In a business environment ruled by high volatility, uncertainty, complexity, and ambiguity (VUCA), manufacturers in process industries are increasingly embracing emerging digital technologies to transform operations, control costs, reduce downtime, and improve profitability.
To address these challenges, companies in the process industries have set smart manufacturing goals in applying digital transformation technology to manufacturing operations. Yokogawa believes, for many end users, autonomous operations is the destination to achieve their smart manufacturing goals.
Currently, all companies are at some stage of automated operations as a starting point.
IA2IA is what Yokogawa foresees as the transition from Industrial Automation to 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.
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 is responsible for all safety-critical functions. In a fully autonomous operation (no human intervention required), the cognitive system is responsible for all aspects of the operation including safety.
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 majority of production processes and aids in workflow and maintenance tasks but requires human oversight and intervention to properly deal with anything that’s 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.
Companies have several business objectives they are targeting when they push for greater autonomy through smart manufacturing initiatives. For some companies, improved productivity is a focal point, while others are more directed toward operational efficiency. In any case, there are some objectives such as improved worker safety that are universally desirable targets.
For more information, please see "Key Business Objectives" under details.
New technologies are revolutionizing the way that plants operate and are facilitating a shift whereby physical tasks and decision-making processes are being made more autonomous. The technologies span across information technology (IT) and operational technology (OT) areas. Yokogawa's strength comes from its combination of both IT and OT expertise.
For more information, please see "Enablers in Industrial Autonomy" under details.
Yokogawa envisions an additional transcendent step of “Symbiotic Autonomy” on top of operations, which focuses on more collaboration across industries as well as eco-systems such as the energy sector.
In Symbiotic Autonomy, the autonomous operations of multi-collaborating ecosystems are brought together to look beyond an individual plant and achieve autonomous interaction of data and resources between separate plants. In a world that expects corporations to consider their operations from the point of view of world sustainability, this approach can deliver multi-win outcomes for a broader range of stakeholders.
Yokogawa is taking a leading role to make symbiotic autonomy a reality across the world.
Yokogawa carried out research with a diverse range of industry experts and end users to find out how 500 decision makers are preparing for industrial autonomous future which can obtain improved efficiency, higher reliably, greater production flexibility and increased work place safety while also benefiting the bottom line. For more information about the research the advancement of industrial autonomy, please click here.
Traditionally, the process industry focuses on four improvement areas: HSSE (health, safety, security, and environment); efficiency; asset availability; and human reliability. Some of these areas are interdependent. For instance, human reliability has a profound effect on all the other areas. Efficiency can impact safety and security and so on. Even though these focus areas haven’t changed significantly over the years, companies are still struggling to adequately deal with them. They are continually seeking better ways to reduce the adverse effects these challenges have on their operations, which ultimately improves the bottom line.
Furthermore, the industry faces emerging challenges such as changing business environment, value chain visibility, and optimization of sustainability.
The latest and the biggest challenge is how to deal with the COVID-19 pandemic. COVID-19 has made it extra challenging to keep workers safe, continue operations, and improve production flexibility.
From reducing downtime to improved quality management and regulatory compliance, companies have several business objectives they are targeting when they push for greater autonomy through digital transformation initiatives. Some challenges will be common to all operations, others will vary depending on region, industry, company—and even by division or site. For some companies, improved safety is a focal point, while others are more directed toward reducing product defects or improving reliability, resiliency and sustainability. In any case, there are some objectives such as improved productivity and efficiency that are universally desirable targets never far from a CEO’s mind.
Reduced risks associated with human involvement in the production work place. 48% of respondents said their companies put productivity as a key target.
Increased flexibility and efficiency through automation of physical / decision making tasks. 40% named operations efficiency as one of their key objectives.
Worker safety has always been a priority. With COVID-19, safety along with remote operations are the top investment priorities in the next 3 years.
Industrial autonomy can help in terms of improving overall output by optimizing processes and reducing downtime, or at the individual level by augmenting workers with additional tools and decision making to improve their productivity. Yokogawa knows the challenges its customers face and can set them on the right path for their transition from industrial automation to industrial autonomy.
Beyond the headline KPIs, a myriad of other benefits can be realized with industrial autonomy. For example, supporting sustainability efforts tied to the prominent Sustainable Development Goals (SDGs) through initiatives such as AI-supported real-time energy optimization. In addition, improving worker safety and de-manning dangerous applications are flagged as being able to reduce insurance premiums, costs from injury compensation, and worker days lost. It can also improve quality by identifying faulty goods on a production line or further upstream, adjusting a production process to ensure quality tolerances are met.
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.
All the advanced digital technologies built on the reliable and sustainable architectures are already available. How these technologies are integrated and applied to the industrial autonomy is essential to the journey.
Yokogawa & KBC have years of experience and expertise to implement transformation programs starting from a very early consulting phase. Yokogawa & KBC understand what key issues must be solved and what types of technology are available to solve the issues.
AI is an important enabler of data analysis, decision-making, and providing autonomous functionality. AI comes into play in the handling of large volumes of data, and in supporting predictive analytics that can be applied to applications such as asset health monitoring, process optimization, and quality-management. However, the benefits of analyzing data without domain knowledge are limited. The value comes when these technologies are combined with expertise that can identify both the business pain points alongside understanding the context of the data feeding the algorithms. This will allow AI to be utilized for self-learning, self-healing, and self-optimizing autonomous operations.
Robots and drones are another important enabler of industrial autonomy. Robots and drones are being used for monitoring, surveillance, and supporting people to perform tasks in hazardous environments. Robots and drones are doing tasks such as leak detection, corrosion monitoring, chimney inspection, and so on. As more intelligence is embedded, they will become more versatile and perform more operational and maintenance tasks autonomously.
Digital transformation encompasses the entire enterprise. DX applied to manufacturing is called Smart Manufacturing. Yokogawa believes for many end users, autonomous operations is the destination of smart manufacturing.
The transition from industrial automation to industrial autonomy requires sensing and a digital infrastructure that spans the entire operation and integrates data, smart devices at the edge, bulletproof hardware and software to deliver the required level of flexibility, adaptability, resilience, and eventually, autonomy.
Yokogawa is preparing the way for a future in which industries will make the transition from industrial automation to industrial autonomy (IA2IA). We offer a wide range of smart manufacturing solutions and digital consultancy services that will help customers on their digital transformation journey towards autonomous operations. For more information about Yokogawa’s Smart Manufacturing solutions, please click here.
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