IoT-Driven Cold Chain Tracking Provides Quality Control on the Go

Dec 21, 2017

For some products, maintaining strict temperature controls during shipping is critical. Certain pharmaceuticals and vaccines require an unbroken chain of refrigerated environments to guarantee safety and effectiveness. Frozen foods need low temperatures to prevent bacterial growth, which can cause serious illness if consumed. Fresh produce depends on a specific conditions to maintain shelf life once it reaches market.

From production to storage to shipping, it takes multiple technologies to ensure perishable goods reach their destination without compromise. Not only must these products strictly stay within a specific temperature range, but handlers may also need to maintain other environmental parameters, such as changing weather-related humidity and pressure, equipment maintenance and anticipating rises in theft risk along the way. By tapping into sensors and the data they generate, AI-driven data insights are transforming the industry like never before, monitoring sensitive products during transit, identifying whether a shipment is at risk for damage and taking preventive or corrective action.

A picture of a truck.

AI-Driven Traceability

Beyond recognizing better delivery routes, AI can offer real-time assessments of the safety and quality of food and pharma products. By offering those real-time insights, opportunities are created to change the conditions affecting products. Making food and pharma two places where traceability is critical. That traceability extends to consumers’ interactions with cold chain products. AI can help in four categories:

  • Descriptive Analytics can help provide context to analytics, enabling a better understand of the story behind the data, to reduce false positives for a smarter system. It can greatly boost sensor fusion and the integration of combined data from multiple sensors, for example to add vibration alerts corresponding to the higher temperature that occur when a package moving away from the cold chain. If a package falls over in a truck, for example, temperatures won’t rise, preventing false positives for product losses. Examining data post-trip AI can rank the quality of service as well as ranking the service provider.
  • Diagnostic Analytics enable automated decisions to reduce ping rates if data is running low, or to prioritize certain messages over the others. It can also suppress redundant data, reducing data bandwidth.
  • Predictive Analytics can provide theft forecasting based on data from a combination of sources including combinations of weather plus location, a rainy day plus low visibility day, or a holiday plus time of the day with location. Predictive analytics can rank quality of service in near real-time, even before a trip is over. It can also provide tremendous equipment insights offering everything from battery to sensor failure predictions.
  • Prescriptive Analytics enables maximization of good outcomes. From best mitigation factors to optimizations for the most efficient routs, prescriptive analytics streamline operational efficiency across the cold chain. This includes optimizing green strategies for reduced carbon emissions and lower energy costs.

Blockchain for Cold Chain

There’s never been a more exciting time for those working in the cold chain industry. But what does the future hold? Looking ahead five to 15 years, blockchain promises stronger layers of security for AI-driven networks. With enterprise interest in blockchain heating up, 39 percent of all companies, including 56 percent of companies with more than 20,000 employees, are already looking at blockchain implementation. Linking blockchain solutions to existing product journeys could very well provide even stronger traceability across the entire cold chain — from farm to pharmacy.

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The post IoT-Driven Cold Chain Tracking Provides Quality Control on the Go appeared first on IoT@Intel.


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