Ask The Expert

Food Safety as Business Infrastructure

By Azure Edwards, M.S.
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Fifteen years after FSMA reoriented food safety around prevention, the technical infrastructure is largely in place. What is becoming visible at this maturity point is the layer beneath it — the business decisions, governance structures, and organizational design that determine whether that infrastructure actually holds under real operational conditions.

This five-part series examines food safety through the business realities that leaders already navigate: profitability, risk, growth, brand trust, and organizational function. Not to reframe food safety as a business problem, but to make visible what the industry has earned the right to see clearly. Food safety outcomes are shaped upstream of the technical program, in the structures responsible for decision-making and execution. Each article stands alone. Together they trace a single line of thinking about where the conversation goes next.

The Business Layer of Food Safety

Fifteen years after FSMA reoriented the food safety conversation from response to prevention, the industry is taking stock of how far it has come and beginning to ask what comes next. The technical infrastructure that the regulation called for is largely in place. Preventive controls, environmental monitoring, supplier verification, documented systems designed to demonstrate control: organizations have invested heavily in building these programs, and the investment has mattered. What is becoming visible at this maturity point, precisely because the technical layer is now developed enough to examine clearly, is the layer beneath it. The decisions that shape how work actually happens, the authority structures that determine who can act and when, and the resource allocations that establish what the system can realistically do under pressure. The industry recognizes this layer. Food safety is earning a seat at the leadership table, and the conversation arriving with it is more sophisticated than it has ever been. What is still being built is the shared language that allows that recognition to move from individual insight into organizational practice — the clarity that lets the governance layer function not just as something experienced professionals can describe, but as something the organization can deliberately act on.

That gap between recognition and shared operational language is where most recurring food safety instability actually lives. A single deviation is an event. The same deviation returning across multiple corrective action cycles, under different operators and different supervisors, despite documented resolution, is something else. It is the friction that has become familiar, and the category of work the organization has silently learned to expect rather than eliminate. Fifteen years of investment in preventive infrastructure has produced something valuable that the industry hasn’t fully used yet — a record precise enough to show, over time, not just what went wrong, but what keeps returning and why. The correction closed the record. It did not reach the source.

Consider what that looks like on the floor. A food manufacturing facility has a recurring GMP issue: sanitation tools left on the floor rather than returned to storage after use. The expectation is documented, the procedure exists, and the team has been trained far more than once. When the issue surfaces again, the response follows the familiar path: a reminder, a retraining, a corrective action that closes with appropriate documentation. And for a period things improve… until they don’t. What finally shifted the outcome wasn’t a stronger procedure or more consistent enforcement. It was a different question: not what are people doing wrong, but what is the system making it easier to do? When I examined the actual conditions rather than the behavior, the answer was immediate. Storage locations were positioned away from where the tools were used, the hardware didn’t fit the tools being issued, and returning equipment properly required extra movement that, under the pace of a working shift, simply didn’t happen reliably. Once the storage locations were repositioned and the hardware matched the tools, the issue resolved without additional training, without escalation, without any of the interventions that had been tried before. The behavior changed because the conditions changed.

What that case reveals extends well past its specific details. Through multiple corrective cycles, the investigation had been aimed at the people in the system — their knowledge, their habits, their compliance — when the actual source of the pattern was sitting in the design of the environment they were working in. This is the structure of most recurring food safety problems: not absent standards, not insufficient commitment, but a mismatch between where the response is directed and where the condition actually originates. The organization had a functioning program and genuine investment in food safety outcomes, but neither were sufficient to stabilize a condition that lived upstream of where the program was looking. That gap between where the system looks and where the condition lives is precisely what the governance layer is responsible for closing, and precisely what the industry’s next conversation needs to address.

Food safety is one of the few functions in a business where this gap becomes consistently legible to both the people running the floor and the people running the business. A corrective action log read as a list of resolved tickets tells you how responsive the system is. The same log read as a transcript of what keeps coming back tells you something different — which areas generate repeated entries, which responses cycle through without producing stability, which categories of work the organization has learned to absorb as routine rather than resolve at the source. That second reading requires treating the pattern across entries as more informative than any individual entry, and asking what organizational conditions would have to be true for this pattern to keep generating itself. The data to answer that question already exists in most operations. What’s needed is the orientation to read it at the right level — one that connects what operators see on the floor to the decisions that executives are positioned to change.

The layer that determines whether those conditions get addressed is not the technical program layer. Everything built over the last fifteen years — the controls, the monitoring, the documentation infrastructure — operates within conditions established further upstream: in how decisions get made about work design and resource allocation, in how authority is distributed and what happens when it’s exercised under pressure, in how competing priorities get resolved when production demands and safety requirements arrive at the same moment. Those decisions, and the organizational structures that make them, are what food safety outcomes are actually built on. When that structure is coherent, the technical programs beneath it tend to function as designed. When it isn’t, those programs compensate by absorbing strain, generating more corrective activity, and requiring more verification while the conditions producing that activity remain in place.

Reading the pattern accurately means asking questions at the right level of the system, not about the procedure that was missed or the person who was present. What decisions and structures established the conditions those people were working within? That inquiry moves the conversation out of the technical program and into the business itself: into how the organization is structured to make and carry decisions under ordinary operational pressure, and whether that structure is coherent enough to support the systems that depend on it. What it costs when it isn’t (corrective cycles, absorbed inefficiency, work that keeps having to be done twice) is where the stakes become most visible to leadership, and most familiar to the people closest to the work.

Big data

AI Is Becoming a Practical Food Safety Equalizer for Small and Mid Sized Manufacturers

By Matthew Kang
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Big data

For small and mid sized food manufacturers, the real food safety challenge is often not the absence of programs. It is the difficulty of executing them consistently with limited people, limited time, and limited system support. AI does not replace food safety culture, trained employees, or management accountability. What it can do is reduce documentation drag, connect fragmented records, and give small plants better visibility into the daily factors that affect both compliance and performance.

Most small food plants already have some version of HACCP, sanitation procedures, allergen controls, supplier documentation, and corrective action forms. On paper, the structure exists. The problem is that these programs often live in separate places. Some information is in handwritten logs. Some is in Excel files. Some sits in email trails. Some remains in the memory of one or two experienced employees. In a large company, those gaps are often absorbed by specialized teams. In a 20 person plant, they become part of the day’s friction. USDA FSIS guidance for small and very small establishments reflects that reality by offering practical compliance guidance for smaller operations rather than assuming large company infrastructure.¹ ²

That is one reason AI matters. Not because it is futuristic, but because small food companies and facilities need tools that help them execute what they are already supposed to be doing.

FDA’s Food Traceability Final Rule makes that challenge even more visible. For foods on the Food Traceability List, firms are expected to maintain linked records around Critical Tracking Events and Key Data Elements so food can be identified and removed from the market more quickly when necessary. FDA has also said that, under current law, it does not intend to enforce the rule before July 20, 2028.³ ⁴

I did not introduce AI as a food safety system. At first, I was simply trying to make our ordinary plant records easier to use. What surprised me was how quickly those same operating records turned into food safety records once they were organized properly.

