From IIoT to AIoT: What is the Smart Industrial Internet of Things?

Author: Vasily Churanov, Director of the Mechanical Engineering and Metalworking Division of the Tsifra Group of Companies

In recent years, the term “Internet of Things” (IoT) has become one of the most popular in the tech world. It promised to connect billions of devices, making our lives more convenient and efficient. However, the development did not stop there. The next stage was the Industrial Internet of Things (IIoT), which brought revolutionary changes to production processes. Today, a new term is coming onto the scene – AIoT (Artificial Intelligence of Things), combining the capabilities of the Internet of Things with the power of artificial intelligence.

What is IIoT?

IIoT is the application of Internet of Things technologies in industry. It involves the use of sensors, data collection and analysis systems to improve the efficiency and productivity of production processes. IIoT allows enterprises to obtain objective, real-time data on the condition of equipment and use it to optimize processes and minimize downtime. For example, sensors can track equipment wear and tear and perform maintenance only when it is really necessary, which significantly reduces costs.

Over the past decade, IIoT has already proven its effectiveness. Implementation of the Industrial Internet of Things allows enterprises to achieve significant improvements in productivity and cost reduction. Companies using IIoT can reduce equipment downtime by 20-30%, which leads to a significant increase in profitability. For example, the implementation of the IIoT solution "Dispatcher" at UEC-Salut reduced unscheduled equipment downtime by 12% and increased the equipment utilization rate by 40%. IIoT also helps improve product quality due to more precise control of production processes.

Transition to AIoT

AIoT combines the capabilities of IIoT and artificial intelligence. The idea is to not only collect data using sensors, but also analyze it in real time using machine learning algorithms. This allows not only to identify problems, but also to predict their occurrence. Simply put, it is a smart industrial internet. For example, an AIoT system can warn in advance about a possible equipment failure based on data on its operation and previous failures.

The AIoT market is showing significant growth and is projected to expand rapidly in the coming years. In 2024, the AIoT market is expected to reach approximately US$9.98 billion with a projected CAGR of 32.7%. This could grow to US$31.05 billion by 2028. In comparison, the IoT market is significantly larger but growing at a slightly slower rate. In 2024, the IoT market size is projected to be around US$714.48 billion and it could reach 4,062.34 billion by 2032 with a CAGR of 24.3%. IIoT is also showing significant growth driven by the advancement of manufacturing technologies, healthcare, and smart cities, with a projected CAGR of over 23% between 2024 and 2030.

Thus, although the AIoT market is smaller in absolute terms compared to the overall IoT market, it is expanding at a faster pace due to the synergistic benefits that come from combining AI and IoT technologies.

Bosch factories have learned to track inefficient production processes and optimize them in real time using AIoT. This has reduced production time by 30% and increased the flexibility of production processes. AIoT helps improve logistics, inventory management, and even predict demand for products. In medicine, AIoT allows for the creation of smart systems for monitoring patients’ conditions, predicting possible complications and warning doctors in a timely manner.

One of the most anticipated results of AI in technical systems is the ability to predict the timing of preventive maintenance. For example, in a real workshop, logistics management requires the use of highly reliable algorithms. Such algorithms depend on many factors – dynamically changing production plans, operational conditions on the routes of movement of physical objects, safety requirements and other parameters. It is especially important that some factors are directly related to AI tasks, such as image recognition, voice control and collision avoidance.

Judging by the publications and reports of such large holdings as Siemens, Volkswagen, General Electric, the most frequently solved problems where AIoT can really bring significant benefits in the manufacturing sector are:

• Predictive service for the technical condition of equipment: AIoT systems allow for early planning of preventive maintenance, which ensures the durability and reliability of equipment.
• Logistics optimization: Managing the movement of objects on the production floor, whether tools or large machines, is becoming more efficient thanks to AIoT. Such systems use data from sensors, video cameras, and other devices to optimize routes and prevent collisions.
• Quality control: AIoT systems can monitor dynamic parameters such as temperature, vibration, and electrical characteristics, allowing for immediate response to deviations from norms and improving product quality.
• Inventory Management: AIoT helps businesses manage inventory by predicting material and tool requirements to minimize costs and avoid stockouts.
• Digital twins: AIoT technologies enable the creation of digital twins of production equipment and processes, making it possible to simulate and optimize their operation without risk to real systems.

