Exploring Innovative AI Use Cases in Manufacturing
The work of an enterprise in the manufacturing industry is a constant overcoming of obstacles such as low productivity of processes, shortcomings in production, and costly maintenance of machines and systems. The sources of these problems lie in the use of inefficient manual labor, the human factor, and the lack of information in real-time which result in overspending, loss of quality, and inability to seize opportunities. In particular, the problems of identifying defects often remain unresolved due to the handicraft and simplification of the procedures for their search. At the same time, artificial intelligence (AI) technology opens up new perspectives for addressing these challenges.
AI optimizes equipment maintenance and avoids errors. Learning opportunities while processing and analyzing large amounts of information make it possible to set up production cycles in such a way that they show greater efficiency and accuracy with less manual labor. In addition, AI is handing out tools that help make informed decisions faster and manage inventory more economically. Read more about the role of AI in the management of an industrial enterprise below.
AI's potential in manufacturing: Key technologies and techniques
The global market for AI in manufacturing reached $3.8 billion in 2022 and is expected to make $68.36 billion by 2032, with a growth rate of 33.5% over the forecast period from 2023 to 2032. The main challenges that can be solved with the help of AI in manufacturing are the need to optimize processes, increase efficiency and reduce costs. Automation of routine operations by minimizing human resources makes it possible to shift the focus to solving strategic issues, thereby improving productivity. For this, the following Artificial Intelligence technologies are used:
- Machine learning. Analyzing large amounts of information to identify patterns and make predictions help draw precise timing when, for example, maintenance may be required due to a probable failure. Proactive service is always cheaper than emergency repairs, it also reduces downtime.
- Computer vision that works with AI algorithms that process information from cameras and sensors. This technology is applicable to finding defects, tracking production lines, and automating the quality control process.
- Natural Language Processing, which provides understanding of human language by machine algorithms. In manufacturing enterprises, NLP is used to analyze textual information from customer reviews, technical literature, etc. The technology allows you to track the trends and moods of the audience, thereby improving your product.
- Robotics - collaborative robots and robots that can be used to perform routine or dangerous tasks.
- Data analytics - analysis of a large amount of information that will help improve production processes and increase efficiency in specific areas of work.
AI use cases in manufacturing
The application of AI in the manufacturing industry provides an opportunity to improve the quality and increase the efficiency of processes. Here are the main use cases for AI in manufacturing:
- Quality control system and defect detection. The use of computer vision technology enables to automate visual checks which consist of the analysis of pictures or videos of products from the lines. Comparison of the data with the established quality parameters identifies and points defects out. So the overall level of product quality increases, and consumer/client loyalty to you and the product becomes even stronger.
- Optimization of the supply chain by analyzing different data sources, i.e. forecasting demand, production capacity, and stock levels. Making the right decision to increase or, conversely, reduce production is aimed at minimizing waste, promptly solving the problem of shortage, or, conversely, excess of goods.
- Process optimization through data analysis using ML. Algorithms learning cycle times, energy consumption, material consumption, and subsequent suggestions for optimization lead to improved productivity and operational efficiency.
- Demand forecasting. By studying historical data and market demand, AI algorithms offer forecasts that can be correlated with output performance and based on which to adjust the operation of the enterprise.
- Energy consumption. AI monitors energy consumption of production facilities in real-time and reproduces the energy loss model. Based on the data obtained, strategies are drawn up to save resources and comply with environmental requirements, which not only reduces energy consumption but also builds sustainable, environmentally friendly processes.
- Predictive analytics - a forecast of probable failures and breakdowns of equipment before the actual breakdown. By anticipating the need for scheduled repairs and maintenance, a manufacturer will be able to reduce repair time and line downtime.
- Real-time monitoring is the collection of data from sensors and machines at present. This ability of AI gives an objective vision of production processes and helps quickly find and eliminate errors in the operation of equipment.
- RBA (Robotic Process Automation) - the use of software bots to automatically perform certain actions and optimize the production cycle. RBA enhances the efficiency of operations, reduces errors to zero, and frees up resources to explore opportunities to improve more complex operations.
- Production design. The ability to analyze AI reviews, market trends, and performance information is indispensable for making accurate decisions regarding product development. AI provides data about customers preferences, their expectations, and features the product should have in accordance with these expectations.
Benefits of AI in manufacturing
The use of AI in the manufacturing industry provides numerous advantages:
- Efficiency and productivity increase through process automation, data analysis, and production cycle optimization. As a result, the number of errors is reduced, and resources are allocated and consumed more efficiently;
- Improved quality control through real-time inspections and automation of error detection processes;
- Cost reduction. AI analysis identifies inefficient areas and finds solutions to improve performance;
- Accurate forecasting based on data obtained as a result of processing historical statements and market trends, comparing information with production data;
- Increased safety due to the inclusion in the production cycle of the activities of robots that perform complex and dangerous actions;
- Real-time data analysis provides valuable information that helps you make the right decisions that optimize costs;
- Innovation. AI, like no other technology, promotes active research and development of innovative features, products that meet consumer needs;
- Improved supply chain management by analyzing data and comparing results with statements of output volumes, stock availability, etc.;
- Stability and energy efficiency are the tasks that are solved by AI algorithms.
How AI is transforming the manufacturing industry: real-world examples
The application of AI automates processes, optimizes production and integrates innovative solutions, which can be seen in the example of such enterprises:
- General Electric is a company that has integrated AI-driven visual inspection technologies into manufacturing processes. Together with ML methods, this has improved quality control and error detection. In particular, engine components are analyzed by AI cameras, which reduces errors;
- Siemens relies on AI for predictive maintenance. Introduction of AI algorithms into the sensors of industrial equipment makes it possible to monitor and detect failures in real-time. Thus, it is possible to predict when repairs will be needed and to schedule planned maintenance;
- Tesla has applied AI robotics to automate assembly operations. For this purpose, robotic arms, equipped with computer vision technology and ML, are used. This implementation has improved efficiency, reduced the number of errors;
- BMW has turned to AI to optimize its supply chain. AI algorithms and data analysis enable accurate demand forecasting, inventory management, and logistics planning, which in turn creates the basis for inventory balancing and logistics route optimization;
- Fanuc, a provider of factory automation solutions, has implemented RPA in manufacturing operations. Software bots are used to perform repetitive tasks such as data entry, and quality control.
Future trends and opportunities
What are the prospects for the use of AI in the future?
- AI in smart factories in Industry 4.0. It is assumed that AI will be used to build interconnected systems to exchange data in real-time. Autonomous regulation of production parameters and optimization of energy consumption will also become a reality.
- Collaborative robots and joint work of a person with a robot. One of the trends in manufacturing industry is cobots, which combine the advantages of AI and robotics. Such robots do not perform operations in isolation, but together with people, showing flexibility and efficiency, the ability to make accurate decisions, adapt to new working conditions, and learn from people. This is possible through the use of computer vision technologies, natural language processing, and gesture recognition.
- AI-based decision support systems for managers. These systems provide leaders with valuable real-time insights, predictive insights, and plausible scenarios to make the right decisions and execute strategies. Accurate forecasting is achieved through the analysis of historical records, market trends, and customer preferences. The advantages are the ability to rationalize resource consumption and balance reserves, risks an assessment, and strategies to reduce them.
The Software Development Hub company is ready to develop solutions for enterprises based on AI. Choosing the development methodology and technologies to realize the vision, we will create solutions that will optimize the production process and meet customer needs in line with increasing digitalization.