Georgia Collaborative Developing AI Manufacturing Workforce

(TNS) — Southern Regional Technical College announced Tuesday that it has been named a partner in the Georgia AI Manufacturing (AIM) Grant. This $65 million, multi-year grant will develop and expand artificial intelligence (AI) manufacturing and workforce development throughout Georgia.

The Georgia AIM project proposes to build a technical workforce training incubator and talent pipeline for autonomous and AI manufacturing technologies. Partners include education, business, government and economic development groups.

Georgia AIM recognizes SRTC's commitment to excellence in technical education and its dedication to fostering innovation in the field of AI, according to a press release from SRTC. Through the development of Innovation Incubators, starting with K-12 partners, SRTC will work to strengthen the pipeline of STEM and AI innovators and entrepreneurs.

Through partnerships with Georgia Tech, Foundational Leadership Entrepreneurship and X-perience (FLEX), Tisk-Task, Southwest Georgia Regional Commission, DOE Office of Rural Education and Innovation, Spark Thomasville, Thomas County Chamber of Commerce, local manufacturers, Thomas County School System and the Thomasville City School System, SRTC will spearhead the development of the Georgia AIM Technology Corridor to connect student innovators with business and industry partners to solve real-world problems, the college said.

The project will provide equity of access to innovation and entrepreneurship skills for rural, underserved students, transforming the region into a hub for advanced manufacturing and AI research and development. SRTC's new director of innovation, Willie Allen, will work to train at least 1,000 students and 100 teachers from the region and expand partnerships with Georgia Tech programs, FLEX, the Southwest Georgia Regional Commission, local manufacturers and K-12 school leaders, the SRTC press release said.

Georgia AIM will also serve as a collaborative platform for industry leaders, and educational institutions beginning with Thomasville City Schools and Thomas County Schools to converge and drive technological breakthroughs. By harnessing the power of AI, this initiative aims to revolutionize manufacturing processes, enhance productivity and foster economic growth throughout Georgia.

To celebrate this milestone, SRTC hosted pilot events July 17 and 20 that brought together industry professionals, government officials, educators and community members to showcase the vision and potential of the Georgia Artificial Intelligence Manufacturing Technology Corridor in the region.

©2023 The Moultrie Observer (Moultrie, Ga.). Distributed by Tribune Content Agency, LLC.

How Artificial Intelligence Is Used In Manufacturing

AI in manufacturing is becoming more common than you might think.

Increasingly, technology plays a major role in how products get made on the factory floor. Manufacturing plants can resemble high-tech laboratories with robotic arms handling repetitive tasks and algorithms, ensuring that products are made according to manufacturer specifications.

Image source: The Motley Fool

Industrial titans like General Electric (NYSE:GE) and Siemens have embraced artificial intelligence as the technology offers a number of advantages, including minimizing defects and errors, reducing downtime, and saving on costs.

In this look at AI in the manufacturing industry, we'll discuss what artificial intelligence is, how it plays a role in manufacturing, and review several examples of how AI is used in manufacturing.

Machine Learning

Machine learning is a technique by which artificial intelligence programs are trained.

What is artificial intelligence? What is artificial intelligence?

Artificial intelligence is a technology that allows computers and machines to do tasks that normally require human intelligence.

This includes a wide range of functions, such as machine learning, which is a form of AI that is trained data to recognize images and patterns and draw conclusions based on the information presented.

There's also computer vision, which is widely used in manufacturing; natural language processing, which includes tools like ChatGPT and other chatbots; neural networks; and robotics, which may be the most commonly used AI application in manufacturing.

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.

5 applications of AI in manufacturing 5 applications of AI in manufacturing

Manufacturing is one of many industries that artificial intelligence is changing. Keep reading to see five ways that artificial intelligence is being used in manufacturing today.

1. Robotics

When you imagine technology in manufacturing, you probably think of robotics. Now, more than ever, robots play a major role in manufacturing.

Companies like Amazon (NASDAQ:AMZN) use robots to move items back and forth and to pick and pack orders. Ford (NYSE:F) uses robots to operate 3D printers, and the robots save time by running them unsupervised overnight, using 3D designs that have been uploaded to the printer. While a human needs to give them the design, the process itself is autonomous.

