Turning Tide on Water Utilities with Digital Transformation

Posted on by Micah Kaplan

Access to clean, safe drinking water is something most of us expect without thinking too much about it. We turn on the tap and it is there. But the mechanics of what happens under the sink, within the pipes, beneath the level of the street, is complex, antiquated, regulated—even politicized.

Much like the smart factory or Industry 4.0, water utilities need to undergo a digital transformation to ensure the safety and efficiency of their operations.

We talk to Dr. Alison Adams, Chief Technology Officer at INTERA, a geoscience and engineering company with a background in water resource problems, about the challenges facing an aging water utility infrastructure, the need for data analytics, and how digital transformation can lead to the sustainability and resiliency we expect from a system that provides such a crucial resource to us all.

What trends and challenges do you see in the water utility industry today?

The water utility industry has always dealt with risk—primarily because of the public health requirement for safe, high-quality drinking water. In this country, water supply is a 24/7 activity. Unlike electric grids, which can have brownouts and blackouts, a water utility system can never go offline.

But now there are the added challenges of climate change, significant economic swings, changes in water-use patterns, increasing water-quality concerns—all those types of concerns. The customer base has also become increasingly aware, and is asking a lot more questions, wanting more transparency from the utility industry.

So the water utility industry must figure out a way to become more responsive to these various crises and risks that they face on a day-to-day operational basis.

How well equipped is the infrastructure of the water utility system to handle these changes?

The actual infrastructure that moves water within the urban environment is very, very old—in some places it’s well over 100 years old. And so operating, maintaining, and upgrading those systems is very, very challenging. Much of our water utility infrastructure is buried in underground pipes—and so is also kind of out of sight, out of mind.

And this has caused the system to be slow in moving toward digital transformation concepts—employing sensors, employing methodologies to be able to view what’s going on underground. But it’s just very difficult to maintain things underground if you don’t have some sort of monitoring system in place.

Much like the smart factory or Industry 4.0, #water #utilities need to undergo a #DigitalTransformation to ensure the safety and efficiency of their operations. @INTERA_Inc via @insightdottech

An additional challenge for the water utility industry to adopt new technologies is the regulatory environment. Water utility industries are very much guided by what the regulations are—water-quality testing, water-quality monitoring, water supply-system monitoring—these are all pretty much driven by regulatory requirements. And regulatory agencies in this country are very, very slow to adopt innovative ways to move toward adaptive decision-making, adaptive management, predictive analysis, scenario planning—all of these types of tools that data analytics through digital transformation puts at your fingertips. Often, by the time you gather enough data for someone to decide, “Oh—looks like we’ve got a problem,” it’s very, very costly for the utility to then fix that problem.

Whereas if we had more capability to use predictive analytics or scenario planning through more data analysis and data information coming into the system, then at least utilities could adapt their management strategies to either avoid impacts or minimize impacts. And that would really help reduce a lot of the cost in mitigating or trying to fix problems that regulatory agencies would actually rather avoid.

What is the best way for water utilities to start the digital transformation journey?

I’ll be honest—with utilities, digital transformation is not an inexpensive proposition. At the very start of the process, it’s difficult to do cost/benefit analysis, because a lot of the benefits that a utility is going to gain at the beginning are difficult to cost. There are going to be savings in staff; there are going to be process savings—for example, bringing your organization together and getting rid of silos.

The next thing to do is assess what you have. Utilities are old organizations, and they’ve built different databases over time. They may have SCADA systems, which are Supervisory Control And Data Acquisition systems. They may have laboratory water-quality systems. They’ll have different databases for different types of data that were needed over time, and these things start to create their own little spaghetti databases. So a lot of digital transformation revolves around bringing all of that data—that is usually very scattered throughout an organization—into some central location so that everyone has access to it.

What type of data is available for water utilities? And how can they turn that data into actionable insights?

For decades, one of the utility’s primary tools has been a SCADA system. It’s great at collecting all types of data related to the operation of a utility—how much water is coming into your water treatment plant; what is the chemical dosing rate, depending upon the quality of that water; what is the quality of the water that you need to deliver to the customer? A lot of this is regulatory. Many utilities also have their own additional criteria for safe water that they want to monitor. Utilities end up with just an enormous amount of data.

And so developing good data analytics is important. Also having those data analytics actually completed at the site where the decision is most effective. For example, if you have a control valve, or you have a chemical-dosing regulator, and you have analytics at the edge—where that sensor is now also coupled with some data analytics as it brings information in—the analysis is performed, and then the decision can be updated and made right there on the fly. Those are some of the ways digital transformation can really transform the way a utility can respond to changing environmental conditions.

What types of tools and technologies can water utilities use in their digital transformation efforts?

One of the first things that many water utilities went for was automated meter reading for water distribution at the individual home, to gain a better understanding of what was happening for their end-use customers. Understanding end-use water needs really helps drive the overall planning and operation of a water utility.

A new piece of technology that is gaining a lot of traction in water utilities today is this notion of a digital twin—where you take your SCADA system or your control system, and you create a duplicate of it digitally. Now you can begin to take your hydraulic models—the models that help you better understand how water moves through pipe, the impacts of pressure and temperature and water quality on the water in the system—and you create this digital twin of your system. Then you can start playing what-if games. You can start making changes. If you have a sudden pressure drop in a certain area, what does that do to your overall system?

Before, it was very difficult to plan even infrastructure upgrades and improvements. You had to take a part of your system out of service. What was that going to do to the rest of the system? And a lot of times those decisions were made mostly on past experience. You would run some hydraulic models, but often those models were very limited. Sometimes you just hoped for the best and had a good recovery plan.

Replicating the hydraulics of your system and then really tweaking it, impacting it, and seeing what happens—seeing where your pressure points are, seeing where your failure points are—has really helped a lot of utilities’ insight, and improved the operation of their systems. It helps them understand where they need to focus their maintenance programs in terms of pipe replacements or pump replacements, or that sort of thing.

That’s where a lot of this is going. I’m also aware of some utilities starting to look more at artificial intelligence as part of their data analytics. Since there is so much data coming into the system, and AI is becoming more ubiquitous in the analytics world, it is kind of a natural transition to move in that direction, to take advantage of big data to really assess large systems.

How does INTERA help water utilities address regulatory concerns while still meeting their digital transformation and innovation goals?

INTERA is really focused on making better use of the data that many of its clients are collecting—taking that data and turning it into actionable information. So it’s not just gathering data and putting it in a database, but turning that into something that now you can act on. Whether you’re doing it to try to respond to a regulatory requirement, or whether you’re doing it just because it makes sense from a business perspective—it’s improving the efficiency of your operation; it’s improving the effectiveness.

How are you working with Intel® to help water utilities address some of these things?

Intel® brings access to technological innovation, and the opportunity to work with a global partner. Intel is also offering grant funding for us to conduct some pilot projects. We are deploying particular types of sensor solutions in the field to demonstrate to water utilities the benefits of some of these digital transformation activities as a proof of concept.

INTERA is working on the data analytics side, the database side, the display of this information to show how we can take that data and turn it into actionable information so that a utility can make decisions differently. And I think this is going to be very, very important going forward—the ability to do these demonstration projects for different utilities of different scales—because not all utilities have equal financial security.

Many utilities don’t have a lot of money coming into their systems to cover the cost of innovation. Most utilities are public entities, and rate setting is a very, very public process. It’s a very political process. Often you just simply don’t get to change the rates—they are what they are. This puts utilities very much at risk to move to innovation, or to do things in an innovative fashion that would probably save them money in the long run.

Can you talk about resiliency and sustainability, and how they fit into the digital transformation of water utilities?

Resiliency is the key to sustainability because resiliency represents the ability to deal with risk. We can’t plan for every risk that’s going to happen out there, but it is possible to develop ways to manage risk through resilient systems. And I think that’s really the key item that digital transformation offers for us. If we had sensors in the field, and data analytics, we could actually predict a flooding event caused by extreme rainfall. And if we are able to predict it, then we can develop mitigation and adaptation strategies to prevent it from becoming a disaster or a crisis.

Where do you think the industry is headed?

I think the industry must head in the direction of being more nimble and adaptive—particularly in terms of operational decision-making and handling changing environmental conditions. It has to be more efficient and effective with the use of all of its resources—its capital funding, its water supplies, and its people resources. And I believe the primary means of obtaining this future is through digital transformation.

Related Content

To learn more about water utility digital transformation efforts, listen to the podcast The Flow of Data in Water Utilities with INTERA. For the latest innovations from INTERA, follow it on Twitter at @INTERA_Inc and on LinkedIn at INTERA.

This article was edited by Kenton Williston, Editor-in-Chief of insight.tech.

Smart Buildings Harness the Power of Data Analytics

Posted on by Micah Kaplan

What do hospitals, factories, businesses, and schools all have in common? They have a big data problem. Organizations struggle to use all the information they are collecting across the enterprise. But when you harness the power of data, transformational possibilities are practically endless. You can move toward achieving sustainability goals, lowering operational costs, and creating more agile operations.

What begins as a quest for distinct, application-focused data often ends with too much information from too many sources.

“People often have a very specific problem they’re trying to solve, but they realize over time they can’t scale that discrete solution to cover other issues,” explains Nathan Kehr, channel manager at Tridium, Inc. “They collect data from many systems and have more than they know what to do with, so they have to turn around and go back to the application side.”

“This cyclical process of adding more data streams and bolting on more applications is common in large institutional and multi-site organizations,” Kehr continues. “Soon they realize they have a data problem.” To solve it, they need a comprehensive solution that can aggregate, process, and analyze data from many sources.

One such solution, Tridium’s Niagara Framework, allows users to move data from disparate sources into an organized framework, allowing enterprises to gain insights and apply logic. “We have lots of provisioning steps built into the framework.” Kehr says. “As you’re bringing new devices, equipment, and other sources online, you can precondition a lot of that data through metadata tagging according to a standard methodology that helps bring order and interoperability.”

From Smart Buildings to Operating Rooms

Based on its Niagara IoT framework, the Tridium platform has many potential use cases, including occupant comfort, energy management, and power-grid responsiveness—across schools, university campuses, hospitals, and smart factories.

The move to smart-building technology is a key goal across all sectors. In this case, the solution collects data from building services such as heating and cooling, ventilation, and lighting subsystems. Using data from connected equipment that can range from big air handlers on the roof to small sensor packs attached to interior fixtures and furniture, the software can monitor usage, calculate energy consumption, and generate reports. It can even feed the data it aggregates and normalizes to other applications, giving facility managers the flexibility and choice of tools they need to reach sustainability goals.

In the fast-moving smart-building space, software developers are quickly advancing the field of special-purpose algorithms. These algorithms are designed to enhance air quality and further optimize energy use within specific building types. The algorithms developed for a data center might be tuned to keep power-hungry computers sufficiently cool and dry.

But the algorithms for a schoolroom are quite the opposite. And the common goal for all facility managers is to recognize and reject the creation of data silos and to maintain an open future growth path.

And it is not just mechanical, power, and lighting systems that organizations may want to link into their building data framework. They can also choose to add security cameras, access control systems, process control applications for manufacturing, and more.

The university campus can also benefit from the same system. Typically built over many years, their different buildings have likely been upgraded in an ad hoc manner. Campuses are rarely unified when it comes to all the equipment and systems that deliver core building services.

“You end up in situations where you have multiple manufacturers and generations of equipment installed, and different contractors coming to service and maintain them,” says Kehr. “Just getting all that data under control gives the university visibility into every system through a single pane of glass, allowing managers to make decisions about prioritizing contractors, scheduling repairs, and optimizing their budgets.”

Tridium’s Niagara Framework is a good fit for hospitals and healthcare settings as well. When preparing an operating room for example, certain parameters need to be met before the procedure can begin—typically involving humidity, temperature levels, lighting, ventilation, and air changes between operations. The solution can effectively regulate the environment for critical surgical procedures, handling mechanicals, lighting systems, and occupancy.

Organizations are wise to look at their existing #DataSystems and craft a plan for maximizing its effectiveness, particularly around #building and #EnergyManagement. @TridiumInc via @insightdottech

Scalable and Secure from Edge to Cloud

Designed with scalability in mind, the Intel® technology-based solution’s edge-to-cloud framework runs the same software in the cloud, on a local server, and at the unitary level on specific pieces of equipment—for example, a fan cooling unit in a building, or smart-factory robot.

This structure allows for more control and allows managers to use a single tool set for programming, servicing, and commissioning all equipment and devices. “There’s also a security element,” Kehr says. “Loading the software at every level allows us to encrypt data all the way down to the individual device.”

Managers access the data through a web-based dashboard system. What they see when they login depends on their position in the organization or area of responsibility. For example, an energy manager might see a dashboard showing consumption and energy costs, while a contractor working on a specific subsystem might see a more detailed report to help them diagnose problems and outages on specific devices.

As technology continues to evolve, organizations are wise to look at their existing data systems and craft a plan for maximizing its effectiveness, particularly around building and energy management.

“It’s very much a worthwhile endeavor to sit down with your operations and IT teams and say, ‘What do we have? What are we doing today? And do we have a plan for the future?’” says Kehr. “Even if you just went through a controls retrofit or new construction project five or six years ago, a lot has changed. And how we’re using technology to derive more value from data has evolved quickly and significantly during that time.”

This article was edited by Georganne Benesch.

Untangling the Full Lifecycle of Digital Signage

Posted on by Micah Kaplan

Digital signage has become so popular that it throws people off when they pull up to a drive-through and find a static menu. Or when they enter a store or restaurant and don’t see any digital engagement. Whether it is a simple web or mobile service, consumers expect some level of digital interaction.

Digital signage gives brands and retailers a way to represent themselves and engage with customers in public, digital form. But keeping digital signage up and running across multiple locations—anywhere from 5 to 5,000 sites—can be an IT nightmare. And technicians can’t exactly run from store to store every time there is a problem to fix.

“Signage is not hard—but it’s extremely complex. It’s more than simply installing a screen and throwing some content on it,” says IV Dickson, Vice President of Digital Signage and Digital Experience at SageNet, a leader in digital signage and retail IoT services. “A screen on a wall playing content is not really difficult.”

What becomes difficult is building architecture around that screen, connecting to hundreds and thousands of other screens, and communicating to different data points. In addition, there are the monitoring, management, and maintenance aspects of all those screens to consider. That’s when it becomes very complex, very fast, Dickson explains.

