Digitalisation helps transform process industries through IT/OT convergence
Published on : Tuesday 05-03-2024
Prasanna Lohar, CEO at Block Stack/President at India Blockchain Forum.
Industrial automation represents the digitalisation of process industries, marking a pivotal shift in manufacturing paradigms. The First Industrial Revolution introduced mechanisation, powered by steam engines, transforming manual labor into machine-driven production. The Second Industrial Revolution brought electricity and mass production, revolutionising manufacturing with assembly lines and standardised parts. The Third Industrial Revolution, or the Digital Revolution, introduced computers and automation, enabling greater precision and efficiency in production processes. The Fourth Industrial Revolution, also known as Industry 4.0, merges physical systems with digital technologies, leveraging IoT, AI, and data analytics for interconnected, smart manufacturing. Industrial automation streamlines operations, enhances productivity, and drives innovation, ushering in a new era of advanced manufacturing and competitiveness.
Top of Form
Here, Industrial automation refers to the use of control systems, machinery, and technologies to automate industrial processes and manufacturing tasks, reducing the need for human intervention and enhancing efficiency, productivity, and safety.
Digitalisation of process industries involves the integration of digital technologies, such as sensors, data analytics, artificial intelligence, and cloud computing, into industrial processes and operations. This digital transformation enables the collection, analysis, and utilisation of data from various sources to optimise processes, improve decision-making, and drive innovation across the entire value chain.
How process industries have historically automated with sensors, actuators, instrumentation and control in their operation?
Process industries have automated their operations by integrating sensors, actuators, instrumentation, and control systems into their manufacturing processes. This automation aims to improve efficiency, reliability, and safety.
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Sensors | Sensors are deployed throughout the manufacturing process to measure various parameters such as temperature, pressure, flow rate, level, and chemical composition | These sensors provide real-time data that enables operators to monitor and control the process more effectively |
Actuators | Actuators are devices used to control and manipulate process variables based on the feedback received from sensors | They can adjust valves, dampers, pumps, motors, and other components to maintain optimal process conditions. |
Instrumentation | Instrumentation encompasses a range of devices used for measuring, monitoring, and controlling industrial processes | This includes instruments such as flow meters, pressure gauges, level indicators, analysers, and controllers, which are integrated into the control system to facilitate automation |
Control Systems | Control systems receive input from sensors, process the data, and send commands to actuators to regulate process variables within desired setpoints | These systems can be simple, like on/off controllers, or complex, like distributed control systems (DCS) or programmable logic controllers (PLC), depending on the complexity of the process |
During my Tecnimont ICB days, I witnessed the seamless integration of sensors, actuators, instrumentation, and control systems in EPC turnkey projects. This integration enabled process industries to automate repetitive tasks, optimise production processes, minimise human error, and ensure consistent product quality. By harnessing these technologies, companies could achieve greater efficiency, reliability, and precision in their operations, ultimately leading to improved competitiveness and customer satisfaction. Eventually we have seen technological advancement in these areas with artificial intelligence, cloud computing, advanced analytics, IoT integration, wireless connectivity, integration with robotics, remote monitoring.
What are the examples of specific technologies or methods that process industries have used to enhance automation in their operations?
Process industries have employed various technologies and methods to enhance automation in their operations. In process industries, automation is pivotal for optimising operations and enhancing productivity. Traditional methods like Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition (SCADA) systems have long been the backbone of automation. However, modern technologies such as Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing are revolutionising automation by enabling real-time monitoring, predictive maintenance, and data-driven decision-making. These advancements drive efficiency, reduce downtime, and improve overall performance in process industries, ensuring they remain competitive in a rapidly evolving market landscape.