The first and most important use case was the daily production report.

A typical report includes labor hours, raw material use, number of batches, yield, run time, and overhead assumptions. But what makes that report valuable is the context around the numbers. A forming machine goes down and creates a one hour delay. A new operator joins the line and throughput drops. A raw material lot arrives with inconsistent quality and forces rework or a change in handling. Before AI, those details usually existed as loose comments. They were written down, but not really used.

That changed once we started combining the numbers and the narrative in one place. After a few weeks, I started noticing which problems were truly random and which ones kept coming back. A yield problem was not always just a yield problem. Sometimes it pointed to operator inconsistency. Sometimes it pointed to equipment instability. Sometimes it started with raw material quality. In a small plant, those issues do not stay in their own lane. They spill into sanitation timing, rushed handling, delayed changeovers, and rework decisions. That is when I realized AI was doing more than saving time. It was helping us see operational patterns we had been living with but not fully recognizing.

A second use case involved incoming raw materials.

In a small food company or facility, receiving is one of the most important control points, but also one of the easiest places for information to become fragmented. We began using simple photo capture of ingredient statements and specification sheets to pull out allergen information, compare those ingredients against non allergen counterparts, and flag price changes. If a supplier raised a price or changed a formulation, that information could be reflected back into costing and into the same day’s production analysis.

This mattered more than I expected. In the past, allergen characteristics, lot information, and pricing changes could all be reviewed by different people at different times. That made it too easy for something important to be noticed late. Once those pieces were pulled together, receiving became much more useful as an early warning point instead of just a paperwork step.

A third application involved process data and compliance follow through.

Post process data logger outputs, for example, became more useful when we reviewed them for patterns instead of as isolated records. If a cooling trend began to drift or a cook step started landing too close to the lower end of a target range, we could see it earlier. The same logic applied when a USDA or FSIS noncompliance record was issued. What used to require digging through prior records, emails, and deadlines could be organized into a more structured workflow. That did not remove the need for qualified review. It still required human judgment and human sign off. But it cut down the time spent assembling information that already existed in scattered places.

Monthly closing and costing created another layer of value. By comparing accounting data with production report trends, it became easier to see whether a margin decline was being driven by labor inefficiency, unstable yield, supplier inflation, or poor scheduling. In a small plant, food safety discipline and operational discipline are closely tied together. Rework, spoilage, excessive changeovers, and weak lot visibility are cost problems. They are also signals of weak execution. Once those signals become visible earlier, management decisions improve.

Production scheduling turned out to be one of the clearest examples of AI’s practical value. In a small facility, the best schedule is not simply the one that fills the day. It is the one that balances labor availability, sanitation windows, equipment uptime, maintenance timing, raw material readiness, and product mix. We began reviewing historical combinations of labor, line setup, batch sequence, and uptime that had previously produced stronger margins and smoother runs. It was not perfect. But it did stop us from planning only by instinct.

That also created a sustainability benefit. Better schedules can reduce avoidable changeovers, overproduction, product loss, and inefficient use of labor and energy. For small plants, sustainability does not begin with a polished ESG report. It begins with running a tighter operation. When inventory is more visible, fewer ingredients expire unnoticed. When schedules are better sequenced, fewer unnecessary runs are made. When traceability is better structured, edible surplus is easier to identify and donate instead of discard. In California, where edible food recovery and organic waste diversion obligations under SB 1383 are part of the operating landscape, those improvements are not abstract. They can affect whether product is simply written off or handled more responsibly.⁹

None of this means AI should be treated casually.

The stronger its role becomes, the more important governance becomes. That is why the NIST AI Risk Management Framework is useful even though it is not a food law. It gives smaller organizations a practical framework for thinking about trustworthiness, transparency, validation, human oversight, and risk management. Published as NIST AI 100-1 in January 2023, it was developed under the National Artificial Intelligence Initiative Act of 2020 and is voluntary, non sector specific, and broadly applicable across sectors.⁸

For a small food company or facility, that does not require a long policy manual. It does require a few clear rules. Which decisions require human sign off. Which records are AI assisted but still human verified. How outputs are checked against current FDA regulations, USDA FSIS guidance, customer requirements, and plant procedures. What data may be uploaded into external tools, and by whom. These questions matter because AI can produce text that sounds authoritative even when it is wrong. In food safety, that is not a minor issue. It is a governance issue.

The same caution applies to digital records. FDA’s Part 11 guidance makes clear that electronic records used in regulated settings remain subject to the applicable predicate rules.⁵ USDA FSIS has also made clear that electronic monitoring and recording records may be used to satisfy HACCP, sanitation, and related requirements, and that electronic records are treated the same as paper records.⁶ ⁷

The food safety world often talks in terms of programs, plans, and frameworks. Those matter. But in small and mid sized manufacturing, the real test is whether those systems can still be executed on an ordinary Tuesday while labor is tight, equipment is acting up, and a late shipment has already disrupted the day. That is where food safety often breaks down. Not in theory, but in execution.

That is why I see AI less as a replacement for expertise and more as a practical equalizer. In a 20 person plant, it can create better visibility, better consistency, and better follow through than the staffing level would otherwise allow.

References

¹ U.S. Department of Agriculture, Food Safety and Inspection Service. Small & Very Small Plant Guidance.
² U.S. Department of Agriculture, Food Safety and Inspection Service. HACCP Guidance. Last updated Jan. 12, 2022.
³ U.S. Food and Drug Administration. FSMA Final Rule on Requirements for Additional Traceability Records for Certain Foods.
⁴ U.S. Food and Drug Administration. Food Traceability List.
⁵ U.S. Food and Drug Administration. Part 11, Electronic Records; Electronic Signatures — Scope and Application. Guidance for Industry. September 2003.
⁶ U.S. Department of Agriculture, Food Safety and Inspection Service. Verifying Video or Other Electronic Monitoring Records. FSIS Directive 5000.9. Aug. 26, 2011.
⁷ U.S. Department of Agriculture, Food Safety and Inspection Service. Compliance Guidelines for Use of Video or Other Electronic Monitoring or Recording Equipment in Federally Inspected Establishments. Guideline ID FSIS-GD-2011-0001. August 2011.
⁸ National Institute of Standards and Technology. Artificial Intelligence Risk Management Framework (AI RMF 1.0). NIST AI 100-1. Jan. 26, 2023.
⁹ California Department of Resources Recycling and Recovery. Food Recovery Questions and Answers.

Collaboration Graphic

The Missing Layer in Food Safety Systems

By Azure Edwards, M.S.
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Collaboration Graphic

Food safety systems are often evaluated through the strength of their technical programs. Organizations invest heavily in preventive controls, environmental monitoring, supplier verification, and documentation systems designed to demonstrate regulatory compliance.

Yet many companies still encounter instability in their food safety programs, even when the required systems appear to be in place.

  • Corrective actions repeat.
  • Audit findings reappear across facilities.
  • Operational practices vary from site to site.

When these patterns emerge, the instinct is often to add more documentation, training, or oversight. But in many cases the issue is not the absence of technical controls. It is the absence of a stable governance structure capable of translating safety expectations into consistent operational practice.

Understanding this governance layer can help explain why food safety systems sometimes struggle to stabilize as organizations grow.

Signals of Governance Instability

When governance structures are underdeveloped, organizations often experience recognizable patterns. Food safety programs may appear complete on paper, yet operational stability remains difficult to achieve. Common signals include:

  • corrective actions addressing the same underlying issues repeatedly
  • compliance programs dependent on specific individuals rather than system design
  • inconsistent practices across facilities, shifts, or teams
  • unclear authority for safety-related decisions

  • reactive responses to audits or inspections

These patterns are often interpreted as training gaps, communication problems, or culture challenges. In many cases, however, they reflect a deeper issue: the governance structures required to translate food safety expectations into consistent operational practice were never fully established.

Governance Exists Before Compliance

As food businesses expand, they move through stages of increasing regulatory oversight. A cottage food operation may manage safety practices informally. Licensed commercial kitchens introduce sanitation programs and basic documentation. Commercial manufacturers implement preventive controls, monitoring systems, and structured records. Enterprise operations standardize these systems across facilities and supply chains.

At each stage, regulatory expectations become more visible and formalized. However, the underlying conditions required to produce safe food do not begin with regulation. They exist before it.

Regulatory frameworks primarily make those conditions observable and enforceable. When organizations grow quickly or transition between operational stages, regulatory oversight often reveals governance structures that were never fully developed.

Common symptoms include fragmented compliance programs, inconsistent operational practices, reliance on individual expertise rather than system design, and reactive responses to audits or inspections.

Organizations frequently respond by expanding documentation requirements or implementing additional procedures. While these interventions can address immediate gaps, they may not resolve the deeper governance instability beneath them.

The Structural Conditions of Safe Food Production

Across all scales of food production, stable food safety governance depends on several core conditions.

  1. The decision authority for safety must be clear. Food safety decisions must be anchored in identifiable operational authority. When responsibility is diffuse or ambiguous, operational decisions may drift away from safety expectations.
  2. Organizations must maintain visibility into where hazards and contamination risks can occur across processes, materials, and the operational environment. This awareness forms the foundation for preventive control strategies.
  3. Operational practices must exist to prevent contamination and control risk. These practices may appear as formal procedures, sanitation programs, or routine operational behaviors embedded in daily work.
  4. Organizations must manage suppliers and external inputs intentionally. Ingredients, packaging materials, and outsourced processes introduce variability into the production system and require structured oversight.
  5. Systems must exist to detect deviations and respond consistently. Monitoring, verification, and escalation mechanisms allow organizations to identify when conditions diverge from expected standards and ensure that appropriate responses occur.
  6. Organizations must maintain records sufficient to demonstrate control. Documentation provides traceability, accountability, and evidence that governance systems function as intended.

These structural conditions exist before audits, certifications, or inspections. Regulatory frameworks formalize and observe them, but they do not originate from those frameworks.

The architecture of food safety governance remains relatively consistent across organizations of different sizes. What changes with scale is how visible and distributed that architecture becomes.

In early-stage operations, governance conditions are often implicit. Safety decisions are managed directly by founders or operators who maintain personal oversight of production activities. As organizations grow, responsibilities become distributed across teams and departments. Preventive control programs become formalized, and operational systems become more structured.

Instability often occurs during these transitions. Oversight mechanisms such as inspections, customer requirements, or certification audits rarely introduce entirely new expectations. Instead, they expose structural conditions that were already necessary but not previously formalized.

Organizations may then attempt to compensate by layering additional documentation or procedures onto an unstable governance foundation. Without addressing the structural layer beneath those programs, stability can remain difficult to achieve.

The Orientation Challenge

Even when organizations recognize the structural elements required for safe food production, another challenge remains: interpreting operational complexity in a way that allows those structures to be built coherently.

Food safety systems operate within sociotechnical environments where technical programs, operational realities, leadership decisions, and human behavior interact continuously.

Attempts to correct one domain without addressing the others often produce temporary or fragile improvements.

Stable systems require an orientation that helps organizations interpret how these elements interact and translate them into coherent governance structures. In practical terms, this means understanding where safety-related decisions are actually made and how risk signals move through the organization. When those pathways are unclear, even well-designed technical programs can struggle to function consistently.

This orientation exists upstream of technical program design. It shapes how organizations interpret regulatory expectations, operational constraints, and risk signals before specific programs are implemented.

Building Systems That Can Endure

Reliable food safety systems must function under ordinary operational conditions. They must withstand staffing changes, production pressure, operational growth, and the variability of real manufacturing environments.

Systems that depend on exceptional individuals or constant intervention tend to degrade over time. Durability emerges when governance structures provide:

  • clear operational priorities
  • defined decision authority
  • consistent escalation pathways

  • shared understanding of risk

When these elements are present, technical programs can operate as intended. When they are absent, organizations often rely on documentation, enforcement, or external oversight to compensate for deeper structural ambiguity.

Seeing the System Clearly

Food safety governance ultimately involves more than compliance or technical expertise. It requires organizational structures capable of translating risk awareness into coherent operational practice.

When organizations understand the structural conditions required for safe food production—and the reasoning patterns that allow those conditions to be built—technical programs become more stable and scalable.

Rather than introducing new requirements, this perspective helps make visible the governance realities that have always existed within safe food production.

Once visible, those structures can be developed deliberately, supporting food safety systems that remain stable even as organizations grow and operational complexity increases.

References

  1. Codex Alimentarius Commission. General Principles of Food Hygiene CXC 1-1969. FAO/WHO.

  2. U.S. Food and Drug Administration. FSMA Final Rule for Preventive Controls for Human Food. FDA.

  3. GFSI. A Culture of Food Safety: A Position Paper from the Global Food Safety Initiative. 2018.

Collaboration Graphic
In the Food Lab

How Rapid Microbiology and AI Are Transforming Modern Food Safety Laboratories

By Wesam Al-Jeddawi, Ph.D.
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Collaboration Graphic

Food safety laboratories are undergoing a significant shift as the food industry faces increasing pressure for speed, accuracy, and transparency. Traditional microbiological methods remain foundational, but they are often too slow to support today’s accelerated production cycles and complex supply chains. As a result, laboratories are adopting rapid microbiological methods, digital data systems, and artificial intelligence to enhance decision-making and reduce risk.

These technologies are not replacing scientific expertise—they are expanding what laboratories can deliver. When integrated thoughtfully, they improve efficiency, strengthen data integrity, and help manufacturers identify issues earlier in the production process.

