Insights from an Industry Webinar on Leveraging Real-Time Monitoring for Meaningful Insights in Food Production.
In this article, we recap insights from an industry webinar exploring how real-time monitoring goes beyond compliance to support data-driven decision-making.
The session featured expert perspectives from:
- Alex Carlyon, Key Account Director at Klipspringer
- Radek Tameczka, Real-Time Monitoring Specialist at Klipspringer
- Stuart Warren, Senior Food Safety Manager at Coca-Cola Europacific Partners
If you are short on time, you will find a bulleted list of key takeaways at the end of each section.
Real-Time Monitoring – An Introduction
What Is Real-Time Wireless Monitoring?
In this section, Alex Carlyon introduced the fundamentals of real-time monitoring, focusing on what it is and what it is not.
He explained that real-time monitoring is the continuous, automated collection of live data, with no delay between measurement and visibility. This live element is critical, as it enables immediate alerts and timely corrective action, helping to prevent spoilage, reduce waste, and maintain control during production.
Real-Time Wireless Monitoring vs. Passive Data Logging
Alex highlighted the value of digitally recorded data, which creates a clear audit trail and allows teams to quickly retrieve and analyse information, something that is far more difficult with manual records.
Importantly, he clarified that real-time monitoring should not be confused with passive data logging. Systems that only provide results after a process is complete offer proof, not control, as there is no opportunity to intervene.
Alex concluded by noting that real-time monitoring is a risk mitigation tool. It strengthens existing food safety systems but does not replace them.
Key Takeaways
- Real-time monitoring involves continuous, automated collection of live data
- Immediate access to live data enables instant alerts and timely corrective action
- Helps prevent spoilage, reduce waste, and maintain production control
- Digital data recording creates a reliable audit trail and simplifies data retrieval and analysis
- Real-time monitoring strengthens existing food safety systems rather than replacing them
BRCGS Requirements and Audit Compliance
Maintaining Conditions and Record Keeping
Next, Alex outlined how real-time monitoring relates to key clauses in Issue 9 of the BRCGS Global Food Safety Standard.
He highlighted that requirements around temperature control focus on maintaining conditions within specification, supported by recording equipment with suitable alarms. Importantly, monitoring must take place at a frequency that allows timely intervention.
Alex also pointed to the emphasis on record keeping, particularly in relation to storage conditions and process control. When it comes to CCP monitoring, systems must be able to detect loss of control and support corrective action.
Continuous vs. Non-Continuous Monitoring Methods
While the standard allows different monitoring methods, Alex noted that non-continuous data limits intervention, often leaving disposal as the only option. This can attract scrutiny during audits.
Finally, Alex addressed the administrative burden associated with manual checks and verification, explaining that the right system can simplify compliance while improving reliability and control.
Key Takeaways
- Temperature control requirements focus on maintaining conditions within specification using recording equipment with suitable alarms
- Monitoring frequency must allow for timely intervention to prevent loss of control
- Strong emphasis on accurate record keeping for storage conditions and process control
- CCP monitoring systems must detect loss of control and enable corrective action
- Delayed data often results in product disposal being the only option, raising audit concerns
- Manual checks and verification create a significant administrative burden
The Business Case for Real-Time Monitoring
Next, Alex outlined the key business benefits of real-time monitoring. He explained that access to live data enables immediate intervention, helping to reduce waste and minimise spoilage. This is particularly important for maintaining continuity of supply and protecting customer relationships.
He also highlighted the value of reliable, tamper-proof data, which supports traceability and provides greater confidence during audits. Ultimately, real-time monitoring delivers enhanced control and peace of mind across operations.
Real-Time Monitoring and Marginal Gains
Novak Djokovic & Marginal Gains
Radek introduced the concept of marginal gains, using the career of Novak Djokovic to illustrate how small, incremental improvements can deliver significant results over time.
