How IoT can improve DIFOT performance

The solution

To overcome the difficulties the cold chain logistics organization was having, we developed an AI + IoT continuous monitoring solution that can provide real-time DIFOTQ; with en route temperature checks, as well as additional shock monitoring and time recording at point of delivery by pallet.

This provided insights into:

1. Product quality – continuous data on en route storage temperature and handling, available at any time during or after the delivery
2. Delivery timestamp and GPS verification
3. Route deviations and delays
4. Pallet management – pallets en route, by customer, by warehouse, or not heard from

Defining what data to collect is critical
In order to create this solution, we identified the data required to achieve these goals. For this solution, it included:

1. CANbus truck performance data
2. Real-time location, temperature and shock data
3. Truck door status
4. Inventory and orders information
5. DC scheduled truck runs

Initial goals of the solution
The first goal was to connect sensors via 4G to the Rayven cloud to collect the right data in a consistent and reliable manner. By ensuring data quality and integrity, we would then be able to provide the following features:

1. Monitoring of real-time data via a web-based and mobile system
2. Device management monitoring ion one location
3. Ability to define business logic so as to meet the client’s objectives
4. Provide alarm and alert notifications via email or SMS messages when there was communication downtime or when a temperature excursion occurred
5. Testing procedure for the application, making sure that all of the above goals are met based on the below solution architecture

Before setting the solution live, we tested four critical aspects of the solution:

Security
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.

Connectivity
Data is transmitted directly to our servers over 4G using a secure MQTT connection. To ensure the system is always working even when data isn’t sent, a heartbeat was also implemented.

Data integrity
Once the devices started sending data, tests we carried out to ensure that it was being received both reliably and was able to be stored in the database.

Industrial data science
The objective of exploratory data analysis was to observe trends by driver, product, and customer. This would enable the organization to:

1. Examine transit duration
2. Compare temperature and damage by product, driver, and customer
3. Provide input to a DIFOTQ measure
4. Monitor turnaround times for pallets by warehouse, driver, and customer
5. Track pallet losses

What’s next?

We are continuing to work with the organization and are building adding to functionality to be allow them to dynamically group and track shipments, enabling them to find broader trends and track performance in different ways across their entire fleet in real-time.