Retailers need to make sure they have all the moving pieces in place—and a way to keep it all up and running, 24x7x365. But if done incorrectly, signage can have negative effects on a brand. Nothing is more frustrating to a customer than engaging with a piece of technology that doesn’t work.

Uncovering Digital-Signage Complications

A case in point is financial institution Tulsa Federal Credit Union (TulsaFCU), which turned to digital-signage displays across its 17 locations as part of its modernization efforts. While it was the right approach to keep it competitive in the financial services industry, making it work long term was a big challenge.

The goal was to provide more than 56,000 members with financial services and timely information such as loan and interest rates. But anytime the credit union’s digital displays stopped working, it had no visibility into the causes, making it nearly impossible to address the situation.

Thinking and designing upfront is crucial. #DigitalSignage doesn’t start to become valuable until you have a network built out over a digital-signage ecosystem. @Arrow_dot_com @SageNetLLC via @insightdottech

As problems progressed, TulsaFCU finally reached out to SageNet for outside help. SageNet was able to quickly pinpoint the issue to the software and media player operations.

“It quickly became clear that the credit union’s signage network had taken a backseat to more pressing operational IT challenges,” says Dickson. “They simply didn’t have the resources to support their digital signage at the appropriate level.”

Creating Visibility into Digital-Signage Displays

Armed with this information, TulsaFCU revamped its underlying digital-signage infrastructure and replaced many system components with media players from Seneca, an Arrow Electronics, Inc. company.

TulsaFCU’s digital signage network now runs at nearly 100% uptime, allowing it to create new digital environments at new locations with the confidence it is working properly.

As part of its solution, Seneca offers the remote management platform xConnect to allow users to scale, monitor, manage, and maintain their digital-signage solutions. xConnect comes equipped with powerful remote management capabilities from Intel vPro® to provide a deep view into what’s happening within a system and the ability to access the telemetry and data to report upstream.

“The ability to marry xConnect capabilities with vPro allows us to understand all the way down to the board level what the device is doing: Is it overheating, are we having network and communication issues, or is there a problem with the OS?” says Dickson.

The Value Proposition for Digital Signage

The credit union is just one example of how organizations can overcome problems when looking at the whole picture. According to Dickson, it is not typically the technology that gets in the way of digital signage; it’s the lack of a clear business objective.

Organizations will often start thinking about the types of content and how they want to display it. Instead, they need to start by identifying the goals they want to achieve from their digital-signage solution and where the organization is going. For instance, how many touchpoints, displays, and sites will they have or build up to in the future?

“When you focus on speeds, feeds, or otherwise, a project doesn’t always get accomplished because you’re usually thinking about the wrong stuff. But suppose you’re focusing on business objectives and outcomes and allow the technology to play along with that. In that case, those are usually the projects that win,” says Kevin Cosbey, Business Development Manager for Arrow Electronics, an Intel® Solutions Aggregator.

All that thinking and designing upfront is crucial because digital signage doesn’t start to become valuable until you have a network built out over a digital-signage ecosystem, Cosbey explains.

“If you want the life of that network to be valuable to your organization over time, then you have to put all the pieces together days and months before day one to get to day 1,000,” he says. “The purpose and management of the technology after installation are key to the value proposition.”

Working with Seneca, SageNet, and Intel® from the beginning enables customers to identify and address all their gaps up front and create a solution tailored to their specific needs.

“Digital signage is such a unique architecture in that it’s relatively new for many folks. That is why it’s so important to make sure you pick the right partners that will be there for the long term and look out for you,” says Cosbey.

Related Content

Listen to what else Cosbey has to say about the future of digital signage on the IoT Chat podcast.

This article was edited by Georganne Benesch, Associate Content Director for insight.tech.

The Flow of Data in Water Utilities with INTERA

Posted on by Micah Kaplan
Dr. Alison Adams, Water utility management, water utility services, safe drinking water

[Podcast Player]

You expect the water you drink to be clean. But do you know what goes into maintaining high-quality drinking water? There are endless federal, state, and local rules and requirements to safeguard water quality. These regulations are often complex and inhibit a water utility’s ability to innovate.

On top of that, water utilities are dealing with a shortage of water supply, changing climate challenges, and a lack of funding. Water utilities need access to clean data and analytics to transform the water supply chain and overcome increasing obstacles. Listen to this podcast to learn about the obstacles utilities are facing, what decisions they are making, and how they are improving their water supply management systems.

Our Guest

Our guest this episode is Dr. Alison Adams, Chief Technology Officer for the environmental and water resource consulting firm INTERA. She was first introduced to the company when she was working as the CTO of Tampa Bay Water, one of the largest water utilities in Florida. INTERA helped assist Tampa Bay Water in making strategic operational decisions regarding several water supply management initiatives. After retiring from Tampa Bay Water, Dr. Adams joined INTERA as a principal water resources engineer. She was promoted to CTO in May 2021.

Podcast Topics

Dr. Adams answers our questions about:

  • (3:11) Water supply management challenges and trends
  • (5:20) How to overcome aging infrastructure
  • (9:19) Where digital transformation starts
  • (15:03) The importance of data analytics
  • (17:56) The tools and technology that can help
  • (24:32) Governmental rules and regulations standing in the way
  • (31:16) Resiliency versus sustainability
  • (33:11) The future of the water utility industry

Related Content

To learn more about the ongoing changes among water utility companies, read IoT Adoption Takes a Culture Shift. For the latest innovations from INTERA, follow them on Twitter at @INTERA_Inc and on LinkedIn at INTERA.

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Transcript

Kenton Williston: Welcome to the IoT Chat, where we explore the trends that matter for consultants, systems integrators, and end users. I’m Kenton Williston, the Editor-in-Chief of insight.tech. Every episode we talk to a leading expert about the latest developments in the Internet of Things. Today we have our Senior Editor, Christina Cardoza, joining the podcast to talk about efforts to digitize the water supply chain with Dr. Alison Adams, the Chief Technology Officer of INTERA.

When you turn on your faucet, you just expect there to be clean water. But the process it takes to get to your faucet is not as simple as turning a knob. Water utilities have to first jump through a number of regulatory, technical, and infrastructure hoops to ensure the safety and quality of the water supply. In this episode, Christina talks to Dr. Adams about how a digital transformation, powered by data and analytics, can ease the burden on water utilities, and provide a more resilient, sustainable, and innovative future. But, first, let me hand it over to Christina and our guest.

Christina Cardoza: Thank you for that great introduction, Kenton. And welcome to the podcast, Dr. Alison Adams.

Dr. Alison Adams: Thank you, Christina. It’s a pleasure to be here today.

Christina Cardoza: Why don’t we start off. If you could tell us more about INTERA and your role there?

Dr. Alison Adams: Yes. Founded in 1974, INTERA is an employee-owned geoscience and engineering company with deep roots in solving complex water-resource problems, and effectively assisting utilities with these difficult issues. My role as the Chief Technology Officer is to bridge together data, analytics, and technology so that data is transformed into actionable information, which supports decision making.

Christina Cardoza: What did you do before coming to INTERA? What brought you to the company?

Dr. Alison Adams: Before INTERA I was the Chief Technical Officer at Tampa Bay Water, which is a large water utility located in west central Florida. And I spent most of my career there on improving decision making and data utilization through this digital transformation process. When I was working on my PhD at Colorado State University I got very interested in the role of data, coupled with human decision making, in this role of decision making for water utilities. When I went back to Tampa Bay Water after finishing my PhD, I just became very involved in moving the agency forward in this world.

And while I was at Tampa Bay Water we had employed the services of INTERA to assist us in several complex modeling projects. We had a very complicated groundwater/surface-water model that we were developing, and INTERA helped with that development process—along with some other decision-support tools, which we needed to make operational decisions regarding water supply management. And when I retired from Tampa Bay Water, INTERA asked me to join, and it seemed like a very natural extension to the type of work that I had been doing there for years.

Christina Cardoza: I didn’t realize you had already formed a relationship with INTERA before joining there. That’s great. You mentioned as part of your role you’re really helping to solve some of the challenges around utilities, and it’s funny, because you don’t normally think of water utilities being something that generates a lot of data, or something that needs to undergo a digital transformation. So can you just talk about some of the trends and challenges happening in the water utility industry today?

Dr. Alison Adams: Well, the water utility industry today is faced with an increasing number of issues which are competing for its resources. And many of these issues really kind of came to being about 20 years ago when we had the economic decline, which really caused water utilities to recognize a declining revenue stream in the face of growing infrastructure needs, and in growing climate change challenges. Now, the water utility industry has always dealt with risk—primarily because of the public health mandates of drinking water and the requirements for safe, high-quality drinking water—but with the added challenges of climate change, significant economic swings, changes in water-use patterns, increasing water quality concerns, and those types of concerns—it has caused the customer base to become increasingly aware and asking a lot more questions and wanting transparency from the utility industry.

And so, compounding issues, along with the risk that the water utility industry faces—and the primary risk is related to the fact that in this country, water supply is a 24/7 activity. Unlike electric grids, which can have brownouts and blackouts, a water utility system can never go offline. And so the pressure to always provide water to the tap is a very real pressure that water utilities face. The water utility industry must figure out a way to become more responsive, more—not reactive—but the ability to handle the various crises and risks that they face in a day-to-day operational basis.

Christina Cardoza: You mentioned the power grids and electrical utilities. I know that part of their digital transformation efforts have become challenging because they’re dealing with a power grid infrastructure and architecture that has been virtually untouched for years. And so they’re trying to deal with how to modernize this legacy infrastructure. What is the state of things in water utility? How equipped is the architecture and the infrastructure to handle these new evolutions and changes?

Dr. Alison Adams: Well, I think there’s a lot of similarities there, because much of the water infrastructure—the actual infrastructure that moves water within the urban environment—is also very, very old infrastructure. In some cities, in some places it’s well over 100 years old. And so the ability to maintain and upgrade and operate those systems is very, very challenging. Much of our water utility infrastructure is all buried—underground pipes—and so kind of out of sight, out of mind. And this has caused it to be slow in moving toward digital transformation concepts—employing sensors, employing methodologies to be able to view what’s going on underground. And also it’s just very difficult to maintain things underground if you don’t have some sort of monitoring system in place. So there’s some similarities there.

The water utility industry also, as an industry, is an old industry that has been very, very slow to change. It’s an industry that’s built around a lot of “people trust,” over decades of working with each other—which is also new in today’s environment, where you have a different workforce who is not really interested in working for a utility from the ground up. It’s a changing workforce that actually wants to utilize and take advantage of more technology, more information at their fingertips, more understanding of the system. And the utility industry has been kind of very slow to adapt to that, to adopt those new ways.

Part of the challenge, though, for water utility industry to adopt to new technologies is the regulatory environment. Water utility industries are very much guided by what the regulations are. Their water quality testing, their water quality monitoring, their water supply system monitoring are all pretty much driven by the regulatory requirements. And regulatory agencies in this country are very, very slow to adopt innovative ways to want to move toward adaptive decision making, adaptive management, predictive analysis, scenario planning—all of these types of tools that data analytics through digital transformation provides at your fingertips, the regulatory agencies are like, “Well, you know, we haven’t really worked that out in the past. Our regulations have been fixed. They’ve been this way for decades—they seem to work. Let’s just continue monitoring the situation, and we’ll see if we have water quality problems.” Or you have an environmental problem due to your groundwater pumpage, and oftentimes by the time you gather enough data for someone to decide, “Oh—looks like we’ve got a problem,” now it becomes very, very costly for the utility to then correct what they’re doing and try and fix the problem.

Whereas if we had more capabilities to use predictive analytics or scenario planning through more data analysis and data information coming into the system, then at least utilities could adapt their management strategies to either avoid impacts or minimize impacts, and that would really help reduce a lot of the cost in mitigating, or clean up, or trying to fix problems that regulatory agencies actually would rather avoid, but they seem to have some reluctance in employing the technology needed to actually avoid those situations.

Christina Cardoza: Great. Lots to unload there. I want to come back to all the regulatory requirements and regulations that water utilities are having to deal with today. But before we get there, talk to me more about how water utilities can start this digital transformation journey. Where is the best place for them to start tackling some of these challenges, and is there a best way or best practice to do so?

Dr. Alison Adams: I’ll be honest—with utilities, digital transformation is not an inexpensive proposition. And oftentimes it’s one that, at the very start of the process, it’s difficult to do the cost benefit analysis that a lot of utilities want to do when they’re deciding whether it’s a go/no go on projects. Because a lot of the benefits that a utility is going to gain at the beginning are going to be difficult to cost—because they’re going to be savings in staff; they’re going to be more process savings. You’re going to streamline what is going on in your organization. You’re going to bring your organization together and get rid of the silos—the engineers working in one department, the operators working in another department, your water quality lab working in a—planning all of these, which are typically very separate departments and areas within a utility, they must come together to work as an entire business. So you have to have that executive leadership because of funding and the organizational changes that digital transformation will result in your utility.

The next thing that you need to do is assess what you have. You need to—and that might sound kind of odd—but utilities are not new organizations; they’re old organizations, and they built different databases. They may have a SCADA system. They may have a laboratory water quality system. They’ll have different databases that people were collecting different types of data that were needed over time—whether it was monitoring a well field, or some regulatory requirement, or some planning activity—their demand data, these things start to create lives of their own, their own little spaghetti databases. You need to assess what all that looks like, so that now you can start forming a plan of where do we want to go, and how do we get there?

And a lot of the digital transformation revolves around bringing all of that data—that is usually very scattered throughout an organization—into some central location, so that everyone has common access to all of your high-quality data. Your regulatory reporting is one of the easiest, most effective ways to demonstrate the benefits of digital transformation, when you can automate the regulatory reporting that every utility goes through. And a lot of times that’s a very—the regulatory reporting is a very costly activity in terms of staff time, the number of staff that you employ to do that. And automation can absolutely reduce the cost that a utility spends in that area. Once you kind of make the assessment of where you are, and then you build a plan of where you want to be, then you go about implementing that plan.

And another key aspect is, don’t take on more than you can assimilate at a given time. Digital transformation is a huge organizational change, and if you try and do everything at once, you’re going to overwhelm your staff. They’re going to be so busy with all the change-management activities, they’re going to say, “Well, what about my regular job? What I’m here to do.” And they’ll get frustrated. Staff will either leave, or they just won’t participate in the process, and you’ll end up with a lot of failure. So it’s a process that it has to be deliberate, but it has to recognize the time it takes. And it can take years for a utility to go through this process, and that’s okay. You need to be adaptive in how you’re making decisions to implement certain technologies. The technology world is changing every day. There are new sensors, there are different database systems, there’s different communication protocols—and you have to be willing to scan across the horizon, take advantage of what there is today, make decisions, move forward, and do it in a much more incremental and deliberate fashion.