Cobots or collaborative robots are also commonly used in warehouses and manufacturing plants to lift heavy car parts or handle assembly. Often, cobots are capable of learning tasks, avoiding physical obstacles, and working side-by-side with humans.

Expect robotics and technologies like computer vision and speech recognition to become more common in factories and in the manufacturing industry as they advance.

2. AI in quality control

Quality control is a key component of the manufacturing process, and it's essential for manufacturing. Making machines like automobiles can be deadly if there are errors.

Here, AI is also being applied in a number of different ways. AI-powered vision systems can recognize defects, pull products or fix issues before the product is shipped to customers.

Semiconductor companies, including Samsung (OTC:SSNL.F), Google (NASDAQ:GOOGL) (NASDAQ:GOOG), and NVIDIA (NASDAQ:NVDA) also use machine learning for quality control, optimizing chip design, and improving manufacturing.

Semiconductor

A semiconductor is a basic element or compound substance that conducts electricity in certain situations.

Quality control is one area where AI systems consistently outperform manual testing processes done by humans. AI machines are also able to optimize production and figure out the root cause of a problem when there is an error.

3. AI in predictive maintenance

Maintenance is another key component of any manufacturing process, as production equipment needs to be maintained.

This is where predictive maintenance comes in. Predictive maintenance analyzes data from connected equipment and production equipment to determine when maintenance is needed. Using predictive maintenance technology helps businesses lower maintenance costs and avoid unexpected production downtime.

Companies like IBM (NYSE:IBM) provide predictive maintenance technology that Washington, D.C., uses to help maintain its water hydrants, for example. Similarly, C3.Ai provides predictive maintenance systems for utility companies, such as an electric grid serving more than 7 million customers. Its system relies on machine learning to prevent asset failures before they happen.

4. Automation

Automation is often the product of multiple AI applications, and manufacturers use AI for automation in a number of different ways.

Robotic process automation (RPA) is the process by which AI-powered robots handle repetitive tasks such as assembly or packaging.

One 2022 survey found that 43% of manufacturing businesses already use RPA. The benefits they've found from automation include a reduction in operational costs by up to 40%; an increase in the manufacturer's control over processes; improved employee performance; and significantly lower downtime.

Some companies that use RPA in manufacturing include Whirlpool (NYSE:WHR), which uses robotic process automation to automate its assembly line and handle materials. So-called bots are also used for quality control checks.

Supply Chain

A supply chain is a series of steps taken to obtain raw materials, turn them into a product, and deliver the product to consumers.

5. AI in the supply chain

Manufacturing goes beyond what takes place on the factory floor. It's crucial for every manufacturer to have a well-managed supply chain so they have the parts they need when they need them.

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.

BMW (OTC:BMWYY) for example, uses AI to predict demand and optimize inventory. In one example, the company installed an AI application to prevent the transportation of empty containers on conveyor belts. The tech also decides if a container needs to be attached to a pallet, and finds the shortest route for boxes to be disposed of.

AI systems can also take into account data from weather forecasts, as well as other disruptions to usual shipping patterns to find alternate route and make new plans that won't disrupt normal business operations.

Will artificial intelligence revolutionize the manufacturing industry? Will artificial intelligence revolutionize the manufacturing industry?

It's clear that artificial intelligence is already having an impact on the manufacturing industry. That influence will continue to grow.

Unlike some other industries, generative AI technologies like ChatGPT seem less likely to have an impact on manufacturing. But other forms of AI tech, like robotics and machine learning applications, will push the envelope, helping manufacturers produce goods more efficiently, relieve human labor, and eliminate errors that lead to product recalls or even real-world accidents.

Some forecasts estimate that the opportunity in artificial intelligence will be worth trillions of dollars. If you're looking to invest in AI manufacturers, you can consider some of the stocks above or take a look at other AI stocks, machine learning stocks, or AI ETFs.

Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool's board of directors. John Mackey, former CEO of Whole Foods Market, an Amazon subsidiary, is a member of The Motley Fool's board of directors. Jeremy Bowman has positions in Amazon.Com. The Motley Fool has positions in and recommends Alphabet, Amazon.Com, and Nvidia. The Motley Fool recommends Bayerische Motoren Werke Aktiengesellschaft and International Business Machines. The Motley Fool has a disclosure policy.