He explained that the same principle applies in food manufacturing. Businesses should identify areas for improvement, measure them effectively, and build gains gradually. Radek highlighted energy as a key opportunity due to rising costs. He demonstrated how the real-time monitoring of inputs such as temperature, humidity, CO2, door activity, and vibration can uncover inefficiencies and enable early intervention.
“What Gets Measured Gets Managed.”
Practical examples showed how small changes can add up, from identifying refrigerant leaks and inefficient chillers to reducing the time fridge doors are left open. Radek also noted the value of combining data sources, such as temperature and door monitoring, to reveal insights that would otherwise go unnoticed.
Finally, Radek emphasised the fact that real-time data supports evidence-based decisions, helping sites reduce costs, improve efficiency, and contribute to wider sustainability goals. He concluded with a simple principle: “what gets measured gets managed.” Identifying opportunities for marginal gains is the first step towards meaningful improvement.
Key Takeaways
- Small, incremental improvements can deliver significant long-term results
- Energy usage is a key area for optimisation, particularly as costs continue to rise
- Live data enables early intervention, preventing small issues from becoming costly problems
- Combining multiple data sources reveals deeper operational insights
The Current State of Food Safety & Why Change Is Happening
Why Is Change Happening?
In this section of the webinar, Stuart explained why we are at a turning point for food safety and monitoring, with several technologies now coming together.
He highlighted that sensors are becoming far more affordable and widely used, allowing factories to monitor equipment performance and environmental conditions in real time, including vibration, temperature, and humidity.
He also noted the role of cloud-based systems, which now make it easy to store and access large volumes of data without needing complex on-site infrastructure.
The Growing Impact of AI
Stuart pointed to the growing impact of AI and machine learning, which means this data can now be used more effectively to support day-to-day decision making.
Finally, he explained that the combination of AI and robotics is creating more intelligent systems that can learn and adapt, driving a shift in how modern food manufacturing operates.
Key Takeaways
- The food safety sector is at a turning point, driven by the convergence of multiple technologies
- Sensors are becoming more affordable and widely adopted
- Real-time monitoring of equipment and conditions is now more accessible
- Cloud-based systems enable easy storage and access to large volumes of data without complex on-site infrastructure
Understanding AI & Its Practical Role
What Do We Mean By AI?
Stuart explained that AI is an umbrella term, best understood as the use of data to support and improve decision making.
He outlined how machine learning enables systems to learn what normal looks like, using data such as time and temperature to identify early signs of drift before limits are reached. This supports a more predictive approach to food safety.
He then described deeper layers of AI. Deep learning can process more complex inputs such as images, video, sound, and vibration, supporting applications like predictive maintenance. Generative AI helps automate reporting, dashboards, and training content, reducing administrative workload.
Practical AI – What It Is & What It Isn’t
Looking further ahead, Stuart referenced emerging stages of AI development, including systems that can match human reasoning, exceed human capability, and eventually operate autonomously with minimal human input.
Stuart also clarified what AI is and is not. It is not a “black box” or experimental concept, but a practical tool that identifies patterns, flags risks early, and supports human judgement rather than replacing it. Importantly, it also provides transparent, auditable data for traceability.
The Evolution of Food Safety Systems
Stuart outlined the evolution of food safety systems, moving from paper-based records and lagging indicators, to digital dashboards and connected systems, and now towards predictive models that identify risks before they occur.
Finally, Stuart highlighted practical applications already in use, including environmental monitoring, predictive maintenance, and intelligent cleaning systems. He explained how combining these data sources can help identify risks linked to equipment, processes, and human behaviour, enabling earlier intervention and a more dynamic approach to risk management.
Key Takeaways
- AI is a practical tool for identifying patterns and risks early to support human decision making
- AI contributes to improved traceability through transparent and accessible data records
- The next stage is predictive models that identify risks before they occur
- Practical applications already include environmental monitoring, predictive maintenance, and intelligent cleaning systems
Moving to Predictive Food Safety
From Supplier Audits to Supplier Intelligence
Stuart outlined a shift away from traditional food safety models towards a more predictive, data-led approach. He explained that the historic model of fixed supplier audits and periodic reviews is evolving into dynamic, risk-based scoring, where emerging global and operational signals can be used to identify risk earlier. This allows businesses to focus attention where risk is developing, rather than where it has historically existed.