Christina Cardoza: Yeah, that is such a great point. I feel like, especially with COVID 19, there has been such a rush and acceleration for digital transformation. But it’s important to remember that this is a journey, and there’s not going to be a hard deadline or a hard stop, and it really is going to take time. And so if you want to do it successfully, you have to make sure that you’re putting the time and the effort in, and you’re not rushing things.

Dr. Alison Adams: Each step along the way you need to let your staff kind of get settled into the new way of doing things and get comfortable, and then move to another increment. Also, like I said, this journey can be expensive, and you need to plan for those expenditures and pace that out based upon your own economic situation at the utilities, and not overwhelm yourself by trying to spend too much money too fast.

Christina Cardoza: Exactly. And talking about the expenses and the funding, you mentioned data: how important it is to assess where you are, and then connecting all of those siloed technologies and departments. Data is also going to be very important in these journeys to keep track of how you’re doing—allowing stakeholders to see the health of projects and where the bottlenecks are and where they can improve. So, what type of data is available for water utilities, and how can they turn that data into actionable insights?

Dr. Alison Adams: Well, our water utility collects a wealth of data. For decades one of the utility’s primary tools has been a SCADA system, which are Supervisory Control And Data Acquisition systems. That’s been their main bread-and-butter, industrial-control system. SCADA systems are also great at collecting all types of data related to the operation of a utility—whether it’s how much water is coming into your water treatment plant; what is your chemical dosing rate depending upon the quality of that water; and the quality of the water that you need to deliver to the customer. A lot of this is regulatory, required data for what’s the quality of the water coming into a plant. What’s the quality of the water leaving a plant? Is it not only meeting safe drinking water requirements, but many utilities have their own criteria for safe delivery of water that they also want to monitor and make sure that the water is of a high standard to its customers.

So utilities end up with just an enormous amount of data that, if you don’t figure out how to use that data, you can become easily overwhelmed. And so developing good data analytics—also the move to having those data analytics actually completed at the site where, or at the point where, the decision is most effective. For example, if you have a control valve, or you have a chemical-dosing regulator, if you have analytics—what we refer to as analytics at the edge, where that sensor is now also coupled with some data analytics as it brings information in, the analysis is performed, and then the decision can be updated and made right there on the fly to change a critical control element of a water utility. Those are some of the ways where digital transformation can really transform the way a utility can respond to its changing environmental conditions, and still continue to deliver the high-quality water that it’s responsible for.

Christina Cardoza: A lot of this reminds me of the digital transformation initiatives happening in the manufacturing space right now. There are a lot of differences, but the whole, ensuring the safety and efficiency of operations, using predictive analytics, processing data at the edge—all reminds me of what’s happening in the smart factory right now, and they’re using a lot of automation and AI sensors to find those answers, find those insights, and get that data. So, what sort of—I know it’s a culture change first, and then the tools and the process will help—but I’m curious what sort of tools and technologies are water utilities looking to take advantage of in this transformation?

Dr. Alison Adams: Well, I think one of the first things that many water utilities went for is the automated meter reading for the water distribution at the individual home. That technology has been out and about now for 10-plus years, and that’s one of the first pieces of technology in the field that utilities went to, to gain a better understanding of what was happening at their end-use customers. Because that ultimately—understanding the end-use water needs—really helps drive the overall planning and operation of a water utility.

A new piece of technology that is gaining a lot of traction in water utilities today is this notion of a digital twin—where you take your SCADA system or your control system, and you create a duplicate, a replicate, of it digitally, so that you can now begin to take your hydraulic models—the models that actually help you better understand how water moves through pipe, the impacts of pressure and temperature and water quality on the water in the system and deliver it to the customer—you create this digital twin of your system, and then you can start playing what-if games. You can start making changes. If you had a sudden pressure drop in a certain area of your system, what does that do to your overall system?

Before, a utility—absolutely, it became very difficult to plan even infrastructure upgrades and improvements. You had to take a segment of pipe out of service. You had to take a part of your system out of service. Well, what was that going to do to the rest of the system, and how long could we be down? And a lot of times those decisions were made on experience. You would run some hydraulic models, but oftentimes those models were very limited in what they could actually tell you about your system. And then sometimes you just kind of hoped for the best, and you had a good recovery plan if something went wrong.

But these capabilities of a digital twin—where you can replicate the hydraulics of your system and then start really tweaking it, impacting it, and seeing what happens, seeing where your pressure points are, seeing where your failure points are—has really helped a lot of utilities’ insight, and to improve their operation of their system, understand where they need to focus their maintenance programs, understand where they actually need to focus their asset management programs in terms of pipe replacements, or pump replacements, or in that sort of thing.

That’s where a lot of this is going. I’m also aware of some utilities starting to look more at artificial intelligence as part of their data analytics tool. Since there is so much data coming into the system, and AI is becoming more ubiquitous in the analytic world, it becomes kind of a natural transition to move in that direction to take advantage of what is referred to as “big data” to really assess large systems.

Christina Cardoza: I love this idea of a digital twin and the water utilities utilizing this, because I feel like it helps not only plan for today, but also plan for the future in all of this.

Dr. Alison Adams: Oh, absolutely. Yeah. I mean, digital transformation can provide managers with the tools to adapt to changing conditions, which is really kind of critical to a water utility. And one of the areas where they probably had their biggest risk is, water utility operators, they love steady state conditions. They want to turn the plant on and the water, their source water, comes in at a steady rate and they treat it and they deliver it. They don’t really like many changes throughout the system. But I think, today, we do have a lot of changes throughout the system. And many of these digital transformation tools that are starting to become available are really going to help utility managers become more adaptive and more responsive in their day-to-day operational conditions.

Christina Cardoza: And we mentioned assessing risk and risk management a couple of times. And just to give our listeners a full picture, what did risk management traditionally look like for water utilities, and how are they having to change that?

Dr. Alison Adams: Risk management for water utilities was really more or less focused on the economic risk, and also the regulatory risk. The biggest driver for water utility has always been its mandate to deliver high-quality drinking water and to meet the Safe Drinking Water Act—federal Safe Drinking Water Act requirement of what those standards for safe drinking water are. Also, not only are there federal standards, in many different states there’s also different states’ standards. And also there could be different local standards. So a utility is faced with a lot of regulations that they might need to meet.

And so the regulatory requirement often drives the economic conditions. So you have to spend the money to meet the regulatory conditions, but utilities are public entities, most of them. There are private-investor-owned utilities that are regulated separately—usually through a utility commission—and their rate settings are done in a different fashion. My experience has been with a public utility, and rate setting is a very, very public process. It’s a very political process. And oftentimes to set rates at a utility, the answer is you just simply don’t get to change them. They are what they are.

So utilities don’t have a lot of money coming into their systems in order to do a lot of innovation—unless they can get some grant funding, or some other federal access to federal or state monies to do these sorts of things. The pressure to keep your rates low, actually a lot of times puts utilities very much at risk to move to innovation or to do things in an innovative fashion that actually in the long run will probably save them money, but it’s just that notion of spending that first dollar value up front in order to get over the hurdle. Most of the risk, historically, around water utilities is this kind of push-pull relationship between the economics of running a utility, coupled with the regulatory requirements of things that you must do to meet safe drinking water requirements.

Christina Cardoza: Talk to me a little bit more about those government regulations. Do you think this is helping them on their digital transformation journeys? Or can this sometimes complicate things?

Dr. Alison Adams: The regulatory agencies are very much complicating moving in a digital transformation way. The regulatory agencies are very, very slow to change. Most of the regulations actually start at the federal level. The Environmental Protection Agency is the overall guiding, regulatory body to set safe drinking water requirements. They can then pass those obligations down to the states. The states can either adopt the federal standard, or they can adopt standards that are more stringent than the federal requirements. But when you’re either dealing at the federal level—and the problem with the federal level is, of course, they look across all 50 states and they go, “Oh, wow, we don’t really want to change this. These regulations have been working fine from a national perspective for 35 years—since the mid 1970s. They’ve been working for a long time. Let’s don’t monkey with them. Let’s don’t change them.” Whereas a utility might go, “Well, look, if I could employ some data analytics and improve my predictive capabilities and be more adaptive so that I am responding to the actual environmental conditions that we’re measuring in the field—let me do that instead of having to meet your fixed standard.”

But the regulatory agencies are very, very slow to want to accept that type of management strategy. It just probably puts more pressure on them to understand what’s going on. It takes away this kind of fixed standard that’s easy to review on an annual basis, but it’s really causing water utilities to not move in that direction, because they’re going to be driven by what the regulatory requirements are. And if the regulatory agency doesn’t embrace the innovation, then the utility is not motivated to continue with it. They’re going to say, “Well, what’s the benefit to me? I mean, I might improve my management of the system, and I know that ultimately things will be better, but it doesn’t improve me from a regulatory agency’s perspective. And actually they might ding me. They might criticize me during part of the year because they see that I did something that didn’t actually meet their fixed standard just for some portion of the year.” So that’s a large part of the problem.

Christina Cardoza: So how does INTERA help the water utilities in this area? How is the company helping water utilities address regulatory concerns and regulations, and still meeting their digital transformation goals and innovation?

Dr. Alison Adams: INTERA is really focused on taking better use of the data that many of its clients are collecting—taking that data and turning it into actionable information. And recognizing the importance of good decision making—supporting strong decision making with the clients and the regulatory agencies. It’s not an easy lift to get regulatory agencies—INTERA works with many different regulatory agencies, particularly in the mining space, as well as the water utility space—to help them understand the value of these data analytics, supporting these better decision analyses and better decision opportunities. But, again, it becomes still something that the regulatory agencies are struggling with—this notion of adaptive decision making, of incremental decision making.

But we continue to work through with clients the value of this, and the importance and the value of turning their data into actionable information. I think that’s probably one of the key items that INTERA is doing, is really getting their clients to understand it is actionable information—it’s not just gathering data and putting it in a database, but turning that into something that now you can act on. Whether you’re doing it to try and respond to a regulatory requirement, or whether you’re doing it just because it makes sense from a business perspective—it’s improving the efficiency of your operation; it’s improving the effectiveness; your customer base has more transparency. You can demonstrate to your customers—they can actually see what’s going on through this actionable information, through these data analytics. That is really where a lot of the utilities are gaining some ground and recognizing and valuing the digital transformation, is in getting their customers to really understand the nature of the business and what goes on in producing safe drinking water.

Christina Cardoza: Now, I should note, in interest of full disclosure, that insight.tech, the program that produces this podcast, is Intel® owned and operated. How are you working with Intel to help water utilities address some of the things you just talked about? And what has been the value of that partnership?

Dr. Alison Adams: Intel brings access to technological innovation, and the opportunity to work with a global partner. Right now we’re working specifically with Intel—Intel is offering grant funding for us to conduct some pilot or some demo projects, where we are deploying particular types of sensors—solutions in the field to demonstrate to water utilities the benefits of some digital transformation activities. We’re looking at pilot projects and demo sites to give kind of a proof of concept, if you will. Intel is funding those efforts. We’re working with other technology providers that have different types of technology solutions deploying this information in the field.

INTERA is working on the data analytics side, the database side, the display of this information to show how we can take that data and turn it into actionable information, so that a utility can now make decisions. And I think this is going to be a very, very important part, going forward, is the ability to do these pilot projects and demonstration projects for different utilities of different scales. Not all utilities are of equal financial security. Most of the utilities in this country are actually small- and midsize, and don’t have access to funding. So I think these types of demonstration projects and grant projects that Intel is funding are going to be very important to help get these concepts of digital transformation out into the water utility industry.

Christina Cardoza: Now, we’ve sort of been dancing around this next point, hinting at it at least, but a lot of these regulations and grants are a focus towards a more sustainable future. But I’m noticing over the last year and a half, companies and organizations are trying to be as prepared as possible for what’s to come next. So I’m seeing a lot more interest in resiliency and being resilient water utilities. So can you talk a little bit more about those two buzzwords, and how they fit together in this digital transformation?

Dr. Alison Adams: Resiliency is the key to sustainability, because resiliency represents the ability to deal with risk. The World Bank estimates that every $1 invested in resiliency results in $4 saved. I mean, that’s pretty phenomenal to me. The notion of building a resiliency is really—that’s the only way that you’re going to be sustainable. And through that—we can’t plan for every risk that’s going to happen out there, but it is possible to develop ways to manage our way, through resilient systems. And I think that’s really the key item that digital transformation offers for us. Whether you’re responding to a flooding event caused from extreme rainfall that we actually could predict better now if we had the sensors in the field and the data analytics—we can actually predict that flooding event from the rainfall that’s actually occurring in the ground and causing sudden changes in our water quality. We would be able to predict that. And if we are able to predict that, then we’re able to develop mitigation strategies and adaptation strategies in order to prevent that from becoming a disaster or a crisis.

Christina Cardoza: I know we’re near our end of time here. One of my last questions I want to ask you is, what is your vision of where the industry is headed? And how the technology today will enable that future.

Dr. Alison Adams: I think the industry must be headed in a direction to make it more nimble and adaptive—particularly in its operational decision making, in its day-to-day decision making—how it handles changing environmental conditions. The industry has to continue to be more efficient with its use of all of its resources. And by this I mean its capital funding, its water supplies, and its people resources—all of those. We have to be more efficient and effective in how we use that—use all of those. And the primary means in obtaining this future, I believe, is through digital transformation.

Christina Cardoza: Well, thank you again for joining us today, and for the great conversation.

Dr. Alison Adams: Thank you very much. I certainly enjoyed it.

Kenton Williston: And thanks to our listeners for joining us. To keep up with the latest from INTERA, follow them on Twitter at @INTERA_INC and on LinkedIn at INTERA.

If you enjoyed listening, please support us by subscribing and rating us on your favorite podcast app. This has been the IoT Chat. We’ll be back next time with more ideas from industry leaders at the forefront of IoT design.

Digital Signage Puts the Spotlight on Data

Posted on by Micah Kaplan

We’ve all benefited from digital signage, from navigating our way through a transportation hub to discovering new products in retail, to staying up to date in the workplace. But these applications only scratch the surface of what’s possible.

For example, at one school, cutting-edge technology not only saved thousands of dollars, it improved student culture. Rather than investing in a traditional scoreboard, administrators installed digital signage and handed the controls to tech-savvy students.