Traditional methods | Modern technologies |
Programmable Logic Controllers (PLCs): PLCs have been a staple in process automation for decades. These industrial computers are programmed to control manufacturing processes such as assembly lines, robotic arms, and machinery. They automate repetitive tasks and ensure precise control over various parameters. | Internet of Things (IoT): IoT technologies involve connecting sensors, devices, and equipment to the internet to gather and exchange data. In process industries, IoT enables real-time monitoring, predictive maintenance, and optimisation of production processes. For example, sensors embedded in machinery can collect data on temperature, pressure, and vibration, allowing operators to identify potential issues before they escalate. |
Supervisory Control and Data Acquisition (SCADA): SCADA systems are used to monitor and control industrial processes remotely. They collect real-time data from sensors and devices, display it to operators, and allow them to control processes from a centralised location. SCADA systems have been widely used in sectors like water treatment, oil and gas, and power generation. | Artificial Intelligence (AI): AI technologies, including machine learning and deep learning, are revolutionising process automation by enabling machines to learn from data, identify patterns, and make decisions autonomously. AI algorithms can optimise production processes, detect anomalies, and predict equipment failures, leading to improved efficiency and reduced downtime. |
Distributed Control Systems (DCS): DCSs are specialised control systems used in industries where complex control algorithms and high reliability are required. They consist of multiple control units distributed throughout a plant, each responsible for a specific process or area. DCSs enable centralised monitoring and control of industrial processes and have been used in chemical, petrochemical, and refining industries. | Robotics and Automation: Advances in robotics and automation technologies have led to the widespread adoption of robots in process industries. Robots can perform various tasks such as material handling, assembly, and packaging with speed, precision, and consistency. Collaborative robots (cobots) are increasingly being used alongside human workers to improve productivity and safety. |
| Cloud Computing: Cloud computing provides scalable and on-demand access to computing resources and data storage over the internet. In process industries, cloud-based automation platforms enable remote monitoring, data analytics, and collaboration. They facilitate access to advanced analytics tools, machine learning algorithms, and digital twin simulations, empowering organisations to optimise operations and make data-driven decisions. |
| Blockchain: Process industries, such as pharmaceuticals, food and beverage, and manufacturing, utilise blockchain to track and trace raw materials, components, and finished products throughout the supply chain. By recording transactions and data on a tamper-proof blockchain ledger, companies can ensure transparency, immutability, and authenticity of product information, enabling more efficient supply chain management, compliance with regulations, and prevention of counterfeit products.
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It makes sense for organisations to keep reskilling and adopting these technologies and methods to enable process industries to achieve higher levels of automation, efficiency, and productivity while ensuring the safety and quality of their products.
What role does digitalisation now play in the transformation of process industries with the convergence of IT/OT (Information Technology/Operational Technology)?
Digitalisation plays a pivotal role in transforming process industries through the convergence of Information Technology (IT) and Operational Technology (OT). This convergence blurs the traditional boundaries between IT systems (such as enterprise resource planning and data analytics) and OT systems (such as industrial control systems and sensors), creating new opportunities for efficiency, innovation, and competitiveness. Digitalisation enables process industries to collect, analyse, and leverage data from across the production chain in real time, facilitating predictive maintenance, process optimisation, and quality control. By integrating IT and OT systems, companies can enhance visibility, agility, and decision-making capabilities, driving operational excellence and unlocking new levels of productivity and profitability. Digitalisation also enables the adoption of advanced technologies such as artificial intelligence, digital twins, and blockchain, further accelerating the transformation of process industries towards smarter, more connected, and resilient operations.
Smart Factory Implementation |
A steel manufacturing company undergoes a digital transformation by implementing a smart factory concept that converges IT and OT systems. The company installs a network of sensors, actuators, and IoT devices across its production facilities to capture real-time data on machine performance, energy consumption, and product quality. This data is integrated with enterprise IT systems such as Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) systems to enable end-to-end visibility and control over manufacturing operations. Advanced analytics and machine learning algorithms are applied to the integrated data to optimise production processes, improve product quality, and reduce waste. As a result, the company achieves higher throughput, lower operational costs, and increased competitiveness in the market.
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We can achieve many pathbreaking benefits for today and tomorrow.
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Definitely with enhanced customer satisfaction, digitalisation empowers decision makers and management to run process industries to drive operational excellence, enhance competitiveness, and adapt to evolving market dynamics by harnessing the synergies between IT and OT.
It should enable organisations to unlock new opportunities for innovation, efficiency, and sustainability in the digital age and move forward to the next orbit as I always mentioned for my CEAT digital transformation framework. (CEAT – Create, Engage, Act and Transform).
