“How can ChatGPT be used by manufacturers to help their businesses?”
Anyone with a computer and a bit of curiosity can pose this question to the ChatGPT online artificial intelligence (AI) tool. With a single prompt, the AI-powered chatbot will sing the praises of digital transformation—a new age for the industry where customer service and lead generation exist on the cutting edge.
Yet, for sector proponents including Cleveland’s Manufacturing Growth Advocacy Network (MAGNET), the technology’s potential for supply chain optimization is the real eye-opener.
In recent years, supply chains have become significantly more challenging to manage, notes MAGNET president and CEO Ethan Karp. Existing vulnerabilities in the flow of raw materials and finished goods were worsened by the pandemic, disrupting new product creation and leaving companies scrambling for answers.
In the ongoing aftermath of COVID-19, manufacturing enterprises are seeking sustainable supply chain strategies that include extensive use of artificial intelligence. The ChatGPT innovation’s ability to understand relationships and analyze huge volumes of data can change how these companies approach everything from sales to materials procurement.
“The functionality of ChatGPT can take data from inventory systems and generate an email to a supplier that says, ‘We need this on X date,’” explains Karp.
Whereas preventive maintenance is perhaps the most talked-about use case for AI tools like ChatGPT, the tech’s pattern identifying abilities can be harnessed for supply chain issues as well, according to Karp.
In theory, the powerful chatbot could forecast supply disruptions—allowing manufacturers to plan for problems before they occur. Additionally, AI can automate routine tasks around order tracking and quality control, reducing costs and improving efficiency.
Previously siloed manufacturing departments and stakeholders, meanwhile, could be brought together by AI. The technology has the industry covered on risk management as well, giving builders lead time on natural disasters or geopolitical events before major supply network disruptions arise.
Enterprise resource planning systems (ERP) are likely the best supply-related application for the nascent chatbot, says Karp. As ERP is integrated into daily business processes, including AI in that equation only makes sense.
“All those functions about communicating with suppliers would be embedded in a software package that becomes more powerful and user friendly,” Karp says.
Western Reserve University professor Michael GoulderDon’t get ahead of yourself
Case Western Reserve University (CWRU) professor Michael Goulder knows very well the possibilities of an AI-assisted supply chain. Along with his duties as a professor at CWRU, Goulder also leads the college’s master of supply chain management program.
In his previous career, Goulder oversaw the supply network for Hudson-based JoAnn Fabrics, giving him a full understanding of the complex system that starts with raw materials and ends when a user receives a finished product. Supply administration done correctly reduces costs and leads to a more efficient production cycle, he says.
Considering how fragile supply lines became during COVID-19, using AI and machine learning to strengthen the system seems an obvious choice.
However, the boundless buzz around AI reminds the CWRU prof of the late 1990s Internet boom and subsequent bust.
“There is a vast overestimation of the speed at which these technologies will be perfected and commercialized,” says Goulder. “It took 10-plus years for the Internet to mature, and likewise it will take longer than people think for AI to mature.”
Though Goulder is cautious about AI’s immediate impacts, there are reasons to be excited about the technology’s future. AI could be fed big supply chain data sets and return thousands of actionable variables.
“The beauty of machine learning is that it will determine the variables that make the most sense,” Goulder says. “That will revolutionize supply chain forecasting when the technology matures.”
Inventory and transportation management are additional circumstances where AI can shine. On the transportation side, artificial intelligence could spit out optimal delivery routes, or exact windows for trucks to arrive or depart a transportation center.
“Companies will have a model about what products are selling in what parts of the country, then start shipping those goods knowing what the demands are,” says Goulder. “The [AI] models will learn and get better over time.”
MAGNET president and CEO Ethan KarpPlacing a bet on AI
Currently, most organizations do not have the sophistication to leverage emerging AI technologies. Any manufacturing firm interested in pursuing digital designs must know how to capture the innovation’s full value, Goulder says.
That means purpose-built analytics rather than half-hearted experimentation with an application like ChatGPT. Goulder says he expects talents around Python and other programming languages will be in demand as artificial intelligence takes hold in manufacturing and beyond.
“Business leaders want highly developed analytical skills—they won’t hire someone if that person doesn’t know Python,” says Goulder. “Those skills are now table stakes. If I was a young person in the supply chain or a mid-career manager, I’d make a big bet on those tools.”
MAGNET’s Karp agrees that ChatGPT cannot just be “bolted on” to a company’s supply chain network. Simply giving the chatbot a few prompts reveals the errors in what passes for its thinking.
“Ultimately, it sounds like a person and makes you believe it’s thinking like a person, but it’s just taking information and smashing it together with no mind toward sense,” Karp argues.
Caveats aside, Karp cannot help but be thrilled by AI’s down-the-line benefits for the manufacturing supply chain.
“There have been conversations about AI for years, but this makes it real for people,” says Karp. “The supply chain [for this tech] makes sense, because there is a lot of communication that goes back and forth. The more real-time [we can get], the better.”