Despite recent developments in international trade and regulation, the trajectory of modern food production continues to point to more complex, interconnected supply chains and increasing regulatory scrutiny. Risk is growing in scale and accelerating, and traditional reactive approaches to food safety and preventive controls are being outpaced. PCQIs and food safety professionals are left trying to catch up, even as they fall further behind.
The secret to staying ahead might be using predictive analytics in preventive controls, powered by big data collected directly and automatically from smart sensors that monitor the entire food production lifecycle in real time. When properly implemented, these systems allow PCQIs to not only improve real-time decision-making but also implement novel, proactive, and data-driven preventive controls that put them ahead of the risk curve.
Predictive Analytics in Food Safety
Predictive analytics techniques aren't new; many have been commonly used in food safety for years. But advances in processing power and machine intelligence, combined with the availability of affordable connected data collection devices, have made it much more powerful and attainable.
Building a data collection and processing system requires a few things upfront. PCQIs must create a program that, first, collects enough data, and, second, collects it quickly enough to be useful. They must also familiarize themselves with analytics and data science tools like digital twins, cloud-based quality management systems, and statistical analysis programs.
The upfront commitment to predictive analytics in preventive controls is well worth it, though. Predictive analytics systems can forecast pathogen risk long before an outbreak by monitoring temperature and humidity, spot potential package integrity failures before reaching consumers, and warn about vendor issues before a supply chain break or failed audit happens.
The Role of the PCQI in Predictive Food Safety
PCQIs are already responsible for developing and overseeing food safety plans and preventive controls inside food manufacturing facilities. Predictive food safety is a natural outgrowth of that role, but one that actually makes PCQIs more effective, freeing them up to focus on big-picture concerns rather than minutia.
The most significant shift when implementing predictive food safety practices is from scheduled, static food safety activities to dynamic, data-driven ones triggered by changes in specific risk profiles. In short, instead of performing the same verification, logging, and auditing at the same time every day, PCQIs allow real-world conditions to point them in the direction of the biggest potential problems.
This lets PCQIs:
- Dynamically adjust preventive controls in response to real risk.
- Optimize resource allocation by focusing effort where risk is highest.
- Identify hazards before they happen.
- Prioritize hazards based on data rather than on routine.
- Strengthen audit readiness with real-time, data-backed documentation.
- Enhance compliance with 21 CFR 117 Subpart C.
Building a Predictive Food Safety Data Infrastructure
A core requirement for making predictive analytics in preventive controls work is collecting enough data to draw statistically valid conclusions and make accurate forecasts. This data must be clean, properly formatted, and capable of powering analysis engines while remaining compliant.
The checklist below can help PCQIs build out the framework for their predictive analytics program:
Data capture
Historical data
Historical data is the starting point for any data collection process. Organizations with digitized records should have no trouble converting them to a format that works with predictive modelling. On the other hand, organizations that still largely rely on paper documents and scans will need to spend some time digitizing records before proceeding.
Regular logging
PCQIs log a tremendous amount of data on a daily basis. That data should be logged digitally and integrated into the data feeding predictive analytics systems. Note that secure storage and data integrity measures, such as audit trails and encryption, are also essential for regulatory compliance.
Compliance logs and corrective actions
Aside from logging the state of equipment, like temperature and humidity, "soft" logs should also be entered into the data pool. These can include sanitation compliance, employee food safety performance reviews, equipment diagnostics, and vendor performance metrics and audit results, to name a few.
Smart IoT sensors
The powerhouse for PCQIs, smart sensors can integrate into most equipment and monitoring routines and provide a steady stream of real-time data directly into a central data repository. This frees food safety professionals from manually checking readings (as long as calibration procedures are followed and regularly verified) while providing much better and more granular data than manual readings could. Suggestions on where to integrate smart sensors include:
- Temperature and humidity sensors in climate-controlled areas
- Air quality or particulate sensors when dealing with grains, spices, or cooking processes that can create airborne particles
- Camera-based packaging integrity sensors at the end of packing lines
- Machine-interface SCADA systems for industrial machinery to log performance second by second
Data validation and governance
Standardized formatting
Ensuring that forecasting and predictions are accurate and consistent over time requires a standard format. Decide on a standardized way to collect, enter, and display data for each variable, and ensure that you maintain standards moving forward.
Validate inputs
Before making any decisions or switching to the predictive framework, ensure that all data is collected accurately and that every reading is valid. Build out a validation and calibration process to monitor and adjust data sources over time.
Assign ownership
Identify where you may need support, for example, in setting up and maintaining IT infrastructure, and assign ownership of those parts of the process to relevant departments and people.
Select and validate models
Evaluate multiple models
Finding the right statistical model — and the AI/ML technology to support it — can be challenging. Evaluate multiple models using historical data and incidents to determine which one is the most accurate, but beware of overfitting to historical events. Note that different models may work best for different processes.
Validate models regularly
Use digital twins — virtual replicas of your facilities, equipment, or processes — to forecast future values for sensor readings and risk conditions. Then, check those predictions against actual values. Set up a regular validation step to do this.
Integrate with your food safety plan and HACCP framework
Ensure that model output matches key food safety metrics
Confirm that the forecasting focuses on the key metrics and indicators important to your facility and food safety plan. It's easy to get lost in analysis paralysis and statistical noise when looking at big data systems; isolate the critical outcomes and create a dashboard focusing on them.
Tie output into risk registers and decision protocols
Make sure that when a red flag comes up, all relevant stakeholders are immediately informed and that there is a process for understanding and resolving it. Proactively design response systems rather than waiting to react to alerts.
Challenges of Predictive Food Safety Systems
Building a functional predictive food safety system is not without challenges. Some of the biggest include:
- Lack of access to historical data: Digitizing extensive paper records can be a monumental lift, especially in facilities that have operated for a long time with minimal changes. Consider using a digitization service to assist, or talk to local schools to see if their data science students could use the experience as interns to help you.
- Capabilities gaps: Not every food manufacturer has a robust IT department that can help set up infrastructure, and not every PCQI feels comfortable reading and interpreting statistical dashboards. Upskilling can help over time; additionally, facilities can bring in food safety consultants to help get staff up to speed, like with AIB International's Assign an Expert program, for example.
- Decision outsourcing: It's important to remember that predictive analytics are meant to support, not replace, decision-making by trained human professionals. Regularly validate outputs and decisions based on these supports, and don't over-rely on the system at the expense of on-the-ground experience.
The Future of Food Safety: Smart Systems Supporting Smart PCQIs
Predictive analytics in preventive controls is a powerful tool. It can help spot risk before it becomes a problem, allow PCQIs to focus on bigger priorities, and enable food safety professionals to manage facilities proactively. In short, it transforms PCQIs from technicians into risk management leaders.
Getting these systems to work for you will require some adjustments. Food safety plans will need tweaking to reflect how data capture happens and decisions are made, facilities must be modernized for integrated sensor and networking capabilities, and PCQIs need to hone their statistics and programming skills to make the most of the changes. But the work is well worth it.
PCQIs who want to get their feet wet can begin with limited-scope pilot programs. Identify a simple use case — converting a refrigerated section to remote IoT sensors placed throughout, for example — and work through that limited implementation while identifying skills, technology, and process gaps that must be filled for a larger roll-out. As you build experience and confidence, roll out more use-cases one by one, dialing in each one before moving on. A slow and steady small-scope approach will make the process easier to approve and more manageable to oversee.
Once you feel confident, the next step is to take these programs and start integrating them into broader, organization-wide risk analysis and quality systems. If you need help getting started, contact AIB International to find out how our expert food safety consultants can help get you going with predictive food safety.
