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Posts tagged ‘Charles Larkin’

Applying Artificial Intelligence to Commercial Refrigeration

Charles Larkin | Director of Data and Analytics, Cold Chain

Emerson’s Commercial and Residential Solutions Business

Over the past decade, artificial intelligence (AI) has become an ever-present aspect of everyday life. From e-commerce and smartphone functions to social media to modern industry, AI and advanced machine learning (ML) algorithms analyze continuous streams of data to derive predictive insights and optimize performance. Although these data science techniques are not new to commercial refrigeration, food retail and foodservice operators have been relatively slow to embrace AI’s vast potential. I recently participated in an ACHR The News article where we discussed AI’s barriers to adoption and how Emerson is helping to prove the value of AI to its customers.

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.

[Webinar Recap] The Journey to Data-driven Refrigeration Insights

Charles Larkin | Director of Data and Analytics, Cold Chain

Emerson’s Commercial and Residential Solutions Business

The utilization of data analytics and data science techniques is rapidly expanding throughout the commercial refrigeration sector. From the implementation of food safety programs to the identification of potential equipment performance issues, operational data can be transformed in a variety of ways to drive operational improvements and business outcomes. In our most recent E360 Webinar, I explored some real-world examples of how our analytics team is helping leverage data to drive improvements in their quality control (QC) initiatives.

When considering the role of data analytics in commercial refrigeration, it is important to understand that data should be viewed as an entry point for discovering larger issues and digging deeper to find root causes. Historically, the management of food safety QC programs has relied on paper-based recording and tracking methods, but these can be cumbersome to maintain, inaccurate and difficult to transform into usable insights. In addition, refrigeration data can be very complex and difficult to interpret and our industry is just beginning to unlock the potential uses of data in these applications.

Thankfully, modern data science techniques and machine learning algorithms are helping to deliver insights that uncover issues previously hidden from food service and food retail operators. Here are a few examples that I discussed in the webinar.

Descriptive analytics of QC programs in foodservice

Traditional hazard analysis and critical control points (HACCP) programs are paper-based checklists that do not easily yield valuable insights. In addition, the process of manually checking the temperatures of coolers, freezers and food (pulp temperatures) — and physically recording all of this data — is labor-intensive, inefficient and often inaccurate.

By leveraging cloud-connected sensors, we are able to digitally record HACCP temperature data and present that information in the form of easy-to-digest descriptive analytics. Through intuitive visual dashboards, foodservice operators can see their HACCP compliance checklist rates — per store or operation on a daily, weekly, monthly or annual basis. By doing so, they can then easily identify patterns that indicate areas of improvement.

Diagnostic analytics of refrigerated shipping container performance in marine transport

Applying data science to the process of measuring condensing unit performance in a refrigerated shipping container has allowed us to leverage more diagnostic analytic capabilities for our customers. These containers are at sea for extended periods of time, typically carrying high-value perishable shipments, so it is critical for operators to continually monitor refrigeration performance, identify issues early and make the necessary equipment or process corrections.

Performance data patterns in refrigeration units can be unpredictable, chaotic and difficult to interpret. By capturing this data over extended periods of time and processing it through advanced analytics techniques, we are able to identify patterns in condensing unit system health and make recommendations. For example, our analytics teams can diagnose when system health begins to decline so the operator can take proactive steps to fix potential issues before they become larger ones. When used across a fleet of shipping containers, we are also able to reframe this data into dashboard views to indicate which containers have issues that need immediate attention at any given time.

Prescriptive analytics in food retail

As we move these concepts into the retail space, we are applying similar techniques used in our food service and marine examples in an environment that can be significantly more complex — with diverse refrigeration systems, compressor racks and display cases to monitor. We are finding ways to make operational data simple for food retail operators to consume and to give them tools to identify precisely when and where they are having temperature excursions or performance issues.

Through a combination of performance dashboards and live alarms, we are able to help our customers assess the health of key assets and identify temperature deviations. This allows them to see which cases were having product deviations and begin the process of figuring out root causes (such as set point changes, defrost effectiveness or myriad other factors). From an enterprise view, these insights give large retailers the ability to monitor and analyze performance across multiple sites and examine why different stores have variations in performance characteristics.

Effective data analytics also provide more insight into which alarms are most indicative of critical performance issues. As a result, we can deliver a reduction in total alarms — on display cases and product temperature probes — which simplifies food retail operations and improves the overall likelihood of maintaining the desired temperatures.

To learn more about how data analytics can uncover insights in your operation, please view this webinar.

 

 

 

[New E360 Webinar] Leverage Data to Optimize Refrigeration System Efficiency

Charles Larkin | Director of Data and Analytics, Cold Chain

Emerson’s Commercial and Residential Solutions Business

Within the ever-expanding scope of commercial refrigeration applications, internet of things (IoT) technologies have a wide variety of potential uses. From helping to preserve food safety and quality to implementing smart maintenance programs, IoT programs can be utilized to address some of food retailers’ most critical operational concerns. In an upcoming E360 Webinar, which will take place on Tuesday, July 20 at 2:30 p.m. EDT/11:30 a.m. PDT, we’ll explore how retailers can utilize IoT initiatives and data-driven insights to achieve key operational objectives.

Attendees of this webinar will gain an understanding of IoT fundamentals and learn how hardware and software can combine to deliver valuable information on equipment performance. By utilizing connected sensors on equipment and installing smart control devices, operators can leverage previously untapped data to uncover real-time and historic insights on refrigeration status, performance trends and overall asset conditions.

Then, using advanced software with powerful machine-learning (ML) algorithms, this data can be processed and further analyzed to deliver more predictive insights, identify preventative maintenance (PM) opportunities, and even develop prescriptive maintenance models.

The upcoming webinar will explore how retailers can unlock the vast potential of data within commercial refrigeration applications, such as:

  • Identifying procedural problems in quick-service restaurants (QSRs) with respect to adherence to their hazard analysis and critical control points (HACCP) programs
  • Measuring the return on investment (ROI) of implementing digital HACCP programs and/or remote temperature monitoring of refrigeration assets
  • Developing algorithms for the marine sector to help provide early detection of potential food safety/quality issues during sea transport (and applying these concepts to food retail)

To learn more about how IoT programs can deliver operational insights in commercial refrigeration applications, please register for this informative webinar.

 

 

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