Skip to content

Posts from the ‘Retail Solutions’ Category

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.

9760-E360_Outlook_September-2017- Applying Machine Learning_Facebook-1200x630

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.

1 fb-03

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).

3 tw-01

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.

The Internet of Refrigeration

Dean Landeche_Blog Dean Landeche | V.P. of Marketing , Retail Solutions

Emerson Commercial & Residential Solutions

I contributed to an article published in Condenser magazine. The focus of the piece was to analyze how networked equipment is aiming to improve safety and operations.

 The Internet of Things – an increasingly massive network of electronically connected systems, devices and people that enables cross-platform data sharing – is creating a large, connected ecosystem across many industries, including refrigeration.

woman choosing ice cream at grocery store freezer

There is continuing growth in remote monitoring with all types of refrigeration equipment, driven largely by the need to safeguard consumers and food, manage energy use, and provide a consistent, effective maintenance program.

Retail groceries have long recognized the importance of connected refrigeration systems, and have high adoption rates of connected devices. Previously, the primary focus was operating alerts and alarms to indicate problems. Now with more points of connection, more sophisticated data from embedded sensor and controllers and advanced analytics capabilities in the ‘big data’ world, the focus has changed to creating more insights that drive specific decisions and actions.

There is much more interest and use of information to prompt action in advance, based on opportunities and trends identified in data patterns rather than reacting to failure modes and alerts. Applied at the system, site and enterprise levels, those types of insight-driven actions have huge implications for cost-saving, labor productivity, maintenance improvement, food safety and more.

Through remote monitoring, equipment owners and their service providers can often detect problems, as they emerge rather than after-the-fact in an emergency breakdown. Major food safety risk and food loss is often avoided, and system operation can be maintained through proactive efforts. The adoption of remote monitoring for refrigerant leak detection is also becoming more common. Advanced data can often identify small leaks up to 30 days prior to discovery by leak detectors.

Today’s smarter systems are making it easier, faster, and highly reliable to implement equipment monitoring and performance processes.

Read the full article here.


Retail and Foodservice 2025: Omni-Channel Proficiency

Ed_McKiernan Ed McKiernan | President, Emerson Retail Solutions

Emerson Commercial & Residential Solutions

Welcome back for the final installment of our series highlighting the top five trends driving change within the grocery retail and chained foodservice markets in the coming years. The final trend we are taking a closer look at is Omni-Channel Proficiency.

Emerson and global research firm Euromonitor International worked together to identify the megatrends impacting retail and restaurant operations and facilities management over the next eight years.

If you haven’t already, you may want to first read our previous posts where we focused on the first four trends: Digital Shoppers, Focus on Convenience,  New Retail Formats, and Experiential Retail.

So what do we mean by Omni-Channel Proficiency? The common misconception is that “omni-channel” refers solely to online consumer engagement and shopping. It is more than that. It has to do with looking at all the different ways retailers need to be available for consumer engagement. A growing number of consumers are no longer coming to the retailer. To capture sales, retailers need to meet consumers halfway and go where they are; they need to be omnipresent.

The omni-channel concept pulls together many of the topics previously discussed within the other trends, including digital devices, customer experience, convenience, and Millennials. Omni-channel consumers have more frequent shopping experiences and spend more money than traditional shoppers. According to recent research, there has been a 23 percent increase in shopping trips among U.S. omni-channel shoppers and a 13 percent increase in spend among the group.

Omni-Channel Proficiency means facilitating sales anytime, anywhere in a seamless way for consumers. These consumers are “always shopping.” They are checking prices or browsing on their smartwatches or phones at all hours of the day and night. They want consistent experiences online and in brick-and-mortar locations.

One way retailers are trying to create this consistent experience is through customer loyalty programs. Unfortunately, many programs are falling short of consumer expectations. The most common mistake that retailers make with their loyalty program is that they treat it as a completely different entity within their brand. For instance, only allowing use of loyalty card online or in brick-and-mortar sites, or putting restrictions on use through the brand’s mobile app.

From a consumer perspective, that can be really frustrating. They want one seamless experience, regardless where or when they are interacting with the brand. And surprising to some, they do not necessarily want everything to be only digital or online. This is supported by the fact that we are seeing traditional online players incorporating brick-and-mortar into their brand experience. For instance, Amazon recently opened a bookstore location and will soon be introducing its curbside drive-up grocery store.

So, what can retail and foodservice organizations do in terms of facility management and operations to help support Omni-Channel Proficiency?

  • Facilities – Ensure seamless interaction between technology, operations, and store design to increase customer engagement and eliminate inefficiencies and lower costs.
  • Supply Chain – Increase focus on tracking inventory and warehouse strategy to expand reach at low costs and fulfill multiple channels from one site.
  • e-Commerce – Implement an infrastructure that supports online interaction and personalized, real-time engagement to slow industry commoditization and build equity against competitors.
  • Human Resources – Offer consistent customer service that allows troubleshooting and product returns from anywhere to increase customer satisfaction and build brand loyalty.
  • Customer Experience – Create consistent experience across formats that enables the ability to shop anywhere, anytime to increase brand loyalty and sales.

As the trends covered in this series illustrate, the grocery retail and chained foodservice environments are quickly evolving. It’s important that operators understand the impact these changes will have on their operations and plan accordingly to ensure strong sales and customer loyalty.

%d bloggers like this: