Tag Archives: predictive analytics

Seafood Analytics CQR

Leveraging Automation for Enhanced Food Safety and Compliance

By Ainsley Lawrence
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Seafood Analytics CQR

The food industry faces increasing customer demand on top of snowballing regulatory concerns, and many are calling for automation to overcome these obstacles. Automation technologies reinforce food safety practices from processing to packaging by revamping sanitation, quality control, and more.

To begin leveraging automation for food safety in your sector today, the most important areas to focus on are automated monitoring systems, growing AI/ML capabilities, and exceeding regulatory compliance.

Automated Monitoring Systems in Food Safety

Automated monitoring systems have become the titanium backbone of modern food safety, offering greater control over critical processes. With human error as a prevalent risk factor for safety incidents, companies can mitigate accidents with automated systems to mitigate this risk by standardizing processes and enforcing predefined protocols.

This paradigm shift in the way we produce food makes food safer, helps keep workers safe, and makes food quality more consistent at large. Automated monitoring systems can help reduce common errors, drive more effective sanitation, and track your most sensitive critical control points.

Error Reduction through Automated Processes

Many small, common errors can be reduced or outright eliminated with automation. In seafood processing, for example, optical sorting machines consistently identify and remove substandard products. Rather than relying on the inconsistent human eye, machines can rapidly assess each item based on precise criteria such as size, color, and texture. Automation enhances human capabilities in this way by minimizing errors due to fatigue, such as in high-volume production sites.

Seafood Analytics CQR
The CQR device from Seafood Analytics measures the freshness and quality of seafood.

 

Consistent Sanitation Procedures

Maintaining sanitary conditions is critical for safety and regulatory compliance in food production environments. Automated cleaning systems, programmed with precise chemical concentrations and application methods, guarantee thorough and consistent sanitation. These systems meticulously track each cleaning cycle, providing auditable records for compliance purposes. In food packaging, robots can make wrapping products safer, identify foreign objects like bone/shell, and greatly reduce fatigue on workers.

Real-time Critical Control Point Tracking

Automated systems excel at monitoring critical control points (CCPs) in food production, dramatically reducing spoilage. Temperature sensors in cold storage facilities transmit continuous data streams, alerting staff to deviations before spoilage occurs. Meanwhile, automated pH meters and metal detectors in processing areas operate tirelessly with pinpoint precision to ensure consistent product quality and safety.

AI and Machine Learning Applications

Automation can only go so far without insight. AI and ML are carving a niche alongside automation, supplementing raw power with vast datasets and analytic powers to identify anomalies. Together, they enable systems to recognize patterns, flag issues, and optimize processes in ways previously unfeasible.

These technologies integrate with automated systems to monitor complex food production networks, uncovering subtle irregularities that might be missed by human inspection or conventional algorithms.

Traceability in Food Supply Chains

Supply chains are notoriously complex and unpredictable to track because they often involve multiple stages, from raw material sourcing to processing, packaging, distribution, and retail. Each step can involve different suppliers, locations, and regulations, making it difficult to maintain a clear, real-time view of where a product has been and what conditions it has encountered.

AI and machine learning address this by continuously analyzing data from various points, creating an interconnected web of information that companies can use to trace products with greater accuracy than ever before. Whether it’s identifying the origin of a raw ingredient or tracking environmental conditions during transportation, AI-driven traceability systems provide granular insights that facility managers can use to make improvements.

Predictive Analytics

Machine learning models trained on historical data and real-time inputs can predict food safety risks before they appear. In food packaging operations, these systems analyze factors such as temperature fluctuations and microbial growth rates to track CCPs and identify issues. Across departments, predictive maintenance algorithms anticipate equipment failures that could lead to contamination. With this insight, managers can reduce accidents, cut waste, and intervene before incidents occur.

