When it comes to artificial intelligence (AI), there is a general feeling of concern across several sectors, including the supply chain. This sentiment is often relayed to He Yuangie, department chair and professor in the technology and operations department at California State Polytechnic University, Pomona, by those in various industries.
“I talk to many people, and their general feeling about AI is concern. Will we still have jobs?” Yuangie said during a presentation at the Association for Supply Chain Management Connect conference in Austin, Texas, in September.
But as Yuangie pointed out, AI is nothing new. It has been studied for more than 70 years, and even Microsoft Excel has been used as an early form of AI for several years.
Yuangie believes that all areas of AI can help solve problems in the supply chain and that there are three basic ways to use the technology: machine learning, deep learning and generative AI.
Generative AI (GenAI), for example, allows supply chain professionals to create something that previously did not exist by using prompts.
“Our prompts really have to be content based. The more specific the prompts, the better the result you get.”
– He Yuangie, department chair, professor, California State Polytechnic University, Pomona
GenAI can help anyone become an analyst by analyzing and predicting physical and information flows within the supply chain. Yuangie noted that the main function of AI in the supply chain is robotic automation, which increases efficiency and overall flow.
Pedro Neto, a supply chain data scientist at HP, said the exciting thing about GenAI is its versatility across different areas in the supply chain sector.
Workers shouldn’t view this as a negative, Neto said. If GenAI is used to handle what he calls the “grunt work,” the soft skills each individual possesses will become more important in the workforce. For example, if you didn’t learn how to write computer code in school, that’s fine; AI can help with that, allowing your unique skills to shine.
Current uses for AI in the workplace include enterprise AI, which employs machine learning, natural language processing and predictive analytics to improve business outcomes and decision-making. Large language models can also recognize, analyze and generate text, and AI can enhance other tools, such as search engines and drawing, coding and writing applications.
Challenges
As with any advancing technology, AI presents challenges. Safety, privacy, security and government regulations are just some of the issues associated with AI. One of the main hurdles supply chain professionals face is related to data quality and reliability.
Neto explained that GenAI is a good “guessing machine” and can analyze data to ensure a company’s operations are aligned.
Neto and Yuangie suggested that when considering future readiness in AI, companies should focus on small-scale benefits in everyday workflows rather than large-scale projects. Continuous learning is crucial to adapt supply chain talent for AI roles and keep pace with the rapid changes the technology will bring.
“This really is an opportunity for personal growth,” said Yuangie.
Neto and Yuangie highlighted examples of how companies like Walmart and Maersk are using GenAI. Walmart, for instance, uses Practum’s chatbot to negotiate with suppliers, with 83 per cent of suppliers reporting a positive experience. Maersk also uses the same platform to negotiate ocean freight rates.
Looking ahead, Neto and Yuangie said agentic AI—which involves more autonomous decision-making, allowing systems to perceive their environment through sight, sound and text—is on the horizon.
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