The Growing Role of Rapid Microbiological Methods

Rapid microbiology has become essential for processors seeking faster turnaround times and more consistent results. Technologies such as molecular assays, automated enumeration systems, and ATP bioluminescence offer several advantages:

  • Faster results, enabling earlier product release
  • Reduced manual handling, lowering variability and labor demands
  • Improved sensitivity, especially for stressed or low-level organisms
  • Digital traceability, supporting audit readiness and data integrity

For many facilities, the value lies not only in speed but in the ability to intervene earlier. Rapid methods allow quality teams to detect deviations before they escalate into waste, rework, or regulatory action.

Artificial intelligence is beginning to influence how laboratories interpret and manage microbiological data. Its most impactful applications include:

  • Identifying patterns across historical testing data
  • Predicting spoilage and contamination risks
  • Automating data checks, reducing transcription errors
  • Supporting root-cause analysis with more complete datasets

When paired with a laboratory information management system (LIMS), AI helps laboratories transition from reactive testing to proactive risk management. Instead of simply reporting results, labs can provide insights that help manufacturers prevent issues before they occur.

Quality Systems and Accreditation Expectations

As laboratories adopt new technologies, accreditation bodies are raising expectations around method validation, documentation, and data integrity. This shift is encouraging labs to:

  • Strengthen quality management systems
  • Standardize workflows to reduce analyst-to-analyst variation
  • Improve documentation for regulatory and customer audits
  • Integrate digital tools that support real-time monitoring

The laboratories that excel are those that combine scientific rigor with operational discipline.

A Changing Role for Food Safety Laboratories

The modern laboratory is evolving from a testing provider to a strategic partner. Today’s labs increasingly support manufacturers by offering:

  • Technical guidance on sampling and environmental monitoring
  • Data-driven insights for continuous improvement
  • Training for quality and production teams
  • Support for regulatory readiness and risk mitigation

This expanded role reflects the growing importance of laboratory expertise in ensuring food safety across the supply chain.

Conclusion

Rapid microbiology, digitalization, and artificial intelligence are reshaping the capabilities of food safety laboratories. These tools enhance—not replace—scientific judgment, enabling laboratories to deliver faster, more reliable, and more actionable information. As the industry continues to evolve, laboratories that embrace innovation while maintaining strong scientific foundations will play a central role in building a safer and more resilient food system.

Food Safety Culture Club

More Than an Obligation: Building a Culture of Food Safety That Lasts

By William Brodegard
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Every food and beverage company has a food safety program.very company has a food safety culture, good or bad. Across the industry, you can see a clear maturity curve. At one end are organizations that meet regulatory requirements because they must – they comply, they document, and they prepare for audits. At the other end are those that embed food safety into everything they do. For them, compliance is a byproduct of strong processes and shared values, not just the finish line. They move past “what’s required” and focus on “what’s right,” creating an environment where food safety isn’t something checked off once a year but lived out daily across every shift and every site.

This shift in mindset represents the real evolution of food safety maturity. It’s the difference between protecting a business and strengthening it. When companies see compliance as the goal, improvement stops at the minimum acceptable level. But when they treat food safety as a reflection of their culture, every metric – from audit scores to customer satisfaction – begins to move in the right direction. Food safety excellence in 2026 shouldn’t really be about policing processes, it should be about empowering people to take ownership. The organizations that reach this stage understand that sustainable quality comes from within, when teams no longer ask, “Do we meet the standard?” but rather, “How can we raise it?”

The compliance plateau

It’s easy to assume that passing audits and meeting regulatory deadlines means a business is in good shape, and many companies think that’s enough.. But compliance alone doesn’t guarantee consistency, and it doesn’t always tell the full story of what’s happening on the floor. Documentation can look perfect while small, unrecorded deviations create hidden risks. Every gap in recordkeeping represents a potential business risk, not just a compliance failure, and those risks compound quietly over time. The reality is that many food and beverage companies hit a “compliance plateau.” They have the right forms, the right SOPs, and the right intentions, but their systems are designed to prove compliance rather than to improve performance. That leaves a lot of potential on the table.

It also breeds complacency. Teams work hard to stay audit-ready but lose sight of why the standards exist in the first place. The focus becomes “what we have to do” instead of “what we could do better.” And in that mindset, root causes stay buried and the same issues resurface year after year. Breaking through requires a shift in perspective that treats every inspection, every deviation, and every data point as a learning opportunity. When companies start asking how they can prevent issues instead of just documenting them, they move from compliance maintenance to cultural improvement.

Culture as a catalyst

A strong food safety program depends on process, but a lasting one depends on people. When food safety becomes everyone’s responsibility, from operators on the floor to leadership in the boardroom, it becomes a shared mission. For this to work, trust and visibility are essential – when teams can see real-time data about performance, issues, and trends, they’re likely to take greater ownership of results. Transparency removes the fear of “being caught out” and replaces it with a sense of accountability and pride. In that sense, the more open the system, the stronger the culture.

Building this kind of environment also means reframing how success is measured. Instead of celebrating clean audits alone, mature organizations recognize and reward proactive behaviors – flagging small deviations before they escalate, taking initiative to correct problems, and learning from near misses. Over time, those actions create a cycle of continuous improvement. Food safety becomes something teams compete to uphold rather than something imposed from above. And because safety is inherently “pre-competitive” – there are no brand wins for getting it right, only reputational harm for getting it wrong – raising internal standards benefits the industry as a whole.

Digitization as an enabler

Culture can’t be established in a vacuum – the right systems need to be in place to support and reinforce it. That’s where digitization becomes a key differentiator. By moving from paper records and delayed reporting to real-time data capture, food safety teams gain the visibility they need to stay ahead of issues. While a paper system merely stores information, often poorly with high risks of error and duplication, a digital system connects people, processes, and performance under one single source of truth. When a deviation occurs, alerts trigger immediately, corrective actions are tracked, and insights feed back into continuous improvement. The result is a living system that evolves with each and every data point.

And this level of visibility and proactivity has a knock-on effect on behavior. When operators see that the data they enter drives decisions and improvements, engagement rises. When managers can analyze patterns across sites or shifts, prevention actually starts to seem possible. Over time, digitization weaves food safety into the nuts and bolts of daily operations, turning isolated checks into connected intelligence. It’s important to note that this technology will never replace human judgment, nor should it, but it does strengthen it by giving teams the clarity and confidence to act quickly and consistently. In the most mature organizations, digital systems are the quiet backbone of culture: always on, always learning, and always reinforcing the commitment to do things right the first time.

Sanitation, misting

How Hard Water Sabotages Your Sanitation Chemicals (And How to Fix It)

By Emily Newton
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Sanitation, misting

Water management in food processing is essential to ensuring the safety of employees and consumers. Disinfection and sanitation are other vital components, but they often influence each other. Hard water in food processing, while common, affects the performance of chemical cleaners. The workforce must recognize the dangers of using sanitizers on hard water — even if they are industry-approved — and implement strategies to mitigate their adverse effects.

Where Hard Water in Food Processing Comes From

Hard water is a persistent problem in the U.S., indicating calcium carbonate concentrations of 121 milligrams per liter or more. The presence of typical hard-water minerals is not a public safety concern for food manufacturers, but it affects how they clean and sanitize. Understanding the many points of entry is the first step in solving the problem.