Setting Up for AI and ML

Preparation and a solid foundation in data management are essential to make the most of what AI and machine learning have to offer. Food processing facilities must prioritize data quality, storage capacity, and scalability to harness these technologies. Companies looking to adopt AI and machine learning should:

  • Invest in Quality Data Collection: AI and ML require high-quality data, so IoT devices and sensors are deployed to gather accurate, real-time data across production stages.
  • Choose Scalable Storage: Opt for cloud-based storage to handle increasing data volumes and facilitate easy access and integration.
  • Select Flexible AI Tools: Choose AI and machine learning platforms that can adapt to changing business needs and integrate with existing systems as smoothly as possible.
  • Train Staff with AI/ML: These technologies are only as good as the workers using them – provide training for employees on how to use AI tools effectively to maximize their potential.

AI can make workflows more efficient, but introducing it should always be met with deliberate planning and testing.

Regulatory Compliance and Automation

Automation tech plays a crucial role in helping food businesses navigate the complex regulatory landscape, which is subject to change. As food safety standards evolve, management should look to not just match but exceed regulatory compliance in anticipation of tightening requirements.

Robust food safety standards are essential for maintaining product integrity and consumer trust, but they only work when combined with automated documentation and reporting. Lastly, a new challenge facing food production is handling human-robot interaction in a Wild West-esque tech frontier.

Food Safety Standards

Regulatory bodies frequently update food safety standards to identify emerging risks and incorporate new scientific findings. Automation helps streamline this process for companies fighting a web of red tape by allowing for swift reconfiguration of monitoring parameters and control processes. For instance, AI-powered testing equipment can be remotely updated to detect new microbial threats without overhauling entire production lines. This flexibility helps companies stay ahead of the regulatory curve and slim costs simultaneously.

Automated Reporting and Documentation

Automated systems are stellar at simplifying food safety compliance, able to effortlessly generate and update detailed, real-time records of every aspect of food production and handling. From temperature logs to sanitation schedules, automated reporting tools compile data into a proper regulatory format and ease administrative burdens. While the primary goal is to demonstrate regulatory compliance, this data also proves itself a treasure trove for companies to improve their practices ahead of regulatory change.

Tackle Human-Robot Interaction

The concept of human-robot collaboration isn’t new, but it’s becoming increasingly more common, and the average food production worker is more likely than ever to work with a robot. This paradigm shift requires a new approach to work, which prioritizes streamlining repetitive or laborious tasks, clear communication, and continuous training as capabilities increase. It’s also worth noting that managers can alleviate worries about ‘being replaced with a machine’ by focusing on how technology supplements humans rather than wholesale replacing them in the workplace.

Workers production line
Workers in a factory sorting food by hand, could be assisted by new robot technology. (Unsplash image)

Final Thoughts

Automation, including robotics, AI, and machine learning, is pivotal in enhancing food safety and compliance across the industry. By using automated monitoring systems, food production sites can reduce human error and standardize processes. At the same time, AI and machine learning provide real-time data analysis and predictive insights if companies are willing to put in the work needed to prepare for automation. In that case, they can help reduce accidents, enhance efficiency, monitor food quality, and keep up with regulatory compliance at a fraction of their previous efforts.

Predictive Analytics for Proactive Food Safety

By Emily Newton
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Many of the most popular predictive analytics use cases revolve around risk assessments and optimization. While businesses largely use them to drive efficiency and financial gains, the same tech can help make the food and beverage industry safer.

Predictive analytics could benefit virtually any industry that applies it. While that means monetary improvements in most sectors, this technology could impact people’s health and well-being in others. Applying predictive analytics in food safety in one such use case.

The Importance of Proactive Food Safety

Foodborne illnesses affect 48 million people each year in the U.S. alone. These diseases are easily treatable in many instances, but as hospitals become more crowded and the population grows, more could result in worse outcomes. Already, 128,000 of these cases hospitalize their victims and 3,000 end in death.

These outbreaks may become a more prominent threat over time, too. Climate change can make certain foodborne pathogens more common and affect food’s nutritional value. The global population is also growing, so available resources must spread further to cover everyone. That could result in more people being unable to access the care they need if they contract a foodborne disease.

Given these concerns, food safety must be proactive. Organizations need to stop outbreaks before they occur to reduce the burden on the health care system and ensure a healthier world. Predictive analytics can support that goal by optimizing several aspects of food safety.