And instead of simply posting points, they leveraged additional data to turn game stats into engaging content, lighting up the screen and motivating the crowd and players. The result was a special bond between two isolated groups—jocks and computer geeks—creating camaraderie on and off the field.

Organizations of all types can be creative with external data—tying in news headlines, weather, traffic, and market information to drive engagement and value. The uses are limited only by the imagination.

Light Up Your Data

“We believe there’s tremendous opportunity for organizations to capture value in the data that they already have. They spend billions of dollars every year gathering data, storing it, and backing it up,” says Doug Bannister, founder and CTO of Omnivex Corporation, manufacturer of Omnivex Moxie Digital Signage Solution.

But only a very small percentage is used to drive value to the bottom line. An abundance of insights is left untapped in silos that could be used to increase engagement, reduce costs, or increase revenue. Digital signage can break down silos and make data useful.

Bannister, whose background is in engineering, launched his company in 1991, providing bank call center agents with real-time stock exchange information. Over the past 30 years, he’s seen the capabilities of digital signs explode and has been part of the transformation.

He likens their possibilities to an ability to combine filing cabinets of different departments, such as sales, HR, shipping and receiving, production, and marketing. For example, manufacturing facilities can post sales metrics in real time, letting employees know if the company is on target with its goals. Retailers can leverage marketing data, using signs to welcome loyalty car rental members and direct them to their vehicles without requiring check-in.

“The Moxie #DigitalSignage platform can be used in hundreds of different applications. We drive entire #airports on it, we drive #StockExchanges with it, we drive #universities with it.” –@dbannister, CTO of @Omnivex via @insightdottech

Digital Signage Increases Engagement

One of the possibilities is improving communication. For example, a university in Alberta, Canada, uses the Moxie software to drive digital screens across campus, posting activities happening around campus and live streaming sporting events for students who can’t make it to the game.

“When the president of the university was dedicating a new building with the media there, they suddenly realized they were missing students,” says Bannister. “The guy in charge of the signs took care of it, posting a message offering free food. And 15 minutes later, they had 2,000 kids for the photo op.”

Smart Digital Signage Improves Safety

Signs can serve more serious functions, such as displaying emergency messages that are improved by connecting the software to sensors in the digital-signage network. For example, a drug company that uses ammonia in its manufacturing process installed the Omnivex Moxie solution at a plant.

“Ammonia spills are big problems,” says Bannister. “If you have a leak and need to clear out the place, the nearest door isn’t necessarily the safest if it’s downwind.”

Signage can connect to edge technology that pulls in wind speed and direction data to determine the best exit route. During normal operations, the signs can display general manufacturing updates. When an alarm is pulled, the signs instantly switch to emergency mode, displaying exit instructions on screens throughout the facility.

The Technology Inside Digital-Signage Software

Smart digital signage is powered by Omnivex Moxie software, a low-code distributed platform that can be installed on-premises or in the cloud. Users can choose from several data connectors such as SQL links for tapping into databases and IO links for IoT sensors, connectors, and relays. Data is pushed in real time with no refresh needed.

“Our competitors tend to come at signage from a graphics or broadcast standpoint, focusing on making a pretty picture,” says Bannister. “We always start with the data. Once you have the data, the connections, and you have it secure, you then say, ‘How do we make it look great?’”

Moxie displays and digital-signage software are built on Intel® processor-driven PCs running Windows. “Intel is the foundation of who we are and the solutions that we deliver to our customers,” says Bannister. “The Moxie platform can be used in hundreds of different applications. We drive entire airports on it, we drive stock exchanges with it, we drive universities with it.”

Systems integrators can use solutions like Omnivex Moxie to grow their businesses. Omnivex is a partner-first company. Instead of selling screens, ads, or content, it focuses on making the platform that enables its partners to do those things.

Omnivex’s digital practice team supports partners by training them on its software and assisting with technical support and sales calls, providing demonstrations, configuration, architecture questions, and product sample applications.

Every organization has data that’s meant to be used—and it can improve lives.

“We would all agree that our lives are better because of the introduction of the automobile, the telephone, and electricity,” Bannister says. “I would argue that our lives will be better because of today’s emerging technologies. Our mission is to connect people with data from artificial intelligence and machine learning. As engineer, I find all the possibilities fascinating and something to watch.”

Retail Tech 2022: The In-Store Digital Experience

Posted on by Micah Kaplan

Related Content

To learn more about the future of retail and the role system integrators will play, read our articles: Retail Analytics Transforms Data to Insights and IoT Solution Factory Builds System Integrator Growth.

Transcript

Corporate Participants

Kenton Williston
insight.tech – Editor-in-Chief

Kristen Call
Intel® – Senior Industry Advisor, Americas RBH

Lisa McGarvey
Tech Data – Director, Solutions Development & Vertical Markets NA

Styrbjörn Torbacke
Advantech – Head of iCity Services Europe

Presentation

Kenton Williston: Hello and welcome to the insight.tech webinar, “Retail Tech 2022:” The In-Store Digital Experience”. I’m Kenton Williston, the Editor-in-Chief of insight.tech and your moderator for today’s session. This panel features experts from Advantech and Tech Data, who are both members of the Intel Partner Alliance, as well as a friend from Intel itself, and we will all be taking a look at the future of retail as shoppers continue to return the stores.

Before we get started, a little bit of housekeeping. I’d like to let our guests introduce themselves. So, Styrbjörn, I’d start with you. Could you tell me a little bit about who you are and what you do at Advantech?

Styrbjörn Torbacke: Thank you very much for the introduction, Kenton. My name is Styrbjörn Torbacke, a Swede. I head up what we call ICT services, which basically is retail, but we’ve also brought a certain amount of smart spaces applications and smart city into that. Headquartered out of Munich, but as said, a Swede residing in Stockholm. I’ve never worked in retail, but sold hardware, software, and AI to retailers for the past 10 years.

Kenton Williston: Lovely. Lisa, I’ll go to you next. Could you please give me a little bit about yourself?

Lisa McGarvey: Sure, Ken, thanks. Hi, I’m Lisa McGarvey. I’m Director of Vertical Markets and Solution Development at Tech Data. I have responsibility for North America, and my team really focuses on vertical markets programs and enablement, as well as really developing solutions that align to vertical markets, and I’m excited today to talk to you about what we’re seeing in the retail market

Kenton Williston: Wonderful, and Kristen, I’ll come to you last, but definitely not least.

Kristen Call: Oh, no worries. I’m so happy to be here with my partners. I’m Kristen Call, I am the senior industry advisor for the RBH, it means retail, banking, and hospitality. Those are my focuses. I actually did spend 25 plus years in retail. I’ve done almost every gig in retail that you can think of, and it’s one of my very passionate topics, and I’m excited to be here with all of you.

Kenton Williston: Fabulous, you said retail, baking, and hospitality?

Kristen Call: Banking—not baking, banking.

Kenton Williston: No, I knew that.

Kristen Call: It’s really early in the morning for me.

Kenton Williston: Just giving you a hard time. OK, very good.

So, in today’s agenda, I want to first take a look at where we are now, as we’re at the end of 2021, talk a little bit about what consumer expectations are in the present moment and how those might be evolving moving forward, and in particular, have a conversation around the interchange between brick and mortar and e-commerce and again, how that might change as we look forward, and then focus on I think what the key topic, really, of today’s conversation is, in-store technology that’s going to provide that really amazing shopper experience. And then I want to look a little bit at some of the implementation questions of how to bring this technology to life and how to make it work most effectively and have the best return on investment, and then, of course, as the title of this webinar suggests, talk some about where things are heading next.

First and foremost, a little bit of a review of where things stand today, and of course, as we all know, the pandemic has really defined what shopping looks like over the last 18 months or so, and I would even say redefine that to a great extent, and it has had a tremendous impact on how stores operate, what kind of experiences customers are having, and pretty much everything you can think of retail related all the way to the supply chain and all the rest.

So, Styrbjörn, I’d like to start with you. What, from your perspective, do you see as being some of the biggest impacts the pandemic had on the retail experience?

Styrbjörn Torbacke: Thanks, Kenton. I think I’m seeing, which isn’t a new thing, but it’s the absolute acceleration of the move from brick and mortar shopping to the online experience. That has been an underlying trend. We’re seeing that, and have seen it for many, many years, but the rate at which that, for obvious reasons, accelerated during the pandemic, not all countries or regions have been as fortunate as me in the Nordics, where shops pretty much have stayed open all through the pandemic, and you’ve gone to more physical kind of measurements, i.e., putting plexiglass in front of the cashier to still allow a physical interaction between people, but without that immediate ability to breathe on someone else. But of course, you’re seeing the trend of both bases, buying online, pick up in store being exchanged for curbside pickups or home delivery, all that.

Kenton Williston: Yes, absolutely, couldn’t agree more, and I think one of the key questions in my mind is how well various retailers have responded to all these new requirements. And Lisa, I’d love to hear your thoughts on that, if there are any other, you think, major issues that have arisen during the pandemic, as well as how have merchants responded to these new circumstances we find ourselves in.

Lisa McGarvey: Yes, I mean, I agree with Styrbjörn had said about the impact of the pandemic has definitely accelerated digital transformation in retail, and that growth and shift and move to e-commerce, and one of the big changes that we’ve seen or acceleration of all retailers pivoting to really implement those omnichannel strategies to create and optimize things like buy online, pick up in store. This creates a faster, more secure, convenient shopping experience, and these are really the types of services that help retailers remain competitive, and help shoppers feel safe during the pandemic. So, we’ve seen shoppers really adopt the curbside pickup and fulfillment, and I believe it’s definitely here to stay because it makes customers feel safe, and it’s also convenient. And we’ve seen companies across the board, the big companies like Bed Bath & Beyond, Michaels, Nordstrom, Dick’s, but even local retailers. I mean, I’ve seen local retailers who didn’t even have websites in my area, build their websites, spin them up, and then also offer curbside pickup because they had to, because it’s crucial to the experience that they need to offer today, during the pandemic and ongoing, and making sure that they’re not playing catch up to their competitor.

So, I think what’s really going to be the big impact is, you know… being precise, being precise on inventory counts and operational efficiency, so wait times. So, that’s really going to set certain retailers apart, making sure they get those things right, and that they have the systems in place to effect those inventory counts and wait times, and be really accurate in what they’re providing to their customers. I was actually reading an article and it said in Target’s third quarter of 2020, their same-day services grew more than 200%, adding $1 billion in incremental sales. So, those are the types of things that are making a difference right now, and where we’ve seen retailers really pivot and change.

Kenton Williston: Yes, totally, and one thing I think is important here is to define some of our terms up front. So, we were talking about retail tech, but of course, retail means a lot of different things, and I think Kristen, to the point, we were just joking about your title, there’s banking, there’s hospitality, it’s quick service restaurants, it’s many, many different things, and I’d love to hear from the Intel perspective, across this broader expanse of industries, what some of the key changes have been.

Kristen Call: I mean, yes, all of the things that have been said right now are, I think, across the board. The in-store pickup, the online presence, be able to shop anywhere, and those are not new themes. I don’t think that the pandemic really created those themes. Those just enhanced the need to have those and we’ve seen some of the laggards come up front, and the ones up front tried to even get better at those types of services. So, it just really depends on those, I call them the retail sub-verticals, because you’ve got grocery stores, big box chains, you’ve got C stores and that, and everyone is having their own challenges, because their businesses are just—they’re different, and I think that I’ve looked at how retailers have responded. You’ve seen some really great innovation and up to the challenge of what it meant, and those are the ones that have survived throughout 2020 and into 2021.

And talking about statistically, so I was reading an article back in early 2020, and it was saying that 90% of US sales still occur in their four walls, and so people still want to shop, but there is something about I want to go and do my research online, I want to look, I want to peer into what these products are, because that’s really hard to do at the store, oftentimes, and that’s where we’re seeing that digital transformation. If I’m standing in front of our product, I want to know more about that product, and so that’s bringing that digital experience into the stores, and that’s not in every single retail sub-vertical, but a lot of the big box, and even the grocery stores being able to do recipes in real-time, if I’m looking at the pasta or something like that. So, I think that innovation is going to continue where it was and where it can be, so retailers to me, I think they’re doing a great job.

Kristen Call: Yes, perfect. I love that point you made about the way folks are thinking differently about how they shop. In particular, this idea that in-store shopping experience can also involve doing things on your phone or other device while you’re in the store, I think is becoming an incredibly important part of the customer experience.

So, I’d love to hear, Styrbjörn, I’ll just circle right back around to you, what you see as some of these new expectations for how people are shopping today, when they’re in the store, how they’re expecting to utilize technologies, whether it’s their phones, whether it’s a digital kiosk or other devices, and how that’s going to impact what retailers need to provide moving forward.

Styrbjörn Torbacke: Thanks, Kenton. Before I move into and reply to that question, I’d like to tie on to something that Kristen mentioned, and it’s that thought about the change and the sub-verticals within retail, where I think it’s quite clear that if you look at grocery, if you look at QSR, they have suffered the least because the Germans were stockpiling on toilet paper at the outbreak, for example, so the grocery retailers were running like crazy all the way through the supply chain and all the way up to front of store, and there it was—it wasn’t so much about the experience. It was about the need to get the goods, why they did well, even if they weren’t being clever about it. So, I think one of the things we are going to see, as we move forward, is the consumer, the customer, having been acquainted with, or accustomed to, the more clever, the more immersive customer experience with other retailers in other sub-verticals, and starting to demand this experience also from the other ones.

So, I mean, if you look at it, let’s face it, today, I don’t need to go to a store. I can do basically anything online. I can have everything delivered to my doorstep within an hour. I can’t get to the store and do what I want to do in the store and get back within an hour. So, it makes absolutely no sense from that point of view, which means that I must individually desire, I must want to go to a store to have that experience.

And then we’re back at what you asked about what specific technologies. I think that the first bit, one thing is going to be about the ease, so how obstacle-free, how seamless, can the retailer make my shopping experience, everything from, obviously in COVID times, how I come in, how I find my products, how I learn about them, how I can select them, and on to Lisa’s favorite subject, how I can pay for them very seamlessly, and get back out of the store.

Kenton Williston: Yes, absolutely, and so Lisa, your name was invoked, I’ll throw things over to you. How do you see technology playing out in terms both of meeting the new customer expectations and, to Styrbjörn’s point, just in general how it is working back of house to make everything come together.

