Building a Sustainable Supply Chain

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To learn more about sustainability, read IoT Paves the Way Toward Smart Sustainability and this report on IoT and Sustainability.

Transcript

Corporate Participants

Christina Cardoza
insight.tech – Associate Editorial Director

Chris Cutshaw
C.H. Robinson – Director of Commercial and Product Strategy for Visibility Products

Jan Hellgren
VINCI Energies, Sweden – Director of Innovation

Presentation

(On screen: insight.tech logo intro slide introducing the webinar topic and panelists)

Christina Cardoza: Hello and welcome to the webinar on Building a Sustainable Supply Chain. I’m your moderator, Christina Cardoza, Associate Editorial Director at insight.tech. And here to talk more about this topic, we have a panel of expert guests from Axians and C.H. Robinson.

So, before we jump into our conversation, let’s get to know our guests. Jan, I’ll start with you. Can you tell us a little bit more about yourself and your role at Axians?

Jan Hellgren: Yes, my name is Jan Hellgren. So, I’m Swedish, and sitting in Stockholm. And Axians is part of the VINCI Group, VINCI Energies, and I’m the Director of Innovation at VINCI Energies. So, I’m working out of our Innovation Center here in Stockholm, it’s called the Hive – Innovations for Good. And what we basically are doing is that we try to find solutions that are good for our planet, and using IT and OT.

Christina Cardoza: And Chris, welcome to the webinar. Can you tell us a little bit more about yourself and C.H. Robinson?

Chris Cutshaw: Yes, thanks, Christina, excited to talk about sustainable supply chains today. My name is Chris Cutshaw, I’m Director of Commercial and Product Strategy at C.H. Robinson.

C.H. Robinson is a $17 billion logistics and supply chain solutions company. We help companies all over the world build automated processes and sustainable solutions within their supply chains. My focus is around our managed service division, which helps roll out technology and global control tower solutions for large companies that move products all over the world. I’m based in Seattle, been with the company 11 years, look forward to the conversation today.

Christina Cardoza: Great. Yes, can’t wait to get into a little bit of how C.H. Robinson is helping customers build those sustainable supply chains.

Let’s take a quick look at our agenda before we get started.

(On screen: slide outlining the webinar’s agenda with image of green plant)

Today, our guests are going to explore why sustainability matters and how businesses can begin down a sustainable path. What that looks like. How to be successful in this area. Sort of the technology partners in this ecosystem. Who can help and what are the right tools and technologies to get you there? And then lastly, we’re going to look at what these efforts are working towards in the future.

So, let’s get started.

(On screen: The State of Sustainability with  illustration of solar panels, windmills, and sustainable solutions in a city)

Here at insight.tech, we’ve been seeing a lot of organizations really start setting aggressive sustainability goals over the last couple of years with so many trying to reach net zero in just a few years.

So, Jan, I wanted to throw this first question to you. What has been behind this move and this adoption to become more sustainable?

Jan Hellgren: More than the obvious that the climate is changing and there are very few people that question that these days. But I will say that what happened in Paris a couple of years ago for the Paris Treaty, we know that legislation is coming. We are going to be forced to change. But then many companies that understand that this is coming understands that if you are really early in this, this can become something that can help your business.

So, either you are in the back seat waiting for somebody to tell you what to do, or you are in the front seat and you can make money out of it.

Christina Cardoza: Great, and we’ll dive a little bit into some of those regulations that you just mentioned. But I’m curious, Jan, if you can expand a little bit on where we are with sustainable businesses today. Like I mentioned, a lot of them are setting aggressive sustainability goals, but how realistic do you think those goals have been and how strong have those efforts been towards those goals?

Jan Hellgren: The goals are realistic in the sense that if we don’t meet them, we’re not going to – it’s going to go really, really bad for us. So, I would like to mention that many organizations now are adopting this three-pillar economy with the PPP. And like in VINCI, VINCI Energies, we have this every month and every quarter, we’re going through our profitable goals, our year result, and we are measuring that and we have the KPIs for that.

On the people side, we now have – since a couple of years – we have KPIs of following safety and the health of our staff. But if this is going to – if they are going to be healthy over time, and we are going to be a profitable company over time, then we need to address the planet part also.

And the planet “P”, addressing CO2e, the CO2 equivalence, if we address that in a smart way, the planet “P” could actually help the profit “P”. And I think that many companies are really understanding this. And I would say that it’s very common here in the Nordics that companies are addressing this heavily. And we are a French company, and VINCI has really aggressive goals in terms of reaching this.

I didn’t mention that many don’t know what VINCI is, but VINCI is a 260,000-employee company, so it’s a really big organization and takes this really seriously.

(On screen: Going green slide with image of a business team holding a plant together)

Christina Cardoza: Great, and Chris, you mentioned in your intro you guys are really working to help businesses set those goals, reach those goals. So, I’m wondering how you’re seeing them get started, what this sustainable journey looks like for a business.

Chris Cutshaw: Yes, definitely building on what Jan was just talking about, this is becoming not only just an incredibly important topic for the globe, but companies are being really forced, both by public perception and by policy, to figure out how they can become sustainable.

So, from a carbon perspective, which is a really critical aspect in sustainability, transportation or supply chain activities outside of manufacturing is going to be the second biggest carbon-producing aspect of companies’ businesses.

So, first and foremost, they need to understand a baseline what’s happening within their supply chain to understand where they can actually focus on improving and eliminating the amount of carbon that they’re producing.

So, we help companies by baselining their modes of transportation. When they’re shifting maybe to airfreight to accelerate something, understanding the impact when they’re doing that. Obviously, it’s important to get product into the market when customers need to, but there’s also tradeoffs that you need to make. Carrying more inventory, understanding where to, let’s say, be more sustainable within your business and make lower impact decisions. So, we baseline their supply chain, and then from a transactional perspective, help them choose in-moment when you have various different metrics to consider like time, cost. And now, we’re actually inputting carbon output into that equation, so they can make a balanced decision.

And then, ultimately, reviewing how you moved against your baseline and making sure that you’re accelerating towards that goal, whatever that may be to improve your sustainability. And that’s the carbon aspect of it.

But we’re also helping companies just have a sustainable process. If you look at right now the talent and labor shortage, especially within supply chain talent is growing exponentially. And so, there’s more jobs than people to fill them. And really, if we help companies build a process where people want to stay, work, and grow, that’s also a part of the sustainability concept that companies are thinking about. How can my supply chain run and accelerate on its own without having to, let’s say, fire hop, or jump from fire-to-fire like we’ve seen many companies do through the pandemic? And then with all these supply constraints and port issues, how can we build a sustainable process through technology, through automation to help them achieve their goals both from keeping talent and retaining talent, but also reducing the amount of impact that they have on the environment?

Christina Cardoza: There is no surprise that there’s a lot that goes into being sustainable from a carbon emissions standpoint to just within your business how it runs and operates. On top of that, there’s all of these other challenges like you both have mentioned, governmental regulations, global green energy initiatives.

So, Jan, I’m wondering if you can expand a little bit on the challenges businesses face when they’re trying to become more sustainable. And how some of these government regulations and global initiatives are putting pressure on them even more.