Emily Newton,

Preventing On-Farm Contamination

Food safety starts with food’s farm origins. Some diseases can spread through pest contamination, and predictive analytics may provide more reliable proactive anti-pest measures than conventional alternatives.

Pest outbreak modeling begins by collecting data on weather patterns, past outbreaks, and known interactions between certain pests and other chemicals or plants. Machine learning models can then predict when rising pest populations are likely and what could stop them. Farmers can then respond as necessary, whether that means spraying the optimal amount of pesticide or companion planting to repel animals before they arrive.

Early experimental models under this umbrella have accurately predicted outbreaks up to seven years in the future — more than enough time for farms to adapt. Even if these warnings spur little more than increased attention to contamination risks, they could significantly impact food safety.

Protecting Food Products in Transit

Predictive analytics can guard crops or animal products in transit once they leave the farm. Unlike pest prevention, this application is less concerned with long-term trends, instead centering around real-time data.

Internet of Things (IoT) sensors can track metrics like shipment temperatures and humidity in real time. With this data, predictive models can identify when current conditions may lead to food safety concerns, such as temperatures rising above safe levels. Once they identify these trends, they alert drivers and other stakeholders to take action before spoilage occurs.

Some available solutions today can monitor core temperatures up to 1 meter away, while others can detect bacteria and gas associated with spoilage. Whatever the specifics, real-time data and machine learning enable fast responses to prevent contamination or stop spoiled products from reaching consumers if prevention is impossible.

Refining Manufacturing Processes

Predictive analytics can also promote proactive food safety in the manufacturing stage. Many manufacturers today are already investing in AI to optimize their production workflows, and the same technology can yield safety improvements.

Take the production of dry pet food — which accounts for 60% of all pet food sold today — for example. These products are prone to cross-contamination from additives or surface contact during extrusion, but these hazards are difficult to identify in a large facility. Predictive analytics can analyze digital twins of these facilities to pinpoint where this kind of contamination is most likely, informing workflow changes to remove or mitigate the risk.

Just as predictive analytics can highlight production bottlenecks, it can alert manufacturers to processes prone to bacterial infection or other health hazards. Advanced models can even suggest alternative workflows to make it easier to ensure the safest possible production process.

Pinpointing Supply Chain Vulnerabilities

Similarly, food and beverage companies can use predictive analytics to identify hazards in their supply chains. Third-party health and safety risks are hard to pinpoint manually, but AI can monitor real-time conditions and analyze past trends to predict vulnerabilities.

Businesses can apply predictive analytics to food supply chains in a few ways. One effective option is to analyze past health department reports to identify suppliers with a history of health and safety violations. Some solutions today can even highlight common themes between reports to reveal what kinds of hazards a company struggles with.

Other supply chain analytics engines can analyze real-time data to predict potential outbreaks in a region’s food supply or growing cross-contamination threats in an area. Food companies can then adjust their supply strategy to avoid sourcing from these problem areas and prevent outbreaks.

Learning From Past Outbreaks

Many supply chains have also embraced predictive analytics for scenario modeling. Applying this practice to food safety can help experts learn where past outbreaks came from to inform preventive measures in the future.

With enough data on past foodborne disease outbreaks, machine learning models could identify trends in their early warning signs. Alternatively, they could highlight how some logistics or manufacturing practices contributed to the disease’s spread. Predictive models can then apply these insights to real-time farm, production facility and health report data to predict incoming cases.

Food processors already use hyperspectral sensors that could help detect early warning signs of undesired microbes, like the release of some gasses. Feeding this data to predictive models alongside information on how past foodborne illnesses emerged and evolved could let them predict new diseases before they affect anyone. Global health agencies and food and beverage companies could enact much more effective mitigation measures as a result.

Predictive Analytics Takes Food Safety Further

Many of the most popular predictive analytics use cases revolve around risk assessments and optimization. While businesses largely use these applications to drive efficiency and financial gains, the same technology can help make the food and beverage industry safer. That will become increasingly crucial as the population grows and climate change worsens the threat of foodborne illnesses.

The use of predictive analytics in proactive food safety is still in its infancy, but early signs are promising. As this technology evolves and more brands capitalize on it, it could make the world a safer, healthier place.

