FOR GE APPLIANCES , the ability to access the right data at the right time has been a game- changer. The company reduced the volume of backorders in its aftermarket parts operation by more than 25%, signi cantly improving availability for customers, thanks to a supplier collaboration agent the company implemented in 2025.
synchronized with supply management and marketing, inventory may sit unsold, leading to price reductions. In other cases, inventory may be misallocated. Some nodes in a network experience shortages, while others carry excess stock, explains Daniel Wang, director with LIDD, a supply chain consultancy. Those navigating the shortages may overorder, consuming working capital and more facility space than they actually need. Compliance risks also come into play, says Max Schlichter, a partner with McKinsey. Incorrect data on country-of-origin or hazardous material classi cations can lead to customs delays, nes, or shipment holds.
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” GE APPLIANCES IS USING DATA AND AI TO IMPROVE HOW WE SEE, UNDERSTAND, AND ACT ACROSS THE LOGISTICS NETWORK . MARCIA BREY VICE PRESIDENT OF LOGISTICS, GE APPLIANCES As GE Appliances’ experience shows, an optimized data strategy can streamline operations, enhance customer experiences, and boost productivity. Conversely, “bad data creates expensive con dence,” says Nicole Brackett, enterprise account executive with TradeBeyond. Partial or inaccurate information can lead organizations to make decisions that result in inventory shortages, delayed shipments, compliance failures, or other budget- busting mistakes. More than one-quarter of the organizations surveyed in a 2025 IBM report estimate that they lose more than $5 million annually due to poor data quality. One reason is that bad data can needlessly tie up working capital. For example, if demand forecasting isn’t
Google Gemini Enterprise, a platform that includes arti cial
intelligence (AI) and other business tools, powers the agent. It automates routine supplier check-ins, con rms order status, and escalates issues when needed, among other tasks. “The opportunity is not just more data,” says Marcia Brey, vice president of logistics at GE Appliances. “It is better signals.” The logistics teams at GE
CHALLENGES TO MEET Freight may be misrouted or
Appliances manage a ow of information across suppliers,
transportation costs in ated because of errors in shipment dimensions, weights, or routing information, Schlichter says. Another challenge is mismatches between what suppliers say they’re shipping and the products that arrive, which can force receiving teams to use inef cient manual reconciliation processes that can create inventory inaccuracies. As companies shift to automated and AI-enabled supply chains, the risks of data mistakes increase. Organizations that train forecasting models or planning systems on incomplete or inaccurate data risk scaling poor decisions quickly, rather than actually improving performance. These systems are only as effective as the quality of the underlying information, Schlichter says. Here are 14 supply chain data mistakes to watch out for.
transportation, factories, warehouses, service parts, and customer delivery. Information that is late, inconsistent, or incomplete can impact availability and cause manual work and delayed decisions. Arti cial intelligence helps the teams identify patterns, anomalies, and early signals that may indicate a disruption, quality issue, or recurring operational problem. As a result, employees spend less time gathering data—they manage more than 700 suppliers and approximately 27 million parts and accessories annually—and more time resolving issues. “GE Appliances is using data and AI to improve how we see, understand, and act across the logistics network,” Brey says.
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