Jan Hellgren: Thanks. And there are so many huge challenges. I wouldn’t say that I’m aware of all these challenges. But a few of them are that if you have – let’s say that you have a goal for 2030, you can’t wait until 2029 to do your changes. You need to break this down into chunkable pieces, so you can act on it right now.

You also – to be able to then make these changes, you need your organization to be aware of what is happening, and you need to be able to engage them. I just have an example from my driving yesterday.

I drove to another city here, it’s about 500 kilometers away from here. The last time I drove, I had gasoline usage that was about 40% higher than this time where I really tried to be careful with my right foot. So, you need your organization to be engaged in doing these changes. And how do you do that? Well, by creating awareness.

And then how will you keep track? Just as Chris said, if you have your baseline, how do you keep track of where you are towards that baseline? I would say that what is happening now also when the companies are starting to address these things is that they… to keep track, they are doing a lot of manual work. So, they are reading invoices, and they convert that into Excel files, and then import Excel files into your ERP system. And it’s creating an enormous amount of administrative burden.

So, there are so many other challenges also, but that would be a few of them.

Christina Cardoza: Great, and I want to touch on something that Chris just mentioned earlier that it’s not only about reducing carbon emissions. There’s really a lot that goes on within the business, within your own workforce that can help it become more sustainable.

(On screen: The Role of the Supply Chain and illustration of  supply chain elements: vehicles, trees, planes, and boats)

So, Chris, can you start off by talking about what does it mean to have a sustainable supply chain, and the role the supply chain plays in these sustainability initiatives?

Chris Cutshaw: Yes, I think as I mentioned before, supply chains are really front and center if you think about manufacturing or whatever your business is to moving goods into market. When you talk about carbon output, that’s really a huge driver.

So, supply chains are in the bullseye, for good or for worse, on how to document, figure out, and identify, one, their output currently, and strategies that they can take to mitigate.

And so, the role of the supply chain in becoming sustainable is also being agile, flexible, connected, and visible. So, really, companies right now are on a journey. So, they’re trying to start by connect all their partners and all of their movements that are happening within their supply chain to gain visibility. The reason you want to gain visibility is to give your customers an understanding of what’s happening. But also for you and your internal systems to be able to understand where everything is at in a complex global environment, and make really critical decisions, and prioritize key metrics in your decision-making process transactionally.

So, you don’t review a quarter and see how you performed and try to change for next quarter. In the moment, when you’re making those critical decisions, are you taking every factor into consideration?

So, companies are trying to connect right now. I’d say the majority of companies right now are just trying to understand what’s happening across the entirety of their supply chain. Then they’re trying to move to more of a predictive phase.

So, can I avoid disruption? Can I see what’s around the corner? Can I identify mitigating strategies to be more resilient? So, if I don’t have to expedite a bunch of airfreight because I don’t have any other alternate sources of supply, if I build a resilient strategy where maybe one of my suppliers goes down, or becomes embargoed by a political – a new geopolitical event, I can already source and have strategies to back up that supply, then I don’t have to go and accelerate airfreight, which is going to produce carbon, and really allow my employees to jump from issue to issue. And I have a plan in place to mitigate that.

So, can I become predictive? Can I get connected? Understand what potentially is going to happen. Then what companies are trying to do is to move into an orchestration where every system, every division, every, let’s say, silo within the company are all reading from the same sheet of music. So, you don’t have logistics yelling at manufacturing, or planning accelerating product through logistics and making them expedite freight. But you’re all saying, “Here are our common metrics, here are our common data assets and common data model that we’re all reading from, we’re all acting from”. And then, eventually, they want to move to a phase where they’re cognitive, where you don’t have humans making decisions, but you have your systems and you really are extending productivity, allowing one person to do 10x more than they’re doing right now. And that’s really the role, and what supply chain leaders are thinking about to take from where we’re at now in a very transactional, manual environment to become cognitive, to become very connected. And then take into consideration priorities that you need to consider every time you’re making a decision. And that includes sustainability, that includes carbon. And you’re taking that decentralized approach away from all the decision-makers that are out there in your potential supply chain. And every transaction you’re making the right decision based on your organizational priorities.

That’s really what we see companies trying to do right now. And we’re helping them with visibility technology. We’re helping them with platforms that allow them to connect to that. And also, connecting with IoT and other types of sensors to understand where everything’s at in a complex global environment.

Christina Cardoza: So, it sounds like just by streamlining your operations, connecting your systems, and understanding everything, what’s going on, looking at the right metrics is helping towards this overall goal of being more green, reaching these net-zero goals.

So, how important or where does the supply chain come in some of these net-zero goals? Is this sort of the first thing businesses should be looking at when trying to reduce their carbon emissions?

Chris Cutshaw: If your business does manufacturing, that’s going to have probably the biggest output or carbon contribution that your company is making. Next up is how you move and facilitate movements of product throughout the world.

And when you’re in those really critical tight decisions, if… and the reason I was talking about becoming connected is a lot of times people are measuring last quarter and seeing how well they did and saying, “Okay, let’s implement these strategies”. Well, when you get into a pinch and you need to make some really quick decisions and maybe a line’s going to be down for production, or you’re not going to meet launch date for a critical launch, you’re not always going to consider environmental impacts in those decisions.

So, if you can actually input those algorithms or that type of decision-making into your process and maybe carry more inventory, maybe be more of a – building in local regions with build-to-order type supply chain instead of just stocking and moving from that perspective, that’s going to help you be more agile in these moments. And also, allow you to align the organization towards a north star.

You don’t have people maybe that are trying to hit a certain P&L or trying to achieve a certain in-stock ratio, but we’re all making a decision based on organizational priorities. And if carbon reduction and your net-zero goal, or whatever that goal is, you need to make sure that you’re considering that every time you make a transportation decision.

Obviously, using ocean and using ground transportation that’s electrified, potentially, or not using a lot of airfreight or direct truckload, that’s going to allow you to reduce your burden and reduce the output of carbon that you’re making. So, we help companies transform their supply chains to say, “Here’s the cost risk and benefit analysis of maybe taking more inventory within your supply chain, shifting to more environmental-friendly modes of transport”. And we help them make that analysis to find where it’s beneficial and then how they can actually do that over time to achieve their goal.

Christina Cardoza: So, it sounds like there is a lot of moving pieces in all of this. Making sure everything is streamlined and correct and moving on the factory floor, if you’re in the manufacturing industry, to ensuring you have the right inventory available, to making sure the right amount is being deployed and delivered on the road.

(On screen: Why Technology Matters slide with image of trees and data points on top of it)

So, I want to talk a little bit into the technology that goes into this. Because we’re talking about a lot of systems connected to each other, and a lot of data and metrics that you need to be collecting. And a lot of this is too much for a human to understand on their own. And since we’re talking about all these systems being connected, Jan, I’m wondering if you can tell us a little bit about the role of the Internet of Things in these green efforts.

Jan Hellgren: So, the Internet of Things is as you say, it’s just a technology, it doesn’t bring any value in itself. But just as Chris mentioned, you can use it for really solving a lot of your challenges.

So, as I mentioned before, one of the big challenges for organizations is this big burden of manual work, to be able to enter this data into your systems. If you’re going to keep track, if you’re going to know your CO2 footprint at any given time, then you need this data. And the interesting thing is that a lot of this data is already digitized. You have it in your existing systems. It’s just that these existing systems are not connected to the internet or to your endpoint.

