While a minority of food companies and healthcare organizations continue to manually collect temperature logs to meet compliance regulations, most enterprises monitoring food, medicine, and vaccines have transitioned to digitalized sensors and probes that automatically measure and store temperature readings. That’s great news.
Even better, more sophisticated methods are now available that enhance the use of digital sensors: temperature monitoring systems that incorporate machine learning (ML) models for advanced analysis of the health and performance of refrigeration and freezing assets that protect perishable products.
Ultimately, the goal of these measures is to collect and analyze large data sets to predict and prevent potentially damaging excursions to ensure product safety and quality, minimize product loss and waste, and maintain compliance through the timely application of corrective actions.
IoT-enabled temperature monitoring provides three fundamental functions to protect food and medical products, the reputation and financial health of the companies that produce them, and the wellbeing of the public that consumes them.
Ensuring safe temperature ranges
The first function is to determine whether perishable products are refrigerated, frozen, (and in the case of retail food service, heated) within their FDA-established safe ranges. These ranges differ for food, medicines, and vaccines.
The second function is to prevent temperature excursions and their potentially costly consequences. An excursion refers to the exposure of a product to temperatures outside of its recommended storage range, which can significantly impact its safety and quality.
Correcting temperature excursions immediately in real time
The third function is to correct a temperature excursion that does occur immediately in real time. Accurate measurement and prevention of excursions are the ideal goal, but many variables can interfere with business as usual that can precipitate an excursion, including:
Because of these unexpected or unplanned events, “set it and forget it” is a dangerous policy when using sensors and probes that cannot account for these issues and, especially, that fail to alert management to take immediate action. Safety and operations professionals need to determine whether food and medicines can be saved or should be discarded. It’s a critical decision that must be made in real time to avoid potentially catastrophic consequences.
Digitalized probes and sensors have been immensely important to increasing the accuracy of temperature monitoring by replacing outdated manual pen-and-paper methods. Sensors continue to be an essential component of a Sensing-as-a-Service solution.
Used alone, however, sensors and probes provide a static model of data collection based on one-time correlations between ambient air temperature and different types of products. Because each sensor reading is unique, it requires an algorithmic model rather than a statistical formula to truly create a comprehensive “big picture” of the environmental condition landscape.
This is precisely the power of machine learning (ML) models: more than merely measure temperatures to check if an excursion has occurred, they analyze large sets of historical data to accurately predict trends to prevent excursions well in advance — before they become an immediate threat.
An ML model is a mathematical representation of patterns learned from large datasets that can be used to identify relationships and make predictions about future outcomes. Using an algorithm, an ML model is continuously “re-trained” every time new data is incorporated into the monitoring system. In turn, the model can simulate a variety of scenarios that provide highly accurate feedback loops about environmental conditions.
Basic digitalized monitoring solutions can identify only basic root causes, forcing management to leverage anecdotal guesswork regarding appropriate corrective action. ML temperature monitoring with advanced risk analysis eliminates that guesswork by giving product safety professionals the knowledge of what specifically is at risk and thus confidence in the actions they take to maintain safety, prevent loss, and satisfy customers.
The benefits of machine learning-enhanced temperature monitoring
ML models offer several significant benefits for monitoring and managing the temperature of food and vaccines, especially within cold chain logistics.
In sum, ML temperature monitoring is a critical practice for industries handling temperature-sensitive products such as food and vaccines, as it ensures product safety and quality, meets regulatory compliance, saves time and money, and promotes customer satisfaction and wellbeing.
Forward-looking food companies and healthcare organizations need accurate, impactful data insights that help ensure product safety and quality and improve the customer experience. Solution providers that operate at large scale can be trusted to collect and analyze years of datasets required for ML model accuracy.
IoT Sensing-as a-Service solutions that provide machine learning-enhanced temperature and condition monitoring combine real-time product traceability across the supply chain to support food and vaccine safety and loss prevention. This, along with streamlined workflows enables optimized asset protection within facilities. Identifying complex root causes of excursions and standardizing operating processes helps resolve issues quickly, saving inventory.
For retail grocery and food service industries, SmartSense’s IoT-enabled continuous condition monitoring prevents cross-contamination and growth of pathogens such as listeria. SmartSense protects perishable food products with 24/7, real-time critical asset monitoring and management and transforms real-time condition monitoring data into prescriptive workflows.
For healthcare organizations, pharmaceutical companies, pharmacies, labs, clinics, and blood banks, SmartSense solutions ensure temperature/humidity compliance with NIST-calibrated temperature monitoring, providing centralized reports and log audits for proof-of-temperature performance. These solutions allow the largest pharmacies and healthcare facilities in the country to easily follow pharmacy refrigeration compliance and improve efficiency.