in Blog

May 23, 2024

Generative AI in Manufacturing Industry: Use Cases

Author:




Edwin Lisowski

CSO & Co-Founder


Reading time:




8 minutes


Generative AI, or GenAI in short, is set to usher in a new age of unparalleled efficiency and innovation in manufacturing and supply chain operations. Its advantages include automating redundant tasks, boosting productivity, and streamlining product development.

But despite its promising capabilities, the breakthrough technology has elicited mixed reactions from key industry players, and for a good reason- appreciating the utility of emerging technology is one thing, but implementing it at scale is another.

Most executives wouldn’t want to go all in on a technology they know little about and one that’s in its infancy, for that matter. However, given the hyper-competitiveness of the manufacturing industry, early adopters might have an edge, and the laggards might never catch up.

In today’s post, we’ll highlight the most notable use cases of generative AI in the manufacturing industry and its many advantages.

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How generative AI is used in manufacturing

The manufacturing industry was already integrating aspects of artificial intelligence into their operations and processes as early as the 1970s. For instance, in 1978, a Japanese professor created SCARA[1], an assembly line robotic arm with a built-in AI called GORDON that could tell where and when products are to be kept. This streamlined the assembly process while helping organize convenience stores in Japan without human input.

Other use cases of generative AI in manufacturing include:

Inventory and supply chain management

Manufacturers can use AI technology to create supply chain models based on real-world data to optimize their inventory management operations. GenAI can draw insights from various data sources, including customer behaviors, purchase history, industry trends, and others, to accurately predict demand.

Manufacturers can also use AI-driven technology to help them make more informed decisions about when to purchase raw materials in bulk. That way, they can prevent unexpected shortages or scenarios where the ordered material exceeds storage capacity, leading to wastage.

Quality control

Manufacturing companies heavily invest in their quality control processes to ensure they only ship non-defective, high-quality products for maximum customer satisfaction. Generative AI optimizes the quality control process by giving manufacturers an extra pair of eyes to inspect their products.

The technology can analyze products, compare them to previously captured images of defective products, and pinpoint defects in newly manufactured objects. Manufacturers can also use AI to generate models that predict the likelihood of defects and suggest ways to sidestep the defects, ensuring top-tier quality products.

Predictive maintenance

Unexpected downtime costs industrial manufacturers a staggering $50 billion annually[2]. These manufacturers can leverage AI-powered tools to predict impending issues with their machines or assembly lines before they cause a complete shutdown.

They can also train the models to use real-time data on temperature, vibrations, voltage, and other factors to identify unusual behavior and automatically schedule maintenance. This predictive maintenance not only saves a bundle in repair costs but also extends the lifespans of their equipment.

Enhancing product design and development

Generative AI in manufacturing can be an invaluable product design and development tool. Manufacturers can use AI to come up with a plethora of product design ideas based on their visions and specific constraints. They can also use it to develop bespoke designs based on client’s preferences and needs, speeding up the design process for better productivity and improved client satisfaction.

Top 5 advantages of using generative AI in manufacturing

Some of the benefits of AI-driven technology in the manufacturing industry include:

Reduced operating costs

Manufacturers can use AI technology to stay on top of issues with machines and equipment and address them before they get out of control, saving them hefty repair or replacement costs. This also reduces downtime and the number of resource-wasting defective products.

Improved efficiency

Manufacturers can use AI technology to boost efficiency in their production lines by automating various aspects of design, machine monitoring, supply chain management, and quality control. The result is faster production times, reduced downtime, greater output, and minimal errors, all contributing to enhanced efficiency across the board.

Enhanced innovation

GenAI boosts innovation in manufacturing by allowing for rapid prototyping and accelerating the design process. Manufacturers can use AI-driven technology to create new designs based on predefined criteria and set conditions within seconds. They can explore different designs and pick the best one.

Better decision-making

Generative AI can analyze copious amounts of data within seconds and draw relevant conclusions to help manufacturers make better strategic decisions regarding various aspects of their trade. They can use AI technology to plan their production and maintenance schedules, inform their expansion plans, and make better recruitment or down-staffing decisions.

Enhanced security

Manufacturers can utilize AI-powered technology to secure their digital assets. AI technology uses complex machine learning algorithms to identify unusual behavior and patterns that may signal cyber threats. These systems also learn from previous breaches and threats to continually improve and provide more advanced security.

