What are autonomous operations?
Autonomous operations are the future of smart manufacturing and represent the last stage in the shift towards autonomy. It is when industry makes the final move away from factory automation to fully autonomous plants and systems.
In the case of automation, equipment, production lines, etc. operate mainly on their own but are controlled by human beings when required. With autonomy, however, technologies like machine learning and AI enable machines to self-sufficiently manage day-to-day manufacturing systems and operations with little to no human interaction. The main objective of autonomous operations is to minimize manual interactions and maximize self-directed plant operations.
Automation typically runs from start to finish with little variation. It is usually a one-dimensional, static sequence of tasks. AI-driven autonomous systems, however, take many layers of legacy and modern applications and infrastructure into consideration. They then use predictive maintenance, smart sensors, digital twins, etc. to monitor, respond, and adapt to large, complex organizational systems. They are therefore associated with many different benefits and can add value to the manufacturing business. For example, they can reduce operational costs, ensure production flexibility, reduce risks, and improve safety.
What is IA2IA?
IA2IA stands for “Industrial Automation to Industrial Autonomy”. This transformation is not a one-step process, but a long journey that involves evolving operations management over time. It is centered around the idea of moving beyond Industry 4.0 into a realm of complete autonomy in industry. Today’s fourth industrial revolution relies heavily on industrial automation: a high degree of automated processes and robotic assistance with connected devices to improve the efficiency and productivity of human-assisted operations management.
Industrial autonomy takes automation to the next level, with self-governing AI learning and making decisions independently. Autonomous operations and factory resources have the capacity to learn and adapt on their own to both predicted and unknown events so that minimal or no human interaction is required. This will eventually allow machines to take over many tasks, such as process operations, maintenance, and planning and scheduling. As a result, unique human potential can be leveraged in other critical areas.
In the real world, companies have varying maturity levels of autonomy. These stages of maturity are divided into five levels. Level 0 represents “Manual,” Level 1 is described as “Semi-Automated,” Level 2 as “Automated,” and Level 3 as “Semi-autonomous.” The final two levels are defined as Level 4: “Autonomous orchestration” and Level 5: “Autonomous operations.” As the levels progress from 0 to 5, the degree of autonomy and the degree of smart manufacturing and control intelligence gradually increases. In Level 0, automation does not factor in at all. All decisions and functions are carried out manually by humans. On the other end in Level 5, however, full autonomy is reached in all plant systems and operations. Humans play zero role in factory operations here, with machines and IIoT technologies doing all the work. A brief overview of the levels and their classifications is illustrated below.
Yokogawa’s IA2IA concept foresees a sixth stage in this transition. It is known as “Symbiotic autonomy”. Its goal is to take autonomous operations one step further to establish cooperation between individual facilities across industries and coordinate autonomous interaction between their assets and systems.
How does digital architecture factor into automation and the different degrees of autonomy?
Digital architecture encompasses a variety of digital technologies and solutions which are deployed to transform the manufacturing industry. They range from big data to smart sensors and connected devices to robotics and artificial intelligence (AI). One very important example of digital architecture that’s helping drive automation and the switch to autonomy is cloud computing.
Cloud computing is essential to the IA2IA movement. The ability to rapidly access computing resources over the internet (the “cloud”) enables plant technologies to process vast amounts of data. As such, autonomous machines can respond to changing conditions quickly and react agilely to adjust and optimize operations management and performance. Cloud computing will also be critical to symbiotic autonomy, as it is able to collect data from multiple locations. This will enable greater harmonization among facilities and their factory floors to help streamline industry supply chains.
AI is another example of digital architecture which is propelling the changeover toward autonomous manufacturing. It is capable of machine learning and reasoning – a process by which computers use mathematical models to learn from large quantities of data and make independent decisions. This will support predictive maintenance so that machines can freely identify irregularities in operations and alert users to take corrective actions.
To stay competitive in times of rapid change and digital transformation, it is pivotal that companies take the leap toward autonomous operations. Yokogawa has the expertise and resources to accompany you down this road.
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