THE SEE-THROUGH SUPPLY CHAIN END-TO-END VISIBILITY
Using AI to remedy data quality issues usually creates a small subset of quality data that can be used for model training, which can then be used to identify gaps in the greater data set. Now that companies have begun to gure out how to leverage data from within their own facilities, they’re looking outside at the data held by suppliers or customers in order to collaborate insightfully with their business partners. “That’s the next frontier of visibility,” Raftery notes. One obstacle to conquering this frontier is trust. Suppliers may hesitate to share upstream sourcing information with buyers because it’s often a competitive advantage. Technology can play a role, by enabling protections around data sharing so that supplier data is not inadvertently shared with potential competitors, Chorn says. IMPLEMENTING GUIDELINES An “inside out” perspective helps when implementing AI and visibility solutions, Jones says. Businesses need to understand their current processes and data, along with the general areas in need of improvement before placing quantities of information within an AI algorithm. Knowing their current operations makes organizations better positioned to become more resilient.
A key part of evaluating AI visibility solutions is determining how the model is trained. Among other questions, supply chain organizations will want to ask vendors how the solution generates recommendations, and the data it uses. When evaluating visibility solutions, it’s important to assess their ability to integrate with existing systems, such as the company’s ERP and WMS platforms, as well as external data sources, like supplier systems and IoT devices. Platforms with APIs or middleware that can provide seamless data exchange and integration can help avoid data silos.
the value of their visibility tools but also re ne and train the system for ongoing improvement. Even as visibility and AI solutions advance, challenges remain. Many companies would like an “agnostic clearinghouse for data,” Raftery says, but it’s not that easy. Often, visibility requires connecting systems built for different purposes, which adds complexity. While the market appears to be moving toward collaboration between suppliers, a silver bullet has yet to be found, he adds. HUMAN AND TECHNICAL CHALLENGES Because different visibility vendors and solutions currently serve different segments of supply chain and logistics functions—say, shipment, product, or inventory visibility—organizations typically need to start by focusing on a portion of their supply chains, and thus a single data hub, as they work toward end- to-end visibility, Titze says. Human nature can also complicate efforts to gain visibility. Some may feel it’s necessary to look at every possible piece of data in one place, Goyal says. To that end, they’ll create data lakes (centralized repositories that ingest and store large volumes of data in its original form) instead of focusing on the applications that will save immediate effort and money. After starting with an area likely to generate the most value, supply chain organizations can work through a roadmap of subsequent segments. “Trying to boil the ocean is a recipe for disaster,” Redecker adds. It’s also critical to consider the return on any investment. Prioritizing exible subscriptions or modular approaches and avoiding unnecessary features can help organizations capture ROI more quickly. End-to-end visibility currently remains more an aspiration than a market reality. “But the capabilities are there and the market is heading in that direction,” Redecker says. Some companies are accelerating toward this in a massive way. “There’s huge opportunity,” he adds. n
As supply chain organizations implement visibility tools, solid
communication and training initiatives are critical. For instance, the supply chain team might launch a new visibility solution, yet leave trade and customs out of the planning, so they don’t know it exists. “You leave a lot of value on the table,” Chorn says. Equally important is the ability to connect insights from a visibility solution to the right teams within the supply chain and the broader business. This often requires data science expertise, explains Mary Rollman, U.S. supply chain leader at KPMG. Such expertise ensures that alerts generated by the system reach the appropriate teams, helping organizations not only maximize
Many food companies now face strict rules and regulations requiring full traceability of agricultural products. As a result, transparency is required all the way back to sources of origin, making greater visibility a top priority.
142 Inbound Logistics • July 2025
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