traveling to remote places to conduct an inspection,” Hill-Treadway adds. ONE DISPATCHER IS A BOT Route optimization was the rst goal when Senpex, a last-mile logistics service based in San Jose, California, started to implement AI. More recently, Senpex has been training AI to work side by side with human staff. “We keep ve dispatchers, and one of them is a bot,” says Anar Mammadov, the company’s co-founder and CEO. A large language model such as ChatGPT can’t tell you how to run a warehouse because it doesn’t have access to that facility’s data. But that type of AI, with its natural language abilities, can play certain roles in the supply chain. “We can train a generative AI model— almost a narrow generative AI model—on interpreting our results,” Moore says. For example, in a warehouse that uses AutoScheduler’s platform, a user could say, in plain English, “I’m a site leader: tell me the 10 things I need to know about.” Based on that person’s role and the current operational data, the AI would respond with an update on inventory that’s running late, stang needs BROAD VS. NARROW The AI that excites most people these days is generative AI, the technology embodied by ChatGPT and similar systems. This AI can create new content—essays, pictures, answers to just about any question—based on the oceans of data on which it was trained. Generative AI is not the best fit for many supply chain challenges, notes Keith Moore, CEO of AutoScheduler.AI in Austin, Texas. That’s because supply chains don’t need models trained on a world’s worth of data, but rather on data from a specific operation. The right tool for the job is what Moore calls narrow AI. “In narrow AI, we take a singular task that can be performed by a person and do it more accurately or faster,” Moore says. throughout the current shift, and other concerns. “That’s where it’s extremely valuable,” Moore says.
Porsche and UP.Labs have formed a joint venture, Sensigo, to leverage AI for automotive diagnostics and repairs. By analyzing data from dealers and OEMs, Sensigo aims to improve eciency and identify potential component issues, enhancing the customer experience.
RISK MANAGEMENT Supply chain platform Inspectorio applies AI to procurement and production. The company’s offerings include solutions such as Quality Risk Management, Responsible Sourcing and Compliance, and Traceability and Transparency. Like Aera Technology, Inspectorio can analyze large volumes of operational data to help human operators make decisions, or it can execute on those decisions automatically. It can also head off problems before they occur. “One of our capabilities is predicting what factory, or what factory’s product, will cause a failure or a defect,” says Chirag Patel, Inspectorio’s CEO. “We can make recommendations on either not using that factory, or inspecting the factory, or asking them to x certain procedures and processes so we can avoid the defect,” he adds. Inspectorio trains the AI to consider a factory’s capacity and competencies, its past performance, results of all the inspections performed there, the types of defects found, and actions taken to eliminate those problems, among other factors. To see the results of an analysis, an operator uses a graphical interface or conducts a chat with the system.
These insights can help a company avoid costly production errors and protect a brand’s reputation. Also, by discovering which factories pose the greatest risk and which perform well, a company can save money on inspections. “If there’s a low-risk factory that we’re sure will have good quality, the brand doesn’t need to have its own inspectors or pay a third party,” says Diego Pienknagura, Inspectorio’s executive vice president of strategy and business operations. “Because Inspectorio is rooted in AI, it will be able to help us mitigate risk,” says Marienne Hill-Treadway, senior vice president, sourcing operations at Centric Brands, a New York-based apparel and accessories rm that contracted to implement three of Inspectorio’s solutions in 2024. “It will give us a lot of data and KPIs (key performance indicators) that we didn’t have before,” she notes. This will help the company work more collaboratively with its vendors. “Also—and this is a big one—we will be able to self-certify good performing vendors, so they can just report in to us, without having to spend money and time
January 2025 • Inbound Logistics 105
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