Why your supply chain risk management plan will fail

The supply chain risk management plans most enterprises are running today are going to fail.

The teams are sharp. The plans are crisp and clear. The dashboards are lit up with alerts. But the trade environment—laden with complex tariffs, rigorous compliance expectations, and geopolitical fragmentation—has changed faster than traditional SCRM could evolve. What started as monitoring for discrete supply chain disruptions now has to support a different set of trade demands.

Evan Smith

Trade regulations now require true N-tier visibility. The Uyghur Forced Labor Prevention Act (UFLPA), complex component-based tariffs, the National Defense Authorization Act (NDAA)—all require upstream traceability, far beyond Tier 1 suppliers, to the components, materials, origins, and inputs that make up products.

Geopolitical competition and fragmentation have made trade a fault line and a weapon. U.S.-China decoupling, Russian sanctions, and rising protectionism are creating upstream risks and product-line disruption that most importers can’t see.

These demands are creating real challenges that traditional SCRM plans and tools cannot address. Landed costs are exploding, as tariff stacking, shifting origin rules, and hidden upstream exposure compress margins. Leadership teams can’t answer basic questions like “What’s our exposure to geopolitical hotspots?” or “Where are our single-sourced risks?” Teams then spend months manually stitching together answers that are wrong by the time they reach executives. SCRM tools were a critical first step for visibility into supplier risks, but blinking red lights on a dashboard don't support the demands of modern business.

SCRM’s outdated and ineffective approach

Pure SCRM tools have inherently limited capabilities. Three tactical problems make even the most well-laid traditional SCRM plan outdated and ineffective:

1. SCRM offers supply chain visibility, but doesn’t put it in the context of actual products. SCRM tools surface risks at the supplier and entity level, not the product level. The result is a high volume of low-context alerts disconnected from products, revenue, or compliance obligations—distant connections between unrelated entities buried alongside a forced-labor-flagged supplier three tiers upstream that could trigger a border detention. Teams either drown in meaningless flags or stop trusting the system altogether.
2. SCRM tools identify risks, but don’t give companies the ability to act on them. SCRM stops at identification—a flagged supplier, a risk score, an alert—without allowing teams to take action within the tool. And many of the costs hitting importers today are driven by changes to trade compliance, which a supplier risk score doesn’t address at all. The result widens the gap between seeing a risk and doing something about it—which is where most companies bleed margin and time. It’s the business equivalent of going to the doctor, being told “you might be sick,” being given no medicine, and then having the exact same experience again and again.
3. SCRM gives companies AI-derived visibility, but doesn’t allow them to verify issues. AI visibility has value in identifying and prioritizing potential risks. But alone, it’s insufficient. The insights generated by AI on public data are noisy and meaningless if they can’t be verified and reinforced with information from trusted sources—supplier data, PLMs, ERPs, global trade systems. Real traceability on actual product value chains is essential, especially when what’s in a product anchors both attestations to regulators and decisions involving millions of dollars in revenue.

Global trade means making decisions about products, and SCRM tools are disconnected from them

To improve margins and efficiency, importers must make decisions about individual products. But SCRM tools don’t offer a complete, verifiable product record, which makes it impossible to reach ground truth.

Consider the various internal teams—from design to sourcing to procurement to supply chain to logistics to trade compliance—that touch a product on its journey from raw materials to store shelves. There is no single source of product truth that they can contribute to, which means there is no single source of product truth from which to pull the information regulators need to clear shipments of goods.

The same goes for the suppliers, logistics providers, and regulators involved in a product’s creation and movement. With SCRM, they share no common operating picture and no ability to exchange information at the product level.

Instead, importers and their supply chain partners get stuck in a reactive cycle that feels productive but isn’t. Teams spend their days triaging endless alerts; suppliers take months to return surveys that feed static, outdated reports; and regulators only have the means to flag issues after shipments have already been detained.

Moving from broken SCRM to trusted trade through an AI-powered source of product truth and collaboration

Moving from broken SCRM into a system that reflects modern trade demands is possible, but requires three elements:

1. A product-level foundation. Importers need a system of record for their products—what they’re made of, where the parts come from, and who supplies them.
2. The ability to connect with supply chain partners. Once a product-level foundation exists, it has to connect to all the partners involved in getting a product from raw materials to the final customer. This includes suppliers, who collaborate on and verify actual product value chains; logistics providers, who are tasked with moving goods efficiently; and regulators, to whom compliance must be proven.

AI workflows that improve with use. Visibility alone isn’t enough. The system has to do the trade work that follows—classification, country of origin, duty calculation, scenario modeling—and it has to get smarter as teams use it. AI on top of a verified product foundation produces results that get more accurate over time; AI on top of public data alone produces alerts that go stale the moment they're generated.

In practice, this looks different from the current model in several specific ways. Suppliers stop responding to disconnected surveys and start collaborating on records tied to actual products. Internal teams stop maintaining separate versions of supplier and product data in separate systems and start working from one shared source. Regulators receive verified product-level information before goods reach the border, turning enforcement from a surprise into a formality. And the data itself improves with use: each verification, each shipment, each interaction with a supplier or a regulator strengthens the underlying record rather than aging it. The result is an operating model that compounds in accuracy over time, instead of one that decays the moment a survey is filed.

The trade environment has fundamentally reset, and isn’t going back to the free trade status quo. Every new tariff, regulation, and geopolitical escalation adds costs that compound across products, suppliers, and geographies—and the pace is accelerating.

Companies operating on fragmented product data will continue to pay a costly tax. Overpaid duties, detained shipments, invisible concentration risks, and hours burned stitching fragments together instead of making strategic decisions—these are the inevitabilities of running on a traditional SCRM plan.

What’s needed to meaningfully change this equation is a network built around products, one that builds trust within the business and with supply chain partners. When companies know their products end-to-end—and when their suppliers, logistics providers, and regulators share and contribute to that understanding—they move goods faster, pay less to get them where they need to go, and adapt fast when rules change and disruption threatens to depress margins.

About the author

Evan Smith is the CEO and Co-Founder of Altana, the AI-powered network for trusted trade. Altana provides an artificial intelligence model of the global supply chain to help governments, enterprises, and financial institutions improve global commerce. Prior to Altana, Evan led enterprise solutions and strategic partnerships for Panjiva, a trade data science company, and led the sale of Panjiva to S&P Global in 2018. Before Panjiva, Evan co-managed a private equity partnership under a family office sponsor, and served as the CEO of IMBU Technologies, a wholly-owned portfolio company, providing textile supply chain automation software. Evan holds a Bachelor’s Degree in Economics from Yale University.

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