The manufacturing industry — with its multi-step assembly lines and needs for quality control — can benefit from automation technologies now more than ever.
That’s where artificial intelligence (AI) can step in and help. Manufacturing companies are already using AI for predictive maintenance, and they show no signs of slowing their stride toward embracing it. According to Aicadium, 93% of manufacturing companies today believe AI will play a crucial role in the sector, according to a Deloitte survey. In addition, the global market size for AI in manufacturing was $5 billion in 2023 and is predicted to grow to $68 billion by 2032.
In this blog post, we’ll cover:
- What is the manufacturing industry?
- What is important to the manufacturing industry?
- What trends are dominant in the manufacturing industry?
- What kinds of AI technologies are used in manufacturing?
- How AI can help manufacturing companies.
- How we’ve helped manufacturing clients.
- Concluding thoughts.
What is the manufacturing industry?
Manufacturing transforms raw materials into completed goods, according to IFS, an enterprise software company focused on industrial and financial systems. The manufacturing industry greatly enriches the GDP and technological advancement. It also promotes job growth in areas like transportation and retail, which encourages highly-skilled workers to join the industry and boost the economy.
Its sectors include production, quality control, supply chain management, and logistics, and its sub-sectors include automotive, electronics, food processing, and textiles.
In general, the manufacturing industry as a whole is divided into six main sub-industries: automotive, chemicals, food and beverage, life sciences, high-tech manufacturing, and industrial manufacturing. Understanding these sub-industries will be crucial for businesses looking to improve operations in any particular industry.
What is important to the manufacturing industry?
The manufacturing industry is facing several challenges, according to IFS. First, supply chain disruptions from events like natural disasters, geopolitical tensions, and demand changes can slow down productivity and increase operational costs.
Also, a labor shortage remains: the need for workers who can handle advanced technologies is increasing, so more employees are needed to fill the skill gap.
Lastly, companies must be careful to comply with regulatory standards for quality control, health and safety, environmental protection, and information and security.
What trends are dominant in the manufacturing industry?
IoT sensors, AI, and advanced robotics are aiding real-time data collection and analysis, which increases efficiency, saves time, and enhances product quality, according to IFS.
Also, manufacturers are working to implement green technologies in addition to reducing waste, which helps them comply with regulations and meet the needs of consumers who care about sustainability.
Finally, digital transformation will continue to improve the manufacturing industry through software technologies like Enterprise Resource Planning, Field Service Management software, Enterprise Asset Management, Asset Investment Planning, and Enterprise Service Management.
What kinds of AI technologies are used for AI in manufacturing?
Numerous AI technologies are utilized in the manufacturing sector for various purposes. These include, but are not limited to, the following:
Digital twin technologies: Virtual replicas that mimic factory processes.
Cobots: Collaborative robots that can work with humans without safety cages.
IoT Sensors: “Internet of Things” devices that collect and exchange data.
Generative custom design: Generative AI platforms can gather and analyze customer feedback and use it to create and enhance product designs.
Factory-in-a box: Portable, multipurpose AI tools that can be easily transported and locally deployed. They are self-contained and often include automation IoT sensors and real-time analytics.
How AI can help manufacturing companies
To begin, AI can help manufacturing companies with numerous problems, from scaling to custom design, according to IBM.
Manufacturers use digital twin technologies to mirror processes, production lines, factories, and supply chains. This speeds up their production time. By digitally mirroring real workflows, AI can monitor and optimize them without humans needing to physically intervene.
Secondly, cobots are designed to work alongside humans. They are used for precise component placement, which allows them to integrate seamlessly into the working environment.
AI also uses sensor data to predict failures before they occur. These predictive abilities save employers both time and money by catching potential problems on the assembly line before they happen or escalate.
Furthermore, AI can create custom products based on customer feedback by adapting its creations in real-time based on what customers say. This is a unique and convenient asset AI has. Its ability to quickly adapt to customer needs helps employers mass-produce tailored, quality products. Generative design technology’s ability to create original parts is already benefiting the aerospace and automotive industries, and it’s making waves in the manufacturing industry as well.
Moreover, “factory in a box” technologies are self-contained, modular manufacturing models that can be easily transported and deployed. These technologies are localized and built with AI-powered automation, IoT sensors, and live analytics, making them invaluable to businesses looking to cut logistics costs.
Lastly, AI can be used for quality control to help companies catch defects with computer vision and machine learning, often with a digital twin companion.
There are several more use cases for AI in manufacturing, but for now, let’s explore how we use AI solutions for manufacturing clients to smooth out the manufacturing process:
A case study on how we’ve helped manufacturing clients
With AI being a great aid to manufacturing workflows, we understand the utility of AI for our manufacturing clients. That’s why we developed an AI Directory and Triage Agent that allowed a trade association that managed over 1,500 companies to handle a high email load.
The three main components of this technology are intent analysis, entity extraction and directory search, and user-facing enhancement.
First, intent analysis uses Natural Language Processing (NLP) technologies to categorize emails as internal (membership, events, etc.) or external (manuals, replacement parts, etc.). Internal messages were automatically sent to the appropriate department for human review.
Second, entity extraction occurred any time an email mentioned a product, company, or brand. The AI agent used contextual text recognition technology to recognize the name, search its ~1500-member database for the matching term, and send the appropriate email in response.
If the AI agent could detect the name of the company the customer was referring to, they sent them a reply with that contact’s details. If they could not, they asked the customer to either verify their public contact or reach out to their point of purchase.
This allowed the association to go from having a clogged inbox to having a smooth email flow and system; with a 90% reduction in manual sorting, the agent now solves most customer inquiries on its own, letting staff focus on higher priorities.
Lastly, we developed the technology further to include a user-facing enhancement system that has a public contact form and chat interface. Here, clients can ask questions, such as “How can I become a member?” and a chatbot offers instant support, which reduces wait times and improves the user experience without needing to add extra staff.
Concluding thoughts
In conclusion, AI can automate and simplify multiple parts of a manufacturing company’s workload. It can adapt to customer feedback, predict errors, create original solutions, and much more.
If these technologies excite you, feel free to contact us. We’d love to learn more about your business and how we can help you.