Steven Sklare, Food Safety Academy
FST Soapbox

What Is Your Company’s Level of Digital Risk Maturity?

By Steven Sklare
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Steven Sklare, Food Safety Academy

The digital transformation of food safety management programs is a common topic of discussion today, across the full range of media including print, blogs, websites and conferences. It has also been generally acknowledged that the COVID-19 pandemic has significantly accelerated the adoption of various digital technologies. However, let’s be clear, COVID-19 may have accelerated the process, but the process was under way as the only way for food companies to efficiently cope with the increase of required compliance documentation for regulatory bodies, such as FDA, USDA, etc., non-regulatory organizations such as GFSI, and customer specific requirements. COVID-19 has added a sense of urgency, as the fragility of both domestic and international supply chains has been exposed with long-term sources of ingredients or equipment being cut off overnight. We must also overlay the need to manage food safety risk and food fraud vulnerability in real time (or even predict the future, which will be discussed further in a future article). The food industry has also had to adjust to dealing with many aspects of work and production without typical face-to-face interaction—a norm of operating within the environment of a global pandemic over the past two years.

What is not clear, however, is the meaning of “digital transformation” or the “digitization” of a food safety management program. What is not clear is what these terms mean to individual organizations. The frenzy of buzzwords, “urgent” presentations, blogs and webinars help to create an improved level of awareness but rarely result in concrete actions that lead to improved results. I admit to being guilty of this very hyperbole—in a previous article discussing “Chocolate and Big Data”, I said, “If a food organization is going to effectively protect the public’s health, protect their brand and comply with various governmental regulations and non-governmental standards such as GFSI, horizon scanning, along with the use of food safety intelligent digital tools, needs to be incorporated into food company’s core FSQA program.” Sounds great, but it presupposes a high level of awareness of those “digital tools”. What is not clear to many organizations is how to get started and how to create a road map that leads to improved results, more efficient operations and importantly, to ongoing improvement in the production of safe food.

Addressing a new concept can be intimidating and paralyzing. Think back to the beginning days of HACCP, then TACCP, then VACCP, and post FSMA, preventive controls! So, where do we start?

Nikos Manouselis, CEO of Agroknow, a food safety data and intelligence company with a cloud-based risk intelligence platform, Foodakai, believes the place to start is for food companies to perform an honest, self-assessment of their digital risk maturity. Think of it as a digital risk maturity gap analysis. While there are certainly different approaches to performing this self-assessment, Agroknow has developed a simple, straightforward series of questions that focus on three critical areas: Risk monitoring practices and tools; risk assessment practices and tools; and risk prevention practices and tools. The questions within each of these areas lead to a ranking of 1–5 with 1 being a low level of maturity and 5 being a high level of maturity. One of the goals of the self-assessment is to determine where your company stands, right now, compared to where you want to be or should be.

While this is not a complete nor exhaustive process, it helps to break the inertia that could be holding a company back from starting the process of digitizing their food protection and quality systems, which will allow them to take advantage of the benefits available from continuous monitoring of food safety risks and food fraud vulnerabilities, artificial intelligence and predictive analytics.

Roberto Bellavia, Kestrel
FST Soapbox

How Integrated Compliance Management Systems Maximize Efficiency

By Roberto Bellavia
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Roberto Bellavia, Kestrel

Managing the complexities of a management system is challenging for any food and beverage company, particularly for the team tasked with implementing the system throughout the organization. That is because every regulatory agency (e.g., FDA, USDA, OSHA, EPA) and voluntary certification (e.g., GFSI-benchmarked standards, gluten-free, organic, ISO) calls for companies to fulfill compliance requirements—many of which overlap. Supply chain and internal requirements can create further complications and confusion.

In today’s “New Era of Smarter Food Safety,” having a common system to organize, manage and track compliance offers an ideal solution. Dynamic tools are becoming available—systems that can manage employee training, pest control, laboratory testing, supply chain management tools, regulatory compliance and certification requirements, etc.

Unfortunately, these systems are often not set up to “talk” to each other, leaving company representatives to navigate many systems, databases, folders, and documents housed in many different locations.

