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Industrial AI refers to the application of artificial intelligence, particularly machine learning, in industrial contexts. It involves processing and analysing operational data to identify opportunities for optimising reliability and performance. This branch of AI develops intelligent systems capable of automating decision-making processes and generating recommendations based on historical data insights. These systems are designed to enhance the understanding and management of complex industrial operations.

Industrial AI is particularly valuable in complex operational environments where it can automate repetitive tasks, enhance decision-making, and increase overall efficiency. It is crucial in industries that require high reliability and precision such as energy, mining, utilities, and motorsports.

Industrial AI is transformative because it uses data-driven modeling to not only follow predefined rules but to actively learn and adapt from historical data. By leveraging machine learning, Industrial AI can dynamically optimise industrial systems by learning from past decisions. For example, it might adjust operational parameters in real-time within a power plant to enhance efficiency and reduce costs, based on what has been most effective in past operations. This ability to continually improve and adapt makes Industrial AI exceptionally powerful in managing the complexities of modern industrial environments.

Industrial operations are inherently complex and dynamic. Industrial AI helps distill large amounts of information into what matters, empowering a workforce to make both fast and informed decisions. It transforms your most important objectives into models that are programmed to search for the best answers based on available data to guide optimal decision-making, specific to the task at hand. In environments rich with data from numerous sensors and system components, Industrial AI provides clear, actionable guidance. As data grows in size, Industrial AI becomes a vital asset, enabling the development of smarter models that guide better decisions over time. These intelligent models evolve with your data, continuously improving their predictive accuracy and operational relevance. This approach doesn't just streamline existing processes; it also anticipates and prepares for future challenges, positioning Industrial AI as an essential tool for delivering a competitive edge.

1. Enhancing Efficiency Streamline operations with reduced manual oversight, decreasing repetition in operational tasks and increasing overall process efficiency. ​ 2. Maximising Asset Performance Enhance operational performance through real-time insight and intelligent automations, which significantly reduce downtime and optimise performance. ​ 3. Reducing Cost & Emissions Proactively monitor and maintain systems to prevent costly operational failures and reduce environmental impact.

While Industrial AI has broad applications, its benefits are particularly notable in sectors like energy, mining, utilities, manufacturing, motorsports and transportation, where complex operations and large volumes of data are common.

Artificial Intelligence (AI) is the broader concept of machines being able to carry out tasks in a way that we would consider "smart". It encompasses any technique that enables computers to mimic human intelligence. Machine Learning (ML), a subset of AI, focuses on the idea that we can build machines to process data and learn on their own, without constant supervision. In machine learning, algorithms are used to discover patterns in data and make estimations or predictions based on those patterns. It's the technology behind many of the sophisticated services we use today, from search engines to recommendation systems and autonomous vehicles.

Industrial AI systems are designed to augment, not replace, engineering roles. AI-based systems cannot replace human creativity and innovation. They excel at automating manual and mundane tasks, such as the early stages of data processing and analysis, freeing humans to focus on more creative and complex problem-solving tasks.

These technologies transform engineering by enhancing precision, efficiency, and innovation. They enable engineers to: Solve complex problems faster and more accurately by analysing vast amounts of data. Predict outcomes such as equipment failures, which can save time and reduce costs. Automate routine tasks, allowing engineers to focus on more strategic work. Innovate by designing solutions that would be impossible or impractical to achieve manually.

Industrial AI is transforming engineering across various domains by enhancing capabilities in surveillance, control, failure prevention, forecasting, and strategic planning: Surveillance: Machine Learning (ML) enables continuous monitoring of engineering systems, helping to anticipate and address potential issues before they escalate. This application is crucial in environments where constant vigilance is required, such as in manufacturing facilities or infrastructure projects. Control: ML-driven systems dynamically adjust engineering processes in real-time, enhancing responsiveness and adaptability. This capability is especially beneficial in automated production lines or in energy management systems where conditions change rapidly. Failure Prevention: By analysing historical and real-time data, ML models can predict and prevent equipment failures. This proactive approach significantly reduces downtime and maintenance costs, which is vital for maintaining high operational efficiency in industrial settings. Forecasting: Machine Learning models excel at forecasting production needs and operational challenges, allowing organisations to make proactive adjustments. This foresight is critical in supply chain management and resource allocation, ensuring that operations run smoothly without interruptions. Strategy: Strategic planning benefits greatly from ML insights, as these systems provide data-driven guidance that aligns operational tactics with broader business objectives. This integration of ML in strategic planning is crucial for long-term sustainability and competitive advantage. These applications showcase how Industrial AI technologies can not only support but actively enhance engineering functions by automating complex processes and providing insights that lead to more informed decisions and improved operational outcomes.

