The manufacturing industry can greatly benefit from AI- and ML-powered solutions. Read this article to get to know how exactly manufacturers can use AI to improve their workflows.
AI has been revolutionizing the manufacturing process in nearly all industries. AI-powered solutions help businesses to improve the quality of their products, boost customer satisfaction, and maximize their revenues. From this article, you’ll get to know how exactly manufacturing companies can benefit from integrating AI into their workflows. If your business doesn’t rely on AI technology yet, it might be the right time to try it!
Table of Contents
Smart Maintenance
Manufacturers spend a lot of funds on the ongoing maintenance of production line machinery and equipment. Each year, companies lose around $50 billion because of unplanned downtime. 42% of this downtime happens because of asset failure.
To increase uptime, businesses need to focus on predictive maintenance. The term “predictive” means that AI strives to foresee upcoming failures of parts, machines, or systems. Such solutions employ machine learning and artificial neural networks. This technology allows manufacturers to achieve the following goals:
- Brief the technicians in advance on which components require inspection
- Make sure the technicians apply appropriate tools and methods to fix the issues
- Schedule focus repairs ahead of time in cases where maintenance is unavoidable
Apart from reducing downtime, this approach helps to extend the remaining useful life of production equipment.
The Rise of Quality
Some manufacturers might struggle to comply with quality regulations and standards for the following reasons:
- Increasing complexity of products
- Very short time-to-market deadlines
At the same time, consumers are not ready to put up with the substandard quality of products. High defect rates and product recalls can tarnish the manufacturer’s reputation and undermine its sales.
AI algorithms can inform manufacturing teams of emerging production faults at an early stage. Here are just a few examples of potential faults:
- Deviations from recipes
- Subtle abnormalities in machine behavior
- Change in raw materials
The earlier you detect abnormalities, the easier and cheaper it should be to fix them.
Plus, AI can collect data about the use and performance of products and generate insightful reports for the manufacturer. Product development teams can rely on these reports to improve their strategic and tactical engineering decisions.
Human-Robot Collaboration
Human professionals shouldn’t be afraid of losing their jobs because of manufacturing automation. As robots take care of repetitive tasks, people should be able to focus on high-priority duties and such spheres that require more complex skills (for instance, coding or design). This shift is not completed yet, so in many manufacturing companies, humans and robots work together hand in hand. People benefit from the collaboration because their work becomes safer and more efficient. In the future, robots should be able to further optimize processes, handle more cognitive tasks, and make autonomous decisions based on real-time environmental data.
Making Better Products with Generative Design
Designers and engineers of the manufacturing company can compose a detailed brief and “feed” it to AI-powered generative design software. The brief can feature the following parameters:
- Material types
- Available production methods
- Budget limitations
- Time constraints
- Restrictions
AI will explore every possible configuration to offer a set of optimal solutions. Machine learning algorithms can test the proposed solutions to identify the best ones. Compared to human professionals, AI and ML software is unbiased and can repeat processes many times without getting tired.
Adapting to an Ever-Changing Market
Manufacturers can use AI algorithms to optimize supply chains and anticipate market changes. Instead of responding to emerging trends, companies can foresee them in advance and behave proactively. To estimate market demands, AI-powered solutions can analyze the following parameters:
- Location
- Socioeconomic factors
- Macroeconomic factors
- Weather patterns
- Political status
- Consumer behavior
Algorithms detect patterns that link these parameters and provide insights to manufacturers. Companies can use this data to optimize staffing, inventory control, and the supply of raw materials.
Augmented and Virtual Reality
The manufacturing industry hasn’t fully adopted the use of virtual reality (VR) and augmented reality (AR) — but it’s only a matter of time. Here are the main reasons why manufacturers would like to integrate these technologies:
- VR enables them to better train product builders who perform assembly or preventative maintenance tasks.
- AR provides real-time reporting driven by machine learning on the factory floor or in the field. These reports are crucial for identifying defective products and areas of operational improvement.
In the future, use cases for AR/VR solutions in the manufacturing industry will become much more diverse.
Energy Management
Many manufacturing companies tend to overlook this sphere simply because it’s too difficult and time-consuming to manage energy consumption without AI. Meanwhile, energy management of production lines can significantly reduce operations costs. If you employ AI to help you with this task, you’ll be able to save a lot of money and use these funds to improve your business processes.
Final Thoughts
Hopefully, this article came in handy and now you better understand the benefits of AI for manufacturing. AI-powered solutions allow manufacturing companies to raise the quality of their products, make better products with generative design, and enhance maintenance. AI technology serves as the basis for human-robot collaboration and helps businesses to adapt to the ever-changing market. AR, VR, and energy management are some of the most promising areas of using AI in manufacturing.