It enters most structures from groundwater sources. As water travels through the soil and other rocks, it becomes laden with additional nutrients and minerals, such as magnesium and calcium. The density of these can vary based on numerous factors, like geography and how far the water has to travel. It also depends on the region’s and the company’s filtration and treatment infrastructure.

How Does Hard Water Ruin Food Sanitation Chemicals

The U.S. Environmental Protection Agency outlines what food-grade sanitation chemicals are approved for use on contact surfaces and equipment. They include chlorine and peroxyacetic acid (PAA), with restrictions being even more noticeable in organic-certified organizations. However, many of these react poorly to hard water.

Common surfactants in sanitation chemicals are ineffective against most divalent cations. When hard-water ions interact with the active ingredients, they can form soap scum. The residue can accumulate quickly, reducing the effectiveness of cleaning agents.

The salt formation is insoluble, so other tools and sanitation chemicals are required to remove it. While it may appear as a mere visual distraction on a food-grade surface, the presence of the insoluble precipitate suggests the disinfectant had a reduced rate of kill against bacteria and other harmful microorganisms.

If biofilms and limescale form, technicians may feel incentivized to use more of their cleaners to remove them. However, research shows that sometimes increasing the amount of the chemical can also have an adverse effect. Tests using 200 parts per million of chlorine, 400 ppm of quaternary ammonium compound and 160 ppm of PAA led to a greater presence of Listeria on stainless steel surfaces. Neglecting targeted cleaning methods could lead to increased foodborne illness outbreaks and product recalls.

Additionally, cleaners such as chlorine and iodine-based options also react poorly to hard water in other ways. Hard water’s acidity can reduce the effectiveness of chlorine by making it less present in an active state. This reduction in efficacy is not visible to the naked eye, making it a deceptively common problem in food processing facilities.

How to Execute Water Management in Food Processing Facilities

If the workforce wants to abate concerns caused by hard water, they must institute a robust water management plan. It must be multipronged, including behavioral shifts and technological implementations to be as comprehensive as possible. Otherwise, it would introduce hazards into the workspace, which would go against the most prominent safety controls, including the Hazard Analysis and Critical Control Points and Good Manufacturing Practices.

Use Cold Storage

Sanitation and disinfection are required parts of workflows to keep food clean and nutritious, but so is the way it is stored. The appropriate storage solution amplifies the effectiveness of all cleaning efforts by preventing bacterial reproduction, machinery failures and scale buildup. It only takes one hour of downtime for an organization to lose thousands of dollars, so leveraging storage to prevent additional cleaning is crucial. Maintaining storage equipment is even more important.

Install Water Softeners and Purification

Companies can remove minerals in the water they use before it hits the production floor. Ion-exchange water softeners and reverse osmosis technologies are among the industry’s most reliable methods for removing calcium and magnesium from water. They take both out of the equation so sanitation chemicals can work at maximum efficiency.

Experiment With Different Formulations

Teams can use conventional sanitizing chemicals with revised ingredients and compositions to fight against hard water if the organization is unable to soften or filter it. Chelating agents are powerful additives because they bind to minerals, preventing them from interfering with active ingredients. One common chelant is citric acid, which may cost more to implement, but it could be cost-effective in the long term by preventing other issues.

Water Quality As the Foundation of Food Safety

Limescale buildup is more than unsightly — it has profound implications for poor sanitation in food manufacturing. Water hardness is a greater health threat in these environments than most realize because it is typically not a concern in other circumstances. However, food experts have a responsibility to understand why their chemicals may not work as well when interacting with hard water. Then, they must collaborate with leadership and local utilities to prevent its transmission into food facilities and protect citizens.

From Chaos to Cruise Control: Generative AI and the Next Era of Human-Centric Warehouse Management

By Michelle Jones
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Managing a warehouse today can feel a lot like going on a beach vacation without a plan. You’ve packed your suitcase, brought sunscreen, and maybe even remembered the snacks, but the tides, the jellyfish, and the long lines at the rental shop all catch you off guard. Traditional warehouse management systems (WMS) can feel the same way. They enforce rules, track inventory, and issue alerts, but when something goes wrong, employees often find themselves scrambling to figure out what to do next. Searching manuals, calling support, or tracking down a colleague who “knows the system” can feel like running down the beach in flip-flops after a wayward volleyball.

Generative AI is changing that experience. Instead of being a rigid, static tool, the WMS becomes a co-pilot, a system that anticipates challenges, responds intelligently, and guides employees in real time. The difference is like stepping from a chaotic, sunburned first day into a perfectly planned vacation, where every detail is accounted for and the experience flows effortlessly.

The Challenges of Traditional WMS: A Chaotic Vacation

Modern warehouses are complex operations under tremendous pressure. Orders continue to climb, delivery windows shrink, and labor shortages make every team member essential. Yet many WMS environments still operate with interfaces and workflows designed for a very different era. The result is:

  • New employees spend weeks learning where to click rather than focusing on their work.
  • Experienced staff hunt for data buried somewhere in the system.
  • Simple questions can spiral into hours of inefficiency.

Day after day, these small frustrations erode productivity, accuracy, and employee satisfaction, just like a beach day where everything goes slightly wrong, leaving you exhausted and frustrated by the end.

Generative AI WMS: The Perfectly Planned Trip

Generative AI flips the traditional WMS experience on its head. Rather than forcing employees to adapt to rigid workflows, AI adapts to them. Questions can be asked in natural language, data is delivered instantly, and repetitive tasks no longer steal attention from high-value work.

Imagine asking your WMS a simple question: “Are any Costco orders at risk of missing their shipment deadlines today?”

Behind the scenes, the system:

  • Pulls real-time inventory data and status of shipment fulfillment tasks
  • References data from labor resource planning, transportation management, dock scheduling, and other warehouse systems
  • Gathers data and context for the orders to identify and escalate at risk shipments
  • Proposes issue resolution scenarios such as changing shipment mode from ground to air

From the employee’s perspective, it’s effortless, like having a vacation guide who already knows the tides, the sunscreen, and the best route to avoid crowds. Operationally, it’s transformational.

Empowering People

The most profound impact of Generative AI is on the human experience. A WMS that can listen, learn, and respond acts like a thoughtful vacation planner who is always available, patient, and proactive. Employees can:

  • Resolve questions independently without interrupting colleagues
  • Learn as they work, shortening onboarding times
  • Access insights via voice commands, mobile devices, or multilingual interfaces

The result is a more confident, engaged workforce, able to focus on meaningful tasks rather than firefighting. Just as a well-planned vacation lets you relax and enjoy the beach, a Generative AI-powered WMS allows employees to focus on execution instead of struggling with the system.

From Reactive to Proactive: Anticipating the Waves

While Generative AI quickly responds to questions, it simultaneously anticipates challenges. By continuously analyzing historical and live data, AI can identify trends, anomalies, and potential risks before they disrupt operations.