Top 5 applications of generative AI in the manufacturing sector

Owing to its versatility, artificial intelligence has a plethora of use cases and applications in the manufacturing sector. Some of the most notable ones include:

  • Research and development
    Manufacturers can leverage AI to gain crucial insights into their respective markets and better meet customer needs. By integrating AI into their R&D processes, manufacturers can spot emerging market trends, analyze product usage, and streamline production while reducing spending.
  • Product design and developments
    With data-backed insights from R&D, manufacturers can use AI technology to generate an array of designs to develop product designs that are unique, cost-efficient, efficient, and, most importantly, what the customers want. GenAI improves continually, so every iteration is better than the previous one until manufacturers get the perfect design.
  • Production and operations management
    Generative AI can optimize production, ensuring a seamless production workflow. AI-powered technology uses analytics for predictive maintenance and to identify potential bottlenecks that hinder production. By eliminating these bottlenecks, manufacturers can fine-tune processes and operate at maximum capacity and efficiency.
  • Financial management
    AI-powered tools can optimize manufacturing financial management tasks. AI can, for instance, can integrate automated systems for financial use cases like accounting, budgeting, financial reporting, and bookkeeping. This ensures accurate cost tracking and updated financial documents. GenAI also utilizes predictive analytics to facilitate efficient resource allocation, allowing manufacturers to deploy funds strategically, anticipate market trends, and make data-driven investment decisions for growth and profitability.
  • Enhancing environmental and sustainability practices
    AI technology can help manufacturers achieve sustainability goals and improve ESG ratings. Generative AI, for instance, utilizes machine learning technology and algorithms to optimize manufacturing processes so they use less energy. AI can also identify areas to reduce waste and suggest more sustainable materials and processes for more eco-friendly production processes and manufacturing environments.

Potential downsides of using generative AI in manufacturing

While AI is poised to revolutionize the manufacturing industry, its adoption also comes with quite a few challenges, including:

  • Expensive to implement
    One of the biggest drawbacks of AI tools in manufacturing is the cost of implementation. AI infrastructure can be pretty costly, especially for startups and small manufacturing plants. Not to mention the ongoing maintenance costs and additional cybersecurity fees.
  • Vulnerability to cyberattacks
    Like other forms of IT infrastructure, AI is vulnerable to cyber-attacks caused by hacking and malware infection. These cyberattacks can disrupt the production process, leading to significant losses.
  • Requires skilled labor
    As an evolving field, AI doesn’t have as many experts and thus necessitates recruiting and training new staff to manage these systems. Since the demand for such experts is high, companies might have to pay top dollar to retain them.

Generative AI’s role in future manufacturing

It’s hard to understate the enormous potential of AI in manufacturing and other sectors of the economy. The technology is in its early stages of implementation, so we have yet to see widespread adoption of AI in manufacturing. However, all indicators point to an AI-centric manufacturing industry, especially since AI is rapidly evolving and becoming more affordable. This will result in improved productivity, more sustainable manufacturing practices, more use cases, and greater competition among manufacturers to the consumer’s benefit.

One lingering question is how AI will impact the manufacturing job market. Will AI displace thousands to millions of workers, and if so, where will the displaced workers go? According to Oded Netzer, a Columbia Business School professor, rather than taking away jobs, AI will help workers with their jobs[3]. Only workers who don’t know how to work with AI will be left without work. That said, the future holds immense promise for manufacturing thanks to generative AI.

Final thoughts

The manufacturing industry is set to benefit immensely from the application of GenAI and machine learning in its core processes. However, the best approach for integrating artificial intelligence into manufacturing would be a careful, step-by-step transition that first examines the existing processes to identify areas that would benefit the most from this technology.

Manufacturers can then devise an implementation strategy and apply the technology incrementally while hiring relevant staff until they achieve full-scale implementation.

References

[1] Sastrarobotics.com. SCARA ROBOTS-Four Decades on and Still the Most Sought-After Robot
URL: https://sastrarobotics.com/scara-robots-the-most-sought-after-robot/. Accessed on May 20, 2024
[2] Forbes.com. Unplanned Downtime Costs More Than You Think. URL:
https://www.forbes.com/sites/forbestechcouncil/2022/02/22/unplanned-downtime-costs-more-than-you-think/?sh=3085a85236f7. Accessed on May 20, 2024
[3] Magazine. business.Columbia. Connect the Dots or be Replaced. URL: https://magazine.business.columbia.edu/sf-23/faculty-views/connect-dots-or-be-replaced. Accessed on May 20, 2024



Category:


Generative AI