So, that might be the case. And some of the data is not yet digitized, but then you have all kinds of sensors for measuring all kinds of gases, and all kinds of emissions. And then in both cases, IoT could really be the carrier of bringing this data to your endpoint.

So, you put your edge gateway where you have the data, and you fetch the data either from existing systems or from sensors. And if you combine that with API integration towards other sources of external parties, then you can really have the data that you need to keep track over time.

Christina Cardoza: And I know capturing and tracking some of this data is a big part of what Axians and VINCI Energies does. So, can you expand a little bit on the ways that you guys help businesses make sense of all this data and how, in turn, that’s helping them become more sustainable?

Jan Hellgren: Yes, we don’t have time to explain that in detail, of course. But I can say that what we normally do is that we put, what you would call, an edge gateway, normally it’s an Intel NUC, and we put that onsite and let that gateway communicate with existing systems. It could be an existing SCADA system or existing PLC, or whatever sensor which is out there already. Or we put other IoT devices there also, bringing all this data into the edge device, where you then combine this data and convert it into whatever protocol you would need in the back end.

And then we send this to an IoT hub, normally Microsoft Azure. And on the back end of that IoT hub, we are then using this GreenEdge Platform that we created for calculating, for aggregating, and presenting the CO2 footprint of your company.

We are addressing the full Scope 1, Scope 2 and, of course, not all of Scope 3 yet, but a really important part of Scope 3 is already addressed also.

Christina Cardoza: Perfect. And Chris, you mentioned the stages of a sustainable supply chain and touched upon some of the technologies or ways C.H. Robinson is helping businesses become successful on this journey. But what other components or technologies do you think is necessary to really be successful?

Chris Cutshaw: Yes, so similar to Jan’s, we also leverage IoT in our partnership with Intel and Microsoft to make that happen.

(On screen: Chris displays his edge gateway device)

I actually have a little gateway device here, it’s about the size of two iPhones. This connects via cellular and GPS technology. So, you can put this on products on a multimodal movement, so often our goods today, especially in North America are being imported from Asia or other locations. So, that means it’s going on a truck. That truck is going to go into a port. That container is going to get put onto a vessel. The vessel is going to go to the destination port. It’s going to be put on a rail. That rail is going to go to a destination rail location. It’s going to be pulled and delivered and dropped off at a distribution center.

So, understanding all of the points in the journey and then each of those modes or movements are actually producing some sort of carbon as a part of that. Understanding even what vessel it’s on. So, I know is it a newer vessel that’s better at fuel emissions? Is it an older vessel that really is actually contributing worse to my carbon footprint? That’s really the true way you can measure your current output. You can definitely make assumptions and say, on average, this is generally what it takes. But to truly identify what’s happening, you need to understand every step of the journey so that you can eliminate or leverage partners that are going to really help you achieve your carbon goals.

And then we have products like our visibility technology, Navisphere Vision. Navisphere is a platform we built proprietarily at C.H. Robinson, over 115 years of development, and we’ve been a company helping companies move supply all over the world. So, we take this visibility technology inputs from a whole bunch of data elements like sensors, like vessels, ports, terminals to combine that data in real-time, show them where their inventory is at, show them where their freight-in-motion is at, and allow them to do more reporting and analytics on on-time performance, carbon, inventory in full, things of that nature.

And we also have rolled out a product across C.H. Robinson called Emissions IQ. This is really the first GLEC-certified, which is an industry body that’s internationally recognized on reporting and understanding carbon output by mode of transport. So, we have a GLEC-certified dashboard that companies who are leveraging C.H. Robinson can quickly with a few simple setup items, can understand from a transportation perspective what is their current baseline as it’s moving through our systems, and help them plan and identify areas of opportunity where maybe they don’t need to be using airfreight, maybe they don’t need to be expediting parcels and there’s consolidation opportunities.

So, if I send 20 shipments out from one place to another place, can I consolidate those into a much more heavier weight, heavier dense type movement, which is going to improve the utilization of my transportation?

So, we use IoT technology, we use visibility platforms, we connect that data through streaming architecture and API architecture. And then we baseline and help companies understand their analytics in real-time. One of those components is carbon output and baselining their carbon emissions.

Christina Cardoza: Now, this all sounds great in theory, but I’m curious of how this actually looks like in practice.

(On screen: Sustainable Businesses in the Real World slide with illustration of various metrics and reports)

So, Chris, I’m wondering if you have any examples of customers that you’ve helped, the challenges they face, how you stepped in, and how they really utilized the technology from C.H. Robinson to meet their sustainable supply chain goals. And you don’t have to name names if you can’t, but any examples you can provide would be great.

Chris Cutshaw: Yes, well, one we can name that I just did in the last answer that we’ve publicly announced our partnership is that of Microsoft.

So, Microsoft has made an ambitious goal. I believe by 2030 they want to be carbon-neutral, and by 2050, they want to be carbon-negative, replacing all of the carbon that they’ve ever produced as a company. Very ambitious goals. And then when that comes down through the organization, Microsoft’s supply chain, again, is in the bullseye saying, “Okay, you guys are a big producer, help us figure out a mitigate strategy”.

So, first and foremost, we help them identify all of these systems and different processes, different decisions that were happening within their global complex network. They service over 100-I-think-70 different countries, shipping 10 to 20,000 SKUs every year. So, understanding and having a platform roll out to be able to track and connect all of those transactions is the foremost step that companies need to take to then be able to change and influence change over time.

So, you need some sort of execution platform, whether it be through your manufacturing or supply chain process that helps you see in real-time, connect to your partners, and make really in-the-moment decisions that are based on your priorities as an organization.

So, we’ve rolled out our global TMS, which is a transportation management system, put it in Azure, which is their cloud-hosted cloud solution, and we host our products in their systems to help build out sustainable processes, track their cargo, which is very prone to potential theft or damage. So, IoT capabilities and understanding what light, temperature, humidity, tilt, shock of every container, every pallet that’s moving in their supply chain. And giving them that live streaming global common data model is an example of how we’ve built this with a real-life customer.

And I would say that doesn’t come with a flip of the switch. Anybody who is going to tell you, “Hey, you just turn this thing on, all your problems are going to go away”. I would say that’s probably a bit of snake oil. This takes iteration. This takes a lot of focus, a lot of buy-in across many different parts of your organization to really influence change. And you need to find partners and technology platforms that are going to allow you to do it that are future-proof, and that grow with you over time.

So, we have a managed service that goes along with our technology that brings people and process, that combines the global technology to help them evolve and transform over time, and stay consistent with what’s happening in the industry and make the right decisions.

So, that would be an example, and we have many others just like that here at C.H. Robinson. We support over 100,000 customers and have about 75,000 carriers that are connected to our platform from all different sizes. So, it’s a journey. You definitely have to make the investment. You have to jump in. You have to iterate. And that’s how we found success in helping companies really build sustainability from a practical sense.

