The Modern Era of Traceability: Harnessing AI to Navigate FDA Compliance

In the complex, interconnected world of global food supply chains, food safety and traceability have ascended to the forefront of pressing concerns.

Recognizing the need for enhanced measures, the U.S. Food and Drug Administration (FDA) introduced the final rule on Requirements for Additional Traceability Records for Certain Foods (Food Traceability Final Rule) on November 2022.

This pivotal rule demands comprehensive recordkeeping from all parties involved in the manufacturing, processing, packing, or holding of foods included in the Food Traceability List (FTL).

The final rule serves as a cornerstone of the FDA’s New Era of Smarter Food Safety Blueprint and aligns with Section 204(d) of the FDA Food Safety Modernization Act (FSMA).

It aims to expedite the identification and removal of potentially contaminated food, thereby reducing instances of foodborne illnesses and deaths.

The rule requires those handling foods on the FTL to maintain records containing Key Data Elements (KDEs) associated with specific Critical Tracking Events (CTEs). Also, these parties must provide necessary information to the FDA within 24 hours or an FDA-approved timeframe.

Emerging technologies, particularly Artificial Intelligence (AI), can play a pivotal role in helping organizations comply with these new regulations.

This article outlines a comprehensive guide to compliance with the FDA's Traceability Rule and how AI can aid this process.

Understanding and Identifying Compliance Requirements

Firstly, it's crucial to understand the rule's purpose and scope.

The rule is designed to ensure robust tracking and monitoring across the food supply chain.

Review the FTL to confirm if your products are listed. If they are, the rule applies to you, regardless of your position in the food supply chain.

After determining the rule's applicability, the next steps are identifying the Critical Tracking Events (CTEs) and Key Data Elements (KDEs) in your operation.

CTEs are stages in the food supply chain. These CTEs are: harvesting, cooling (before initial packing), initial packing (RAC and other than a food obtained from a fishing vessel), receiving, first land receiving (food obtained from a fishing vessel), transforming, and shipping.

KDEs are the particular types of information that must be established and maintained at each CTE. They could include details like the description of the food, reference records, quantity and unit of measure, and more.

Managing Traceability Lot Codes with AI

A significant requirement of the FDA's Traceability Rule is the assignment and management of Traceability Lot Codes (TLCs).

A TLC is a descriptor, often alphanumeric, used to uniquely identify a traceability lot within the records of the firm that assigned the TLC.

The rule requires a TLC to be assigned to a food on the FTL in certain situations such as initially packing a raw agricultural commodity (RAC) other than a food obtained from a fishing vessel, performing the first land-based receiving of a food obtained from a fishing vessel, or transforming a food.

If an FTL food is received from an entity exempt from the final rule without an assigned TLC (unless you are a retail food establishment or restaurant), it's your responsibility to assign one.

However, in conducting other activities (e.g., shipping) for a food on the FTL, a new TLC should not be established.

Once a food has been assigned a TLC, the records required at each Critical Tracking Event (CTE) must include that TLC. All Key Data Elements (KDEs), including the TLC, must be linked to the relevant traceability lot.

This is where AI can be a game-changer. AI-powered systems can automate the process of assigning and managing TLCs. They can generate unique TLCs when necessary, associate these codes with relevant KDEs, and automatically record them in every subsequent CTE. This helps ensure the TLC remains linked to the food product as it moves through the supply chain, providing accurate and efficient traceability.

Furthermore, AI systems can use machine learning algorithms to analyze patterns in the data linked to specific TLCs. This can aid in swiftly identifying anomalies or potential issues related to specific lots, thereby enabling rapid responses to food safety issues.

AI-powered tools can also facilitate swift and efficient retrieval of records associated with specific TLCs.

If the FDA requests information, AI systems can quickly find and provide all the relevant records linked to a given TLC, thereby ensuring compliance with the 24-hour response requirement.

The Power of AI in Record-Keeping and Data Analysis

At this juncture, AI can begin to play a pivotal role.

The FDA's Traceability Rule requires comprehensive record-keeping processes that go beyond traditional methods.

The FDA's Traceability Rule implies that businesses are free to choose their method of record-keeping, as long as it meets the requirements outlined by the rule. This opens the door for AI-based solutions that can significantly enhance record-keeping efficiency and accuracy.

AI can aid in the development of customizable electronic record-keeping templates that adhere to the FDA's Traceability Rule requirements. These templates, such as an electronic sortable spreadsheet and reference document, could be designed to automatically capture the necessary Key Data Elements (KDEs) and Critical Tracking Events (CTEs) related to each food item.

An AI-based system can create these templates and adapt them to the specific needs of each firm, ensuring both flexibility and compliance.

The FDA Traceability Rule's reference document aspect presents a significant opportunity for AI application. These documents are key in establishing a detailed food traceability framework as they contain some or all key data elements (KDEs) critical for tracking events (CTEs) in the food supply chain.

Managing these reference documents manually can be a complex and labor-intensive task, especially for large organizations dealing with vast quantities of diverse food products. This is where AI can be transformative.

AI-powered tools can significantly simplify data entry and management. AI systems can automatically capture and store KDEs in the appropriate formats, reducing manual effort and minimizing human error. Through natural language processing, AI can even interpret and extract data from unstructured sources, such as emails or notes, and incorporate them into the records as necessary.

Moreover, AI can aid in the analysis of the extensive data generated within the supply chain. Machine learning algorithms can identify patterns and anomalies, enabling swift identification and removal of potentially contaminated food from the market, aligning perfectly with the objective of the FDA's new rule.

Implementing and Testing Your Traceability System

The next steps involve designing and implementing the traceability system in your organization.

This process may require technological solutions, staff training, or procedural changes. AI comes into play here by providing real-time tracking of food items throughout the supply chain. AI-powered IoT devices can monitor temperature, humidity, and other environmental conditions, ensuring the food's quality and safety during transit.

After implementation, it's crucial to test the traceability system's effectiveness through regular audits, recall exercises, and system tests. AI systems ensure prompt response times to FDA information requests by swiftly retrieving and providing the necessary KDEs, ensuring organizations meet critical compliance deadlines.

Refinement, Monitoring and Fostering Compliance Culture

Continuous refinement and improvement based on system tests and audits are essential for an effective traceability system. AI, with its continuous learning and adaptation capabilities, can optimize the traceability system over time, enhancing its accuracy and efficiency.

Staying updated with changes to the FDA's Traceability Rule and other related regulations is vital for maintaining compliance. AI systems can automate regulatory monitoring, providing alerts when new updates or changes are released.

Finally, fostering a culture of compliance within your organization is essential.

While AI and other technologies can aid in ensuring compliance, the importance of human understanding and adherence to the rule cannot be understated. Regular training and workshops can ensure that your team understands the importance of traceability and the role they play in food safety.

In conclusion, the FDA’s new Traceability Rule signals the beginning of a new era of smarter food safety, bolstering transparency and accountability across the food supply chain. In navigating the path to compliance, AI tools offer invaluable assistance, from advanced record-keeping to real-time tracking, predictive analytics, swift response times, and continuous learning.

By embracing these innovative technologies, organizations can uphold food safety, protect consumers, and stride confidently into the future of the food industry.

Remember, that the FDA has set the date of January 20, 2026 for enforcement of this rule.

Don't wait until last minute to be in compliance!

Sincerely,

Radojka Barycki

P.S: Click the link to attend to my upcoming audio event: https://bit.ly/43qoLZM