Transforming Tradition: Addressing AI Implementation Struggles In Manufacturing Industries

Summary

By focusing on culture changes and selecting the right AI vendor, traditional manufacturers can successfully implement AI and stay competitive in the market.

Transforming Tradition: Addressing AI Implementation Struggles in Manufacturing Industries

The COVID-19 pandemic has highlighted the vulnerability of traditional manufacturers to supply chain disruptions.

Many leaders in traditional industries now recognize the urgent need to embrace digital transformation and implement AI. They understand that these technologies can help them better manage their supply chains, mitigate disruptions, and predict demand more accurately. However, the challenge is that these companies are typically labor-intensive operations without in-house data teams, and they also face internal resistance to change.

This article examines the typical challenges faced by traditional manufacturers and provides solutions for overcoming them.

Why do traditional manufacturers struggle with implementing AI?

The lack of endorsement from high-level executives and managers in traditional manufacturing companies is often the primary impediment to their digital transformation endeavors. When digital transformation initiatives are isolated within technology teams, there is often a barrier to gaining acceptance among factory staff. This is partly because technology teams do not fully understand the challenges faced in the factory and select solutions that are not suitable for driving real change.

Another common issue is that these initiatives are not fully championed by managers and department supervisors, giving the impression that they are not important and can eventually be ignored with a bit of finesse.

A further issue is the lack of realistic and meaningful key performance indicators (KPIs) set for the implementation of these initiatives. Manufacturing is driven by KPIs, and without clear and meaningful goals, staff will view them as abstract objectives that will not be evaluated.

Traditional companies may also lack the necessary sensitivity to technology to make the digital transformation a success. It can be challenging for them to understand the potential of AI and new technologies and how their adoption can positively change day-to-day operations.

To foster meaningful change, two critical aspects must be prioritized. Firstly, organizations must cultivate a cultural shift and secure broad support for implementing AI. Secondly, selecting the right AI vendor is imperative, as an ill-fitting solution can undermine long-term success.

1. Fostering employee buy-in for AI and digital transformation endeavorsInstigating AI implementation requires top-level commitment, and organizational leaders need a well-defined roadmap for driving change.

Top management needs to ensure clear communication of the benefits of AI adoption to effectively engage other leaders and employees. It is crucial to demonstrate that AI is intended to enhance their work rather than be perceived as a threat or a means of replacing them.

Conducting hands-on demonstrations with teams is crucial, as the benefits of AI often appear abstract to non-technical staff members. Witnessing AI in action allows them to gain a clear understanding of its implementation and its potential to improve processes and make their lives easier.

Equally important is providing leaders and staff with adequate training to effectively navigate AI implementation. Learning from previous middling attempts, our customer, BenQ Materials, took a proactive approach by conducting intensive training courses for supervisors and organizing AI classes within individual departments, which led to a successful implementation process.

2. Selecting the ideal AI vendor for traditional manufacturingOutside of culture changes and getting company-wide buy-in, the other big challenge for traditional manufacturers is choosing and implementing the right AI vendor. Many vendors are not geared for the specific challenges that manufacturers face.

When considering AI vendors, manufacturers need to consider the following:

Domain Knowledge: It is important to choose an AI vendor who has domain knowledge of the manufacturing industry because they will have a better understanding of the real-world environment in which the AI/ML algorithms operate.

Industry Reputation: Look for a solution that has a good reputation in your industry. Try to find a company that has a client list that includes similar-sized manufacturers as yours.

ROI: Before seeking out an AI solution, businesses must identify the specific problem they need to solve and insist on a demonstration of the quantified value and ROI that the potential AI partner can deliver.

Team Background: Many newer startups may be technically impressive but don't have experience implementing enterprise solutions in traditional companies, which is the most difficult part. Look for a vendor with a mature and experienced team that will be able to demonstrate and get buy-in from your staff members.

Flexibility: Consider how attentive the vendor will be to your specific needs and how willing they will be to make customizations.

By focusing on culture changes as well as employee buy-in and selecting the right AI vendor, traditional manufacturers can successfully implement AI and stay competitive in the market.

About The Author

Jerry Huang is the co-founder and CEO of Profet AI. Previously, he worked in global software companies such as IBM, SAP, and PTC and other companies in the manufacturing industry for 20 years before starting his own company, Profet AI, an AutoML enterprise solution for the manufacturing sector.

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