A key theme was the importance of weak signals, such as small changes in temperature, vibration, microbiological trends, operator behaviour, and CIP performance. While strong signals are easy to identify, he emphasised that the real value lies in detecting subtle changes early, before they develop into failures.
Turning Data into Action
Stuart explained that AI helps translate weak signals into actionable insight, moving beyond alerts to recommendations and explanations of why risk is changing. This supports earlier intervention, better decision making, and stronger evidence for audit and compliance purposes, while still keeping humans accountable for final decisions.
He also introduced the concept of digital twins, which allow teams to simulate processes and test scenarios without risk to product, people, or brand. This enables more proactive optimisation of production and maintenance planning.
The Future of Food Safety
Stuart was clear that AI does not replace human judgement. Instead, it reduces guesswork, removes blind spots, and supports more consistent decision making. He also stressed that success depends on having high-quality, relevant data, as well as the right investment, governance, and system integration in place.
He outlined a practical progression from data readiness through to prediction and prevention, and ultimately towards autonomy, where systems may eventually adjust processes automatically within defined controls.
Finally, he noted that the future of food safety governance will move from audit-based assurance towards continuous, real-time, predictive engagement with regulators, enabling greater transparency, shared data, and earlier collaboration across the industry.
Key Takeaways
- Food safety is shifting from traditional, audit-based models to predictive, data-led approaches
- Supplier management is evolving from fixed audits to dynamic, risk-based supplier intelligence
- Focus shifts from historical issues to areas where risk is actively developing
- Weak signals (e.g. small changes in temperature, vibration etc.) are critical for early risk detection
- Digital twins allow simulation of processes to test scenarios without real-world risk
- Human judgement remains essential, with AI supporting, not replacing, final decisions
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Webinar Q&A
Energy Efficiency & Building Trust
In the Q&A session, the speakers addressed a range of questions around the adoption of real-time monitoring and AI in food manufacturing.
On the question of whether energy efficiency is becoming the main driver for real-time monitoring projects, Alex and Stuart agreed that food safety and quality must always remain the primary motivation. However, they noted that energy, water, and utilities are increasingly important secondary drivers, alongside wider benefits such as cost savings and reduced labour.
When asked about building trust in AI-driven systems, Stuart explained that transparency and early engagement are key. He highlighted the importance of bringing teams on the journey, showing how AI supports rather than replaces human decision-making, and embedding it gradually into everyday processes.
Vibration Monitoring & Accessibility
On the topic of predictive vibration monitoring, Stuart confirmed that these systems can identify early warning signs of equipment failure. While individual sensors provide useful signals, the real value comes from trend data across multiple assets, enabling earlier and more reliable intervention before breakdowns occur.
Finally, the discussion turned to whether real-time monitoring is only suitable for large manufacturers. Stuart emphasised that it is suited to businesses of all sizes, with relatively low-cost sensors now capable of delivering meaningful insights. Even small-scale implementations can help identify trends and drive practical improvements over time.
Key Takeaways
- Real-time monitoring can deliver cost savings and reduce labour requirements
- Building trust in Artificial Intelligence requires transparency and early engagement with teams
- Gradual implementation helps embed AI into everyday processes and build confidence
- Predictive vibration monitoring can detect early signs of equipment failure
- Real-time monitoring is accessible to businesses of all sizes, not just large manufacturers
So there you have it, the key takeaways from our recent industry webinar on the role of Real-Time Wireless Monitoring in the food production industry.
If you have any questions about real-time monitoring, you can contact our team on 01473 461 800 or email sales@klipspringer.com. Alternatively, fill out the form below.
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