Lisa McGarvey: Yes, I think he brought up some good points. I mean, convenience is one piece, right. I mean, and the convenience is exactly what was said, right. Ensure that you’re getting that same easy experience that you got through e-commerce, that you get that in-store, right, and it’s fast and convenient and seamless. This includes checkout lines. No one wants to be waiting in line, right. They want tap-and-go payment methods, contactless payment, self-checkout. These are all natural extensions of the digital experience, and they provide fast, frictionless ways to pay with minimal lines, right. So, it’s that—it’s that experience and having that same convenient experience, but it’s also personalization, right. And personalization relies on data, and it’s not just about collecting data, but really knowing how to use it. And retailers must really invest in enabling their store associates to be able to have customer profiles, know who they’re talking to, instantly create personalized experiences. Customize messaging on kiosks or menus when a person enters a store.

You know, now more than ever, it’s so important that employees of the stores are using more personalization when they’re reaching out to their customers. And one of the best ways to build customer loyalty is through loyalty programs. And we’ve seen a lot of that in recent times. I mean, we’ve always had them, but we’re seeing them even get more personalized, right, and leveraging more analytics and data to help identify more marketing opportunities.

And retailers are putting more—you know, a spin on their traditional loyalty programs. They’re supporting more in-store shopping, adding more personalized offerings. You’ll see early access to sales, right, or charitable events, or exclusive experiences. And that just really helps with that personalization that’s so critical right now for retailers.

Kenton Williston: Yes, absolutely. And Kristen, I know—speaking of this analytics idea, this is an area that’s been very important to Intel, I think, you know, for quite a while now, but certainly during the pandemic. And what are some of the ways in which you’re seeing the idea of customer analytics and being able to deliver a better experience evolving?

Kristen Call: I’m going to split it into a couple of sections here, actually, I could do it in quite a few different—split it in different ways. But you know, you’ve got your traditional ingestion of data, it’s going up into your cloud, it’s going into your data centers, it’s getting consumed there and then extracted for statistical analysis later, right. So, that’s yesterday’s—today’s data looked at tomorrow or in the future.

You know, what we’re trying to see—what we’re seeing now is, is that the data that is being generated at the source, which is at the store, is going to be—you know, and analyzed there in real-time, and doesn’t have to necessarily go back to the cloud, because we’re looking at, you know, so much data. And do you really need all of that to go up into your cloud? Well, I think that the retailers are seeing that they can have a choice now.

Now, that doesn’t make the deployment or the—and you know, putting that kind of a solution in the store easy, because how much data do you need and how much compute do you need? All those questions make it a very complex challenge, but that’s why we have fabulous system integrators to help solve some of those problems and—

But those are where—you know, Lisa was hitting on that personalization. So, is my top customer walking in the door? Who is—how many people are in line? Do I need to bring another associate up if I don’t have, you know, an instant way to checkout? So, it’s really providing, not just personalization, but I think if you take a look at retailers that are complacent, I call it a little bit of complacency, where I’m just going to—I’m going to let my customer sit in line for, you know, three or four seconds. And three or four seconds in retail, that’s like a lifetime, right. But I’m going to change that out and I’m going to really be on top of the game and make sure that my customer—I welcome them into my store, and then I’m going to get them out quickly, because they came to purchase something at my store. So, I see a difference in that way for retailers to really set themselves apart from each other.

So, it’s, you know, an interesting playing field in today’s world, but those are the kinds of challenges that I see that retailers are taking on.

Kenton Williston: Wonderful. And something that I’m hearing, I think, clearly from all of you is how critical the idea of having a very, very tight connection between the electronic, online, on-device experience and the physical experience have become. So, I’d like to hear a little bit more about what that dynamic looks like today, where it’s going next.

And Kristen, I’ll stick with you here. How has this balance between in-store and online shopping changed? And where do you see this going next?

Kristen Call: Well, I think before we get to change, we have to look at how systems are either siloed or inoperable. And I think that retailers are trying to take that piece on. Some of it—again, we have, you know, retailers that are advanced in this area, and other ones are laggers. So, it’s really how do I bring my systems together so that they can talk?

I have my online store and then I have my in-store, and oftentimes, they are not the same, right. They are very separate, the pricing is separate, and it’s a different challenge in where you’re competing. And so, I think retailers are really trying to look at how can I bring my systems together? How can I allow customers to have the best pricing? You know, some of those—the same challenges that we had before 2020 still exist here in 2021, and I think they’re putting a lot more focus on how to bring those systems and making them operable, and that they seem to the customer as one system.

Kenton Williston: Absolutely. And Styrbjörn, from the Advantech perspective, what sorts of things are you seeing your customers doing? What’s Advantech doing to help bring these worlds together?

Styrbjörn Torbacke: Oh, what we’re doing on our side is I think it’s taking to understanding what we’re good at and where we need partners. So, I mean, Advantech is a hardware manufacturer first and foremost, so we provide the platforms, we provide the edge compute that Kristen talked about, the ability to turn the data around in-store than not necessarily sending everything into the cloud.

And I’d really like to tie onto what Lisa said there about putting this information that you’ve gathered into good use with a store attendant. It is the people in the store that make the experience for the shopper, for the customer. So, sending things up for statistical analysis is great, but I talk about the democratization of insights and that is the sharing of people—of that insight with the people on the shop floor, because they are the ones that are going to make the real difference in the shopper experience.

So, we try to provide our platforms, be part of discussions like this, work with great partners like Tech Data, with their system integrators that sit with the special knowledge of understanding both the domain retail, as well as being very versed in the systems.

And I think one thing we’ve seen—and I don’t know if this is, and this is an open-ended question to you guys who are more US-centric than I am, many—and we’ve had massive shop death throughout the UK during the pandemic, and all the headlines have been “This company has been operating for 150 years, and now they’re gone, that entire chain has closed down”. And I think part of that is because they have sat on old systems, very siloed systems, unflexible sometimes, written in programming language that is hardly around even. And it makes it so much more difficult for them to adapt. Whereas the ones that come out of the online world, even if they have a physical presence and do brick and mortar stores, typically, is my experience have built that on more modern technology, more adaptable, and platforms that are more adaptable to at least multi-channel.

I will make a claim and say that you cannot decide to be omnichannel as a retailer, because omnichannel is in the perception of the customer. You can be multi-channel, that is the strategy, you can decide to do it by being present in multiple channels, but whether the customer gets the experience that you’re one and the same regardless of where and how they interact with you, that’s really only they who  can tell you whether you succeeded in it or not.

Kenton Williston: Yes, absolutely. And I really want to come back—I think this question about having flexible systems, integrating systems together is incredibly important, something I want to touch on again a little bit later in our conversation.

But first, Lisa, I’d love to hear, as a systems integrator, you’ve got a pretty broad perspective on what’s happening. I think there’s a really interesting question here of how merchants should even be thinking about this question, multi-channel, omnichannel, I mean, these are terms people have been talking about for years now. But my sense is, to Styrbjörn’s point, you know, it’s really—the industry may need to rethink even what these terms mean.

So, Lisa, where do you see the industry needing to take this conversation next to actually deliver something that the customers truly perceive as, “Yes, this is an omnichannel experience where I’m getting that consistent excellent experience, whether, you know, I’m in my home, I’m out and about, I’m the store”, regardless of where and how I’m shopping, it feels the same to me.

Lisa McGarvey: Yes, no, thanks, Kenton. And I want to touch on—Kristen made a good point that physical stores are still very much a part of the customer journey, right. Customers are looking for those shopping experiences. So, when we talk about omnichannel, I mean the physical store is still a part and piece of that.

But, I mean, when I think about omnichannel, it’s really empowering retailer brands to reach consumers at every touchpoint of the buying experience, right, whether it’s online, in-store, and providing a relevant consumer experience across all the channels wherever they want to buy, right. So, that, to me, is what the omnichannel looks like.

Now, I mean, that’s continually changing, right, how consumers want to buy. But we know that it’s critical that they want what they want, when they want it, where they want it, and that’s what omnichannel is really about, making that experience across all those different purchasing ways in the buyer’s journey and engaging in multiple ways across diverse platforms, understanding and ensuring that customers are getting that consistent experience. And that’s what’s so important. And that’s where I really think a lot of the things that we talked about, you know, providing that shopping experience in the brick and mortar, the digital, and making sure that it’s seamless, frictionless.

And it really comes down to personalization, right. Responding to the interests of your consumers. Make sure you understand who they are, engage with them, know what they want, and they will be more loyal and they’ll come back to you as a retailer. So, personalization.

The other one is really about, you know, making sure that when they do come into a store or go online, they’re getting that experience that’s fast, efficient, and frictionless, right. So, making sure that they get that experience.

And then the third is social integration. And I don’t think any of us have talked about the social integration yet, but I think that’s really important, and that’s the next piece where we’re seeing that omnichannel is really touching audiences in—through social media platforms, and creating more opportunities for your consumers to share content, promote your products and services, and really offers you different ways to engage and interact.

So, I think, you know, that’s really about—so, personalization, convenience was the word I was looking for, the fast and efficient checkouts and whether it’s online or in-store, and then the social integration piece. Those three pieces are really, you know, important to the buyer’s journey.

Kenton Williston: Yes, that’s great. I love the point you’re making about how broad of a vision retailers really need to have to deliver an amazing experience. So, I want to dive a little deeper, specifically into that in-store experience and the technologies that are really making a difference there.

So, Lisa, I’ll just stay with you here. What do you see as being some of the most critical technologies being deployed today to transform that in-store experience?

Lisa McGarvey: Yes, I think we talked about, you know, data analytics, but we didn’t really get into artificial intelligence and machine learning have really introduced a new level of data processing, right, and it really deepens the business insights that retailers can use. And they can use it for personalization. But also, you know, as we look at supply chain and logistics, artificial intelligence can be used for demand forecasting, and that’s to support, you know, the shopping experience, whether it’s in-store or online to make sure that you know what inventory you have and be able to forecast that. And also, you know, leveraging different, you know, cloud-hosted point-of-sale systems, we talked about that a little bit.

But I’m really seeing artificial intelligence start to ramp-up. And you know, you look at Amazon AI and what they’ve done. They’ve already introduced, you know, checkout free stores. You just go in and walk out with your product, because of the technology that they’re using.

We look at things like AI chatbots, and these are able to provide even a higher level of customer service that improve, you know, searching. They can send notifications about new things in the store, suggest similar products. So, that’s—you know, if you already bought this, you might want to look at this. And that’s that personalization, right, it really focuses in on that.

Price optimization, right. Stores need to stay flexible with their pricing, be able to adjust quickly for pricing and promotion. So, artificial intelligence can be used to be looking at those types of pricing forecasting strategies.

So, I think there’s a lot there around AI in, you know, also the retail supply chain. You know, restocking, looking for different products, history of sales, looking at locations or trends, promotions, how weather impacts things, right. So, a lot of opportunity in AI.

And then, obviously, IoT still has massive potential for the retail industry. We’re seeing a lot around, you know, beacons and, you know, the ability to leverage people’s smartphones, pick up their signal, send them coupons as they enter a store that are personalized depending upon what they’re looking at.

Other IoT technologies, you know, RFID tags, smart barcodes gives you visibility to where the products are. I was just reading, Lululemon uses RFID tags to update and track inventory, and it’s improved their inventory accuracy to 98%.

So, solution—technology solutions like that are critically important as we see both in-store and online experience.

Kenton Williston: Yes, absolutely. And I think one of the key things that’s been really changing—over the last, I mean, pick any timeframe you want, a very short timeframe, because it’s been happening so fast—are the areas of AI, machine vision. And I think those are incredibly powerful in terms of building on some of those existing technologies like beacons, RFIDs, and providing just an incredible level of visibility into how customers are behaving and how to serve them best.

Kristen, I know this is an area that’s been very important to Intel. So, I’d love to hear some of your perspective on how retailers, you know, whether that’s grocery stores, fashion, whatever it is can best make use of these technologies.

Kristen Call: Yes, there’s a lot of different areas that computer vision can make a big difference for retailers and enhance the employee. And as we have seen in the news, you know, hiring is a big problem right now, and getting people, you know, as an associate has been challenging.

So, one of the ways that computer vision can help, it can augment the associate. It can also enhance the customer experience and the quality. So, for example, we’re seeing computer vision being deployed to watch the food areas, right. So, how is the cook prepping the food? Are they cross-contaminating? Or—all the way to loss prevention. You know, watching and making sure that the items get scanned and alerting if someone is doing, you know, a swap with products, or it was a 4011, or whatever, the banana trick. So, a lot of ways that we can utilize cameras to be able to do operational efficiencies or help associates know when shelves are empty, or help, you know, customers understand what a product is. Cameras can be used in so many different ways. And that computer vision is a very—we’re seeing it deployed now, and like you said, Kenton, you know, two years ago, no retailer would have thought about deploying a computer vision solution. And today, we’re starting to see it deployed more and more to solve a lot of different types of problems.

Kenton Williston: Yes, awesome. And Styrbjörn, I’d love to hear more from you, and particularly, you’ve talked a lot about helping the personnel in-store and, you know, Kristen is absolutely right, it’s been very, very tough in every industry, but certainly retail is leading here, very, very difficult to hire people, you know, get folks trained up, get people with the right experience. So, how do you see the technologies helping them and, you know, more broadly, where do you see some of these leading edge technologies, like AI and machine vision playing a role?

Styrbjörn Torbacke: As you pointed out, Lisa, again, there are many areas where we very clearly have one and the same vision, and that is the importance of the store staff for the experience of the customer.

When I hear about the technology and all the great things that can happen with it as well, I think there is a “but” in here. And the “but” being that some retailers will not have done their homework and will not have understood the key point that it is about creating the customer experience. And how are you going to cater to the needs and desires of the customer if you don’t understand them.

And there, if you think that just implementing technology, be that state-of-the-art or whatever, is going to save your customers from voting with their feet and taking their business elsewhere, then you need to be very careful.

But as we pointed out, said by Kristen, said by Lisa, RFID, we talk about vision. There are different types of vision. Us, in Europe, being very focused, of course, on GDPR, and integrity of people. We don’t like cameras, especially not in the UK, even though it’s the most CCTV-covered country in the world, next to North Korea probably, we’re still looking at it—and there are alternative technologies. So, LiDAR, for example, that will give you the benefits of the computer vision without actually being able to see you. It will detect an object, it will classify it as a person, obviously, you’re losing out on information on that. You will not be able to do a gender classification, you will not be able to do an age group classification of that object, but you’ll be able to recognize that you have a person, and you can then track their behavior in-store.