It’s like having a travel planner who already knows the best beaches, the shortest lines, and when the tide will turn. With this foresight, teams can prevent small annoyances from ruining the day like inventory bottlenecks, process delays, and service issues are addressed before they escalate, keeping operations running smoothly.

Rolling Out AI Without a Tan Line

Replacing a WMS doesn’t have to mean a disruptive, all-at-once change. While a Generative AI–powered WMS represents a new foundation, its value doesn’t need to arrive in a single “big bang” moment.

The most effective transformations roll out AI capabilities incrementally, starting with focused, high-impact use cases that align to real operational needs. Early wins build confidence, demonstrate value, and allow teams to adapt naturally as the platform expands into deeper optimization and autonomy.

This approach helps ensure adoption feels supportive rather than forced. Employees experience AI as a co-pilot that improves how they work from day one, not a system imposed on them overnight. Best practices include:

  • Starting with small pilot use cases in real operational scenarios
  • Providing clear, practical training instead of abstract theory
  • Involving employees early to build trust and familiarity

When introduced thoughtfully, AI-driven WMS transformation delivers lasting value without leaving teams feeling burned out in the process.

The Future of Autonomous, Adaptive Warehouses

The WMS of the near future is becoming increasingly autonomous and adaptive. Visual AI, real-time optimization, and dynamic route planning are not science fiction, but they are tools already helping reduce waste, improve throughput, and make warehouses more resilient.

A Generative AI WMS can adjust strategies on the fly, allocate resources in real time, and anticipate workflow disruptions. Much like a cruise ship that reroutes its course smoothly to avoid storms and crowded ports, these systems keep operations on track, even in complex, high-pressure environments.

From System of Records to system of Intelligence

Generative AI is transforming what a WMS is expected to do. Instead of just recording transactions and enforcing rules, it becomes an intelligent, human-centered partner that listens, learns, and acts.

Traditional WMS are like chaotic, poorly planned vacations that are functional, but stressful and full of surprises. Generative AI WMS are like meticulously planned trips where everything is anticipated, every decision is guided, and every moment is optimized. Employees are empowered, decisions happen faster, errors decrease, and operations flow smoothly.

The next era of warehouse management goes beyond automation. It’s adaptive, collaborative, and designed to make the complex feel effortless. When your WMS can “ask and answer questions” like a seasoned travel planner, the whole operation runs better, and everyone enjoys the experience along the way.

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The Importance Of Recall Preparedness for Supply Chain Resilience

By Roger Hancock
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At recent meetings with the FDA and USDA in Washington, DC—which I attended as Co-Chair of the steering committee for the Alliance for Recall Ready Communities, along with Gillian Kelleher and Dr. Darin Detwiler—the agencies provided updates on their recall modernization efforts. They both acknowledge the increasing challenge of complex supply chains, and continue to prioritize recall process improvements. They expressed strong interest and support for continued collaboration with the Alliance and the industry as a whole.

We updated the agencies on the Alliance’s efforts, explaining how our workgroups are finalizing draft models for a supply chain recall process, recall simulations, and standardized recall data. We plan to pilot implementation of the Recall Ready Community model in the first half of 2026. Ultimately, both meetings had similar takeaways: now is the time to address recall management as an important part of resiliency in increasingly complex supply chains.

Resilient Supply Chains: Connectivity, Communication & Action

With federal policies and priorities continuing to shift under the current administration, companies need to stay focused on protecting consumers and their businesses. While regulatory agencies have committed to improving the recall process, the industry must still shoulder the responsibility of protecting consumers when something goes wrong.

The way recalls are managed impacts consumer trust, public health, business continuity, and brands’ reputations, for better or worse. The negative impact of recalls often grows exponentially when companies and their trading partners are reactive vs. prepared. That’s where resilient supply chains come in.

A resilient supply chain allows food companies to anticipate and mitigate risk, identify and contain issues quickly, and absorb disruption without losing control. It shortens recovery time, reduces financial and reputational damage, and satisfies regulatory compliance. Just as importantly, it builds confidence—with consumers, regulators, and trading partners—through clear communication and decisive response.

This level of resilience is built on preparation. Recall modernization is a critical part of that preparation. Modern recall management treats recalls as a shared supply chain process, not isolated company events. It replaces siloed systems and fragmented workflows with connected data, standardized communication, and coordinated execution across partners.

Trademarks of a Resilient Supply Chain

Individual companies can’t be entirely resilient on their own. True resilience is built across the supply chain, through shared systems, aligned expectations, and coordinated action with trading partners. A resilient supply chain:

  • Enables fast, accurate data flow
  • Coordinates recall plans with trading partners in advance, and
  • Practices for recalls collaboratively.

Resilience is characterized not just by how quickly a company reacts, but by how well the entire supply chain works together. The following trademarks separate resilient supply chains from reactive ones:

  • Built-in visibility – Trading partners have real-time insight into product movement, testing, and crisis response.
  • Actionable data – Clean, structured information empowers better decision-making, data sharing, and response.
  • Clear, fast communication – Predefined protocols, easy to access contact data, and customized templates help trading partners distribute the right messages to the right people without delay. This helps key stakeholders—including trading partners, consumers, and regulators—take quick, proper actions.
  • Calculated adaptability – Resilient trading partners have the ability to shift sourcing, adjust operations, or re-route product without compromising safety or traceability.
  • Interoperability – Systems work together across functions—testing, traceability, recall execution—rather than operating in silos.
  • Dynamic training – Supply chain partners must prioritize ongoing training, regular practice, scenario planning, mock recalls, and post-incident reviews to test, learn, and improve. Working collaboratively helps trading partners prepare for real-life recalls so they can act quickly, confidently, and properly to reduce risks, damage, and disruption.
  • Coordinated responses – Resilient supply chains work together, ensuring a coordinated, integrated response to recall management. Think about recalls as supply chain activities, not individual company activities.
  • Proactiveness – Resilient supply chains are proactive, not reactive, working continuously to improve safety and quality, mitigate risks, and address issues before they become widespread problems.

Resilience isn’t a backup plan, or a measure of how well a company improvises under pressure. It’s the result of deliberate preparation—building systems, aligning partners, and practicing responses long before a recall occurs. True resilience assumes disruption will happen and ensures the supply chain is equipped to respond with clarity, coordination, and control when it does.

Work Together to Protect Public Health

Effective recall management starts well before a food safety issue is identified. Resilient supply chains also work to minimize the chances of a recall occurring in the first place. This includes proactive risk monitoring activities and the use of tech tools to flag potential safety risks early, helping prevent breaches and subsequent recalls.

Still, disruptions will continue to happen. With the right systems and processes in place, companies can identify and contain affected products faster, communicate clear instructions, and reduce risk to public health, brand reputation, and consumer trust.

As the industry looks ahead, preparedness is a practical place to focus—within individual organizations and across the supply chain—long before the next recall demands it. That focus aligns with ongoing recall modernization efforts at both the agency and industry levels, as resiliency is increasingly recognized as essential in today’s complex, global supply chains. Progress will depend on putting those shared frameworks into practice across the supply chain.