The Importance of Data Collection in Modern Industry

At the current stage of information technology development, data collection plays a key role. Data is the “new oil” of the digital age, and it is becoming the basis for making informed decisions and developing strategies. In the context of IIoT and AIoT, data collection allows enterprises to:

• Monitor and analyze production processes: The collected data provides an overview of the current state of production equipment and processes, allowing problems to be identified and resolved in a timely manner.
• Increase productivity: By analyzing data, businesses can optimize their processes, reduce costs, and increase productivity.
• Develop predictive models: Data is used to create models that can predict future events, such as equipment failure or changes in product demand.

An example of such data collection solutions in the Russian Federation is the industrial equipment monitoring system "Dispatcher". This system collects data from any industrial equipment and allows building production and repair management systems based on real-time data. Starting to implement such systems, making it part of a short-term strategy, is the most correct step for modern industrial enterprises. Monitoring systems of the "Dispatcher" class can become an important tool for the transition to the "Data Economy", which the state has taken as its focus.

Digital Transformation Steps to Prepare for AIoT

To successfully implement AIoT systems, enterprises need to go through several important stages of digital transformation. Here are the stages identified by Vasily Churanov, Director of the Mechanical Engineering and Metalworking Division of the Tsifra Group of Companies:

1. Assessing the current state and developing a strategy: Before starting a digital transformation, an enterprise must conduct a comprehensive analysis of the current state of its IT systems and production processes. Based on this analysis, a digital transformation strategy is developed, including goals, objectives and deadlines for achieving them.
2. Infrastructure upgrades: Collecting and processing large amounts of data requires modern infrastructure. This includes installing sensors and data collection devices, upgrading networks and server hardware, and ensuring reliable and secure data storage.
3. Implementation of data management systems: the creation of an effective data management system, which includes the collection, storage, processing and analysis of data, is a key element of digital transformation. It is important to ensure the integration of all systems and devices to create a unified information environment.
4. Developing and implementing analytical tools: powerful analytical tools are needed to analyze data and create predictive models. These may include platforms for big data analysis, machine learning, and artificial intelligence.
5. Staff training: The success of digital transformation depends on the level of training and competencies of employees. Regular training of staff is necessary so that they can effectively use new technologies and tools.
6. Pilot projects and scaling: the implementation of AIoT systems should begin with pilot projects. This will allow you to identify potential problems and shortcomings, as well as evaluate the effectiveness of new technologies. After the successful completion of pilot projects, you can begin scaling the solutions across the entire enterprise.

AIoT is the evolution of cyber-physical systems that can make decisions at the machine and equipment level. The transition from IIoT to AIoT should start with the creation of local intelligent control systems at the level of individual machines and equipment, and then move on to more complex integrated systems capable of managing the entire production process. I believe that AIoT has the potential to significantly improve production processes by using data and smart algorithms to optimize equipment operation, but at first it is necessary to choose cases for AIoT implementation that will not face serious resistance due to bureaucratic and social barriers at the plant. In addition, it is important to look for customers who are interested in real-world use of AIoT technologies and are ready for such innovations.

Conclusion

AIoT is a logical continuation of the development of the Internet of Things and artificial intelligence technologies. It has significant potential to improve production processes and create new opportunities in various industries. However, it is important to understand the real meaning of the term AIoT and not to fall for marketing gimmicks. Ultimately, the success of AIoT will depend on its ability to bring real benefits and solve specific business problems.

As AIoT continues to evolve, businesses should keep a close eye on new opportunities and evaluate their applicability to their processes. Whether AIoT is just another marketing ploy or a true revolution, its potential is exciting now and promises significant changes in the future.