The Solution: Compliance Management Systems

An integrated compliance management system (CMS) is intended to bring all these tools together to create one system that effectively manages compliance requirements, enables staff to carry out daily tasks and manage operations, and supports operational decision making by tracking and trending data that is collected daily by the team charged with implementation.

A CMS is used to coordinate, organize, control, analyze and visualize information to help organizations remain in compliance and operate efficiently. A successful CMS thinks beyond just access to documents; it manages the processes, knowledge and work that is critical to helping identify and control business risks. That may include the following:

  • Ensuring only authorized employees can access the right information.
  • Consolidating documents and records in a centralized location to provide easy access
  • Setting up formal business practices, processes and procedures
  • Implementing compliance and certification programs
  • Monitoring and measuring performance
  • Supporting continuous improvements
  • Documenting decisions and how they are made
  • Capturing institutional knowledge and transferring that into a sustainable system
  • Using task management and tracking tools to understand how people are doing their work
  • Enabling data trending and predictive analytics

CMS Case Study: Boston Sword and Tuna

In early 2019, Boston Sword and Tuna (BST) began the process of achieving SQF food safety certification. We initially started working with BST on the development, training and implementation of the program requirements to the SQF code for certification—including developing guidance documents for a new site under construction.

The process of attaining SQF certification included the development of a register of SQF requirements in Microsoft SharePoint, which has since evolved into a more comprehensive approach to overall data and compliance management. “We didn’t plan to build a paperless food safety management system,” explains BST President Larry Dore, “until we implemented our SQF food safety management program and realized that we needed a better way to manage data.”

We worked with BST to structure the company’s SharePoint CMS according to existing BST food safety management processes to support its certification requirements and overall food safety management program. This has included developing a number of modules/tools to support ongoing compliance efforts and providing online/remote training in the management of the site and a paperless data collection module.

The BST CMS has been designed to support daily task activities with reminders and specific workflows that ensure proper records verifications are carried out as required. The system houses tools and forms, standards/regulatory registers, and calendars for tracking action items, including the following:

  • Ambient Temperature
  • Corrective and Preventive Action (CAPA)
  • Chemical Inventory/Safety Data Sheets (SDS)
  • Compliance Management
  • Customer Complaints
  • Document Control
  • Employee Health Check
  • Food Safety Meetings Management Program
  • Forklift Inspections
  • Good Manufacturing Practices (GMP) Audit
  • SQF Register
  • Maintenance (requests/work orders/assets/repairs)
  • Nightly Cleaning Inspections
  • Operational/Pre-Operational Inspections
  • Sanitation Pre-Op Inspections
  • Scale Calibration
  • Sharp/Knife Inspections
  • Shipping/Receiving Logs
  • Thawing Temperature Log
  • Thermometer Calibration

Key Considerations for Designing a Successful CMS

An effective CMS requires an understanding of technology, operational needs, regulatory compliance obligations and certification requirements, as well as the bigger picture of the company’s overall strategy. There are several key considerations that can help ensure companies end up with the right CMS and efficiency tools to provide an integrated system that supports the organization for the long term.

Before design can even begin, it is important to first determine where you are starting by conducting an inventory of existing systems. This includes not only identifying how you are currently managing your compliance and certification requirements, but also assessing how well those current systems (or parts of them) are working for the organization.

As with many projects, design should begin with the end in mind. What are the business drivers that are guiding your system? What are the outcomes you want to achieve through your system (e.g., create efficiencies, provide remote access, reduce duplication of effort, produce real-time reports, respond to regulatory requirements, foster teamwork and communication)? Assuming that managing compliance and certification requirements is a fundamental objective of the CMS, having a solid understanding of those requirements is key to building the system. These requirements should be documented so they can be built into the CMS for efficient tracking and management.

While you may not build everything from the start, defining the ultimate desired end state will allow for development to proceed so every module is aligned under the CMS. Understand that building a CMS is a process, and different organizations will be comfortable with different paces and budgets. Establish priorities (i.e., the most important items on your list), schedule and budget. Doing so will allow you to determine whether to tackle the full system at once or develop one module at a time. For many, it makes sense to start with existing processes that work well and transition those first. Priorities should be set based on ease of implementation, compliance risk, business improvement and value to the company.