Dynamic optimisation is a method used in various fields such as economics, engineering, and management to make the best decisions over time. This approach involves continuously adjusting the variables in a system or process in response to changes in the environment or system state to achieve the best possible outcome. Here are some key points about dynamic optimisation: Time-Dependent Decisions: Unlike static optimisation, which seeks to find the best solution under a fixed set of conditions, dynamic optimisation considers that conditions may change over time. Therefore, the solutions must adapt dynamically to these changes. Modeling and Algorithms: Dynamic optimisation typically involves mathematical modelling of the system, where differential equations describe how system states evolve over time. Algorithms such as dynamic programming, control theory, or other numerical methods are then used to find optimal solutions. Applications include: Engineering: In process control, dynamic optimisation helps in adjusting operational parameters in real time to ensure the most efficient process operation, such as adjusting the temperature, pressure, or flow rates in chemical processes to maximise yield or minimise energy consumption. Finance: Used in portfolio management, where the allocation of assets is continuously adjusted in response to fluctuating market conditions to maximize returns or minimise risk. Supply Chain Management: Dynamic optimisation is used to adjust schedules and routes in real time in response to changes in demand or supply, traffic conditions, or other logistical challenges. Benefits can include: Increased Efficiency: By continuously adapting to new data, dynamic optimisation ensures that systems operate as efficiently as possible under current conditions. Improved Performance: Systems are able to respond to changing environments proactively, which can improve overall performance. Cost Reduction: Optimising operations dynamically can lead to significant cost savings over time by avoiding inefficiencies and exploiting opportunities as they arise. Dynamic optimisation is particularly valuable in systems where external conditions change rapidly or where it is crucial to respond quickly to new information. It ensures that operations remain optimal even as new data becomes available or as the system's goals and constraints evolve.

Artificial Intelligence (AI) and Its Subfields Artificial Intelligence (AI): Simulation of human intelligence in machines to perform tasks like reasoning, learning, and problem-solving. Machine Learning (ML): A branch of AI where algorithms learn from data to make predictions or decisions without being explicitly programmed. Deep Learning: An advanced ML technique using neural networks to analyse factors in decision-making, similar to the human brain. Industry 4.0 and Related Technologies Industry 4.0: The fusion of advanced manufacturing/industrial operations techniques with the Internet of Things to create smart, automated factories. Smart Manufacturing: The application of AI and ML in manufacturing, enhancing efficiency and productivity. Internet of Things (IoT): Network of interconnected devices exchanging data, crucial in smart homes, cities, and industries. Industrial Internet of Things (IIoT): IoT applied in industrial settings, improving safety and efficiency. Edge Computing: Processing data near its source to reduce latency, often used in IoT applications. Cloud Computing: Delivery of computing services over the internet, including storage, processing, and analytics. Control Systems and Models Control Systems: Mechanisms regulating other devices or systems, often employing feedback loops. PID Control: A widely used feedback control system in industries, balancing precision and stability (PID stands for proportional-integral-derivative). MPC Control (Model Predictive Control): Advanced control method predicting future outcomes for optimal operational adjustments. Industrial Automation and Networking SCADA (Supervisory Control and Data Acquisition): High-level process supervisory management using computers and networked data communications. IT/OT Integration: Merging data-centric IT systems with operational machinery and sensors in OT for improved operational efficiency. Purdue Model: A framework for industrial control system architecture, categorizing various ICS elements. Other Related Concepts Cyber-Physical Systems (CPS): Integrated computer and physical systems, prevalent in automation and smart grid technologies. Digital Twin: A virtual representation of a physical object or system, used for analysis and simulation. Predictive Maintenance: Techniques predicting equipment failures, allowing timely maintenance to prevent downtime. Human-Machine Interface (HMI): User interfaces connecting humans to machines, systems, or devices. Key Terms in Modern Technology Automation: Making processes operate automatically, reducing human intervention. Autonomy: Systems performing tasks independently and adaptively. ‘Smart’ Technology: Devices or systems using advanced algorithms for tasks requiring human-like adaptability. Smart Assets: Assets integrated with data-driven technologies for improved control and management. Model: Simplified representations of systems, either knowledge-driven (based on expert understanding) or data-driven (learning from data). Information and Operational Technology Information Technology (IT): Handling information using computers and other technologies, like networks and databases. Operational Technology (OT): Technology for running operations, including machinery, sensors, and control systems. Industrial Assets: Physical and non-physical assets used in production, such as machinery, buildings, software, or data. This cheat sheet is intended as a high-level guide. For a comprehensive understanding, further exploration of each topic is recommended. Stay tuned for future insights on ‘Building an Autonomous Operations Program’.

It’s advisable to have a solid data management foundation, but starting with advanced analytics can simultaneously highlight areas needing attention in your data strategy. This can expedite improvements in data handling and quality, providing a clearer path to leveraging machine learning effectively.

Companies can begin by: Identifying specific challenges that AI can address, such as bottlenecks in production or high maintenance costs. Partnering with AI experts and solution providers who understand the specific needs of engineering. Investing in training and resources to upskill engineers and integrate AI tools effectively. Starting small with pilot projects to demonstrate value before scaling up successful applications.