How do low code, edge computing, and cloud computing contribute to the flexibility and modularity of plants in the context of process industries?
Low code, edge computing, and cloud computing play crucial roles in enhancing the flexibility and modularity of plants in process industries, enabling them to adapt to changing demands and optimise operations. Low code development accelerates application deployment, edge computing enables real-time data processing at the plant level, and cloud computing centralises data management and analytics.
Method and Technology | Description | Example | Use Case |
Low Code Development | Low code platforms empower plant engineers to quickly develop and deploy custom software solutions without extensive coding knowledge | In a chemical manufacturing plant, engineers use a low code platform to build a monitoring and control application. With intuitive drag-and-drop interfaces, they create dashboards to visualise real-time data on temperature, pressure, and chemical composition. This flexibility allows them to iterate rapidly and tailor the application to evolving process requirements |
Edge Computing | Edge computing brings computational power closer to the data source, enabling real-time processing and analysis at the plant level. | In an oil refinery, edge devices installed on critical equipment collect sensor data and run machine learning algorithms to detect anomalies. By processing data locally, the refinery reduces latency and enhances responsiveness, improving operational efficiency and minimising downtime. |
Cloud Computing | Cloud computing provides scalable and centralised data storage, processing, and analytics capabilities. | A food processing company leverages the cloud to consolidate production data from multiple plants. With cloud-based analytics tools, plant managers access real-time insights and optimise operations remotely. Additionally, cloud integration with suppliers and distributors enables seamless collaboration and supply chain visibility. |
In my view post Covid-19 and considering Future of Work and current Business Trends, together, these technologies enhance the flexibility and modularity of process plants, allowing them to respond quickly to changes, optimise operations, and drive continuous improvement. Successful organisation should be on cloud and edge computing infrastructure. Eventually I can see the banking industry also started utilising these tools during development and monitoring branch operations.
Could you elaborate on the specific benefits that the integration of cutting-edge technologies, such as digital twins and artificial intelligence, bring to process industries?
The integration of cutting-edge technologies like digital twins and artificial intelligence (AI) offers several specific benefits to process industries.
First let’s define them.
Digital Twins: Digital twins are virtual representations of physical assets, processes, or systems. In process industries, digital twins enable real-time monitoring, simulation, and optimisation of production processes.
Artificial Intelligence (AI): AI encompasses various techniques such as machine learning, deep learning, and natural language processing. In process industries, AI algorithms analyse large volumes of data from sensors, equipment, and operational systems to identify patterns, anomalies, and opportunities for optimisation.
By integrating digital twins, AI and other cutting edge technologies like Blockchain, process industries can achieve synergistic benefits such as:
Enhanced visibility and control over operations through real-time monitoring and predictive analytics.
Improved efficiency, reliability, and safety of production processes.
Greater transparency, traceability, and compliance with regulatory requirements.
Enhanced collaboration and trust among stakeholders across the value chain.
Accelerated innovation and continuous improvement through data-driven insights and optimisation.
Use Case Supply Chain Traceability with Blockchain and Digital Twins
In a pharmaceutical manufacturing company, digital twins are deployed to create virtual representations of production processes, including formulation, packaging, and distribution. Blockchain technology is integrated into the supply chain management system to record and securely store data related to the production, transportation, and storage of pharmaceutical products. Each step of the production and distribution process, from sourcing raw materials to delivering finished products to pharmacies, is recorded as immutable transactions on the blockchain. Consumers can access a transparent and tamper-proof record of the product's journey by scanning a QR code on the packaging, verifying its authenticity and ensuring compliance with regulatory standards.
Impact and Benefits:
Enhanced transparency: Blockchain ensures transparency and traceability throughout the supply chain, enabling consumers to verify the authenticity and quality of pharmaceutical products.
Compliance assurance: Immutable records on the blockchain facilitate compliance with regulatory requirements, audit trails, and quality control standards.
Counterfeit prevention: Blockchain-based traceability systems deter counterfeiters by providing a secure and tamper-proof record of product provenance and authenticity.
Supply chain optimisation: Real-time visibility into the supply chain enables proactive management of inventory, production, and distribution processes, improving efficiency and reducing waste.
