The FDA's Food Safety Modernization Act Section 204 requires food retailers to produce complete, end-to-end traceability records within 24 hours of a regulatory request. For most large grocery organizations, that deadline is operationally impossible, not because the data doesn't exist somewhere, but because the systems architecture was never built to preserve it durably.

Most enterprise databases store the current state and overwrite the history of transactions. The reason why is simple: they are unable to scale to the level needed to store every transaction and every event that occurs in their systems. As a result, this leaves compliance teams (within any industry) unable to reconstruct, for example, when a shipment from a vendor was accepted, who handled it, or what conditions surrounded the decision.

McKinsey's 2024 Global Supply Chain Leader Survey found that only nine percent of organizations consider their supply chains currently compliant with new regulatory requirements. Compound this with the fact that there were more than 1,900 food recalls in the US in 2024 alone, with the average major event costing tens of millions of dollars before brand damage is factored in. The problem is structural;  the solution is architectural.

This article discusses several significant factors for enterprises that need to deal with regulatory compliance. We’ll examine:

1. What are the requirements of the FSMA

2. Why traditional database architectures fail to meet FSMA standards

3. How the EU AI Act creates an identical compliance obligation for AI-driven systems (retail or not), and 

4. How event sourcing transforms traceability from a recurring audit crisis into a permanent operational capability

What Does FSMA Section 204 Actually Require from Food Retailers?

The Food Safety Modernization Act, signed into law in the United States in 2011, which has since been strengthened continuously through the FDA, represents the most sweeping overhaul of U.S. food safety law in more than seventy years. Its central premise is a philosophical shift: from reacting to foodborne illness outbreaks to preventing them before they happen.

For most of the law's history, food retailers could treat compliance as a documentation exercise; gather some records, maintain a binder, hope no one looks too closely. In recent years, the FDA has changed that.

FSMA 204 establishes the Food Traceability List which applies to a specific catalog of high-risk foods including leafy greens, fresh fruits and vegetables, eggs, and ready-to-eat foods. And demands that any entity handling these foods maintain what the FDA calls Key Data Elements at every Critical Tracking Event in the supply chain.

A Critical Tracking Event is defined as: any moment when food changes hands, changes form, or changes location. 

Examples of a Critical Tracking Event are:

• Growing

• Cooling

• Shipping

• Receiving

• Transforming

Each of these events must be documented with specific data, and that data must be traceable end-to-end, from the farm field to the store shelf.

The FDA's requirements mandate that covered entities provide traceability records within 24 hours of a request. When a contamination event is detected and people are getting sick, regulators need answers in hours, not weeks.

The penalty for non-compliance ranges from warning letters and import alerts to criminal charges and operational shutdowns. For a major grocery retailer, a single high-profile recall tied to inadequate traceability records can result in hundreds of millions of dollars in lost revenue, legal exposure, and brand damage that takes years to repair. 

It’s not uncommon for there to be nearly 2,000 food recalls in a given year. The average direct cost of a major recall runs into the tens of millions before brand damage and litigation are even factored in, with some global events exceeding $100 million in total economic impact. 

Why FSMA Traceability Compliance Is So Hard for Complex Retail Organizations

The instinctive response for most enterprises is to treat traceability as a data problem. Many assume they need better records. So the organization deploys a new ERP module (expensive and often a long deployment), adds a labeling system, or stands up a traceability portal. Data gets recorded somewhere. The boxes appear to be checked.

But the FDA does not just want data. It wants causal data. It wants to know not just that a shipment arrived, but why it was accepted or rejected. It wants to know not just that a product was transformed, but the precise conditions, sequence, and actors involved in that transformation. It wants a chain of custody so complete and coherent that an investigator could reconstruct every hand that touched a head of romaine lettuce from a field in Salinas Valley to a store shelf in suburban Ohio.

That is a systems architecture problem, not a records management problem.

Modern retail supply chains are incomprehensibly complex. A single location in a large grocery chain may receive products from thousands of suppliers. Each supplier has its own systems, its own data formats, its own event cadence. 

Internally, fulfillment systems, pricing systems, inventory systems, and transportation systems all operate as semi-autonomous domains, often built by different teams at different times using different technical philosophies. McKinsey's 2024 Global Supply Chain Leader Survey found that 90 percent of supply chain leaders say their companies lack sufficient digital talent to meet their digitization goals. 

The fundamental failure mode is this: most of these systems only store the current state. When a shipment is accepted, the database record is updated. The previous state, the reason, the context, the sequence of events that led to the decision, is overwritten and gone. No history exists. The causal chain cannot be reconstructed because it was never captured in the first place.

When the FDA sends a 24-hour data request, the organization finds itself in quiet panic. Engineers are pulled from feature work, analysts begin querying logs, someone building a spreadsheet. The answer that emerges is approximate and not reliably reproducible, and took a lot of manhours to get there. 

PwC reports that 77 percent of executives say compliance complexity has negatively impacted their business. These are not outliers. They are mainstream enterprises with a structural problem, not a process problem.

FSMA Is Not Alone: How the EU AI Act and Other Regulations Demand the Same Causal Audit Trail

FSMA is one piece of a much larger wave of accountability-first regulation over every industry that touches consumers or handles consequential decisions.

In financial services, the EU's Digital Operational Resilience Act (DORA) requires operational resilience and the ability to reconstruct the sequence of events leading to any system failure. In healthcare, HIPAA audit trail requirements are being interpreted with increasing strictness as health systems deploy AI-driven diagnostics and treatment recommendations. 

The SEC's evolving ESG disclosure requirements are pushing companies to document environmental and social impacts deep into supplier networks; the kind of multi-tier supply chain visibility that most organizations still cannot provide. McKinsey also reports that for a typical consumer company, over 80 percent of carbon greenhouse gas emissions originate deep in the supply chain, making that visibility not only a regulatory requirement but an existential business question.