Finally, the CMS will not work well without getting the right people involved—and that can include many different people at various points in the process (e.g., end user entering data in the plant, management reviewing reports and metrics, system administrator, office staff). The system should be designed to reflect the daily routines of those employees who will be using it. Modules should build off existing routines, tasks, and activities to create familiarity and encourage adoption. A truly user-friendly system will be something that meets the needs of all parties.

Driving Value and Compliance Efficiency

When thoughtfully designed, a CMS can provide significant value by creating compliance efficiencies that improve the company’s ability to create consistent and reliable compliance performance. “Our system is allowing us to actually use data analytics for decision making and continuous opportunity,” said Dore. “Plus, it is making remote activities much more practical and efficient”.

For BST, the CMS also:

  • Provides central management of inspection schedules, forms, and other requirements.
  • Increases productivity through reductions in prep time and redundant/manual data entry.
  • Improves data access/availability for reporting and planning purposes.
  • Effectively monitors operational activities to ensure compliance and certifications standards are met.
  • Allows data to be submitted directly and immediately into SharePoint so it can be reviewed, analyzed, etc. in real time.
  • Creates workflow and process automation, including automated notifications to allow for real-time improvements.
  • Allows follow-up actions to be assigned and sent to those who need them.

All these things work together to help the company reduce compliance risk, create efficiencies, provide operational flexibility, and generate business improvement and value.

Angel Suarez, EAS Consulting Group
FST Soapbox

Regulatory Cross Cutting with Artificial Intelligence and Imported Seafood

By Angel M. Suarez
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Angel Suarez, EAS Consulting Group

Since 2019 the FDA’s crosscutting work has implemented artificial intelligence (AI) as part of the its New Era of Smarter Food Safety initiative. This new application of available data sources can strengthen the agency’s public health mission with the goal using AI to improve capabilities to quickly and efficiently identify products that may pose a threat to public health by impeding their entry into the U.S. market.

On February 8 the FDA reported the initiation of their succeeding phase for AI activity with the Imported Seafood Pilot program. Running from February 1 through July 31, 2021, the pilot will allow FDA to study and evaluate the utility of AI in support of import targeting, ultimately assisting with the implementation of an AI model to target high-risk seafood products—a critical strategy, as the United States imports nearly 94% of its seafood, according to the FDA.

Where in the past, reliance on human intervention and/or trend analysis drove scrutiny of seafood shipments such as field exams, label exams or laboratory analysis of samples, with the use of AI technologies, FDA surveillance and regulatory efforts might be improved. The use of Artificial intelligence will allow for processing large amount of data at a faster rate and accuracy giving the capability for revamping FDA regulatory compliance and facilitate importers knowledge of compliance carrying through correct activity. FDA compliance officers would also get actionable insights faster, ensuring that operations can keep up with emerging compliance requirements.

Predictive Risk-based Evaluation for Dynamic Imports Compliance (PREDICT) is the current electronic tracking system that FDA uses to evaluate risk using a database screening system. It combs through every distribution line of imported food and ranks risk based on human inputs of historical data classifying foods as higher or lower risk. Higher-risk foods get more scrutiny at ports of entry. It is worth noting that AI is not intended to replace those noticeable PREDICT trends, but rather augment them. AI will be part of a wider toolset for regulators who want to figure out how and why certain trends happen so that they can make informed decisions.

AI’s focus in this regard is to strengthen food safety through the use of machine learning and identification of complex patterns in large data sets to order to detect and predict risk. AI combined with PREDICT has the potential to be the tool that expedites the clearance of lower risk seafood shipments, and identifies those that are higher risk.

The unleashing of data through this sophisticated mechanism can expedite sample collection, review and analysis with a focus on prevention and action-oriented information.

American consumers want safe food, whether it is domestically produced or imported from abroad. FDA needs to transform its computing and technology infrastructure to close the gap between rapid advances in product and process technology solutions to ensure that advances translate into meaningful results for these consumers.

