How AoFrio’s software engineering is evolving to enable IoT insights for cooler and freezer fleets.
Here at AoFrio, we understand the importance of connectivity for our customers. It helps them optimise their refrigeration hardware and reduce inefficiencies and costs including power use, locating and recovering assets, unscheduled maintenance and restocking. Our mission is to build systems that enable our valued customers and partners to make informed decisions on their fleets and their usage based on the data our IoT hardware and software solutions provide.
What we are seeing in the industry is that the role of software engineering has evolved over time in the IoT landscape. Businesses are looking at technology that offers support for growth, seamless communication between various channels, and that can collect and retain a high level of data for insights generation with minimal human interference.
We are helping our customers attain this value by ensuring that software applications are capable of handling vast amounts of data transfer between the hardware, software solutions and the Cloud while maintaining the requirements for security, scalability, and performance to meet the growth demands. Our Track app and controller can store up to 5 months of fleet data if required. In other cases, our system is efficient to send the data to the cloud as soon as there is connectivity for cleansing and insights generation.
At AoFrio’s Software Engineering function, we use the following key principles to define the
non-functional characteristics that are truly important to support each business we work with and their growth. These principles also work as a guide for our technical and architectural decision making.”
As IoT solutions become more advanced due to emerging technologies entering market, we are continually exploring ways to automate our systems for quicker service delivery and to find out how can we leverage cutting edge technology such as Machine Learning (ML) for customer benefit. An example of this is a current project using ML to analyse the data from our SCS controllers to detect malfunctions, prevent failures and characterize user behaviours. The ML algorithms enable automatic identification of cooler behaviour anomalies that provide faster and more predictive information about fault detections in the field. This ensures AoFrio’s products are helping customers save money on their equipment and avoidable labour costs.