Inbound Logistics | July 2026

events happening across an operation. Then they may be able to respond and prevent or mitigate problems. FAILING TO SPEAK UP Employees need to feel comfortable speaking up once they notice data that doesn’t look right. “The faster you catch it, the less of a mess you will have,” says John A. Evans, president and chief executive ofcer with Evans Distribution. “Everyone involved needs to keep a close eye on their end to communicate discrepancies and solve issues quickly.” That is especially true when working with large accounts that encompass thousands of orders daily. Data accuracy helps everyone do their part without costly delays, mistakes, or disruptions, and is an important tool for identifying issues and mitigating risk of disruption. NOT CONFIRMING THAT A LOGISTICS PROVIDER CAN MAINTAIN DATA VISIBILITY When engaging logistics partners, shippers should conrm that they can provide visibility across the entire transport chain, Gritz says. A company’s own data is only as strong as the data it

Reliable, accurate sourcing data is crucial for companies expanding their supplier base or sourcing regions. Platforms like TradeBeyond enable data processes that support operations.

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” A CLOSE EYE ON THEIR END TO COMMUNICATE DISCREPANCIES AND SOLVE ISSUES QUICKLY. JOHN A. EVANS PRESIDENT AND CEO, EVANS DISTRIBUTION THE FASTER YOU CATCH DATA MISTAKES, THE LESS OF A MESS YOU WILL HAVE. EVERYONE INVOLVED NEEDS TO KEEP DATA THAT ISN’T AI READY As more supply chain organizations implement articial intelligence, they need data that’s “AI-ready,” says Vasileios Plessas, director analyst with Gartner. Data quality is key. While nearly all—94%—of supply chain leaders Gartner recently surveyed are willing and trying to integrate AI within their supply chains, only 17% say they’re actually able to scale it. The obstacle, according to about half, is data

quality, Plessas says. To truly leverage AI, data needs to be connected and cross- functional, rather than siloed. It also needs enough business context that an AI engine can both see and understand it. For instance, the same supplier may appear under several different names, leading an AI tool to see one supplier as three, and assume the company’s supply base is more diversied than it actually is. IGNORING DATA GOVERNANCE, OWNERSHIP, AND STEWARDSHIP Organizations can use technology to clean data, but they can’t correct their way out of bad data, Plessas says. Instead, they need to prevent bad data from recurring. Strong data governance with protocols and policies can help keep bad data from entering the supply chain. An organization might require duplication checks before allowing new suppliers to be entered into the information system, for instance. Identifying data owners is also critical. Without a sense of ownership, employees may fail to maintain the integrity of the

receives from its partners. INSUFFICIENT

SUPPLIER VISIBILITY Many companies know their

direct suppliers but lack visibility to their second- and third-tier vendors. As supply chains become more diversied, these blind spots become harder to manage. DATA THAT DOESN’T REFLECT REALITY The most damaging data mistakes usually aren’t the result of a typo. Instead, they occur when data looks ne in a system but doesn’t reect operations. For example, records may show that inventory exists, but it’s actually damaged

or not available. The organization may make decisions based on the faulty information.

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