Christina Cardoza: I love how you said it’s not a flip of a switch. I think a lot of times companies get frustrated when they get on these journeys because they don’t see results fast enough. But like you mentioned, it really is a journey and we keep talking about hitting these net-zero goals or these sustainability goals. But is there really an endpoint to this? Once you’ve reached that net-zero goal, is your sustainability effort over, or what happens after that?

Chris Cutshaw: I think we have some big targets to hit. I don’t know that anybody could project out. But I know one thing that we constantly evolve as a people and as humanity. And I would imagine that once we get there that we’re going to find some other targets that we’re going to go after, or we’re going to find some new innovative ways to move and build products.

Potentially, and this could really impact our industry, but is there 3D printing or micro-fulfillment, ability to build and manufacture in-region with very consistent and sustainable processes that are sourcing from the countries in which they’re manufacturing.

So, can we build processes that eliminate redundancy, eliminate complexity, allow us to fulfill customer needs immediately without impacting the environment? So, I think maybe a lot of companies will achieve carbon-neutral by offsetting their carbon, but not eliminating. So, I think we’ll actually want to go after full elimination and how can we move in maybe an electric or nuclear way with many different modes, I think, is really exciting to think about. But I know one thing, we will find some other things to chase after if we achieve this goal.

Christina Cardoza: Absolutely. And Jan, you mentioned the GreenEdge a couple of times. I’m wondering if you can expand on some of the businesses or industries the GreenEdge has been helping with sustainability efforts. And where it comes in on the sustainability journey.

Jan Hellgren: So, GreenEdge is actually a generic solution. So, what it does is that it takes in data from many different sources, converting it into CO2e. And it also converts whatever actions you’re taking for your coming year in terms of getting or letting out less CO2 emissions, or other gases for that sake.

So, what you will have is a baseline that you measure all your emissions against. And the interesting thing is that you will measure it on the lowest level of your company. So, it would be your business unit, and perhaps after that, you will aggregate the data up to your regional unit and up to what we call a Pole, or on a top level. And depending on what role you have in your organization, you would like to see the emissions of whatever you are responsible for. And so, it can be used for ourselves. We have a very, very diverse business. We do it for real estate. We do it in a utilities business. And for industry.

But as I said, since it’s using data from whatever sensor or whatever system, it could be used in almost any vertical.

Christina Cardoza: Now, we mentioned some other big names in this conversation. Chris, you mentioned Intel and Microsoft, and I should mention that the insight.tech program is an Intel-owned publication. But I think it’s clear that these goals are aggressive, there’s a lot that goes into it and no one company can do it alone.

(On screen: The Power of Partnerships slide with image of a human hand about to shake hands with an illustrated hand made out of green plants.)

So, Chris, I’m wondering how else you’ve been working with Intel and Microsoft and other organizations, and what has been the value of the partnership ecosystem to meet sustainability goals.

Chris Cutshaw: Yes, starting – I think the comment you made there is you cannot do this on your own, neither us as a provider nor our shippers or customers that are moving freight. We need to all collaborate and collectively build solutions that are going to help us achieve these goals.

And how we partnered with Intel has been really on a technology front. So, they’ve helped us manufacture and build IoT devices, deploy those devices across a variety of different customers.

Now, one way we’re using them is to track what cargo ships they’re on, what trucks and rails that they’re on. We also are able to track if anybody opens a container when they shouldn’t be, or if there’s potential damage. Or let’s say we’re shipping in the winter, we’re shipping berries and raspberries from South America into the US, we want to make sure those maintain a certain temperature and they don’t get spoiled and damaged. That’s sustainable too that we don’t throw away a bunch of food that we don’t have to. So, IoT and Intel has helped us build those technologies.

And other ideas on partnership and things we’re thinking about. So, in our industry, we have this term a lot of companies that we compete with, we’re really frenemies. In some way, we are competing but we’re also helping a customer build solutions. So, can we have collaborative solutions that go across our four walls as a company, but keeping in mind the priority of us as a civilization that we need to be doing the right thing by the planet, we need to be doing the right thing by taking advantage of the data and the technology that’s out there, so we can create these really innovative solutions. And we’re not always focused, necessarily, on our bottom line, but a collective output.

Now, that’s a huge statement, and I would say there’s a lot of other priorities that get in the way of that. But the more we can keep that into consideration as governments are thinking about how they foster and almost enforce that collaboration, I think that’s a big push on partnerships and finding the right folks within our industry that can help us achieve these goals. And collectively, can we help shippers that are really moving products, that are manufacturing products achieve these goals?

And just to close on that, Intel and Microsoft have been huge partners from a technology side to help us deliver that.

Christina Cardoza: I love that point you made about frenemies. You’re absolutely right. Everybody is sort of competing, but at the same time, there’s a lot of different pieces that go into all of this. And different businesses or organizations might have more knowledge or expertise or software in one area than another. So, it really takes a team to put it all together.

Jan, can you expand on some of the technology partnerships that Axians and VINCI is working with to make this happen?

Jan Hellgren: Well, on the OT side, there are a lot of them. On the IT side, I would say the main partners are Intel and Microsoft. So, we’re using Intel hardware, just as Chris said, for gathering the data through an IoT gateway. We have the microservices platform that makes it possible to do any kind of security management on the Intel NUC and any kind of updates. Having an automatic way of managing a big quantity of gateways at a time. And we are sending the data to the Microsoft Azure IoT hub.

And the interesting thing there is with – I know that all of you guys know about Microsoft Azure, but the ones that are listening to this might not be. But you have an extremely big toolbox of solutions for creating any kind of value with this data that you get for sustainability. And these toolboxes we have been using for creating our solutions, but we are also helping organizations to make use of this toolbox. We have Microsoft Azure MVPs and architects that we use to help our customers to meet their goals, using our solution or creating their solutions on their own.

Christina Cardoza: Great points. And I know – we already mentioned how this is a journey and once we hit our goals, which is aggressive and way out in the future, there’ll be even more goals to reach, or even new technologies to apply, different ways to be doing things.

(On screen: Securing a Sustainable Future slide with image of solar panels and windmills)

But I’m wondering if we can stop and look into our crystal balls a little bit at what we can expect in the near-term future. What is this all working towards and how far do you think our reality of becoming more sustainable and meeting these goals are? Chris, I’ll start with you.

Chris Cutshaw: Yes, I think, in our industry, in transportation – just look at North America. So, the average truck fleet or a company that owns a truck is about one to five trucks. And so, you have a crazy large industry that’s being run by a bunch of micro companies that are making decisions in the moment. So, I think the ability to electrify and take different solutions to those different smaller companies to allow us on every shipment across North America, and start there and then move into other areas of the world as that technology becomes available. Can we replace those fleets with more sustainable options?

I think another interesting thing is a lot of our imports into the US, from an ocean perspective, are moving on vessels. And there’s a lot of cool initiatives, and I’ve seen some really interesting concepts of huge, large sailboats that are drafting and not using actual engine power to go and move forwards.

So, if you really can have an electric vessel or a sailboat, a large sailboat that’s moving containers into the US, that’s being picked up by an electric truck and delivering it, you can eliminate carbon on an international movement, in our current supply chains.

And then I think another thing that we’re thinking about as an industry and governments are thinking about, especially coming out of this pandemic, is how can we be sustainable within our own region and not rely on international trade as much?

