Amid all the talk about whether artificial intelligence is a bubble, the supply chain and logistics industries have become breeding grounds for seemingly genuine uses of the technology. Companies like Flexport, Uber Freight, and dozens of startups are developing different applications and winning blue-chip customers. But while AI helps Fortune 500 companies pad their bottom line and justify the next layoff to Wall Street, the right use of the tech is also proving useful to smaller businesses.
Netstock, an inventory management software company founded in 2009, is working on just that. It recently rolled out a generative AI-powered tool called the Opportunity Engine that slots into its existing customer dashboard. The tool pulls information from a customer’s Enterprise Resource Planning software and uses that information to make regular, real-time recommendations.
Netstock claims the tool is saving those businesses thousands. The company announced it has served up one million recommendations to date, and that 75 percent of its customers have received an Opportunity Engine suggestion valued at $50,000 or more.
While tantalizing, one of those customers, Bargreen Ellingson, a family-run 65-year-old restaurant supply company, was initially apprehensive about using an artificial intelligence product. According to chief innovation officer Jacob Moody, old family companies do not trust blind change a lot. He stated he could not have gone into the warehouse and said a black box was going to start managing inventory.
Instead, Moody pitched Netstock’s AI internally as a tool that warehouse managers could either choose to use or not use, a process he describes as eagerly but cautiously dipping their toes into AI.
Moody says it is helping avoid mistakes because it sifts through myriad reports his staff uses to make inventory decisions. He acknowledged the AI summaries of this information are not 100 percent accurate, but said it helps create signals from the noise quickly, especially during off-hours.
A more profound change Moody has noticed is that the software made some of Bargreen Ellingson’s less-senior warehouse staff more effective. He highlighted an employee with a high school diploma who has worked there for two years. Training this employee to understand all the inventory management tools and forecasting information will take time, but he knows the customers and what goes on the trucks. For him, he can look at the system, see the AI-driven insight, and very quickly understand whether it makes sense. This makes him feel empowered.
Netstock cofounder Kukkuk understands the hesitancy around new technologies, especially because so many products are essentially mediocre chatbots attached to existing software. He attributes the early success of Netstock’s Opportunity Engine to a few things. The company has more than a decade’s worth of data from working with retailers, distributors, and light manufacturers. That data is tightly protected to adhere to ISO frameworks, but it powers the models that make the recommendations. Netstock is using a combination of AI tech from the open source community and private companies.
Each recommendation can be rated with a thumbs up or thumbs down, but the models also get reinforced by whether the customer takes the suggested action or not. While that kind of reinforcement learning can lead to weird and sometimes harmful results when applied to things like social media, Kukkuk said he is chasing different incentives. He does not care about eyeballs like Facebook and Instagram do; he cares about the outcome for the customer.
Kukkuk is wary of expanding those interactions due to the limitations of current generative AI tech. While it might make sense for a customer to converse with Netstock’s AI about a recommendation, Kukkuk said that could ultimately lead to a breakdown in accuracy. He described it as a tightrope to walk, because the more freedom you give the users, the more freedom you give a large language model to start hallucinating. This explains the Opportunity Engine’s placement in the typical customer dashboard, where suggestions are prominent but easily dismissed. It is not like Google Docs cramming 20 AI features down a user’s throat.
Moody said he appreciated that the AI is not in-your-face. They are not letting the AI engine make any inventory decisions that a human has not looked at and screened. He said if they ever get to a point where they agree with 90 percent of its suggestions, maybe they will take the next step and give it control, but they are not there yet.
It is a promising start at a time when many enterprise deployments of generative AI seem to go nowhere. But if the tech gets better, Moody said he is nevertheless worried about the implications. Personally, he is afraid of what this means and thinks there is going to be a lot of change, and no one is really sure what that will look like at Bargreen. It could lead to fewer data science experts on staff. But even if that means moving those employees out of the warehouse and into the corporate office, he said preserving knowledge is important. Bargreen needs people who deeply understand the theory and philosophy and can rationalize how and why Netstock is making certain recommendations to ensure they are not blindly going down the wrong path.