5 Examples of AI Uses in Manufacturing The Motley Fool

12 Risks and Dangers of Artificial Intelligence AI

artificial intelligence in manufacturing industry examples

Although these are much more infrequent than humans, it can be costly to allow defective products to roll off the assembly line and ship to consumers. Humans can manually watch assembly lines and catch defective products, but no matter how attentive they are, some defective products will always slip through the cracks. Instead, artificial intelligence can benefit the manufacturing process by inspecting products for us. Quality assurance is the maintenance of a desired level of quality in a service or product. These assembly lines work based on a set of parameters and algorithms that provide guidelines to produce the best possible end-products. AI systems can detect the differences from the usual outputs by using machine vision technology since most defects are visible.

  • Moreover, Topaz AML reduces false positives in transaction monitoring and enables banks to mitigate unwanted, time- and cost-intensive investigations.
  • AI will perform manufacturing, quality control, shorten design time, and reduce materials waste, improve production reuse, perform predictive maintenance, and more.
  • The results themselves can be difficult to understand — particularly the outcomes produced by complex algorithms, such as the deep learning neural networks patterned after the human brain.
  • Equilips 4.0 then offers real-time quality measurement and operational statistics for manufacturing processes.

With the help of AI software, hardware sensors, machine data, and AI, the maintenance team can identify the major failures. To stand up in this competitive race, manufacturers have to adopt a data-driven business model. Contrary to common conviction, the evolving AI doesn’t make the number of vacancies in manufacturing shrink. The manufacturers may not need as many employees on the production line as they would in the past – however, as they’re moving towards a data-driven business model, they will search for more analysts and data scientists. Visual inspection powered by machine learning algorithms can also track whether workers on the production floor are wearing safety gear and adhere to health and safety regulations.


The performance of algorithms typically improves when they train on labeled data sets. This type of machine learning strikes a balance between the superior performance of supervised learning and the efficiency of unsupervised learning. Chatbots powered by natural language processing are an important AI trend in manufacturing that can help make factory issue reporting and help requests more efficient.

The extreme price volatility of raw materials has always been a challenge for manufacturers. Businesses have to adapt to the unstable price of raw materials to remain competitive in the market. AI-powered software like can predict materials prices more accurately than humans and it learns from its mistakes. The COVID-19 pandemic also increased the interest of manufacturers in AI applications.

Top AI Companies in Manufacturing Industry 2023 (Updated)

However, an AI can easily sort through sensor data of a manufacturing machine and pick out outliers in the data that clearly indicate that the machine will require maintenance in the next several weeks. AI can do this in a fraction of the time that a human would spend analyzing the data. That’s why manufacturers often use artificial intelligence systems for supply chain optimization, focusing on demand forecasting, optimizing inventory, and finding the most efficient shipping routes. Altogether, artificial intelligence capabilities allow manufacturers to redeploy human labor to jobs that machines can’t yet do and to make production more efficient and cost-effective. To be competitive in the future, SMMs must begin implementing advanced manufacturing technologies today.

artificial intelligence in manufacturing industry examples

Manufacturing companies can use AI in various ways to improve safety on the production floor. The first example of such application – already mentioned in the context of energy efficiency – is lighting automation. Using it, they can respond to real-time demand for lighting, brightening up particular areas once it’s needed. Tracking defects and leaks with preventive maintenance algorithms also fall under this category.

Although this means certain AI technologies could be banned, it doesn’t prevent societies from exploring the field. In fact, AI algorithms can help investors make smarter and more informed decisions on the market. But finance organizations need to make sure they understand their AI algorithms and how those algorithms make decisions. Companies should consider whether AI raises or lowers their confidence before introducing the technology to avoid stoking fears among investors and creating financial chaos. Many of these new weapons pose major risks to civilians on the ground, but the danger becomes amplified when autonomous weapons fall into the wrong hands.

artificial intelligence in manufacturing industry examples

Data collected on one production line can be interpreted and shared with other branches to automate material provision, maintenance and other previously manual undertakings. Robotic employees are used by the Japanese automation manufacturer Fanuc to run its operations around the clock. The robots can manufacture crucial parts for CNCs and motors, continuously run all factory floor equipment, and enable continuous operation monitoring. Although implementing AI in the industrial industry can reduce labor costs, doing so can be quite expensive, especially in startups and small businesses. Initial expenditures will include continuous maintenance and charges to defend systems against assaults because maintaining cybersecurity is equally crucial. Factories without any human labor are called dark factories since light may not be necessary for robots to function.

Top 15 Big Data Technologies You Need to Know in 2023

Complex models can produce accurate predictions, but explaining to a layperson — or even an expert — how an output was determined can be difficult. Unsupervised machine learning algorithms don’t require data to be labeled. They sift through unlabeled data to look for patterns that can be used to group data points into subsets.

After Australian State Visit to D.C., Washington and Canberra Must … – Foreign Policy

After Australian State Visit to D.C., Washington and Canberra Must ….

Posted: Wed, 25 Oct 2023 19:31:52 GMT [source]

Sign up for our newsletter and don’t miss out on the latest insights, trends and innovations from this sector. Manufacturing is responsible for a big part of energy consumption worldwide and thus, improving energy efficiency is one of the most crucial roles of AI in this sector today. To stop climate change, we’ll need to switch to fully renewable energy sources sooner or later – but meanwhile, we can try using the energy in a more thoughtful, sustainable way. With its subsidiaries and strong distribution network, the company has its presence across Europe, Asia-Pacific, Latin America, and the Middle East & Africa. The subsidiaries of the company include PGI Compilers & Tools, Icera, Uli Electronics Inc, and ModViz, Inc.

It is becoming easier and less expensive to address these needs thanks to technological advancements like 3D printing and IIoT-connected devices. Adopting virtual or augmented reality design approaches implies that the production process will be more affordable. So, it’s time to explore the relationship between artificial intelligence and manufacturing. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month. Cem’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.


On a company level, there are many steps businesses can take when integrating AI into their operations. Organizations can develop processes for monitoring algorithms, compiling high-quality data and explaining the findings of AI algorithms. Leaders could even make AI a part of their company culture, establishing standards to determine acceptable AI technologies. For most innovative manufacturing companies, the number of applications for artificial intelligence will no doubt continue to increase, as computational resources become less costly.

How does manufacturing benefit from AI?

Additionally, it can spot market shifts and improve manufacturing supply chains. Quality assurance may be the main benefit of artificial intelligence in manufacturing. Businesses can employ machine learning models to spot deviations from typical design criteria, flaws, or consistency issues that a normal person might miss.

artificial intelligence in manufacturing industry examples

Read more about https://www.metadialog.com/ here.

How AI is Proving as a Game Changer in Manufacturing – Use … – RTInsights

How AI is Proving as a Game Changer in Manufacturing – Use ….

Posted: Sat, 14 Oct 2023 13:50:25 GMT [source]