Now, that won’t necessarily be the best thing for our company. But it will be the best thing for the world if as a country, as an international body, can we find a way to manufacture, procure, and deliver sustainably within region and not have to rely on moving things across the world in an elongated amount of time. So, that’s going to speed up transit. That’s going to reduce carbon.

So, a lot of those things are coming to fruition, and there’s going to be a lot of automation. So, autonomous vehicles are not too far down the road. I’d say in the next five years, we’re going to see the middle mile taken out of supply chains from a driver perspective. So, can we focus that talent elsewhere? And really, that’s going to allow us to be more sustainable, to run more consistent networks and not have to see a spike in expedites or a spike in cost that we normally see in the different cycles that we operate in within supply chain.

And I also think the ability to process, consume, and predict the amount of data that we’re able to intake now is growing exponentially day over day. So, you’re going to be able, as a company, to receive a lot more information, make more real-time decisions, and allow your people to be more productive and feel more empowered within their roles.

As I mentioned earlier, I feel that with some of the algorithms, with some of the capabilities that are coming to bear within our industry, people may become 10x more productive, and so we don’t have to grow and build these large teams to get things done. But we can sustain with our current size as we become more automated and more capable with some of the solutions that we’re rolling out.

So, all of those things are – I like to see the glass as half full. We have a lot of challenges on the horizon but if we can come together as different organizations and think about the best way to solve some of these challenges, I feel very confident that the future is bright for us as an industry.

Christina Cardoza: Great. I want to repeat one of the points you made, which was a lot of these things – some of these things that businesses are doing may not benefit the company the most, but it is going to benefit the world and making it better. And that’s really what’s behind all of these goals and initiatives. So, I love that you said that.

Jan, is there anything you want to expand on what a sustainable future looks like for Axians and VINCI?

Jan Hellgren: Yes, so I just want to thank Chris for the half-full-glass, because looking into the crystal ball right now, it’s not very pleasant. The emissions are increasing not decreasing, so it’s kind of scary.

I would… I think it’s not about technology. A lot of the technology that we already have would help us a lot in achieving the goals. But this is – it’s a question of do or die. Businesses have to start changing right now, and we need to keep track in doing this. And when I say do or die, for our company, if we’re not addressing the sustainability issue, we will probably not be competitive in the very near future, and we would, as a company, die. But if we are not, as a society, addressing this really, really fast, then it’s not looking good at all.

So, thank you, Chris, I really liked what you were saying. And I have actually the same opinion as you have.

Christina Cardoza: Great. Well, unfortunately, we are running out of time, and I know we covered a lot and there’s still so much more that we could cover.

So, before we go, I just want to throw it back to each of you for any final key thoughts or takeaways you want to leave attendees with today.

Jan, I’ll throw this one to you first.

Jan Hellgren: Yes, just what I’ve already said. If we address this fast, we have a really, really good competitive advantage towards whomever we are competing with.

I just want to mention an example from Sweden that I heard just last week that we have a company that is called Green Steel, so they are using hydrogen to create steel. They haven’t produced not one kilo of steel yet, I think, but they already have a 10 billion order book.

So, if you’re addressing this really fast, you have a competitive advantage and we should use that.

Christina Cardoza: And Chris, any final key thoughts or takeaway?

Chris Cutshaw: Yes, I appreciate the time today, nice talking with you, Jan, and Christina, thanks for moderating.

I would say that, really, as a company if those that are listening to this want to learn more about what we can offer and how we help identify and baseline and produce solutions that can become more sustainable within your business or supply chain or transportation needs, please reach out. chrobinson.com. You can ask to talk to an expert. We’re happy to walk through what we’re doing now and just be even more consultative in how we think about the future and how we think about sustainable practices within our industry.

So, thanks for the time and I look forward to what we can achieve together.

Jan Hellgren: And I would like to add the same as you were saying, Chris. That goes for Axians and VINCI Energies. Whomever would like to contact me, you can contact me personally and I will address the right person to talk to.

Christina Cardoza: Great. Well, with that, I just want to thank you both again for joining the webinar today and for your insightful conversation.

(On screen: Thank you slide)

I also want to thank our audience for listening today. If you’d like to learn more about building a sustainable supply chain, I invite you all to visit insight.tech where we have a wealth of articles and podcasts on the subject.

Until next time, I’m Christina Cardoza with insight.tech.

(On screen: insight.tech logo and thank you animation)

The preceding transcript is provided to ensure accessibility and is intended to accurately capture an informal conversation. The transcript may contain improper uses of trademarked terms and as such should not be used for any other purposes. For more information, please see the Intel® trademark information.

Intelligent Video Analytics Activate Smart Cameras

What do city streets, university campuses, sports stadiums, and factories have in common? Video cameras. They span a wide variety of locations and serve all sorts of needs—from traffic control to entertainment, manufacturing employee safety.

But there’s a big challenge in all these scenarios. Video cameras may be widespread, but they’re not smart enough to meet business goals or provide an adequate ROI.

Most security footage is observed by humans in real time. And even the most diligent person will experience distraction and lapses in concentration. But thanks to advances in processing and analytics, businesses and municipalities can use the video camera systems they already have in place to gain insights and information never possible before.

“A camera without analysis is almost a dead investment,” says Hani Elgebaly, CEO of AvidBeam, a video data analytics company. “Because human security personnel have natural limitations, organizations need analytics to process noteworthy occurrences.”

AI + Deep Learning = Smart Cameras

After spending 19 years leading R&D projects, Elgebaly co-founded AvidBeam in 2014. Since then, the company has developed a processing engine based on big data tools and deep learning modules to recognize and classify objects and patterns from real-time video streams. Elgebaly notes, “At AvidBeam, we have multiple applications enabling security, workplace safety, operational efficiency, and business intelligence. Our applications use AI and deep learning to extract useful insights from video streams and store them in a database or show them to customers in a visualized user interface.”

With his technical and business background, Elgebaly sees the value in utilizing cutting-edge technology like that of Intel®. “Intel offers the powerful processors crucial for video analytics operation—from servers to smart cameras, to mobile devices,” he states. “The technology helps us deliver optimal performance and enhanced experiences.”

The AvidBeam solution was developed using training data and conventional AI tools such as TensorFlow, Tensor RT, and Café. The solution can detect, track, classify, and analyze video streams to extract objects, people, events, alarms, and more.

Extracted data is visualized through a web browser, so you can view it on any device, such as a laptop, tablet, or mobile phone. And because the solution synthesizes big data analytics to process and analyze massive amounts of video streams in near real time, it can scale to serve smart cities and enterprise organizations.

With the scalability of #AI-powered #video processing and analytics, applications can transform a wide array of use cases—from municipalities to #retail to #manufacturing. @AvidBeam via @Insightdottech

Intelligent Video Analytics Protect Privacy

Adding new technology to traditional cameras promises to achieve safety goals while still maintaining privacy. “We respect personal privacy, and don’t maintain an individual’s data,” says Elgebaly. “We’re providing tools for analysis, and in retail for example customer data stays on premise. And from that perspective, we comply with GDPR and other privacy regulations.”

And with the scalability of AI-powered video processing and analytics, applications can transform a wide array of use cases—from municipalities to retail to manufacturing.

