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Posts from the ‘Retail Solutions’ Category

Beyond IoT to Digital Transformation in the Modern Supermarket

Ed_McKiernan Ed McKiernan | President, Cold Chain

Emerson Commercial & Residential Solutions

Accelerate America recently published an article about how the Internet of Things helped a New York- based supermarket, Price Chopper, facilitate data acquisition and operate more efficiently. This blog provides additional perspective on that article and the evolution from IoT to true digital transformation.

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It’s nearly impossible to discuss best practices within the supermarket industry without bringing up the subject of the Internet of Things (IoT). The IoT is a network of electronically connected systems and devices (refrigeration cases, ovens and other facility systems) enabling cross-platform data sharing through embedded electronics, sensors, software and network connectivity.

IoT-connected facility technologies can remotely monitor store equipment which, in turn, provides system data and equipment analysis. This can then be used to generate reports and create an operationally efficient ecosystem of devices and machinery.

Initially, these connected technologies were used to set up operating alerts and alarms that indicated system faults or equipment failure. Then, after more sophisticated sensors and controllers were engineered, the focus shifted to advanced analytics, which allow facility managers to predict system failures and other problems hours or even days in advance. As a result, retailers have improved system reliability and energy efficiencies while preventing costly equipment failures.

Beyond facility and asset management, IoT-based technologies are applied every day throughout the food supply and distribution chain. From the farm through processing, transportation, distribution and — ultimately — retail outlets, a broad range of connected technologies helps extend and ensure food safety. They validate and manage temperature, humidity and other conditions, track transportation time and location, automate record-keeping and improve other handling processes. This sophisticated cold chain management helps maintain fresh food to the point of consumption, reduces food waste, improves food safety, and drives compliance with the Food Safety Modernization Act (FSMA) and other regulations.

Now, the challenge is to move from the functional benefits of IoT to the true digital transformation of businesses. In this emerging state, businesses rely on IoT as a foundational element for rethinking and reinventing their processes, while also redesigning their physical presences.

Price Chopper, a supermarket chain based in Schenectady, N.Y., is a real-world example of how food retailers are engaging in this transition. An early adopter of IoT solutions, Price Chopper installed electronic expansion valves (EEVs) on its case controllers, then deployed multiple temperature, pressure and valve sensors to gather data on EEVs, defrost, lighting and fans. The data revealed opportunities for energy optimization and provided Price Chopper’s facility managers with performance insights to predictive analytics.

The success of this effort prompted Price Chopper to install sensors in every energy-consuming load in its stores — including refrigeration, lighting, ovens and ventilation systems — and link them to a building control system. The initiative produced a tremendous amount of data, which allowed managers to fully optimize energy efficiency while quickly alerting them of servicing leaks and other malfunctions.

Executive leaders at Price Chopper have indicated that they’re planning to extend their IoT initiatives with the goal of meeting the organization’s other operational objectives.

To learn more about how supermarkets are leveraging the power of IoT, read the full article here on pages 26–27.

As you read the article, think about how foundational IoT can enable a reinvented approach to the grocery environment: transforming consumers’ shopping experiences, building customer loyalty and creating new business opportunities. Can facility and system data be consolidated with and correlated to other information within the retailer’s domain? If so, how could that be used to create new operational insights and profit opportunities? What data can be harvested from food’s long journey to stores, combined with store traffic information, and blended together with consumer preferences or menu trends to attract shoppers more frequently to their favorite retailer?

Those are among the possibilities as we move from foundational IoT to true digital transformation of retail.

Modernizing the Middle of the Store

JohnWallace_Blog_Image John Wallace | Director of Innovation, Retail Solutions

Emerson Commercial & Residential Solutions

This blog summarizes an article from our E360 program, entitled Cooling the Middle of the Store to Heat up Sales.” Click here to read it in its entirety.

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The perimeter of most grocery chains has always been at the forefront of the customer experience, occupied by shopper-friendly delis, fresh produce and bakeries, among other things. The middle of the store? That’s where you’d find the less sexy necessities like canned goods and other grocery staples.

Emphasizing the perimeter and stockpiling the middle with necessities was a dependable strategy. But times are changing. Case in point: a major food retailer discovered that in recent years, as the middle of the store began to shrink, so did overall revenues, with some major brands seeing as much as 2.8 percent drops per quarter. It needed a way to boost profits in the middle of stores.

The solution was to add new, low-profile refrigerated units to showcase more exciting products and packaging, bringing the pizzazz and flair of the perimeters to the middle of stores. But keeping these units working properly and monitoring their performance in this central location was the real challenge.

The necessity of maintaining consistent temperatures in refrigeration units exposed to ambient air meant stores would have to hook up sensors to monitor and control the temperatures in free-standing cases. However, these sensors required wiring that would need to be encased inside the stores’ walls — which would disrupt customers and cost stores a decent amount of money.

Emerson Retail Solutions presented one client with another option, which required no wiring at all.

Emerson’s Wireless Sensor System allowed the grocery chain to connect temperature probes, product simulators and other refrigeration sensors in critical refrigeration equipment throughout their stores, running around the perimeter and filing into the middle. This system also allowed the chain to collect key data that helped store managers monitor perishables which, in turn, allowed for maximized shelf life, reduced shrinkage and ensured safety.

The wireless module inside the cases transmitted data from the probes, product simulators and other sensors to a remote wireless gateway overhead. That gateway then converts the wireless signals into usable, real-time information, allowing for constant monitoring and data that can be used for supervisory controls. The signal sent from the module is strong and reliable enough to reach up to a 100-foot radius, all while using a minimal amount of energy. Repeaters can boost this signal even more, allowing for reach across the entirety of stores.