There is a lot we humans can learn from data generated by machine learning and because of that learning curve, FDA is not expecting to see a reduction of FDA import enforcement action during the pilot program. Inputs will need to be adjusted, as well as performance and targets for violative seafood shipments, and the building of smart machines capable of performing tasks that typically require human interaction, optimizing workplans, planning and logistics will be prioritized.

In the future, AI will assist FDA in making regulatory decisions about which facilities must be inspected, what foods are most likely to make people sick, and other risk prioritization factors. As times and technologies change, FDA is changing with them, but its objective remains in protecting public health. There is much promise in AI, but developing a food safety algorithm takes time. FDA’s pilot program focusing on AI’s capabilities to strengthen the safety of U.S. seafood imports is a strong next step in predictive analytics in support of FDA’s New Era of Smarter Food Safety.

John McPherson, rfxcel
FST Soapbox

End-to-End Supply Chain Traceability Starts with High-Quality Data

By John McPherson
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John McPherson, rfxcel

End-to-end traceability technology across the food and beverage (F&B) supply chain has many benefits for companies at all nodes of the chain, not least of which is the ability to act to prevent problems such as irreversible damage, loss, and theft. For these technologies to best deliver on their promise, however, they need standardized and quality-assured data. F&B supply chain stakeholders need to take steps to achieve effective data management to truly take advantage of the benefits of traceability and real-time monitoring technologies.

Since FSMA was introduced in 2011, actors across the F&B supply chain have had to change their behavior. Prior to FSMA, companies tended to react to events; today, proactive and preemptive measures are the norm. This is in line with what the legislation was designed to do: Encourage the prevention of foodborne illness instead of responding after their occurrance.

F&B manufacturers and distributors rely on technology to help predict potential obstacles and mitigate issues along their supply chains. But expressing a desire to embrace technologies such as real-time monitoring solutions and predictive analytics isn’t enough to achieve ultimate supply chain efficiency. Only by taking the necessary steps can companies get on track to ensure results.

Any company that is thinking about deploying a traceability solution has a lot to consider. Foremost, data must be digitized and standardized. This might seem challenging, especially if you’re starting from scratch, but it can be done with appropriate planning.

Let’s examine what F&B companies stand to gain by adopting new, innovative technologies and how they can successfully maximize data to achieve end-to-end supply chain traceability.

New Technologies Hold Huge Potential for F&B Supply Chains

The advantages of adopting new technologies far outweigh the time and effort it takes to get up and running. To smooth the process, F&B companies should work with solution providers that offer advisory services and full-service implementation. The right provider will help define your user requirements and create a template for the solution that will help ensure product safety and compliance. Furthermore, the right provider will help you consider the immediate and long-term implications of implementation; they’ll show you how new technologies “future-proof” your operations because they can be designed to perform and adapt for decades to come.

Burgeoning technologies such as the Internet of Things (IoT), artificial intelligence (AI) and blockchain are driving end-to-end traceability solutions, bridging the gap between different systems and allowing information to move seamlessly through them.

For example, real-time tracking performed by IoT-enabled, item-level sensors allows companies to detect potential damage or negative events such as theft. These devices monitor and send updates about a product’s condition (e.g., temperature, humidity, pressure, motion and location) while it is in transit. They alert you as soon as something has gone wrong and give you the power to take action to mitigate further damage.

This is just one example of how data from a fully implemented real-time, end-to-end traceability platform can yield returns almost immediately by eliminating blind spots, identifying bottlenecks and threats, and validating sourcing requirements. Such rich data can also change outcomes by, for example, empowering you to respond to alerts, intercept suspect products, extend shelf life, and drive continuous improvement.

As for AI technologies, they use data to learn and predict outcomes without human intervention. Global supply chains are packed with diverse types of data (e.g., from shippers and suppliers, information about regulatory requirements and outcomes, and public data); when combined with a company’s internal data, the results can be very powerful. AI is able to identify patterns through self-learning and natural language, and contextualize a single incident to determine if a larger threat can be anticipated or to make decisions that increase potential. For example, AI can help automate common supply chain processes such as demand forecasting, determine optimal delivery routes, or eliminate unforeseeable threats.

