Artificial Intelligence in Manufacturing: The Role of AI in the Automation of Decision-Making Processes in Smart Factories

Industry is currently undergoing a profound transformation, driven by technological progress. The concept of Industry 4.0, which includes digitization, automation, and integration of various production systems, has become a new reality for many enterprises. Artificial intelligence (AI) plays a key role in this revolution. In the past, factories were places where repetitive tasks were performed by people or simple machines. Today, intelligent algorithms take on more and more responsibility, analyzing huge amounts of data, optimizing processes, and making autonomous decisions. It is AI that makes it possible to create smart factories that are more efficient, flexible, and adaptable to changing market conditions.

Artificial Intelligence in Manufacturing Industry – Automation of Production Processes in Smart Factories

Industry 4.0 is a concept that combines automation, digitalization, and integration of production systems with cutting-edge technologies such as AI, the Internet of Things (IoT), and data analysis. The central element of this approach is smart factories, i.e., automated production plants that can independently manage processes, make decisions, and adapt production to changing market conditions. Artificial intelligence plays a key role here, enabling the analysis of huge amounts of data in real time and the implementation of dynamic changes in production processes. It is therefore not surprising that the value of the global market for artificial intelligence in the manufacturing industry in 2023 amounted to $3.2 billion, and by 2028 it is expected to increase to $20.8 billion.
AI is used in many areas of production, including the following:

• Predictive Analytics – AI algorithms analyze data from sensors and production systems to predict potential machine failures, thus preventing downtime and reducing maintenance costs.
• Process Improvement – artificial intelligence allows for the optimization of processes such as production planning and inventory management. AI algorithms analyze large amounts of data to find the most effective solutions.
• Quality Control – AI-based vision systems enable automatic inspection of products for defects, which allows for improving quality and reducing the number of defective products.
• Robotization of production – AI-equipped robots are able to perform more complex tasks, such as assembly, packaging, or welding. By using artificial intelligence, robots become more flexible and adaptable to changing conditions.

Automation of Decision-Making Processes in Smart Factories with AI

One of the most important applications of AI in smart factories is the automation of decision-making processes. Thanks to advanced algorithms, artificial intelligence can process data from sensors and production systems to make more precise and quick choices. Decision-making AI has a huge impact on reducing errors, increasing operational efficiency, and optimizing production processes. Traditionally, many decisions in the manufacturing sector are made based on experience, intuition, and available historical data. However, in a dynamic industrial environment, this approach may not be sufficient. Artificial intelligence allows for the following:

• Real-Time Data Analysis – sensors installed in machines and production equipment provide huge amounts of data on their operation. AI algorithms are able to analyze this data in real time, identifying any anomalies and trends.
• Forecasting – based on the analysis of historical and current data, AI can predict future events, such as machine failures, changes in product demand, or fluctuations in raw material prices.
• Optimization – AI algorithms are able to find optimal solutions to complex problems, such as production planning or resource allocation.
• Learning from Mistakes – thanks to machine learning mechanisms, AI-based systems are able to learn from their own experiences, which allows them to continuously improve the decisions they make.

For example, the use of artificial intelligence in factories can help automate decision-making processes related to production planning. By analyzing demand data, material availability, and machine performance, AI algorithms can create an optimal production plan that minimizes costs and shortens order lead times. They can also manage the entire supply chain, optimizing the flow of materials from suppliers to end customers, predicting product demand, optimizing transportation routes, and managing inventory levels. However, automation of production processes, automation of logistics processes, and automation of warehouse processes are not everything. AI can also support decision-making related to quality control, identifying defects and deviations from the norm, and even planning maintenance work, among other things.
Automation of decision-making processes through AI is a key element of digital transformation in industry. The benefits that can be experienced by the “factories of the future” of Industry 4.0 include the following:

• Increased productivity – smart factory automation allows for faster decision-making and optimization of production processes.
• Cost reduction – by predicting failures and optimizing energy consumption, production costs can be significantly reduced.
• Improved product quality – automated quality control allows for the detection and elimination of defective products, which results in an improved company reputation.
• Increased flexibility – AI-based systems are able to quickly adapt to changing market conditions.

Artificial Intelligence Use Cases in Manufacturing: Agilent Technologies

Agilent Technologies – a manufacturer of scientific and research instruments – is using artificial intelligence to automate decision-making processes in its factory in several innovative ways that significantly improve production efficiency and product quality. Key areas of AI application in their business include predictive testing to improve performance, quality control improvement programs, sample-based testing to reduce waste, and the lights-out factory concept to improve testing.

Agilent Technologies has implemented artificial intelligence to optimize its product testing processes by deploying 250 Industrial Internet of Things (IIoT) stations that use AI algorithms to analyze previous test results. These systems learn from collected data, identify patterns in historical data, and automate routine testing processes. As a result, product testing time was shortened, which improved the length of work cycles by 23%.

In response to growing customer expectations and the increased complexity of assembling scientific and research instruments, Agilent Technologies is using AI to analyze the root causes of quality issues. AI-based tools, such as time series modeling and natural language processing, enable the company to identify the root causes of the underlying causes of a quality shortfall faster and more accurately. As a result, the company has reduced production downtime by 51%. To achieve the goal of net-zero emissions by 2050, Agilent Technologies has created a multidisciplinary team focused on big data architecture and analytics. This team uses AI to simplify sample testing processes without compromising product quality. Using advanced machine learning algorithms, the company has reduced the amount of recycled waste by 53% and increased productivity by 31%.

Agilent Technologies also introduced the concept of a “lights-out factory”, a fully automated technology that conducts product testing without human intervention. Thanks to the use of process robotics and AI technology, the company can easily detect bottlenecks, achieve sustainable production, and has managed to increase productivity by 33%. The key to this solution was the creation of a harmonized IIoT system with real-time sensors. The Agilent Technologies example shows how artificial intelligence can transform decision-making processes in smart factories, improving production efficiency and reducing costs, while meeting modern requirements for sustainable development.

If you are interested in topics such as smart manufacturing Industry 4.0, artificial intelligence in production, smart factories, industrial robotics, automation of production processes, smart manufacturing solutions, or generally cutting-edge technologies in industry, schedule a free consultation with Marcin Jabłonowski – Managing Director and AI Solutions Architect at Pragmile. We have already managed to support many companies in implementing decision-making automation using artificial intelligence. We will be happy to explore your needs and help your business grow.