Analyst Insight: The complexity of export controls has exploded over the last several years, with the U.S. more than doubling the number of restricted buyers since 2019. Manufacturers need data and system integration to avoid violations.
Managing trade restrictions has always been a manufacturing supply chain challenge, but the difficulty is quickly ratcheting up. Since 2019, the U.S. has more than doubled the number of restricted buyers. Even more significant changes may be on the horizon, with the Trump administration reportedly drafting rules that would require government approval to ship AI accelerator chips anywhere outside the U.S.
The tightened export controls are part of the U.S. government’s effort to maintain the country’s leadership in semiconductor technology. These policies sparked retaliatory actions from China and other nations, resulting in continuous policy changes.
These regulations layer on top of a diverse global supply chain. More than 70% of high-tech transactions take place in the channel, passing through multiple distributors and resellers before reaching the end customer. The convoluted supply chain makes it difficult for manufacturers to track potential sales to denied entities. Failing to identify these transactions can expose companies to substantial penalties.
Chip makers need supply chain visibility to maintain global compliance with these growing restriction lists. Visibility requires good data, but many companies lack confidence in their information. According to Model N’s 2025 State of Revenue Report, 90% of surveyed leaders are concerned about the quality of their revenue management data. Completeness, timeliness and accuracy are the biggest pain points, which present significant hurdles to identifying end customers.
The aggressive trade policies and the extreme demand for AI chips also create incentives for gray market activities. Unauthorized brokers and intermediaries have opportunities to reroute scarce components to denied entities for a premium price. These activities put manufacturers at increased risk for regulatory violations and undermine revenue management activities, such as pricing strategies and demand forecasting. Model N’s report found 95% of high-tech decision makers are concerned about gray market activity.
Complying with export restrictions and preventing unauthorized transactions will require manufacturers to improve their supply chain data.
High-tech companies manage hundreds to thousands of end customers, many of whom may have multiple aliases, subsidiaries or name variations. Maintaining a clean, well-governed customer master is critical to accurately identifying potential matches with denied party lists. Companies must also account for “parties of concern”— entities whose ownership, identity or end use cannot be fully verified and require additional scrutiny before shipments proceed.
Semiconductor manufacturers must also maintain a deep understanding of the channel to track where their products end up. They rely on channel partners to submit POS data, ship-and-debit claims and rebate requests. While collecting this information is common practice, the bigger challenge is data quality.
To fix these gaps, companies need to rethink their data strategies for channel management. Start by moving from rigid data warehouses to flexible data lakes that handle raw, unstructured information. Then, adopt canonical data models to ensure that data points hold the same meaning in all systems, resolving differences between varying partner reporting formats as well as internal platforms.
AI further improves data quality by automating the enrichment, cleansing and structuring of channel records. This capability creates better datasets for both daily operations and advanced analytics. With this foundation, manufacturers can execute supply chain audits to map product journeys.
Compliance cannot be limited to a single checkpoint. Manufacturers must detect and act upon potential sales to denied or restricted entities across several systems in the revenue operations stack, including CRM platforms that manage customer relationships, ERP software that processes orders and shipments, and revenue management tools that handle downstream processes such as ship-and-debit and rebates.
These systems must also support continuous screening of customers, channel partners and transactions against evolving government restriction lists. The canonical data model enables companies to harmonize and consistently leverage compliance, customer and transaction data.
AI tools continuously monitor transactions to identify potential problems, and can flag repeated AI chip shipments to distributors in historically low-demand regions. These systems can also automatically screen customer and partner records against denied party and restricted entity lists as they change. Systems can automatically parse out transactions to determine end-customer information and check the names against denied party lists. Manually conducting this analysis would take too long, if the organization noticed the anomaly at all.
The insight allows companies to block or reroute the shipments before they reach the customer. Manufacturers can also implement safeguards to prevent future incidents, such as pre-approvals for distributors in that region.
While the U.S. government’s pending restrictions are far from final, semiconductor manufacturers can’t wait for confirmation to prepare their infrastructure. Improving channel data management supports flexible systems that adapt to any new rules.
This article was originally published in SupplyChainBrain.