Smart Cities are especially well-suited to take advantage of video analytics. The technology helps cities create efficient, safer, more livable environments.

For example, traffic management can provide both safety and efficiency, with more and more cities implementing video analytics not for just to detect violations. Improving conditions for pedestrians and bicyclists, reducing roadway congestion, and improving air quality can all enhance city living.

“We discover new use cases on a daily basis, and we believe the industry is just scratching the surface of the potential of video analytics,” says Elgebaly.

The Value of a Partner Ecosystem

One common concern for businesses is preserving their investments by integrating video analytics into existing platforms and workflows. Not only can use the video systems they already have in place, AvidBeam technology works with a wide range of video management solutions.

“We integrate with Video Management Systems such as Milestone so that alarms and alerts are shown in the user interface of the VMS,” notes Elgebaly. “Our Milestone partnership is critical, as customers find us through their marketplace. Fortunately, all of our applications have native integration with Milestone, so we can accommodate current and future use cases.”

AvidBeam also values its partnerships with solution providers and system integrators. Elgebaly adds, “We continually offer new opportunities for our partners that don’t have the skills necessary to deploy these solutions for their customers. As a software-only provider, we focus on the technology and allow our partners to sell and install hardware and offer services.

The Future of Video Processing and Analytics

While video analytics technology may have many applications, we are still in the early adopter phase. “The future is very exciting. In addition to applications for Smart Cities and manufacturing, technology is poised to spread rapidly to diverse industries such as transportation systems, hospitals, and retailers,” says Elgebaly.

Virtual Collaboration in Ultrasound Advances Clinical Care

We often view healthcare through the lens of how it improves the patient-provider relationship—whether it’s timely access to a patient’s medical history or telehealth checkups. What we don’t see is the care provider collaboration, mentorship, and training that go on behind the scenes.

In the wake of the pandemic, healthcare systems have dealt with ongoing staffing capacity and personnel shortages. Burnout has contributed to higher turnover. “This can lead to hospitals and clinics not having the right number of staff or the expertise for a variety of exam types,” says Eddie Henry, Global Marketing Director for Ultrasound Digital Solutions at GE Healthcare, a leading provider of healthcare technologies.

A healthcare system may have multiple locations across a city or state, which expands access but also poses challenges. This means collaboration and knowledge-sharing have become vital, especially among more experienced providers and those early in their careers. Newer clinicians may encounter difficult patient cases or exam types, and in these moments, they could greatly benefit from talking to and learning from experienced colleagues. But these experts aren’t necessarily co-located with the clinician on the front line. New technologies can solve these collaboration challenges by providing a seamless, secure way for technicians and other clinicians to connect—anywhere, anytime.

Connecting Clinicians and Delivering a Virtual Ultrasound Experience

The GE Healthcare Digital Expert Connect solution brings sonographers, physicians, and remote care providers together to learn from one another and better serve patients. The virtual, interactive, peer-to-peer collaboration platform drives precision health by empowering clinicians to connect with colleagues in real time to get their questions answered, improve clinical decision-making, and deliver more coordinated, personalized patient care.

Digital Expert Connect allows users of GE ultrasound equipment to connect virtually with peers within their network. The HIPAA-compliant collaboration tool allows clinicians to work together on a patient’s case—all from a tablet powered by Intel®.

They can share their screen so a colleague can see exactly what they see in an ultrasound. The platform’s live annotation features also allow remote clinicians to provide real-time feedback on a patient’s case. Through the interactive interface, providers can easily communicate with colleagues, ask for advice, and get an expert’s opinion about a particular exam or scan.

Henry says using Digital Expert Connect for Ultrasound can benefit clinicians in several ways. Sonographers working for a healthcare system spread across a wide geographic area can use the tool to get after-hours support from a lead sonographer or even a physician, walking them through a particular exam and answer their questions (Video 1).

https://www.youtube.com/watch?v=bnKlr1sYz90

Video 1. Virtual, real-time collaboration and training for ultrasound clinicians help improve clinical workflow. (Source: GE Healthcare)

“I’ve heard directly from sonographers saying, ‘Sometimes I feel like I’m in that room with a patient, but I feel alone,’ so having that ability to connect with someone really quickly and really discreetly just helps their confidence level.”

The tool also allows sonographers to connect with OB/GYNs, radiologists, cardiologists, and other providers who have requested patient scans. They can work together with the exam images that they need, which ultimately saves physicians time when they read or interpret these images post-exam.

Jay Hanrahan, Global Product Manager for Ultrasound Digital Ecosystems at GE Healthcare, says Digital Expert Connect also can help healthcare systems reduce exam errors and avoid re-scans.

“Let’s say a sonographer does a scan of a patient and moves it to an IT system where a radiologist can view it. They look at those results and say, ‘We didn’t get what we needed to see, so we’ll need to get the patient back in for a re-scan.’ If you can have that communication occur during the scan, you can make that correction in real time. The patient doesn’t have to come in a second time and the whole process is more efficient because of that,” Hanrahan says.

Driving Change Across the Healthcare Continuum

Digital Expert Connect can help clinicians who must support a large geographical area. Within the system there typically would be a central hub where most of the clinical expertise is consolidated. At other sites, some of which are rural, clinicians may have complementary expertise and skills.

“The platform elevates the capabilities of those rural centers, because you’ve got these experts that are able to be there virtually,” says Hanrahan. “It also improves the satisfaction and the life of the patients because they can get that care locally rather than having to go to the big city every two weeks, which can be very disruptive to their family life.”

Digital Expert Connect is improving clinical workflows and making healthcare systems more efficient as they deal with ongoing staffing capacity challenges. Even more important, the solution drives collaboration and a virtual ultrasound experience that can lead to improved clinical outcomes and more personalized care. Clinicians shouldn’t have to travel miles to collaborate with their colleagues, and patients shouldn’t have to do the same to access the clinical expertise they need.

“We’re helping our customers, whether it’s a sonographer, radiologist, cardiologist, or someone in the women’s health space do what’s absolutely right for and best for their particular patient,” Hanrahan says.

 

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

Getting the Smart Factory to 20/20 Machine Vision

In the past couple of years, manufacturers have been under a lot of pressure to streamline their operations. One way to do that is through the transformation to smart factory. That in itself can mean a lot of things, one being the use of camera systems for machine vision. Throw AI into the machine vision solution, and that solution could seem more intimidating than the problem, particularly without data scientists or AI developers on hand.

David Dewhirst, Vice President of Marketing at Mariner, a provider of technology solutions leveraging IoT, AI, and deep learning, breaks the situation down for us. David spotlights Mariner’s crucial area of expertise—harnessing machine vision and AI for quality assurance on the factory floor, because, as he points out, quality needs to be paid for one way or another. And paying for it up front, on the factory floor, sure beats paying for it in a reputation for shoddy product.

What does it mean to be a smart factory?

I like to draw a distinction between data and information. Data is just the inputs—and they’re everywhere. You need to somehow transform that data so that you can do useful things with it as information. When I’m thinking about a smart factory, or a connected factory, I’m thinking about all of the information that’s inherent on the factory floor. So how do you connect all the data together? And how do you process that data to get useful results out of it—to get information? As well as availing yourself of new sensors and technology to really advance the state of the art in manufacturing.