And why is that important? Well, it’s because—and again, now I’m taking very much the perspective of the customer rather than the underlying retail systems, and that’s just my passion, that’s why I’m going here. We are social constructs as individuals, so it’s difficult to ask us what we want, and how we behave in-store, because you’re not necessarily getting a truthful answer. One being the fact that we simply haven’t reflected over what we do, and therefore, we’re inadvertently misleading in the information we’re conveying. And another one is the social construct that, “Well, if I’m heading into a liquor store and meeting my Sunday school teacher on the street outside, maybe I’d be more prone to walking past rather than going in”. So, there are multiple aspects of that.

And there are technologies that allow us to get the benefits of the IoT sensors without necessarily having them. So, I guess what I’m saying is that there is a time and a place for all of these technologies, more or less. And the key is you get the real values when you bring them all together.

So, I talk about the three Cs when you talk about insight. It’s the Comparing, Combining, and Correlating of different data sources, because that gives you the same as a 1D, 2D, 3D picture gives you a different level of completeness of your understanding. And when you then bring in the RFID on the product side and start building that entire system, it becomes extremely powerful.

Kenton Williston: Absolutely. And you’ve given me a perfect segue into what I wanted to talk about next, which are the challenges associated with these technologies. And I think there are two important fronts, one of which, Styrbjörn, you mentioned. You know, it’s not just technology for technology sake, right, it has to make sense to the customer. Their experience has to be good, they have to be comfortable with it. And then, of course, there’s just the raw challenges that we brought up earlier, and Styrbjörn I’ll thank you for this as well, you know, some of the integration elements, right. Like everyone has their existing systems, how do you incorporate the new things you want to do right alongside with what you’ve got in your existing infrastructure? And then, of course, there’s the complexity of everything we’re talking about. There’s a lot of moving parts here. How do you successfully implement and bring all these things together?

So, of course, these are great questions for a systems integrator to talk about. So, Lisa, can you give me some of your thoughts on some of the key challenges you’re seeing your customers encounter and some of the main thoughts they can keep in mind as they’re considering how to move forward?

Lisa McGarvey: Yes, so, I mean, we’re really seeing, not just in retail, but in all industries, right, the amount of data gathered by businesses is growing at an alarming rate, so—but the number of staff available to analyze it is staying the same or less, right. So, retailers need to look at that and—you know, what’s important then is making sure all the data is being used in a correct way and not contributing towards the data silo problem, right, to some of the problems that you talked about. And finding the right technology solutions that can handle the amounts of data being generated and ensure that it’s, you know, supporting things that retailers need as far as, you know, marketing efforts and personalization.

So, we’re starting to see and the market’s starting to see the data scientist approach, right. We’re seeing more retailers focus on hiring data scientists for more of those integrated marketing approaches, being able to leverage that data. And you know, data is only going to become more prevalent as time goes on, so we need to—it’s part of that whole omnichannel view and what we need to focus on as retailers.

So, you know, that’s definitely what we’re seeing from a data perspective, right. And again, you know, it’s not just the retail industry, but we’ve seen that they’re really focusing more on that, trying to do more with less, and get those data scientists, you know, lined up to help them understand how to leverage the data that they have.

You know, especially as we’re heading into the holidays now, right, this is—it’s going to be a little bit critical to start, you know, getting ahead of these things and really looking at what are some of the things that they already have in place that they can leverage and really double-click on, right, to support.

You know, if we look at what’s going on, you know, right now. I just read an article that—you know, I think over half the—over 50% of shoppers, you know, have already done their Christmas shopping by the end of October. And you know, I don’t know about you, but I keep seeing “shop early, shop early”, right, because of all the logistics issues.

So, making sure that those technologies are in place for what’s coming right away, right. So, making sure that you ramp up your curbside offers, your buy online, pick up in-store, making sure that you have your inventory control systems in place preparing for what’s to come in the next few months with—and even in the next month, right, for the early shopping.

So, I mean, luckily there is a lot of AI-based technologies available to resellers—I’m sorry to retailers, and we do specialize in helping our channel partners have an ecosystem that they leverage with partners like Intel and Advantech, where they can provide the right solutions, looking at the retailer’s needs, and just help them determine what are their business needs, and what is the right technology to help support those business needs.

Kenton Williston: Perfect. And speaking of ecosystem and Intel’s role, Kristen, I’d love to hear from you. You know, of course you’ve got kind of that early view into the front, leading edge of technology. And where do you see some of the biggest hurdles for retailers as they’re implementing these technologies and what can they do to overcome them?

Kristen Call: Yes, I think that if you break it down, so many different aspects, right. So, Styrbjörn, you said it really well, you’ve got to look at the problem. What is the problem that the retailer’s trying to solve, and what are their social hurdles, their legal hurdles, there’s all kinds of hurdles that they have to consider when they’re trying to deploy these types of solutions? And you know, you can easily cross the bridge when you’re talking about a business problem and then technology to adopt to solve that business problem, right. And that’s a big challenge right there, first of all.

And in retail, if you want to take a look at the structure of retail, oftentimes, you have your business and your IT, and they’re not the same, right. Then, oftentimes, depending on the relationship, that could be a big hurdle just to go in and talk to the right person about how—you know, what are the challenges and how can we help solve those challenges. So, that’s one piece.

The other piece is once you get to, “OK, this is the problem that we want to solve”, that’s to understand all the data that you need to ingest, and it’s usually coming at the edge. And a lot of retailers are still trying to evaluate, what does that mean? What hardware do I need to have? And how do I ingest that data? And how do I keep it safe? Where’s the security pieces that I don’t want to get my fish tank hacked? I’m sure we’ve all heard that in the news, right. So, there’s a lot of things to consider there.

And then once I get it into a place where I can actually look at the data, back to Lisa’s point of data ingestion and getting the insights, you know, how do I then put it in a way that is consumable to whoever the person that needs to see this data.

So, there’s a big journey here and that’s why we partner with folks like Advantech and Tech Data. They’re here to help solve a lot of those challenges. And Intel, you know, we walk side by side bringing, you know, our architects, and our, I call them super smart people in our org, that’s one of the great things I love working at Intel is everyone is up for the challenge to support our retailers, to support our partners in order to solve these challenges.

Kenton Williston: Absolutely. And so, I’d like to talk about some practical examples of where you folks have done these things. And Styrbjörn, I’m particularly interested, you know, one of the topics that we have touched on, and in particular, you’ve brought up, you know, in the European context, the German context, in particular, how challenging it’s been for some retailers to update their systems and to move into the current era. Do you have some examples that you can share with us, or maybe an example, given the time we have, of where Advantech has worked with a customer to help them along this journey?

Styrbjörn Torbacke: I think what we’ve done, we’ve done with several of the retailers throughout Europe has been to try—and this is back to Kristen’s point about addressing the correct problem.

So, I mean, a lot of the investments that were taken were driven by the pandemic and driven by compliance rather than a sound business mind in “If I make my investment now, what am I going to get out of it?”, because the pandemic, hopefully, at least will reside and we’ll see a—maybe not the same world as before, but we’ll see something that is more similar.

And you’ve had those that have invested in facial cameras, just linked to a touchscreen that shows whether you have an augmented temperature or just showing how many people are in-store as an isolated number, back to the silo discussion.

If you, instead, which we then have tried to do, leveraging again the ecosystem that we have around us and the different insights and inputs our partners can put into this, make them understand that if they do invest in something that will have a business value in the post-pandemic world, maybe going for a slightly more expensive integrated system that allows them to gather data and insights throughout the store, edge servers to do some of the analysis and maybe even to not just do analysis, but turning the local insights, the in-store insights into actional, compelling events for the consumer, they can build on that.

They will still solve the basic function of compliance, how many people are in-store, does an individual have the right to enter the store et cetera, but their investment has a long lifespan afterwards.

And I think those are discussions and the most interesting ones that we’ve been having with retailers throughout Europe.

Kenton Williston: Absolutely. And I think those two ideas of having enough capability, having enough flexibility to not only do the job you need to do today, meet the pressing need, but give yourself the freedom to do something more beyond that are incredibly important.

And Lisa, I’m wondering, maybe you’ve got some examples that build off that, or just in general—

Lisa McGarvey: Yes.

Kenton Williston: —you showcase where retailers should be thinking as they move forward.

Lisa McGarvey: Yes, I think as we all said, we’ll continue to see that accelerated digital transformation initiative in retail. And as a part of those strategies, we’ve all talked about AI and edge compute playing a key role to automate day-to-day tasks, you know, provide real-time response, enhance the consumer experiences, as we’ve all mentioned.

And then, you know, I don’t know that we’ve touched as much on the health and safety-centric solutions, but we’ll, you know, continue to see a focus there as, you know, satisfying the customer, still, you know, making sure that they’re safe, healthy. So, we’ll still see that.

But I’ll just hit on a few things that we’ve recently seen, more specific solutions. So, inventory control and theft prevention, leveraging RFID technology. We’re seeing a lot of focus there. You know, the ability to automate inventory, asset tracking for loss prevention, for real-time visibility into where assets are at the location, managing that in real-time 24/7, track it by date or time or location. So, we’re seeing a big emphasis on that.

Smart cameras and computer vision. Kristen talked about computer vision. That’s so important right now to being able to monitor in-store occupancy, the flow of shoppers in-store. And also, again, that focus on safety and social distancing, those smart cameras and computer vision can be used to support that as well.

And then, really, we’ve seen an uptick in interactive media, which we haven’t really talked a lot about but, you know, displays and specifically that digital signage that really supports the customer experience in retail. And those digital displays have now become more engaging, more informative. They don’t require a physical touch. So, we’re seeing more focus on that interactive digital signage and digital displays to create those more personalized experiences.

And then one thing I would like to note is, I think, partnerships are really important and really support innovation, not only partnerships on the technology side of the house between, you know, companies like Tech Data, Intel, Advantech, our channel partners, but also partnerships in retail. And I think a good example of that that I, you know, have read a lot about is Coles and what they’re doing with major brands like Sephora and Lego to drive foot traffic, and Amazon. You know, those types of partnerships really drive the innovation that we’re starting to see for the future of retail. And it’s needed both on the retailer side as well as the technology side. And it’s needed for us to enable—to help our retailers. No one can do it alone, and I think that’s the big thing.

Kenton Williston: Absolutely. And so, just to wrap up our conversation, I would like to talk a little bit more about how we move together, as an industry, into this new future. You know, I think, to your point Lisa, and Styrbjörn, and Kristen you’ve both touched on this point, it’s an effort that’s going to require a lot of different technologies, a lot of different experts. And I think, really, a full ecosystem of folks working together to move into this future.

And Kristen, I’d love to hear—you know, of course, you’re working with Intel, as part of Intel, with some of these big marquee names, you know, what you see folks doing to put in place, you know, this foundation we’ve been talking about to enable you to move into this great unknown that is the future of the retail industry.

Kristen Call: Yes, so if you look at some of the retailers that—I call them, they’re the leaders, right, they’re blazing the path. You’ve got your Walmarts, your Targets, you know those are not just retailers, they’re now software companies, right. So, we see this—you know, Amazon, they’re the ones who set that standard. And so, it’s a big question, are they a retailer or a technology company? And the lines are becoming blurred.

And you look at this slide, you know, the robots are—they’re not far away. We’re seeing them in the warehouses now to be able to do pick and pack, to be able to bring those products upfront for dark stores, those kinds of technologies are real and they’re being tried and tested, and being implemented.

So, we’re being asked now from retailers, you know, how do I now have a robot to actually put products on my shelf? And I’m like that sounds a little futuristic still, we’re not quite there, but that’s where we all want to get. And I think it’s really understanding how this technology can enhance.

I don’t hear any retailer trying to say, “I want to replace. I don’t want to replace a person. I want to enhance a person. I want to make their job quality better”, right. I think the market now is, “I have to provide an experience for my associate, and so how can I have technology help in those cases?”.

So, yes, it’s—you know, I don’t know about you guys, but I’m old and when I think about this kind of stuff, this feels like, you know, Buck Rogers and way back when. This is the exciting future of where a retailer really wants to take it but, you know, the last mile of—you know, when looking at autonomous vehicles, I think Walmart just announced a partnership here in Phoenix that they’re going to be looking at autonomous delivery, and—Amazon’s been doing it for some time, thinking about how do I fly the product to you.

So, those are real—companies are really looking at these types of things. There’s a lot of challenges, of course, but as you live in that agile world, if you don’t try, you’ll never fail, and if you don’t fail, you’ll never succeed. So, I’ll pass it—yes, so exciting stuff.

Kenton Williston: Yes, absolutely.

Styrbjörn Torbacke: I don’t think you’re that far away, actually, when it comes to replenishing the stores. I was on a customer visit, I actually got to travel, in the Netherlands last week and stayed in a Van der Valk Hotel where the hotel restaurant had a robot busboy. So, basically, the waiter would take the dishes once we’re finished eating, and place them on a very silent, elegant, dark, shiny busboy that came gliding across the carpet. And when she then placed the dishes on that, very quietly took it around following the outer perimeter of the restaurant and disappeared into the kitchen.

So, you’ve got a little bit of assistance there in actually deploying the dishes from the table to the robot, or vice versa if you were to take it the other way. But also, the autonomous delivery vehicles, yes, Advantech are involved in a number of those kinds of projects. So, it’s definitely a reality and a lot more companies than what you would regularly see are involved in looking at these as real business opportunities.

Kenton Williston: Absolutely. So, Lisa, I’ll give you an opportunity to kind of have a last word here. What do you see coming in the surprisingly not so distant future? And what are some of the things retailers can do to ensure their readiness?

Lisa McGarvey: Yes, so, there’s a buzzword out there, it’s called “Phigital”, and it’s the physical and digital at the same time, a completely connected world. And I think that retailers need to really be thinking about that. And it’s all about looking at affirmations from social media, focusing on implementing technology that supports an incredibly personalized experience. And you know, that’s—everything needs to be customized for the consumer and there’s going to be a lot more in-store experience, as well as online.

And I think—you know, as we think about that and as the technology remains promising and retailers see that promise, there’s still a lot of work to do, right. There’s still a lot of work to do to move beyond experimentation. You know, things like robots like we just talked to, to being able to actually deliver tangible business value and those are the tangibles that are going to be able to help retailers extract that value from data across all of their organizations. And that’s where we can really help. That’s where partners like Tech Data, Advantech, Intel can help our retailers, because we focus on specialization, and we have the ecosystem of skills and capabilities across the business continuum to help reduce complexity and really help retailers monetize the opportunity, leveraging technology.