The return on investment can vary based on several factors including the specific use case, the scale of implementation, and the operational efficiency gains. However, we have seen clients observe significant cost savings, improved asset utilisation, reduced maintenance costs, and increased performance, which together contribute to a strong ROI, often realised within a few months to a year after deployment.

When defining KPIs for an Industrial AI project, it’s all about aligning with your core business goals to maximise impact. Here is a framework to get started: Efficiency: Aim to boost operational efficiency. Track how well the AI reduces manual tasks and streamlines workflows, which should lead to less redundancy and more efficient operations. Asset Performance: Focus on improving the performance and reliability of your assets. Measure the effectiveness of real-time adjustments and predictive analytics in reducing downtime and maximising asset output. Cost and Environmental Impact: Set goals to lower operating costs and minimise your environmental footprint. Use metrics that reflect how proactive monitoring and maintenance contribute to operational cost savings and less environmental impact (e.g. carbon emissions) from operations.

Starting your journey with our Industrial AI solutions is designed to be straightforward, engaging, and highly structured: Contact Us: Begin by reaching out through our website or contact our customer service team. We're ready to start the conversation and learn more about your specific needs and challenges. Discovery Project: We then move into a dedicated discovery phase where we work closely with you to pinpoint specific areas within your operations that could benefit from our NEXGINEER solutions. This collaborative stage is crucial for ensuring that our strategies are perfectly aligned with your operational goals and that the solutions we propose will deliver measurable improvements. Scope Pilot Project: Following a successful discovery phase, we'll scope out a pilot project. This is a critical step where we demonstrate the real-world effectiveness and benefits of our Industrial AI solutions in a controlled part of your operations. It’s designed to provide tangible evidence of how our technology can drive efficiency and performance benefits in your industrial operation. Full-Scale Rollout: With the pilot project validated and its benefits confirmed, we proceed to a customised full-scale deployment. This rollout is tailored to integrate seamlessly with your broader operational frameworks, ensuring that the AI solutions enhance your processes across the board. This phased approach is meticulously planned to ensure that each step, from the initial contact to the comprehensive implementation, is executed with precision. Our goal is to minimise risks while maximising the potential for significant operational improvements and success with Industrial AI.

Minimal input is needed - often as little as 15 minutes per week. Our solutions are designed to integrate seamlessly, allowing engineers to focus on their core responsibilities while benefiting from enhanced decision-making support.

Our solutions are engineered to function autonomously with minimal manual oversight. We set the objective functions of the system to align with your asset management goals, enabling it to autonomously optimize operations and learn from its actions. Engineers have the option to provide feedback and tweak parameters if necessary, but daily supervision or manual validation is not required. This design enables the system to learn and adapt autonomously while still allowing for human oversight when desired.

Our solutions are distinguished by a robust foundation of engineering excellence and industry expertise, ensuring they are: Engineering-Led: Developed by engineers, for engineers, our solutions prioritise engineering and physics principles, ensuring that every Industrial AI application is deeply rooted in the technical realities of industrial operations. Closed-Loop Proven: Demonstrated in real-world applications, our Industrial AI solutions have been proven as far as in closed-loop control of industrial systems. They have been proven to deliver reliable insights and automate decisions, enhancing both efficiency and accuracy in industrial control applications. Built to Integrate: Designed to seamlessly fit into your existing operations, our solutions facilitate easy integration with minimal disruption. They are tailored to meet the unique demands of your systems, enabling engineers to utilise our AI-driven tools with little to no need for extensive training, supported by intuitive interfaces and comprehensive ongoing support.

Each operation has unique characteristics and requirements. While our solutions are based on proven frameworks, they require customisation to fully capture the value and ensure seamless integration into existing systems.

We build our NEXGINEER applications and platform on Amazon Web Services (AWS), which is renowned for its robust security infrastructure. AWS complies with the highest industry standards, ensuring superior data integrity and confidentiality. We ensure all data is encrypted, both in transit and at rest, and continuously update our systems to protect against emerging threats. This commitment to security ensures that our solutions are not only scalable but also secure and reliable for industrial applications.

Industrial AI is a broad and complex field where success depends on a combination of accurate data-driven modelling, deep industry knowledge, and precise engineering expertise. At SIG ML, we leverage our strong industry and engineering background to guide our development of solutions. This ensures that every project is not only technically sound but also directly aligned with your operational challenges and goals.

While high-quality data improves model accuracy, Industrial AI systems are designed to work effectively even with lower-quality data. They can also help identify data quality issues as part of their operation, improving data handling over time.

Historical data is beneficial for calibrating our ML-based models in the NEXGINEER applications, but it is not mandatory for all operations. Our models are designed within a framework that respects physical laws and established engineering analysis methods, allowing for effective operation even in settings with limited data availability. This flexibility ensures that our solutions can be adapted to diverse industrial contexts, regardless of the historical data volume.