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 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.
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:
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:
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.
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.
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.
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:
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.
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.
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.
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.
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.
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.
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.
Building a functional predictive food safety system is not without challenges. Some of the biggest include:
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.