Blockchain has garnered a lot of buzz this year. As a decentralized and distributed data network, it’s a technology that might help with “unknowns” in your supply chain. For example, raw materials and products pass through multiple trading partners, including suppliers, manufacturers, distributors, carriers and retailers, before they reach consumers, so it can be difficult to truly know—and trust—every partner involved in your supply chain. The immutable nature of blockchain data can build trust and secure your operations.

To date, many F&B companies have been hesitant to start a blockchain initiative because of the capital risks, complexity and time-to-value cost. However, you don’t have to dive in head-first. You can start with small pilot programs, working with just a few stakeholders and clearly defining pilot processes. If you choose the right solution provider, you can develop the right cultural shift, defining governance and business models to meet future demands.

To summarize, new technologies are not disruptive to the F&B industry. If you work with an experienced solution provider, they will be constructive for the future. Ultimately, it’s worth the investment.

So how can the F&B industry start acting now?

How to Achieve End-to-End Traceability

Digitize Your Supply Chain. We live in a digital world. The modern supply chain is a digitized supply chain. To achieve end-to-end traceability, every stakeholder’s data must be digitized. It doesn’t matter how big your company is—a small operation or a global processor—if your data isn’t digitized, your supply chain will never reach peak performance.

If you haven’t begun transitioning to a digitalized supply chain, you should start now. Even though transforming processes can be a long journey, it’s worth the effort. You’ll have peace of mind knowing that your data is timely and accurate, and that you can utilize it to remain compliant with regulations, meet your customer’s demands, interact seamlessly with your trading partners, and be proactive about every aspect of your operations. And, of course, you’ll achieve true end-to-end supply chain traceability.

Standardize Your Data. As the needs of global F&B supply chains continue to expand and become more complex, the operations involved in managing relevant logistics also become more complicated. Companies are dealing with huge amounts of non-standardized data that must be standardized to yield transparency and security across all nodes of the supply chain.

Many things can cause inconsistencies with data. Data are often siloed or limited. Internal teams have their own initiatives and unique data needs; without a holistic approach, data can be missing, incomplete or exist in different systems. For example, a quality team may use one software solution to customize quality inspections and manage and monitor remediation or investigations, while a food safety team may look to a vendor management platform and a supply chain or operations team may pull reports from an enterprise resource planning (ERP) system to try and drive continuous improvement. Such conflict between data sources is problematic—even more so when it’s in a paper-based system.

Insights into your supply chain are only as good as the data that have informed them. If data (e.g., critical tracking events) aren’t standardized and quality-assured, companies cannot achieve the level and quality of information they need. Data standards coming from actors such as GS1 US, an organization that standardizes frameworks for easy adoption within food supply chains, can help with this.

There are many solutions to ensure data are standardized and can be shared among different supply chain stakeholders. With recent increases in recalls and contamination issues in the United States, the need for this level of supply chain visibility and information is even more critical.

Data Security. Data security is crucial for a successful digital supply chain with end-to-end traceability, so you must plan accordingly—and strategically. You must ensure that your data is safe 24/7. You must be certain you share your data with only people/organizations who you know and trust. You must be protected against hacks and disruptions. Working with the right solution provider is the best way to achieve data security.

Incentive Structures. Incentives to digitize and standardize data are still lacking across some parts of the F&B supply chain, increasing the chances for problems because all stakeholders are not on the same page.

Companies that continue to regard adopting traceability as a cost, not an investment in operations and brand security, will most likely do the minimum from both fiscal and regulatory standpoints. This is a strategic mistake, because the benefits of traceability are almost immediate and will only get bigger as consumers continue to demand more transparency and accuracy. Indeed, we should recognize that consumers are the driving force behind these needs.

Being able to gather rich, actionable data is the key to the future. Industry leaders that recognize this and act decisively will gain a competitive advantage; those that wait will find themselves playing catch-up, and they may never regain the positions they’ve lost. We can’t overstate the value of high-quality digitized and standardized data and the end-to-end traceability it fuels. If companies want to achieve full visibility and maximize their access to information across all nodes of their supply chains, they must embrace the available technologies and modernize their data capabilities. By doing so, they will reap the benefits of a proactive and predictive approach to the F&B supply chain.