The EU AI Act, now in effect, requires that high-risk AI systems (those used in employment, credit, infrastructure, and essential services) meet three core obligations: log every decision for traceability, explain retroactively why a specific decision was made, and make those explanations available to regulators on demand. For technology leaders, these requirements have similarities to what FSMA demands.

For any retailer operating in the European market, or deploying AI-driven pricing, demand forecasting, inventory allocation, or customer segmentation tools, it is a present compliance obligation. 

Gartner identifies end-to-end supply chain traceability as one of the defining technology imperatives of 2025, noting its importance not only for monitoring perishable goods but for ensuring compliance across an expanding landscape of environmental, safety, and now AI regulations.

The same systems architecture that fails FSMA's demand for causal supply chain traceability will fail the EU AI Act's demand for explainable AI decisions. 

A system that overwrites state rather than preserving history cannot tell you why it did something, whether the action was accepting a shipment of spinach or approving a price adjustment algorithm's output. Regulations change, but the underlying architecture problem does not.

What Happens When a Major Grocer Fails an FSMA Audit: A Real-World Supply Chain Crisis

A major U.S. grocery retailer, operating hundreds of locations, discovered this convergence at the worst possible moment. The organization had been building a modernized event-driven architecture to handle fulfillment, pricing, and transportation in-house. By conventional measures, their system was well-built. Engineers were experienced. The distributed architecture handled high transaction volumes reliably.

Then a compliance audit revealed a single, devastating fact: the grocer could not prove why shipments had been accepted or rejected at their receiving docks.

That gap put the organization in direct conflict with FSMA Section 204's four core traceability obligations:

• End-to-end traceability — tracking product lineage from manufacturer through back-door receiving to the retail shelf

• Causal forensics — identifying exactly what truck a product rode on, who handled it, and the specific conditions of its transit

• Audit-ready decision trails — proving exactly why a shipment was accepted or rejected at the point of arrival

• Tamper-proof immutable history — maintaining records that satisfy high-stakes financial and safety control-gap standards

A 24-hour FDA data request was not something the current architecture could satisfy. Beyond the immediate regulatory risk, the grocer's inability to reconstruct historical decisions also blocked its AI initiatives because risk officers would not approve AI-driven fulfillment decisions that could not be audited after the fact. The compliance gap and the AI readiness gap turned out to be one and the same.

Bolting a traceability layer on top of the existing architecture was evaluated and rejected. The systems were updating state without preserving history, a traceability wrapper cannot reconstruct what was never captured. The only viable path was to fix the underlying architecture.

How Event Sourcing Solves FSMA Traceability Requirements by Design

Event sourcing is an architectural pattern built on a simple but radical idea: instead of storing the current state of a system and overwriting the past, you store every event that led to that state. The complete history is the database. Nothing is ever deleted or overwritten. 

Applied to modern enterprise supply chains, it transforms compliance from a continuous fire drill into an architectural guarantee.

Axoniq built the leading platform for event sourcing with Axon Framework and Axon Server as a foundation. Doing event sourcing correctly at scale is substantial. 

For the major grocer, abandoning their DIY event sourcing and migrating to Axon Framework 5 and Axon Server transformed the compliance picture and so much more. The shift from eventual consistency with distributed saga patterns to atomic consistency with a unified event store meant that every receiving decision was now recorded as an immutable event with full context: the state of the system at the time of the decision, the conditions of transit, the identity of every actor involved, the specific sequence of events that led to acceptance or rejection.

The 24-hour FDA response window went from impossible to trivial. Auditors’ questions became an instant query against a complete event history.

In addition to immediate compliance, the complete event history became a strategic asset. The grocer could redefine business boundaries and shift from product-centric to customer-centric fulfillment models, without migrating data. New organizational rules could be applied retroactively to years of event history in milliseconds. Features that previously required quarters of data migration work could now ship in weeks.

And when the AI compliance conversation arrived for this grocer, the foundation was already in place. Every AI-driven decision in the fulfillment system captured the exact state of the world at the time of inference: the demand signals, the inventory positions, the pricing inputs, the model version.

Deloitte's analysis of FSMA 204 implementation makes the business case clear. Traceability reduces the cost and scope of recalls, improves inventory management, and turns compliance data into operational intelligence that drives supply chain improvement long after an audit is closed.

How to Turn Regulatory Compliance Into a Retail Competitive Advantage

The organizations that will struggle with FSMA 204 and the EU AI Act are the ones treating compliance as a documentation exercise, creating fragile bolt-on audit trails that satisfy neither regulators nor internal teams. 

The organizations that will thrive are the ones whose architecture makes traceability the natural byproduct of normal operations. Where every event is already captured, already immutable, already queryable, already causally linked. Where an FDA request is answered in minutes, not days. Where AI decisions are explainable not because someone built an explainability layer, but because the complete decision context was preserved by design.

For the grocer in this story, what began as a compliance crisis became a nine-month elimination of production delay. The regulatory burden actually became the foundation for AI-ready infrastructure that will differentiate the business for years.

New rules across food safety, supply chain, AI governance, ESG disclosure, and operational resilience are all converging on the same underlying demand of needing to be able to prove what happened, prove why it happened, and prove it fast. Organizations that build that capability into their architecture today will move through future compliance requirements without friction. 

Organizations that keep patching explainability on top of fundamentally incomplete systems will spend the next decade in the same panic loop they are in today.

Learn how Axoniq is helping the world's most complex retail organizations turn supply chain traceability from a liability into a true competitive advantage.

[ Read the full case study → ]