The State of Food Safety in 2026: Risks, Technology, and What FSQA Leaders Are Prioritizing Next

By Paddy McNamara
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Food safety in 2026 sits at a critical inflection point. Global supply chains remain fragile and volatile, regulatory scrutiny continues to intensify, and consumer tolerance for food safety failures is at an all time low. On the other side of the coin, food safety and quality assurance (FSQA) leaders are being asked to do more with fewer resources, manage risk proactively, respond to incidents faster and more effectively, and demonstrate compliance across increasingly complex operations. According to Mars company FSQA Director Vera Dickinson “coupling food safety with innovation is just a logical thing.”

The past year underscored a key truth: traditional, manual approaches to food safety management are no longer sufficient. As we move into 2026, FSQA executives are prioritizing digitization, data integration, and predictive technologies, not as “nice to have” tools, but as foundational capabilities for protecting public health and brand trust going forward into the future.

According to Brendan Niemira, IFT Chief Science and Technology Officer, “our food system is under pressure like never before. Climate change, resource scarcity, geopolitical disruptions, and rising consumer demands are creating unprecedented challenges. In 2026, those challenges will only intensify, but with those challenges comes opportunity for the food science community to turn uncertainty into innovation, complexity into clarity, and challenges into solutions.”

3 Persistent Pressures Defined Food Safety in 2025

1. Continued Supply Chain Complexity

While most problems that arose within the pandemic have eased, but with the U.S. tariffs policy changing so frequently, global sourcing still remains volatile. Ingredients often cross multiple borders, increasing exposure to contamination risks of country-specific germs, inconsistent regulatory oversight, and traceability gaps.

Larry Rehmann, former Diamond CEO an Senior Operations Leader said “food companies are in the business of managing risk.” FSQA teams are now responsible for monitoring risk well beyond their four walls and what they traditionally handled  and that has become an increasing focus as supply chain complexity has grown.

2. Heightened Regulatory Expectations

Regulators are demanding faster access to records, stronger preventive controls, and clearer accountability, all piled onto the plate of the FSQA. In the U.S., enforcement of the Food Safety Modernization Act (FSMA) continues to emphasize prevention, traceability, and rapid access to digital records. Similar regulatory trends are emerging globally.

3. Rising Consumer and Retailer Scrutiny

Word of food safety incidents travels faster than ever. Social media, online news, and retailer compliance programs amplify the reputational and financial damage of recalls, even when public health impacts are limited. As a result, food safety has become a board-level concern rather than a back-office compliance function.

When something goes wrong, almost everyone hears about it, all thanks to our age of exponentially increasing social media communication. On top of this, recent consumer trends have shown that the public has grown much more attentive to the quality of the food they consume in recent years and decades. This combined with the breadth of social media creates a difficult reputational playing field to please the public eye.

Food Safety by the Numbers: 2025 Snapshot

Despite advances in regulation and technology, foodborne illness remains a significant global public health issue. In the United States, the Centers for Disease Control and Prevention (CDC) estimates that 48 million people experience foodborne illness each year, resulting in approximately 128,000 hospitalizations and 3,000 deaths annually. That’s about 7.25% of the U.S. population affected annually. These figures have remained relatively consistent year over year, underscoring the persistent nature of food safety risk. Peter Begg, Lyons Chief Quality Officer, underscores this truth. He noted that “microorganisms don’t care who you are.”

Globally, the World Health Organization (WHO) estimates that 600 million people fall ill from contaminated food each year, leading to 420,000 deaths worldwide

Food recalls were also frequent in 2025, with pathogens such as Listeria monocytogenes, Salmonella, and undeclared allergens continuing to be among the leading causes of regulatory action.

Top Priorities for FSQA Leaders in 2026

As organizations look ahead, several priorities are emerging as central to food safety strategy.

1. Proactive Risk Management

FSQA leaders are shifting away from cause and effect, incident-driven approaches toward proactive risk identification. This includes earlier detection of deviations, real-time monitoring of critical control points, and the use of predictive analytics to prevent issues before they escalate into recalls. Vera Dickinson, Founder, InnovaQ & Former FSQA at Mars & Mondelēz, sees AI being the copilot for food safety leaders in this respect. Instead of worrying about job replacement with AI, Dickinson encourages food safety leaders to adopt it and use it to more efficiently manage risk.

2. End-to-End Traceability

Traceability expectations are expanding beyond “one step forward, one step back.” Regulatory agencies and trading partners increasingly expect organizations to demonstrate end-to-end visibility across suppliers, co-manufacturers, and distribution channels, communication across the board. Faster traceability has been shown to reduce recall scope and response time

3. Continuous Audit Readiness

Rather than preparing for audits periodically, FSQA teams are prioritizing continuous audit readiness. Bryan Armentrout, VP at Whitewave Foods, said, “audits main; risk assessments prevent.” Digital recordkeeping and standardized workflows are becoming essential as regulators expect immediate access to complete and verifiable documentation. This also goes a long way in the eye of the public, a sort of, “nothing to hide,” approach.

4. Workforce Enablement

Labor shortages and high turnover continue to challenge food safety operations. Leaders are investing in systems that simplify training, reduce manual paperwork, and enable frontline teams to execute food safety programs consistently and accurately. The real challenge comes from finding the balance of efficient and timely onboarding that leads to proficient and effective workers.

The Expanding Role of AI in Food Safety

Like a growing tidal wave, artificial intelligence is moving from experimental use cases to practical application within food safety programs, being deployed on the front lines more and more frequently.

AI-enabled systems are increasingly used for anomaly detection, identifying patterns or deviations in operational data that may signal emerging risk. Predictive models can help prioritize inspections, preventive maintenance, and corrective actions by analyzing historical and real-time data more timely and effectively than a human counterpart.

Additionally, AI is being applied to document intelligence, supporting faster analysis of audit reports, corrective action records, and compliance documentation. These tools help FSQA professionals focus less on administrative review and more on risk mitigation and continuous improvement, staying ahead of the curve.

However, AI is not replacing food safety professionals. Instead, it brings both worlds together, augmenting human expertise, enhancing visibility, speed, efficiency, and decision-making across complex food systems.

The Benefits of Technology for Food Safety Programs

Across the industry, digital transformation is delivering measurable benefits:

Faster recall response: Digital traceability systems enable organizations to identify affected products and locations in hours rather than days, again, allowing for much more proactive responses.

Improved compliance confidence: Centralized digital records reduce the likelihood of missing or incomplete documentation during inspections, keeping everyone on the same page.

Stronger cross-functional collaboration: Integrated platforms allow quality, operations, procurement, and leadership teams to operate from a single source of truth.

Roger Hancock, CEO, Recall InfoLink, went on the record as saying that “connected technology improves visibility, traceability, and recall response efficiency. While progress has been made, siloed systems and disconnected data make it harder to manage recalls effectively. The industry is finally shifting toward more responsive, tech-enabled food safety workflows”

Reduced financial impact: The Consumer Brands Association estimates that the average direct cost of a food recall can exceed $10 million, excluding long-term brand damage, making prevention and early detection financially critical.

