AI-Powered Inventory Management Software Saves Thousands for Small Businesses: How Bargreen Ellingson Embraced AI Without Blind Change
In the ongoing debate about artificial intelligence (AI) bubbles, it’s the supply chain and logistics sectors that are witnessing genuine applications of the technology. Companies like Flexport, Uber Freight, and numerous startups are developing various AI-based solutions, attracting big corporate clients.
While AI is boosting the profit margins of Fortune 500 companies (and often justifying layoffs to Wall Street), its effective use is proving beneficial for smaller businesses as well.
One such company, Netstock—a software firm specializing in inventory management that was founded in 2009—has recently introduced a generative AI-powered tool called the “Opportunity Engine.” This tool integrates seamlessly into the existing customer dashboard and utilizes data from the customers’ Enterprise Resource Planning (ERP) software to make real-time recommendations.
Netstock asserts that this tool is saving businesses thousands. Last Thursday, they announced that they have provided one million recommendations so far, with 75% of their customers receiving an Opportunity Engine suggestion valued at $50,000 or more.
Initially hesitant to use AI, a family-run restaurant supply company named Bargreen Ellingson decided to approach it cautiously. According to the chief innovation officer, Jacob Moody, “old family companies are wary of blind changes.” Instead, they introduced Netstock’s AI as a tool that warehouse managers could choose to utilize or ignore—a strategy Moody describes as “tentatively dipping our toes into AI.”
Moody claims that the AI is helping avoid errors and assists in sifting through numerous reports used by his team for inventory decisions. Although the AI summaries are not 100% accurate, they help create signals amidst the noise, especially during off-hours.
The most significant change Moody has observed is that the software has made less-senior warehouse staff more effective. He highlighted an employee in one of Bargreen’s warehouses who has worked there for two years and possesses only a high school diploma. Training this employee to understand all inventory management tools and forecasting information would take time, Moody said, but the AI-driven insights help the employee make quick decisions and feel empowered.
Netstock cofounder Kukkuk understands the skepticism towards new technologies, particularly since many products are merely mediocre chatbots attached to existing software. He credits the early success of Netstock’s Opportunity Engine to several factors: the company’s extensive data from working with retailers, distributors, and light manufacturers for over a decade; and a combination of AI technology from open-source communities and private companies that powers the recommendations.
Each recommendation can be rated with a thumbs up or thumbs down, and the models also get reinforced based on whether the customer follows the suggested action. Kukkuk is cautious about expanding interactions due to the limitations of current generative AI technology. While it might make sense for a customer to engage in a conversation with Netstock’s AI about the usefulness of a recommendation, Kukkuk believes this could ultimately lead to inaccuracies.
This is why the Opportunity Engine is located within Netstock’s typical customer dashboard. The suggestions are prominent but easily dismissible. Unlike Google Docs overwhelming users with multiple AI features, the AI here remains unobtrusive.
Moody appreciates this non-intrusive approach: “We’re not allowing the AI engine to make any inventory decisions without human review and approval.” He suggests that if the technology improves, they may eventually give the AI more control, but only when it agrees with 90% of its suggestions.
It’s an encouraging start in a time when many enterprise deployments of generative AI seem stagnant. However, Moody expresses concerns about potential future implications. “Personally, I am concerned about what this means,” he says, expressing fears about the impact on job roles and the need to preserve knowledge within the company.