How are manufacturers doing on this journey to becoming smart factories?

In fact, there is a high project-failure rate in this space. But you have to do it anyway, because all of your competitors are doing it. If you don’t, you’re going to be left behind.

In my observation, when these projects fail it’s because manufacturers haven’t actually thought through what they’re trying to do. They know they need to do this cool thing, but they may not necessarily be doing it to solve a specific problem. But that’s how I think any smart-factory initiative should proceed. If you’re charged with digital transformation in your factory, find the use case that may not be the coolest thing that you can do, but that solves the biggest, hairiest problem. Our solution is very pointedly aimed at improving defect detection in factory, so that’s one kind of use case.

It’s also important to find those use cases where you can sell your project both below and above—to the engineers who are impacted by it, but also to the decision-makers who cut the checks. And then you’ll be on a clear middle path towards smart factory. Clearly identifying your use case will help you sell it, and it will also help you solve it; if it’s a defect-detection problem, you can go looking for companies like Mariner that specialize in that. And from there, maybe you’ll identify other use cases that you can tackle later on.

The best way to start identifying these use cases is to talk to the people who have the problems. Talk to the people on the factory floor, the engineers—the boots on the ground. They will often be aware of day-to-day problems; they may even be suppressing problems, or just trying to ameliorate problems that they would love to have a solution for if you just asked them. Also talk to the people above you. Say to them, “What is costing us money?”

What’s the importance of machine vision to the smart factory?

When we talk about machine vision or camera systems or computer vision in the factory setting, those are typically fixed cameras in a fixed position with a fixed type. They are very bespoke to the production line. They will be designed in their placement, their lighting, their setup, in order to be targeted to the specific product on that production line. Their importance is in their ability to improve the quality control process.

There is the concept of total cost of quality, right? You’re going to spend money on your factory floor to have good quality that goes out the door. Or, if you don’t do that, you’re going to have a lot of returns, and you’re going to have warranty claims. Not spending money on the quality costs on the factory floor means you’re still going to spend money on quality costs; it’s just going to be in canceled contracts and bad brand association.

“If you’re charged with #digital transformation in your #factory, find the use case that may not be the coolest thing that you can do, but that solves the biggest, hairiest problem.” – David Dewhirst, @MarinerLLC via @insightdottech

The cheapest, highest ROI way to pay this cost is to do the quality work on the factory floor. This isn’t a new concept. Ever since the first assembly line in Dearborn, Michigan, you’ve had guys at the end of the line looking at products and doing quality control. Machine vision systems, or camera systems, to help do that have been around for decades. They are useful because they can present a very consistent look from piece to piece, from part to part to part to part. It always looks the same because the camera, as I said before, is very fixed and situated.

How does AI help take this process to the next level?

For the past several decades, machine vision systems have been very good at solving binary problems. For example, is there a hole in this piece, or is there not a hole in this piece? That’s a binary thing: yes or no. It’s very easy using traditional programming, which relies on those true/false questions to come up with a true/false answer.

But what happens when your problem isn’t binary? What happens when, instead of just asking is it a hole or not a hole, what happens when you’re looking at, for example, is this an oil stain on fabric or is it a piece of lint? They’re both kind of fuzzy. Maybe the stain is a little bit fuzzier and the lint is less fuzzy, but you have to draw an arbitrary line between the fuzziness levels. Then what happens if there is lint that is a little bit fuzzier than where you drew the line? That gets called defect. What happens if the stain is a little less fuzzy than you thought it would be? That will escape, because you might think that it’s lint. That’s where AI comes in.

With machine learning, with deep-learning techniques, you don’t need to draw an arbitrary line for a true/false answer. You can just train the AI with enough samples of stains and lint, and the AI will learn on its own what the difference is. AI can solve those kinds of challenges that weren’t really solvable before with just traditional programming, so you can oftentimes get your machine vision system, your camera system, to do what you hired it to do and what it has never really done a good job at.

What can manufacturers do if they have a lack of IT or AI support?

At Mariner, we use a tool. We ask your quality guys to take all the images you have of your product that show defects, upload them to the tool, and draw a little box around them. That lets your quality guys do what they’re good at—looking at these images and pointing out defects. We can take advantage of that and then do the part we’re good at, which is the data science. Our data scientists will build that AI model so you don’t need data science guys on the factory floor. We’ve got you on that.

Other companies with other solutions and other spaces will ship prebuilt models. Those may or may not work, depending on how closely those prebuilt models match what your particular situation is on the factory floor.

Where is all the data collection and processing happening—the edge or the cloud?

It depends. If you have 10,000 sensors all over your factory and you’re generating terabytes of information, you’re going to have to do it in the cloud. In machine vision there’s a little bit less reliance on the cloud. Mariner, with our Spyglass Visual Inspection solution—SVI—actually uses a hybrid solution. And that’s because, for the real-time defect-detection work, we don’t have time to make a round trip to the cloud. We’re doing our actual defect detection and the AI-inference work on the factory floor because then, even if you lose internet connection, your production isn’t shut down, your factory isn’t shut down.

We do also make use of the cloud. SVI is designed to run headless, without anybody standing around, but engineers can go back and review the decisions that the AI has made. If the AI got something wrong, the engineers can correct it. That will go up to the cloud. And if the AI model needs to be retrained, we can do that in the cloud because it doesn’t require real-time connectivity.

How do you work with other partners in this ecosystem to make it all come together?

Number one, we don’t sell cameras; we are an AI software-as-a-service solution. If you need cameras, we work with a vision integrator that will get you the right camera. By and large, we don’t care what the camera is; we can make use of any camera you already have, or work with you to get one.

Partner number two, because we need some powerful processing capabilities, we work very closely with Intel® and Nvidia, both on the factory floor. We ship AI software as a service that, ironically, will arrive to you on a server box. We do that because then we can build those server boxes to do what we want. So we have Intel® Xeon® chips in there for really muscular, beefy processing, and we have Nvidia cards in there for extra GPU power.

We also partner on the cloud with Microsoft, typically in Azure. There are a lot of prebuilt services and other capabilities in Azure that we can make use of, and also be certain about security and speed and all those other important things.

Anything else you would like to add?

You may not need Mariner’s solution, but you will need to move forward with industrial IoT and AI. Actually, you may or may not need AI, given your use case, but you are going to need to have industrial IoT of some kind. Mainly I would encourage people to think about the use cases and the situations that are right for them. Find that hook, get in, and don’t be the last guy.

Related Content

To learn more about defect detection, read A Guaranteed Model for Machine Learning and listen to Product Defect Detection You Can Count On: With Mariner. For the latest innovations from Mariner, follow it on Twitter at @MarinerLLC and LinkedIn at Mariner.

 

This article was edited by Erin Noble, copy editor.

AI-Assisted Diagnostics: The Future of Cancer Detection

For cancer patients, getting a swift and accurate diagnosis is critical for their prognosis—and peace of mind. But if the screening is done by an endoscopy, the process is more complex. Typically, doctors look for lesions with specialized cameras, but limitations leave the door open to oversight and errors. In fact, about 25% of all colorectal neoplasms, or cancerous tumors, are missed by experts using this standard process.