Will Food Recalls Increase or Decrease in 2026?

The outlook for 2026 is mixed. In the short term, recall activity may remain steady or increase slightly, not necessarily because food is becoming less safe, but because detection, testing, and reporting capabilities continue to improve. Greater transparency often results in earlier identification of issues that previously went undetected. Think of it this way: a new wave of recalls in 2026 doesn’t signify steps backward, but rather shows the growth and advancement of food safety technologies doing its job better, catching already established food safety issues earlier and more frequently.

Over the longer term, organizations that adopt preventive, data-driven food safety systems early are expected to experience fewer large-scale recalls and more targeted product withdrawals, reducing both public health impact and business disruption.

What This Means for the Future of FSQA

Food safety in 2026 is no longer defined solely by compliance. The most resilient organizations are those that treat food safety as a strategic, technology-enabled function, supported by real-time data, predictive insight, and continuous improvement. Those willing to get ahead of the game will come out on top.

As regulatory expectations rise and supply chains grow more complex, the gap between digitally mature food safety programs and legacy, traditional approaches will continue to widen. For FSQA leaders, the path forward is clear: proactive risk management, enabled by data and technology, is essential to protecting both public health and brand trust in the years ahead.

Why Cold Chain IoT Sensors Are Your First Line Of Defense Against Recalls

By Emily Newton
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For the cold chain, Internet of Things (IoT) sensors act as the first line of defense by continuously generating valuable condition information. Even small fluctuations in temperature can reduce shelf life. Food manufacturers and distributors would be wise to utilize this solution.

Common Challenges Within the Cold Chain

The cold truth about conventional temperature monitoring is that it’s error-prone. Personnel receiving trucks for grocery stores record temperature gauge information on a paper ledger attached to a clipboard. They may enter those figures into a digital spreadsheet, but the process is largely manual, which entails significant reliability flaws.

The recorded value upon arrival does not reflect the shipment’s actual temperature history. Spoiled food has significant implications for human health. Inaccurate readings, miscommunications, information gaps and noncompliance are common problems in the cold chain. Human error and disparate recordkeeping systems exacerbate the issue.

Even slight temperature excursions can significantly impact product quality. While most food spoilage microbes thrive in the 68° Fahrenheit to 104° Fahrenheit temperature range, they can grow rapidly in the temperature danger zone, which is just a few degrees away from the ideal refrigerator temperature.

How Existing Cold Chain Solutions Fall Short

Food produced for human consumption cannot reach consumers when temperature abuse during storage supports spoilage and the growth of pathogenic bacteria. Since even minor deviations can cause issues, conventional data loggers no longer meet industry needs. They are unreliable at best and erroneous at worst.

Say a temporary power outage occurs. By the time the refrigerated truck arrives at its destination, the thermometer may display an acceptable temperature, while the containers are still in the temperature danger zone. The discrepancy between the fridge and packaging temperatures could impact product quality.

IoT sensors enable precision monitoring. In one study on table grapes — which are exceptionally sensitive and must remain at negative 33 ° Fahrenheit — researchers found inadequate airflow in containers can create hot spots. The temperature between the control and ventilated units deviated by around 30% on average.

The researchers demonstrated the superiority of existing monitoring solutions. Industry professionals need an accurate, reliable solution to ensure product safety and quality. This has never been more crucial, as consumer awareness regarding healthy eating and wellness is on the rise. Organic food sales reached $63.8 billion in 2023 alone.

IoT Technology Is Your First Line of Defense

Many companies ship temperature-sensitive products using coolants such as dry ice — the solid form of carbon dioxide (CO2) — to maintain a stable, low-temperature environment while in transit. Solid CO2 creates no waste or water, making it safe to include in shipments.

Several factors can influence dry ice’s effectiveness, posing a problem since precise control is crucial. Generally speaking, dry ice sublimates at a rate of 8% every 24 hours, converting from a solid to a gas.

This process may occur more quickly, depending on the material’s size and shape. Blocks boast the slowest sublimation rate and longest shelf life among all types, making them ideal for most distribution applications. However, pellets are excellent for flash-freezing in case the truck’s cooling mechanism malfunctions.

Regardless of the type used, the temperature inside a container can change during the sublimation process. If the sublimation rate is significantly higher than expected, spoilage may occur. This is where IoT comes in. It offers an unprecedented level of visibility into the cold chain, helping mitigate temperature excursions in real time.

Implementing IoT Sensors for the Cold Chain

Since modern IoT sensors are discreet and affordable, implementation is relatively straightforward. Sensors — regardless of the type — cost just 40 cents on average. Businesses can continue relying on cost-effective, reliable solutions like dry ice because they can easily retrofit their fleets instead of overhauling them.

For the cold chain, IoT sensors are the first line of defense. They make temperature logging more convenient, accurate and inexpensive. Professionals can track shipments’ conditions in real time as the technology establishes a comprehensive, verifiable record.

They can go beyond temperature monitoring, measuring metrics like humidity, location, truck door status and water leakage. In addition to enhancing recordkeeping, this technology enables proactive intervention. For example, decision-makers can adjust a truck’s route to avoid a delay or extreme weather conditions.

As a result, food manufacturers reduce spoilage and wastage. Expenses associated with shipping will decrease, as they will no longer need to compensate for product losses. Increased visibility supports data-driven strategies, ensuring safety, quality and compliance.

Considerations for Effective Implementation

While standard IoT temperature sensors for the cold chain are effective, industry leaders should consider implementing the latest, most advanced solutions to maximize their returns. Either way, implementation should be relatively straightforward.

One research group developed a cost-effective temperature and humidity monitoring system built on IoT services and long-range, wide-area (LoRaWAN) networks. Sensors wirelessly transmit data to a LoRa gateway, which forwards the information via Wi-Fi, Ethernet or cellular networks to a central cloud server for processing, analysis and storage.

Multiple gateways can simultaneously receive the data transmitted by a LoRa node. Companies often deploy many gateways in a given area to strengthen the network’s reliability. Redundancy in the data transmission process increases the likelihood of successful delivery and minimizes the chances of data loss due to communication interruptions.

Ambient IoT is an emerging class of connected devices that harvest energy from their surroundings, including through vibrations, magnetic energy fields, light and thermal gradients. Conventional sensors can operate on a wireless power infrastructure, but they typically run on batteries. Retailers must either replace them or recharge them, which can be tedious and costly.

Generally, the investment is worth the return. However, batteries themselves introduce restrictions regarding device size, placement and lifespan. A grocery distribution center with 60 dock doors across 500,000 square feet could spend millions of dollars on a comprehensive system. It could deploy an ambient IoT system for 10 to 20 times less.

Preventing Food Recalls With IoT Sensors

Food manufacturers and distributors would greatly benefit from implementing IoT sensors in the cold chain. Cutting-edge solutions like ultra-low power ambient IoT would enable them to embed sensors virtually anywhere. Sending information to the cloud in real time could help them transform communication, driver accountability and recordkeeping.