Kenton Williston: Wonderful. Well, that’s a perfect place for me to say thank you to all of you for joining us today. And I’d also like to say thank you to our audience for listening in. And if you’d like to know more about the latest and greatest in retail technology, and in particular, the latest coming from Tech Data and Advantech, you can navigate over to insight.tech to learn more.

Scale AI into Next-Gen Robot Surgeons

Posted on by Micah Kaplan

The healthcare industry has always been on the cutting edge of AI. Take, for example, the work on an inferencing engine in 1972 at Stanford University. Known as the MYCIN expert system, it successfully leveraged AI to diagnose blood infections in patients based on reported symptoms and medical test results.

Now healthcare AI is at it again. In this case, it’s making surgical robots smart enough to perform automated tasks, as demonstrated by a research partnership between UC Berkeley, Intel® AI Labs, and Google Brain. One project is Motion2Vec, a semi-supervised representation learning algorithm deployed on a da Vinci surgical robot, learning how to suture wounds completely autonomously.

Motion2Vec is still being refined, but someday it could support AI surgical robots that are more precise than the most practiced human surgeons.

#Healthcare #AI is at it again. In this case, it’s making surgical #robots smart enough to perform automated tasks. @congatecAG via @insightdottech

The Hidden Costs of Robotic Systems

What’s limiting the advancement of fully autonomous surgical robots isn’t so much AI models but the complexity of underlying hardware, regulatory constraints, and the costs of each.

The control modules in these systems are often proprietary, relying on discrete, fixed-function CPUs, GPUs, crypto processors, or ASICs for each required task. The result is a complex system architecture comprising multiple components that not only limit what a robot can do today but also inhibit its ability to scale into the future.

To make matters worse, once these designs are certified to functional safety standards, they are almost set in stone. The time and cost of recertifying to medical safety standards trumps any advantage of upgradability. At that point, it often makes sense to build or buy a completely new system.

But with such high price tags, these systems should be upgradable—a truth that will become even more pointed as AI technology continues to evolve. The problem is that an AI surgical robot has so much going on it’s not really one system at all.

“On an AI robot system, there’s vision required to recognize what’s happening,” says Christian Eder, Director of Marketing at congatec AG, a leading supplier of embedded computer modules. “There is all this motion control and real-time processing necessary, and of course you have to maintain high levels of safety. So that’s an essential application: to combine things like vision and motion and safety.”

This is where 11th Gen Intel® Core™ vPro® and Intel® Xeon® W-11000E Series processors (previously known as Tiger Lake H) come in.

Integration: The Prognosis for Next-Gen Robotics

These 11th Gen processors overcome the challenges of multidisciplinary systems by essentially consolidating them in a 10 nm chipset. This includes heterogeneous multicore processing performance, low-latency and deterministic communications, and even functional safety measures, all in a configurable thermal design power (TDP) as low as 25 W.

On the processing front, devices in the portfolio offer up to eight CPUs. But it’s really the surrounding compute features that make 11th Gen processors stand out in complex use cases like surgical robots.

  • Integrated Intel® UHD Graphics support CV and/or AI workloads with a parallel option that executes these tasks more efficiently. The graphics units also free up CPUs for other tasks like control, network management, and general computing.
  • Hardware-accelerated partitions allow various cores, graphics units, and other components to operate as independent virtual platforms with Intel® Virtualization Technology.
  • Deep-learning inferencing performance achieves higher-performance instructions than previous generations via Intel® Deep Learning Boost.

But sheer performance isn’t the only way the new chipsets enable next-gen robotic systems. For example, once an output is generated by a Motion2Vec inference, that command must be executed on robotic motors and actuators in real time to guarantee procedures are performed in the correct order.

The 11th Gen Intel Series processors ensure that this hard real-time performance leveraging Time Sensitive Networking (TSN) and Intel® Time-Coordinated Computing (Intel® TCC). TCC synchronizes IP blocks within the processor and is supported by tools that reduce jitter and protect real-time applications from interference.

Other aspects of the new devices focus specifically on functional safety. Built-in hooks map processor hardware and firmware with the Intel® Functional Safety Essential Design Package (Intel® FSEDP), which significantly reduces safety certification effort.

The high level of hardware integration and on-ramp to FuSa compliance lends itself to simpler, more streamlined development—easing upfront component expenses and certification costs later. But what about the significant cost savings that can be achieved from upgrading, rather than reengineering, a robotic system?

Standard Modules Equals Development Savings

No amount of chipset performance or integration will protect from the cost of upgrading to a more functional robot. But new embedded hardware standards can.

The PICMG COM Express and COM-HPC specifications are embedded computer-on-module (COM) standards that leverage modular, two-board architectures. In both instances, the bottom card serves as an I/O pathway into a system like a robot—which allows the top processor module to be exchanged for one with more performance if the interfaces between the two remain compatible.

The only difference between the two standards is that COM Express serves existing designs while COM-HPC was architected to support next-generation interfaces, processors, and the higher TDPs that come with them.

As a result, developers who design around COM Express can easily adopt next-gen modules like the conga-TS570. Those starting anew can take advantage of 11th Gen Intel Core processors via solutions like the congatec conga-HPC/cTLH. And in the future, they can replace it with a COM-HPC module based on the next generation of chipsets without having to redesign the entire system.

“The beauty of these processors is that they operate within the same power envelope as previous-generation Intel Core processors, but we get much more performance. And we have this AI acceleration already on the processor,” Eder explains. “In just one low-power package that does not require any extra power supplies or any extra cooling, we can continue with the level we were at before, but with much more functionality.”

With an enabling hardware foundation in place, AI technologies like Motion2Vec should progress to a point that not only assists physicians but replaces them in some cases. This will make healthcare more accessible to all, both economically and geographically.

All these doc bots need is a more flexible brain.

A Data-Driven Manufacturing World with RoviSys

Posted on by Micah Kaplan

As the world becomes increasingly digitalized, it is becoming increasingly difficult to smoothly integrate technology into every aspect of an industry. For instance, IT and OT teams don’t always share a lot of context in the manufacturing world. But digital transformation isn’t going away. So is there a way to bridge the gaps between the engineers, the operators, and the people who hold the purse strings?

We talk to Bryan DeBois, Director of Industrial AI at RoviSys, a leading automation and information solutions provider, about the reality of digital transformation in an industrial setting. What can digital transformation bring to the shop floor? What are the pitfalls to avoid? Where does AI fit into the concept? And how can you get your team to buy in?

What does digital transformation mean in a manufacturing context, and why is it important?

The way I look at it is there is still so much room to grow in manufacturing. There’s so much value that digitalization can bring to manufacturing because we’re typically five to seven years behind the times in terms of adoption of technology. Our industry is highly risk averse, so they’re not going to adopt the bleeding-edge technology. All those things combine to bring about a great opportunity for digitalization.

Also, unique to manufacturing, is this idea of legacy equipment. We regularly see equipment that is 15, 20, 25 years old. We have a customer right now that is operating a generator that was actually installed by Thomas Edison. So that’s about 100 years that this generator has been operating. But it’s still there, it’s still running, and we’ve instrumented it and we have control over it. We’ve got modern tools around it, but there’s just not really a compelling case to replace that generator for that customer at this point. And that’s the case we see with a lot of customers.

I know for me, personally, I think that manufacturing is the lifeblood of any economy. The fact that we have so much available to us that’s of higher quality—and it’s cheaper and it’s prevalent—is a direct result of our ability as humans to manufacture things and to produce products of high quality. I really see it as a deeper need as a society because that’s what raises the bar and the quality of life for everyone, frankly.

#Digitalization is the path to make #manufacturers more productive, more efficient. And frankly, also to make the lives of those folks who actually operate the plants better. @rovisys via @insightdottech

And I strongly believe that digitalization is the path to make manufacturers more productive, more efficient. And frankly, also to make the lives of those folks who actually operate the plants better. Nobody wants to compile Excel reports. If I can put in a logging function that automatically records all those values—besides the fact that you’re going to get more accurate data out of that equipment—that’s just a better situation for the operator. They can stay focused on what they typically like to do, which is operate the machinery.

Ultimately, what we’re trying to create is a data-driven culture. We worked with a company that presses aluminum wheels. They were focused on continuous improvement, and they had dollars allocated every year for continuous improvement. But often those dollars simply went to the loudest voice in the room. But they said that after we were there and we did this project with them, those funds, those limited funds, would now go to whoever had the best data story.

What is the overall state of digital transformation in the industry, and what are some common pitfalls?

I definitely think that we’re past the hype cycle now, in digital transformation, and we’re unfortunately starting to see some of those early projects fail. I think that there’s definite trepidation around this.

The first pitfall is that companies don’t start small. We are very big on “walk before you run,” or “phased approach,” or whatever you want to call it: small projects, small wins, and getting that momentum going. Then roll that into the next project, and the next project. That’s where we’ve seen the most value; that’s where we’ve seen the most progress.

And as you’re defining those smaller projects, you need to be tying them to use cases. So what we do is we put the problem first and we prioritize that. We say: “What’s the most important problem that we can solve with a certain budget, with a certain constraint, on a timeline?” And then we go and solve that problem with technology.

Now that customer has a proven solution to a problem. Now the rollout part—that part’s easy because you have a working solution to a problem. Where else do you have that problem? “Well, we have it at five other plants.” Great. Let’s roll it out to five other plants.

Can you talk about the process: How can a manufacturer successfully start and navigate a digital transformation journey?

We have what I would call a loose path that we typically follow, which is proven, and seems to be the right way to approach these digital transformation projects.

The first thing we’re going to look at is their OT data infrastructure. And we think a historian is such a critical, foundational part of that. So that’s typically the very first thing we ask: “Do you have a historian? Is it comprehensive?” Because another thing that historians do is they become that OT data infrastructure, and then it becomes a common platform to query. As part of that, there are typically networking upgrades that must happen, too.

Then, the next thing we look at is OEE—availability, throughput, and quality. It’s one of the easiest and most approachable ways to start on a digital transformation journey. And along with OEE comes visibility. So many of these projects—once we’ve put in a historian for them and we’ve got some displays that visualize their process—that’s transformative. Sometimes that in itself can lead to so many wins.

Now you can start looking at things like OT data warehouse. Now you can start looking at combining all that process data, and other sources of data on the plant floor. Now you can look at combining all of that into a single data warehouse and starting to query that. And that often is the carrot that’s dangling in front of IT—that’s where they want to get.

Where and when does AI enter the picture?

I just mentioned the carrot that we dangle for IT, which is unlocking this data on the plant floor. So AI then becomes a further carrot. Sometimes it’s a little frustrating being in the role that I’m in because you talk to customers, and they’re super excited to talk to you about AI, and they’ve got big vision about what they want to do around analytics. And unfortunately, then you look at their data infrastructure, and you’re like, “Wait, you’re not anywhere near where you need to be in terms of OT data infrastructure to take advantage of some of this AI stuff.”

Because one of the things that AI needs to feed on is data, and it needs lots of data. And not only does it need a ton of data, but it also needs very, very clean data. That’s just not what we find. And so I tend to have to be the bearer of bad news that there’s a lot of foundational work that we’re going to have to do before we can really take advantage of some of this AI.

And one of the other aspects of AI that I like to emphasize is that all that work that I just described, all of that—you’ve made no money at this point off of it; that was all invested dollars. Until an operator, a supervisor—someone on the plant floor—is making decisions based on the predictions of that model, you haven’t seen a dime from your investment. Everything up to that point has been a giant science experiment. And so it turns out that operationalizing the AI is actually the hard part.

And that’s the part that we’re focused on. In our division we’re trying to marry 30 years of plant-floor experience and information-solution experience with the AI and the data science. And that, I think, is where we’re going to move the needle—focusing on how we actually put those AI systems into operation on a plant floor that has to run 24/7, with operators who often may not trust that AI versus their own eyes, ears, and smell that they’ve developed over many, many decades.

How do you get all these stakeholders aligned on the digital transformation goals and processes?

If you’re leading with use cases, you’re going about it the right way. Start with first identifying what the problems are. And then prioritizing those problems that are the right combination of achievable in a certain amount of time—that are maybe the least expensive to try to bite off but have the biggest impact. We want to identify those pretty early on.

The second thing that we do is, we’ve got ideas about what projects are going to have the biggest impact. So now we take a couple of those off the top, and we go around and do an assessment. We talk to all the stakeholders—we talk to maintenance, we talk to management, we talk to operations—we talk to everyone involved, and we talk about those specific couple of use cases that we’re going to pursue.

We talk about what data we need to make that project a success. What data can your systems contribute to this? And what kind of organizational change management is going to be required to change the way that we operate in this future state? We capture all of that into an assessment.

And then typically we’re getting in front of the purse-string holders, and now we’ve got all the documentation—we’ve got a pretty clear plan of how to get from A to B. Again, we’re focused on specific solutions—forget digital transformation and all of the buzzwords. Here’s a problem you have, and here’s a road map for a solution that would solve that problem, and here’s roughly what it would cost.

And when you lay it all out like that, it’s actually pretty easy to get everyone on board, to get everyone excited, and to get those purse-string holders to say, “Okay, let’s do it. Let’s try this first one.”

How do you see technology helping these digital transformations along?

It’s been really exciting to see the advancements that have happened even in the past five years in this space. And Intel®, of course, is leading the charge on a lot of that. Obviously, you’ve got the edge play, the IoT play—more and more smart devices everywhere. The focus right now seems to be on vision. And there’s a lot of value that computer vision can bring to a manufacturing facility.

But what I’m excited about is what’s next. Where do we start to see some dedicated hardware for processing AI workloads outside of vision? Because that’s going to be really exciting. You also have the cloud to roll out these really big OT deployments, and of course I know that Intel makes a big impact there, too. I think that they are really pushing the envelope on what’s possible.

Any key takeaways to leave for those who might be thinking about embarking on a digital transformation effort?

My pitch has always been: involve OT early. A lot of these projects nowadays are being driven almost exclusively by IT, and that makes a lot of sense for a number of reasons. But it’s so critical to get OT to the table early.

We have a great amount of knowledge about the different technologies and platforms and things that are out there, so we can definitely help guide during the ideation process. We can guide the conversation on what’s feasible, and what’s not, where the lowest-hanging fruit is.

