Artificial intelligence can take a combination of data from sensors, machines, and people and apply it to algorithms designed to optimize operations or lighten manufacturing.
Most organizations lack the skills, scientists, data, and infrastructure readiness to adopt specialized differentiated processes or solutions. Today, most manufacturing organizations have disconnected machines, people and processes, all of which are not particularly suited to AI or machine learning (ML). It is more likely to find a paper than a technical foundation to implement and accelerate artificial intelligence.
In this regard, productive commerce has a long way to go—but don't let that deter your organization from experimenting and investing in artificial intelligence. As with other long-term initiatives, it will take time to implement some of the underlying investments needed to improve employees, change culture, and tackle artificial intelligence.
Read More: AI in the manufacturing industry
1. Improving manufacturing processes
AI is being used in many manufacturing operations to streamline processes and improve productivity. For example, textile company Lindstrom worked with QPR to coordinate and improve business processes and a process management model to ensure future competitiveness and success.
2. Productivity of plants
A digitalization platform from Mitsubishi Power called Tomoni includes controls, instrumentation, data analytics, AI and more. Its aim is to make plants smarter. For example, an average power plant has about 10,000 sensors that can generate over a million points of data every minute. Tomoni takes this mess of data and turns it into a usable form.
3. Preventive maintenance
Compressor manufacturer and oil and gas solutions provider Baker Hughes is using AI to identify maintenance issues. The company has partnered with Microsoft Azure and C3.ai, an AI company, to create an AI-powered application that allows operators to view real-time production data, improve future production and optimize operations for better production rates.
The application continuously uses machine-learning (ML) algorithms to quickly integrate historical and real-time data into production operations and creates a virtual representation of production across the value chain. It also identifies anomalies, evaluates the product and prescribes actions to improve product performance. Engineers can use this to determine which injection wells to tune for higher production output.
4. Product design
Original equipment manufacturer, Sentry Equipment, developed its SentryGuard prototype machine to provide guidance to operators using the Aveva system platform to reduce development time. It provides the ability to analyze sample data, provide alerts, and guide operators to resolution.
5. Zero Touch and Zero Defects
The more people working on an assembly line, the more chances there are for errors to creep in. Hence, many manufacturers are introducing automation and robotics to eliminate defects. But it takes AI to ensure that even the slightest deviation from standard practices and workflows is detected at once.
Advantages of AI
- Natural language processing (NLP) and virtual agents make it easier for users to access data and perform tasks. Users can interact with AI-powered virtual agents via voice or text to get real-time insights and notifications.
- Manufacturers can evaluate the state of the equipment and forecast when maintenance has to be done using AI. You may save 30% on maintenance expenditures and unplanned equipment downtime by using machine learning for predictive maintenance.
- AI offers 360-degree view across warehouses, production lines, and plants. It provides customers with insights to identify quality issues, reduce scrap and make other product improvements. It helps manufacturers increase production throughput by 20% and increase quality by 35%.
- Even for complicated manufacturing processes, AI improves production planning and scheduling. To suggest the ideal scheduling strategy, it evaluates material availability, manufacturing capability, and customer demand.
- Instead of manually counting inventory, you can use AI to analyze inventory and maintain appropriate stock levels. This lowers expenditures and waste for your company.
- In order to enhance the client experience, algorithms can precisely estimate shipment and delivery lag times.
- Deep-learning-based systems improve defect detection by up to 90%, ensuring you maintain pristine product quality.
- AI simplifies manufacturing operations by fully automating complex tasks and requires less manpower to perform. This gives businesses the agility to quickly modify production plans or immediately adjust material flow based on schedule or production changes.
- This technology improves employee productivity by providing easy access to critical insights. Engineers can immediately identify the type of material to use for a particular product, and manufacturers can use the reports to estimate orders.
- By automating repetitive processes and increasing visibility, AI provides a better user experience. Employees spend more time on tasks they enjoy than doing mundane activities that can lead to injury or fatigue.
The future of AI in manufacturing
What comes next for the role of artificial intelligence in manufacturing? There are many ideas about this, some from the realm of science fiction and others as an extension of technologies already in use. An immediately noticeable development is the increased focus on data collection. Artificial intelligence technologies and techniques being used in the manufacturing sector can only do so much on their own. As Industrial Internet of Things devices grow in popularity, use, and impact, more data can be collected that can be used by AI platforms to improve various tasks in manufacturing.
However, as AI application development continues over time, we may see the rise of fully automated factories, product designs made automatically without human supervision, and more. However, we will never reach this level unless we continue the trend of innovation. All it takes is an idea. It may be the integration of technologies or the use of technology in a new use case. Those innovations will change the manufacturing market landscape and help businesses stand out from the rest.
If you have an idea or are looking for ways to apply AI technologies to your business needs in manufacturing, contact us today to take that first step.
