AI is not a new concept for the food retail and foodservice industries. Many prominent retailers are already using AI techniques in customer-focused areas of their businesses, such as personalizing their consumer rewards and loyalty programs. In fact, several leverage in-house data science teams to champion these initiatives. But when it comes to turning AI’s focus toward refrigeration, very few have the domain expertise or experience applying AI to other critical facility systems — which can be significantly more complex and require a completely different knowledge base.
Another barrier to implementing AI in commercial refrigeration is the challenge of aggregating different sources and types of operational data into a useable format. Many food retailers already have some type of control system in place. Since different control system vendors collect and process data differently, it can be difficult to ensure the accuracy and consistency of the data. In addition, many vendor systems have proprietary constraints that don’t allow data to be shared easily.
Although the industry recognizes the potential of AI to deliver value in commercial refrigeration, food retailers and their servicing teams still have questions about its role in their operations. Demonstrating the value of AI across a wide range of food retail applications will be necessary in order to remove these doubts.
Engaging in proof-of-concept trials
At Emerson, one of the most important jobs we have is to provide the expertise and data science programs to build the business case for AI’s potential value to our customers. As a refrigeration controls, components and equipment manufacturer, we are focused on developing AI-enabled controls and integrated equipment that can deliver numerous benefits for operators and contractors alike.
Currently, we are engaging some of our customers in short-term, proof-of-concept trial periods. This gives us opportunities to demonstrate how our AI and ML solutions can integrate with their operations and deliver the potential for long-term, continuous refrigeration performance improvements. Once they see how quickly we’re able to deliver value and offer a return on investment (ROI), they’re much more interested in exploring a longer-term engagement.
The core of AI and ML technologies resides within the system control devices, which are typically incorporated into the equipment itself. By capturing data from sensors, modern equipment controls can perform a variety of key system optimization functions — from system fault protection and diagnostics to performance management and event scheduling. And in many instances, we can enable these capabilities without having to perform a significant retrofit.
Many of our existing customers already have a data-rich infrastructure — including sensors, controls and modems — that we can tap into and begin delivering insights. We often recommend installing additional sensors, which is relatively inexpensive compared to a full retrofit.
Adding up the advantages
As for the advantages that AI offers, not only can it deliver significant reliability and longevity benefits to commercial refrigeration equipment, but it can also address an ever-expanding variety of store operator and contractor concerns. For operators, we’re building data models that help them to optimize food quality and safety and reduce waste — in applicable case types and perishable food categories.
For contractors, we’re developing ML algorithms that are designed to detect asset health or condition issues. Over time, this data will allow retailers and their contractors to:
- Implement more predictive maintenance programs
- Reduce energy costs
- Keep assets running in optimum condition
Today, Emerson is leveraging AI and ML to optimize critical aspects of our customers’ operations. Our solutions utilize sensors that deliver data to powerful control devices — such as the new Lumity™ E3 supervisory control — and integrate with advanced, cloud-based software. By leveraging the deep domain expertise of our refrigeration engineers, we’re able to create data models that maximize refrigeration performance and help our customers to achieve a variety of key food retail and foodservice objectives.
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