Start small, focus on use cases, and build that business case early on—and get those wins. Build that momentum, and start to develop that culture for digital transformation.

Related Content

To learn more about the digitalization of industrial operations, read Demystifying Digital Transformation for Manufacturers and listen to our podcast on Creating a Data-Driven Manufacturing Culture with RoviSys. For the latest innovations from RoviSys, follow them at Twitter at @RoviSys and on LinkedIn at RoviSys.

This article was edited by Christina Cardoza, Senior Editor for insight.tech.

The Agile Smart Factory: Rapid Turn Ready

Posted on by Micah Kaplan

COVID-19 has been particularly trying for the manufacturing sector. Worldwide stay-at-home-orders not only prevented workers from staffing factories but also choked the flow of goods to and from facilities as global supply chains slowed to a standstill.

But despite the odds, some manufacturers were able to continue by minimizing fallout from pandemic-driven lockdowns and materials shortages. Overwhelmingly, these operators had Industry 4.0 technology at their disposal that transformed their infrastructure into smart factories.

A McKinsey study surveyed leaders from 400 manufacturing organizations. It found that 96% of companies with Industry 4.0 technology scaled across locations were able to effectively respond to the crisis (Figure 1). For example, the report highlights a consumer-packaged goods company that used digital twins to simulate the impact of sudden shutdowns and supply chain disruptions, then optimized operations accordingly.

Percentage of companies with Industry 4.0 infrastructure that we’re able to respond to COVID-19
Figure 1. Manufacturing companies with more advanced Industry 4.0 infrastructure were better equipped to respond to the COVID-19 pandemic. (Source: McKinsey & Company)

Conversely, companies without any Industry 4.0 infrastructure struggled through the pandemic. And things don’t appear to be getting any easier. With less cash and fewer engineering resources available in the wake of COVID-19, they risk falling further behind.

This isn’t to suggest these organizations won’t recover. But they must overcome complex digital transformation challenges before they can implement solutions. These challenges include securely connecting the industrial edge to enterprise networks and simultaneously supporting both new and legacy OT systems.

To help manufacturers accelerate development and deployment of Industry 4.0 infrastructure and applications, Intel® designed a suite of industrial-grade digital transformation features into the 11th Gen Intel® Core™ vPro®, Intel® Xeon® W-11000E Series, and Intel® Celeron® processors (previously known as Tiger Lake H).

With enabling tools and #technologies #manufacturing organizations can move their #Industry40 initiatives from strategic planning to tactical implementation. @Portwell_US via @insightdottech

IIoT-on-a-Chip: Tools to Jump-Start Integration

Even though the challenges of IT/OT integration are broad and somewhat abstract, the solutions to them are focused. For instance, coordinating an Industry 4.0 network requires deterministic communications from industrial endpoints to IT servers. But this can’t impact the safe and secure operation of OT endpoints in any way.

Besides scalable computing performance, it’s the integrated safety, security, and real-time features of 11th Gen Core vPro and Xeon W-11000E Series processors that address these needs and make them a game changer for industry 4.0 architects:

  • Support for IEEE 802.1 Time Sensitive Networking (TSN) and Intel® Time Coordinated Computing (Intel® TCC) synchronizes device clocks and timestamp packets for deterministic communications.
  • Protections for data at rest and code analysis—executed to safeguard local and remote industrial systems via the vPro technology.
  • A process framework for more efficient validation and failure analysis via select SKUs outfitted with the Intel® Functional Safety Essential Design Package (Intel® FSEDP).

Of course, these features are only as good as what you can do with them.

To shorten the curve for companies trying to implement digital transformation solutions quickly, manufacturers need a head start in making use of the technologies mentioned above. To accelerate IIoT application development, Intel® frameworks and tool suites maximize the industrial edge computing capabilities on the latest processors.

These development tools address the key technical pillars of Industry 4.0 design—networking, performance, safety, and security.

  • Intel® TCC tools provide a framework and APIs that further reduce latency and improve on real-time performance.
  • Intel® Edge Controls for Industrial decouples hardware and software infrastructure to simplify the development of high-performance, deterministic IoT applications.
  • Intel® Edge Insights for Industrial reference designs combine technologies like Docker containers and the Intel® OpenVINO™ Toolkit to provide real-time, AI-enabled edge analytics and control.

Avert Crisis Today, Optimize for Tomorrow

With enabling tools and technologies manufacturing organizations can move their Industry 4.0 initiatives from strategic planning to tactical implementation. And to do so, operators must contend with a mix of brownfield and greenfield equipment operating on the same infrastructure.

Here, OEMs like Portwell Inc., a leader in Industrial PC and embedded computing, provide automation companies with a forward migration path. Products like its PCOM-B657VGL COM Express Type VI with industry use condition support incorporate 11th Gen Intel Core vPro and Xeon W-11000E Series processors to bring all the networking, determinism, and performance outlined above to manufacturing environments.

The COM Express-compliant devices plug into compatible carrier boards so that legacy systems based can scale. And if more performance or newer features are required in the future, they can be simply swapped out for next-generation modules without having to redesign the entire control system.

The combination of scalable COM Express modules and the highly integrated, Industry 4.0-centric feature set of 11th Gen Intel Series processors are an inflection point for manufacturers amid IT/OT convergence. Without the functionality they provide, organizations may not be able to implement solutions that can mitigate the fallout from crises like COVID-19.

But with them—like the digital twin-enabled consumer packaged goods company—manufacturers can use data as a defense against sudden, drastic change. Or—under more normal circumstances—as an engine of operational efficiency that separates them from the competition.

Remove Friction from IoT Development with Microsoft

Posted on by Micah Kaplan

Companies are putting IoT application projects on the fast track, and the pandemic has only accelerated that trend. But does every organization have the skill sets and resources they need to scale up these projects smoothly? Where does AI fit into the picture? And how do you balance old hardware realities with new software innovations?

We talk with Pete Bernard, Senior Director of Silicon and Telecom in the Azure Edge Devices Platform and Services Group at Microsoft, about the major trends and challenges in IoT development, how to make the life of an IoT developer easier, and why that matters.

What are some of the major trends or challenges in commercial IoT applications these days?

It’s a very solutions-oriented market out there, especially in what I would consider the edge ecosystem. If you talk to commercial customers, they have some pretty complicated problems, and those problems are not solved with a single device or a single app. Quite often it’s about how these systems work together. How do they work together securely? How can they be managed? How can they be deployed?

It’s a heterogeneous space. So, for example, there are AI models that might be trained on the cloud and Azure, and then get deployed to the edge. But how are they optimized for the silicon on the edge? We’ve been working a lot with Intel® on OpenVINO™ and a platform we call Azure Percept, which we launched in March. It’s just one example of where you really need to be able to take advantage of the silicon characteristics and the capabilities of silicon to get the performance and the power to solve problems.

Can you talk about the rise of these really intensive workloads and why they need to be done at the edge?

We’ve seen the evolution of standalone systems, or disconnected systems, and systems connected to the cloud that can just send data up to simple sensors. And now we have the cloud talking to the edge, and the edge talking to the cloud. Basically, you can run a lot of compute on the edge and asynchronously from the cloud, and you have to really figure out how those things can work together.

“What we’re trying to do is take the #IoT friction out, and give people the power of all the optionality that’s out there without making it too complicated.” –@digitaldad @microsoft via @insightdottech

There are a lot of new scenarios out there where you need some pretty high-performance edge computing, and maybe that’s for privacy reasons, maybe that’s for bandwidth reasons. But it’s exciting to see some of the innovation that’s happening out there, and the way people are using it.

Has the pandemic changed how companies are considering their IoT applications?

I think so. I think the internet is one of the heroes of the pandemic, in that it’s kept us all connected and working throughout. But everything has accelerated. All the experiments that were cooking up that we were going to do two or three years from now—they’ve all been deployed.

And you’re seeing a lot more AI-vision work. Automation has really accelerated a lot through the pandemic. And there’s a lot more optimizing of that around healthcare—using a lot more information at the edge to make sure people can have a smooth, authenticated experience. The pandemic has just hyper-accelerated a lot that’s been in the pipeline.

How do we build on this acceleration going forward? Particularly in terms of bringing systems together in new ways.

The systems that are getting deployed can’t be one-off, bespoke systems, right? You can’t hard-code your solution this year, and then hard-code another solution next year. You have to think about what problems you’re going to tackle today, versus two or three years from now, by adding more and more capabilities.

One example is point-of-sale terminals in retail—they’re pretty prevalent right now. People are saying, “I have these point-of-sale platforms—what else can I do with them? Can I add AI vision to provide some kind of security monitoring in my store?” So, we’re seeing humble platforms like that actually becoming edge endpoints in and of themselves that can do quite a bit of compute.

Another thing that we’re seeing is that a lot of people have legacy equipment. There was a supermarket we were talking to recently, and the first thing they said was, “Last year we bought all these cameras, and they’re sitting in boxes.” So, how do you connect legacy equipment and make that more AI capable, more secure, more manageable?

You must think about it systemically. If you have some problem you need to solve, what equipment do you currently have that you can leverage to do it? And then, how do you to scale it when, inevitably, the chips get better and faster and cheaper. So, next year, or the year after, there is going to be even better equipment to connect into the system.

What is Microsoft doing to help with all that?

Obviously, Azure is an incredible cloud business. I think 95% of the Fortune 500 run their businesses on Azure. And what we’re doing from our group is really helping on the edge.

We have something called EFLOW, in which you can run Linux workloads on Windows. So you can have a secure and managed Windows platform—that everyone knows how to manage and deploy—and on top of that you can now run legacy Linux workloads that can be run from the cloud, as opposed to having a separate box that runs Linux. So that’s one example of something that we’re doing out of our team that’s really helping customers solve problems with the equipment they have—with a little bit of new software—which is pretty cool.

The good news is that there are lots of different ways to solve problems in a very cost-efficient, very low-CapEx way. But the downside of that—and I guess that’s why we get paid—is that it’s complicated. You have to take the friction out—with developer tools, with platforms. That’s really what we’re trying to do, is take the friction out, and give people the power of all the optionality that’s out there without making it too complicated.

Would you say that IoT applications have really evolved to a place where it’s more about the software model than the particulars of the hardware?

I think so because the software has to really work across many different pieces of a system. Different pieces of hardware all have to work together, and that’s where the software really comes into play. If you have your business logic and your power apps and all that running on top, it’s a really software-driven exercise. Something like 7% of all Tesla employees are software engineers. And I think in General Motors it’s something like 1% or 2% are software engineers.

I do a lot of mentorships for college students, and they always ask me, “What companies should I work for if I want to be in tech?” And I always tell them, “Well, pretty much any company is in tech these days.” Everyone has to be tech capable, and you have to have software capability built into any company. So from a career perspective that’s exciting for a lot of people coming out of college, because they can pretty much work anywhere if they have software capability.

We are taking advantage of a lot of new semiconductor capability—on lower power, higher tops, higher performance, lower cost—so there’s still a lot of headroom there, and, I think, an opportunity for the hardware to accelerate. Maybe that’s not in the consumer space as much, but on the commercial side everyone’s looking for higher performance, lower power, lower power consumption, lower cost. And that’s going to continue. But really it’s the software that can unlock some of these amazing scenarios.

What do you see as some of the key pain points around scaling up? And how is Microsoft trying to reduce the friction there?

That’s a good question. There’s the “show your boss that your camera could recognize a banana,” and then there’s actually deploying that. And one of the things we’re trying to do is minimize the steps between the demo to your boss and that deployment. Azure Percept uses some pretty cool Intel technology; it’s really a developer kit that enables people to quickly and easily recognize bananas and import their own AI models or use a bunch of models out of the box.

We’re trying to give developers a way to really harvest the work that they’ve done in the POC stage, and not have to do anything over again to get to a full deployment stage. The production hardware may change—you’re going to maybe change models and get something weatherized, or whatever. But the software and AI models that you’ve developed and trained, and the way you’ve managed and deployed them—that’s all sort of production-level code. And being able to develop and deploy on Azure gets you pretty far along when you want to actually do full production deployment.

IT deployments can be incredibly complicated these days. Are there any particular skill sets or resources that companies should be making sure they have shored up?

One of the things we’re trying to do with our tools and Visual Studio and the whole Azure platform is to figure out how to enable embedded developers to become smarter AI developers, and vice versa, so you don’t necessarily have to have two different types of software developer. One software developer can skill up and become really good at all of those things—developing and training AI models, and also writing code to develop and deploy applications on embedded or edge devices. But certainly, the data science and AI capabilities are the new skill sets that are really required for lots of companies these days.

In the old days the IT department would be behind one of those half doors, and you’d walk through it and say your laptop didn’t work, or something. And they’d take it from you and tell you that you could come back for it in a few hours. That doesn’t really happen anymore.

Your IT department is about security and about productivity, and probably doing some custom application development and, hopefully, buying some of these solutions or sourcing some of these solutions and adding their own power apps and other business logic on top for your particular business. I think it’s an incredible opportunity for developers these days to get out of their comfort zone a little bit, and to start experimenting with things like AI.

Where do you see the IoT ecosystem going in the medium to long term?

Every company has a problem, and every company has equipment. And so one of the things that we’re seeing a lot of action on is, “How do I leverage my legacy equipment in the brownfield as opposed to the greenfield?” We’re seeing a lot of activity there: “How can I write new software and applications to work on those platforms?”

And, at the same time, people are planning for the next big hardware cycle: “How do I use 5G and private 5G? And how do I use Wi-Fi 6? And how do I do all kinds of new things with these new vision processors?” So that’s all happening in parallel, but I think brownfield is where there’s a lot of near-term action.

The connectivity side of things has really changed a lot. Can you talk a bit about that?

I think my advice for folks is to keep an open mind when it comes to connectivity. So, there’s Wi-Fi, but also 5G. There’s something called LPWA, or Low Power Wireless Access. There’s Bluetooth Low Energy, which has gotten really good. There are lots of different ways to connect these things together, and people should really keep an open mind about the best way to do that, because there are so many options these days.

Any key takeaways for how to make the life of an IoT developer easier?

We really need to be “customer obsessed.” It sounds a little trite, but it really does mean something. Being customer obsessed means thinking about the solutions, not just the technology. So think about how you can help solve problems for your company or your customers holistically, and assume that there’s a heterogeneous ecosystem out there. Part of your value add is being able to glue that stuff together in a seamless way to solve problems.

To learn more about overcoming IoT development challenges, listen to our podcast Take the Pain Out of IoT Projects with Microsoft.