Saving money and reducing spoilage in cold storage environments.
Monitoring your cold storage environment to ensure that your product is stored at the correct temperature and complies with storage regulations, both during storage and in transport, is critical to many organizations all around the world – including our customer.
The ability, therefore, to predict and/or quickly identify potential problems and issues that may lead to adverse temperature fluxation and take immediate action to prevent them, is highly-desirable to businesses. Not only does it dramatically reduce spoilage volumes, but it allows producers, hauliers, cold storage and stores to find effective new ways to operate their businesses.
The world’s cold storage market equates to over $200bn worth of refrigerated and frozen food products every year, so the impact that AI + IoT technology can make to the sector is massive.
Our real-time Cold Storage Monitoring, Compliance & Energy Efficiency AI + IoT solution.
Our customer wanted to improve the way that they monitor their cold chain, end-to-end. To do this, we created a real-time AI + IoT and predictive analytics solution that monitors asset performance and storage refrigeration temperature set-point values, and which alerts and sends notifications when forecasting that temperatures are going to move beyond the optimum set point parameters.
Defining what data to collect is critical
We started by identifying the key metrics necessary to achieve the desired business outcomes. In this solution these included:
- Storage refrigeration temperature and humidity readings
- Storage refrigeration motor performance
- Storage refrigeration energy performance
- Light status
- Door status
- People counting
Initial goals of the solution
The first goal of this solution was to connect the different data sources to Rayven’s AI + IoT platform and to:
- Collect critical refrigeration, temperature and energy data received via a web-based system
- Define business logic to the application so as to meet the business objectives
- Provide alarm and alert notifications via email or SMS messages
- Collect enough historical data to enable the ability to test different logic to diagnose faults
- Provide the right dashboard to the right user with the right insight across multiple fleets and truck models
- Test the application, making sure all of the above goals are met, based on the below solution architecture
Before go-live, we tested four critical aspects of the AI + IoT solution:
The Rayven AI + IoT platform is built with security as a top priority and our proprietary security architecture ensures that data is secure at all points of the environment.
The solution includes data encryption in transit from device-to-cloud, as well as device authentication; security (Bearer) tokens; SSL, AES and RSA encryption; as well as additional device security checks done via automated polling.
Once securely connected, an Ethernet cable connection from the cold room to a 4G Gateway was determined to be the best solution. In addition, we used a three phase wireless energy meter to collect energy consumption data, which has its own 4G connectivity and Modbus connectivity.
To ensure that the data being received by the AI + IoT solution was current, it was necessary for us to compare the data in the refrigeration rooms with the dashboards and to construct functionality to back-fill data in the event that communication was ever lost. This process is important to ensure data integrity and give our customer confidence that they can make decisions based on the data.
Industrial Data Science
The objective of exploratory data analysis was to create a predictive maintenance Machine Learning data model, which included:
- Refrigeration fault detection and predictive maintenance
- Forecast time in set point
- Anomaly detection in temperature drifting
- Energy forecasting
We are continuing to improve the maintenance forecasting capabilities, using ever-growing data sets. In addition, we plan to add maintenance history records to improve the predictive maintenance modelling for all the assets in the operation.
The cold storage data used to deliver our monitoring, compliance and energy efficiency AI + IoT solution.
New and existing data was used to create the custom applications and business insights.
Key features of our Cold Storage Monitoring, Compliance & Energy Efficiency AI + IoT solution.
All of these features were customized to fit the customers specific business objectives.
Temperature & humidity monitoring
Monitoring of your cold storage environment is vital to ensuring your product is stored within the correct temperature ranges and complies with local food storage regulations, both during storage and in-transit.
Continuous monitoring of energy consumption including power factor, current, voltage, and frequency.
Alerts & notifications
Receive immediate SMS and email alerts for energy alarms, PAC warnings and alarms, or when any metric crosses your preset thresholds.
Identify when filter pressures are rising, system checks fail, or when compressor motors start to misbehave and take proactive action.
Ensure that there is a process in place to have the right people respond based on agreed protocols.
Create custom reporting dashboards and schedule them to be sent at any periodic time schedule to any specific user or user group.
Our Cold Storage Monitoring, Compliance and Energy efficiency AI + IoT solution drives real business outcomes.
Lower cost of maintenance
With centralized continuous real-time monitoring you can avoid the need to visit sites, monitoring critical performance metrics and alerts, as well as proactively maintain your PACs, from anywhere.
Better resource utilization
Sending technicians to monitor equipment or to fix-as-fail is not a good use of your technicians’ time or your money. Send technicians out when they are needed with specific purpose in mind.
Centralized, continuous real-time monitoring allows you to proactively repair and maintain equipment, improving uptime and customer service.
Reduce energy consumption
Compare PAC performance across a site or across an entire fleet to learn what makes some units more efficient than others.
Compliance through prediction
You can predict when you are not going to be compliant and dispatch maintenance crews and fix machines and faults before potentially triggering broader failures.
Less food waste
Always being complaint means there is less of a chance that food and supplies will need to be thrown out due to storage issues.
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Pricing & plans
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