The integration of digital twins and AI brings transformative benefits to process industries, enabling them to optimise operations, enhance product quality, reduce costs, drive innovation, and ensure safety and compliance. These technologies empower organisations to remain competitive in a rapidly evolving market landscape.
How does the trend towards more flexible and modular plants align with broader industry goals, and what are the potential implications for the future of process industries?
In my view, the trend towards more flexible and modular plants aligns with broader industry goals by promoting agility, cost efficiency, innovation, resilience, sustainability, and global expansion. The potential implications for the future of process industries include increased competitiveness, accelerated innovation, enhanced sustainability, and improved resilience to disruptions, positioning organisations for long-term success in a rapidly evolving market landscape.
In 2024, companies are looking to improve customer experience with Go-To-Market strategy, use of data, ease of operations with automation, adherence to regulations and compliance and moreover, data privacy and overall control of the data movement efficiently.
We can achieve this with automation, use of technology and tools, sustainable infrastructure aligned closely with broader industry goals and present several potential implications for the future of process industries.
Agility and Adaptability: By modularising equipment and processes, organisations can reconfigure production lines more efficiently, scale operations up or down as needed, and introduce new products or processes with minimal disruption.
Cost Efficiency: Modularisation allows for standardised components that can be mass-produced, leading to economies of scale and lower overall project costs.
Resilience and Risk Mitigation: Flexible and modular plants enhance resilience and risk mitigation strategies by diversifying production capabilities and reducing dependency on single large-scale facilities.
Sustainability and Environmental Impact: Modular plants offer opportunities to improve sustainability and reduce environmental impact by optimising resource utilisation, minimising waste generation, and implementing eco-friendly technologies, and closed-loop systems to conserve resources and minimise emissions.
If you are looking for Global Expansion, I highly recommend organisations should have modular units in diverse geographic locations, tailoring production to local market needs and regulatory requirements. This facilitates market penetration, enhances supply chain resilience, and enables organisations to capitalise on emerging opportunities in new markets.
Finally Innovation and Technology Adoption by providing a flexible framework for experimentation and implementation. Organisations can integrate cutting-edge technologies into modular units and easily upgrade or replace them as technology evolves. This enables process industries to stay competitive and leverage advancements in automation, digitalisation, and sustainability.
Concluding Remarks
Process industries historically automated their operations by employing sensors to measure variables such as temperature, pressure, flow, and level, and actuators to control valves, pumps, and motors based on the readings from these sensors. Instrumentation and control systems were used to monitor and regulate the processes, ensuring optimal performance and efficiency. Digitalisation plays a crucial role in the transformation of process industries by enabling the convergence of Information Technology (IT) and Operational Technology (OT) with following 4 Focus Areas:
Increased Competitiveness: Flexible and modular plants can respond quickly to market changes, giving companies a competitive edge.
Enhanced Sustainability: Optimised processes and resource utilisation contribute to sustainability goals by reducing waste, energy consumption, and environmental impact.
Improved Resilience: Modular plants are more resilient to disruptions, as they can be easily reconfigured or scaled up/down to mitigate risks and ensure continuity of operations.
Accelerated Innovation: Adoption of cutting-edge technologies and digitalisation fosters innovation ecosystems, driving collaboration and co-creation of new solutions and business models.
This focus on convergence should allow industrial automation in process industries in a better direction to adopt the application of digital technologies to automate and optimise industrial processes, leading to increased efficiency, flexibility, and competitiveness.
(The views expressed in interviews are personal, not necessarily of the organisations represented.)
Prasanna Lohar is an award winning CXO banker, digital architect, industry innovator, board member, fintech influencer, startup mentor, leadership coach, bank advisor and impact investor.
With 24+ years of experience at all levels – Engineering, Management, and Innovation as CXO – Prasanna has worked globally for banks, fintech, micro-finance, engineering, and multi-national companies for digital & architecture transformation. He is closely associated with cutting-edge technology adoption strategy, assimilation, experimentation, innovative customer servicing & engagement, robust architecture implementation, fintech & start-up alignment, open innovation practices, ecosystem collaboration with govt, big tech, fintech, banks and academia.
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