and OT expertise.
While these enabling technologies for industrial autonomy are already in place, their true value depends on how effectively they are integrated to create intelligent, autonomous workflows.
Yokogawa has years of experience and expertise to implement transformation programs starting from a very early consulting phase. Yokogawa understands what key issues must be solved and what types of technology are available to solve the issues.
AI and Robotics: Core Enablers of Industrial Autonomy
Artificial intelligence and robotics are at the heart of the shift from automation to autonomy.
AI enables advanced data analysis, predictive analytics, and autonomous decision-making—powering applications such as asset health monitoring, process optimization, and quality management. However, AI’s true value is realized when combined with deep domain expertise that gives meaning to the data and aligns insights with real business needs.
Robotics—including drones, mobile robots, and fixed automation—are also transforming how physical tasks are carried out. From leak detection and corrosion monitoring to subsea pipeline maintenance and automated pipe-handling on drill floors, intelligent robotic systems are expanding the scope of autonomy in industrial operations. As more intelligence is embedded, these systems are becoming more versatile, taking on increasingly complex maintenance and operational tasks across diverse environments.
Diverse Journeys to Industrial Autonomy
The journey to industrial autonomy is not a fixed path—it varies widely across and within organizations. Some operations have already reached the autonomous orchestration level, where systems independently make decisions. However, most workflows still remain at the automated or semi-autonomous level.
Then what, where, when, and how of introducing industrial autonomy is heavily influenced by a facility’s readiness. Brownfield sites, in particular, pose significant challenges, as they were not designed with autonomy or reduced human intervention in mind. However, these legacy sites also present the greatest opportunity for impact, given their higher energy use, operational inefficiencies, and maintenance demands. In contrast, greenfield sites are increasingly being designed with autonomous operations in mind, incorporating technologies such as robotics from the outset. Ultimately, the journey to autonomy is shaped by application complexity, site conditions, and the ability to integrate technologies effectively.
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