AI can boost manufacturing with its predictive ability, allowing businesses to prepare for maintenance, inspections and rising component costs. It can also improve productivity, quality, safety, and other manufacturing aspects integral to business growth. Learn why AI is becoming necessary in an industry impacted by global economic challenges and supply chain disruptions.

 

Now that we’re in the thick of Industry 4.0, robotics and automation have become must-haves in the manufacturing environment. But when you add artificial intelligence (AI) to the mix, robots can take on higher-level tasks that involve decision-making. With AI, fabricators can deploy complex automation measures, establish a centralized repository for operational data, and quickly adjust output in response to demand changes and production strategies.

This way, businesses can speed up processes, lower costs, and boost accuracy and efficiency on the production floor. 

7 ways AI transforms manufacturing

AI has capabilities depicting intellect, such as smart automation, complex simulations, advanced analytics, and image recognition. One of its applications is machine learning that allows robots and equipment to recognize basic patterns from data and algorithms. Meanwhile, deep learning employs neural networks to study videos and images. AI also enables autonomous objects, like robots and smart vehicles, to complete tasks independently. 

Because of these benefits, AI used in manufacturing will likely expand from a little over US$1 billion in 2020 to over US$16 billion by 2026. This CAGR growth of 57% is due to big data accessibility, enhanced computing power, and higher financial investments. Here are the other ways AI can boost manufacturing. 

1. Better quality control

The global manufacturing industry is likely to employ AI to assess quality. When applied with human discernment and powerful technology, AI can lower the risk of issuing defective products, which adds up costs while harming business reputation.

AI can pinpoint issues in equipment and output that humans and robots might overlook. With sensors, cameras, and the Internet of Things (IoT), AI-powered software spots problems and decides how to deal with flawed items immediately. 

2. Optimizing chatbots

Communication is a crucial part of manufacturing between workers, humans, and machines. Fueled by AI, national language processing (NLP) simulates human language naturally, helping the staff report issues more effectively and address queries within a factory setting. Because of the clear communication NLP offers, problems can be better cascaded and resolved, boosting worker accountability while easing the load on managers.

3. Timely maintenance

Machines breaking down without warning cause major disruptions in manufacturing. Adding to the burden is unplanned spending, which can be avoided with predictive maintenance. AI offers this feature, helping manufacturers know when equipment should be repaired or refreshed, which avoids severe damage and unforeseen machine failure. AI analytics, which study previous and real-time data, can identify metrics, such as heating points, material sturdiness, internal vibrations, etc. This allows business owners to be proactive rather than reactive, letting them make data-fueled decisions. 

4. Predicting trends

Imagine forecasting supply chain movements, commodity prices, and consumer demand. This is what AI-driven software can do, boosting decision-making in inventory management, production schedules, and allocating resources. For instance, matching equipment availability with workforce capacity allows manufacturers to optimize materials while delivering products on time. Meanwhile, AI can monitor and dive deep into commercial trends and consumer preferences, allowing businesses to innovate to target their market better.

5. Sustainability

According to the Boston Consulting Group, AI can significantly contribute to corporate sustainability, allowing companies to earn and save as much as US$1 trillion to over US$2 trillion by 2030. AI can determine which areas consume the most energy and produce the most waste. This enables manufacturers to draft strategies to minimize their carbon footprint and prevent waste. 

6. Product design & development

Because machine learning can emulate how engineers generate products, it can create design alternatives quickly and help improve original 3D models. To produce output, generative design software typically needs information such as basic materials, metrics like weight and mass, execution strategies, and budget constraints. Based on these inputs, the algorithm can develop various designs from which to choose.

Meanwhile, augmented and virtual reality can help fabricators test product models before producing them. With fast troubleshooting and maintenance, manufacturers can release innovative products before the competition. 

7. Warehouse management

The warehouse involves several processes that AI can automate. Constant stock monitoring can enhance inventory management due to continuous data collection. Restocking and understocking can be avoided by optimizing production output. With sensors and smart factories, manufacturers can keep an eye on the floor at all times, keep track of resources, and conduct timely interventions. It’s an effective way to bring down costs while increasing revenue.  

How manufacturing businesses can maximize AI

With so many benefits of AI, which changes should you prioritize? Here are some expert tips to help you adopt a more targeted approach.

• Understand the basics of AI

Before anything else, you must first learn the technology’s fundamentals. What are its components, benefits and drawbacks? Reading about others’ success stories can give you a clear idea about what AI can do—and, more importantly, what it can do for your business. 

• Shortlist the issues

Which problems are the most pressing in your manufacturing process? What is your top business goal? Knowing what you want to address or achieve helps you match the technology with your specific needs.

• Build interdisciplinary teams

Getting members from various departments such as IT, engineering and operations ensures you gain input from different manufacturing aspects. This helps you build a comprehensive plan that covers technical and process-driven dimensions.

• Collect data

Always remember that massive amounts of data power AI. Though quantity matters, so does quality. Ensure that you collect relevant data from diverse sources within your factory. This allows you to develop a database that’s accessible and exhaustive.

• Invest in AI infrastructure

Assess your IT infrastructure to know which hardware and software to purchase for your AI applications. Examples include robust servers, cloud computing capabilities, and software tools.

• Implement, integrate & monitor

Begin with pilot testing on a small scale to study the effectiveness and possible problems before conducting wide-scale implementation. You may have to collaborate with vendors and build new teams to integrate AI solutions with existing infrastructure. Set up systems for continuous monitoring, allowing you to refine solutions as needed. 

 ​As one of the Top 20 EMS companies in the world, IMI has over 40 years of experience in providing electronics manufacturing and technology solutions. 

We are ready to support your business on a global scale.

Our proven technical expertise, worldwide reach, and vast experience in high-growth and emerging markets make us the ideal global manufacturing solutions partner. 

Let's work together to build our future today.