The Emerson Wireless Sensor System can, oftentimes, be installed in just 3.5 hours, potentially accumulating a 70 percent savings in installation costs when retrofitting stores, and cutting construction costs on new retail stores by up to 15 percent. Savings continue after installation by allowing the grocery chain to avoid fluctuating temperatures and reduce energy costs with their highly efficient wireless systems.

This particular grocery chain firmly believes that maintaining food quality is their top priority. Recent changes in the Food Safety and Modernization Act establish that it should be every chain’s top priority. Solutions such as the Emerson Wireless Sensor System allow chains to monitor free-standing refrigerated equipment in their stores, ensuring proper merchandise temperatures and giving customers the confidence in the retailer’s ability to consistently provide fresh and nutritious products — regardless of where product is located.

 

 

 

 

How to Create a Machine-learning Model in Your Enterprise in Six Simple Steps

JohnWallace_Blog_Image John Wallace | Director of Innovation, Retail Solutions

Emerson Commercial & Residential Solutions

This blog summarizes an article from our most recent E360 Outlook, entitled Applying Machine Learning for Facility Management.” Click here to read it in its entirety.

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Machine learning is a subfield of computer science that refers to a computer’s ability to learn without being programmed. Although machines should be able to learn and adapt through experience, human interaction is still needed to produce desired results. Today, many facility management applications — for refrigeration and HVAC systems, for example — have taken a supervised learning approach that utilizes historical data to train an algorithm and predict an outcome from a series of inputs.

To create your own supervised-learning model, businesses can take these relatively simple six steps:

  1. Define the problem. It’s critical to have a keen idea of the problem you are trying to predict or solve, and establish well-defined goals of the application.
  2. Develop a data collection strategy. Data collection is achieved via inputs from a variety of information, including: temperatures, pressures, on-off activities (from motors, etc.) as well as the actions that result from these inputs. Your goal will be to predict the action that will occur for a given set of inputs. Data will be used to both train the learning model and validate the model’s performance.
  3. Create machine-learning models. Based on the training data collected and available inputs, you can create a machine-learning model that uses specific algorithms (math) to predict an action. Since different types of models may perform better or worse for a particular data set, you might need to create multiple models (different math) and then pick the one that performs best based on your data.
  4. Establish a standard. How closely does your model predict the action or result that came out of your training data? A perfect model would anticipate the result every time. While that usually doesn’t happen, the goal is to get as close as possible to achieving the desired results, and then use that model as a standard moving forward.
  5. Test the validation data. Based on the validation data from step two, evaluate the performance of your model. If the validation data doesn’t match up, you may need to step back and select a different training model, and then validate the data again. This is an intricate process. When and if the results do not match expectations, you may have to start from the beginning. Make sure you are collecting the right types of data before running the process again.
  6. Utilize the machine-learning model. Upon completion of your efforts, you should have a model that can be used to predict an action or result based on the available inputs. At some point, input parameters may change or another system modification may be required; in this event, you will need to go back periodically and update the model based on new data.

How Big Data Can Enhance Sustainability Initiatives

ronchapek_2 Ron Chapek | Director of Product Management, ProAct Enterprise Software Services

Emerson Commercial & Residential Solutions

Today, across nearly all industries, corporations are being held to higher standards for sustainable operations and the food retail industry is no different. For convenience stores, much of the drive to be more sustainable is coming from evolving consumer demands. According to a Nielsen’s 2014 corporate social responsibility survey, 55% of global respondents said they are willing to pay extra for products and services from companies that are committed to positive social and environmental impact — an increase from 50% in 2012 and 45% in 2011.

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An increasingly important tool to help operators enhance sustainability efforts to meet changing consumer demands and government regulations is the gathering of data that can be strategically used to reduce waste, increase efficiency and ensure food freshness and safety. One way to accomplish this is through remote monitoring.

Remote monitoring services collect data from sensors that monitor conditions like products and case temperatures. They also provide real-time performance data on critical store equipment, including insights around energy expenditure, equipment operating condition and health, facility maintenance needs, refrigerant leaks and shrink causes.

With remote monitoring, retailers can also control and monitor facility systems across multiple sites and entire enterprises, giving them the ability to monitor food and maintain efficiency throughout the entire chain.

By leveraging the data gathered via monitoring, companies can not only improve efforts to safeguard food and gain operational efficiency, they can also contribute to sustainability efforts.

For more information, read the full article here.

 

Convenience Store Decisions: Gaining Operational Efficiency from BMS Insight

ronchapek_2 Ron Chapek | Director of Product Management, ProAct Enterprise Software Services

Emerson Commercial & Residential Solutions

As convenience stores continue to evolve to adapt to changing customer demands and infrastructure and facility requirements, operators are under increasing pressure to gain operational efficiencies. Of growing importance in this effort are the intelligent applications that allow operators to effectively use the data gathered by building management systems (BMS) and environmental monitoring systems (EMS).

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The challenge of building intelligent applications is to effectively convert rapidly expanding and disparate data sources into visually insightful, prescriptive, actionable and value-adding graphical interfaces across multiple stakeholder departments with a diverse range of usage and persona types. Historically, the static application data was gathered or delivered late, making it hard to determine the action to take. Now with intelligent applications, you have the ability to make decisions and take actions faster on more current data.

Before you can take advantage of these new intelligent applications there are four building blocks to consider putting in place:

  1. Modern Data Architecture that delivers access to a wide variety of data at high velocity and scale.
  2. Advanced Analytics, the science of using a wide variety of data to understand factors that impact customer experience.
  3. Smart Devices all gathering data and sending it through the architecture.
  4. Real-Time Business making decisions in real-time.

Before you take the first step in your intelligent application, think about the business value. Then you will be in a position to effectively use the data to increase operational efficiencies.

For more information, read the full article in Convenience Store Decisions online here.

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