Today, those same cameras are being enhanced with AI and machine learning technology, helping improve patient outcomes. The solution leverages capture cards typically used in video gaming to enable high-resolution graphics that can be displayed on screens in real time. The crisp images are paired with machine learning data that can help doctors identify tumors faster, speeding up a patient’s path to treatment.

“Endoscopy is the base case for AI Analysis because doctors want to understand what is happening inside your body,” says Evelyn Tsai, Marketing Manager for Wincomm Corporation, a provider of industrial and medical grade computer products. “Previously, a doctor would need to have the patient come back to the hospital for more observation and tests. Through AI-assisted diagnostics, they can diagnose in real time whether or not something is neoplasm.”

The AI-Powered Medical Panel PC for New Endoscopic System, called EndoBRAIN, was first deployed in a hospital in Japan. Its speed and accuracy can reduce the costs, time, and risks associated with biopsies and repeated colonoscopies, saving patients the high level of discomfort that would come from enduring multiple procedures. EndoBRAIN can also improve patient diagnostics at remote rural healthcare facilities that are often lacking experienced professionals, who tend to work in large urban hospitals.

“AI-assisted diagnostic doesn’t replace the doctor’s decision; it supports their decision by a prediction training model,” says Tsai. “Experienced doctors may be able to detect neoplasms easier than younger doctors. Through this kind of system, doctors don’t need to rely on years of practice because the computer has learned the experience and can assist with a diagnosis.”

Development Partnership Leads to Innovation

To make such a technology operate properly, different disciplines must work together. For example, the medical specialists must work closely with the embedded technology experts to develop the solution.

One such case is where the Wincomm engineers worked closely with the team at CYBERNET SYSTEMS CO., LTD., subsidiary of FUJISOFT, to develop EndoBRAIN and EndoBRAIN-EYE—tools that deploy AI to detect and analyze colorectal polyps and other lesions in an endoscopy.

The system is integrated with the Wincomm high image processing Medical Panel PC platform and an Olympus endoscope, which was key to getting the regulatory approvals required to bring it to market. In addition to compute performance and design flexibility, the panel PC’s antibacterial design protects against airborne diseases. And the system’s built-in safety protects patient data by avoiding equipment damage from signal and voltage feedback loops.

“While #technology can’t replace the judgment that #healthcare professionals provide, #AI does have the power to impact and enhance the industry in important ways.” – Evelyn Tsai, @WincommCorp via @insightdottech

AI and Machine Learning Improve Diagnostic Accuracy

EndoBRAIN is the endoscopic microscope used to photograph the inside of the patient’s large intestine, as well as the AI software that determines the presence of colorectal cancer using image analysis technology. After “learning” with 60,000 medical records, the tool’s sensitivity rate is 96.9% and its accuracy is 98%, comparable to senior specialists. Using AI to automatically judge key parts of the image enlargement, the diagnosis is brief, reducing patient discomfort and the scheduling and training burden placed on hospital staff.

AI inferencing at the edge—enabled by the Intel® OpenVINO toolkit—is key to providing real-time data required for diagnosis.

“To make the medical imaging process perform smoothly, it must be low latency, almost real time,” says Tsai. “The doctors need to see the screen at the same time they watch the analyzed data. Doctors have different techniques, such as moving the imaging faster or slower. The system must also be able to fit the doctors’ behavior. Intel processors provide the powerful computing performance, high-resolution graphics, and an architecture can support these requirements.”

The Future of AI in Healthcare

Wincomm is also helping systems integrators expand the Medical Edge AI solution to other use cases. The medical edge AI computer platform, powered by Intel® Core processors, can work with a range of servers, camera control units, and medical imaging solutions. In addition to endoscopies, the technology supports a wide range of use cases such as robotic surgeries, ultrasounds, ECGs, and x-rays.

While technology can’t replace the judgment and care that human healthcare professionals provide, AI does have the power to impact and enhance the industry in important ways, says Tsai. First, AI can assist in providing a more accurate diagnosis, such as with the endoscopy solution. Data analytics can be especially impactful for new physicians who haven’t yet collected years of experience.

Second, AI tools can enable intelligent monitoring solutions, which can collect data and save time and resources. For example, patients with a non-critical diagnosis can be cared for remotely, freeing up hospital beds for those with more serious conditions. In addition, AI can help nurses monitor patients from a central location, saving time while maintaining care. And AI solutions can speed up deployment of advanced healthcare to a wider geographic region, including rural facilities that may have a harder time competing for top care providers.

“Edge AI is going to dramatically shift the healthcare industry in the future,” says Tsai. “The cost, time and healthcare service quality will improve, and patients can get faster care.”

 

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

SIs Boost Customer Engagement with Retail Analytics

Knowing what their customers like, what they don’t like, and what piques their interest in real time could be invaluable for retailers, helping inform campaigns, displays, and content. But it seems like you’d need to be a mind reader to gather this information. While loyalty programs provide some information, brick-and-mortar retailers, and the systems integrators that serve them, have had to rely on intuition alone when selling to customers.

For years, online retailers have had an advantage over physical stores because they can gather analytics from visitors’ browsing activity. But AI technology is leveling the playing field. With the help of systems integrators, luxury brands use edge technology to gather and leverage analytics inside physical stores. These insights are used to tell stories that help improve the customer experience, adapt to the changing market, and grow sales. And the timing is perfect, as shoppers return to stores after the pandemic had sent many online.

“Despite one-click shopping, 90% of sales are still transacted in stores,” says Luigi Crudele, CEO of Wonderstore, a manufacturer of AI solutions for the retail industry. “That means retailers have plenty of information to gather about visitors to their locations. Stores are the main place to create high-level relationships with the consumer based on involvement.”

For example, one fashion brand used insights collected by Wonderstore to pinpoint the best day and time to launch its new accessory line. By understanding its customers’ behavior, it was able to increase accessory revenue by 30% the day the merchandise was available in the store.

Another luxury fashion brand uses Wonderstore data analytics to uncover an average of 20% transaction variation between stores. It also found a 15% difference in conversion rates between its highest- and lowest-performing locations. Managers used this information to better understand regional customer profiles and the success rate of sales tactics.

AI Retail Technology Creates a Smart Store

To create a Wonderstore IoT retail analytics solution, Crudele drew upon his wealth of experience in storytelling and branding. His first company created 3D computer animation for video games, and he later launched an agency for developing interactive digital brand experiences, working with Italian brands like Tiscali.

“Brands spend millions of dollars in advertising campaigns and super-shop windows,” says Crudele. “We measure the effectiveness of those messages. With our solution, retailers can measure performance, understand conversion rates, and improve their investments.”

Wonderstore uses sensor technology to collect data about the in-store customer journey. Using computer vision technology, the solution can count, track, and analyze customers, collecting data that includes gender, age, and even emotions. It can measure dwell time, visitor flows, and browsing patterns. The data can be very granular, measuring performance of every single point of interest in the store, such as shop windows, entrances, shelves, fitting rooms, mirrors, and point of sale.

The company relies on the latest computer vision storytelling technology, with best-in-class IoT sensors from Intel®. Meeting GDPR regulations, sensors collect anonymous data that is sent to the cloud to be analyzed and transformed into actionable information. The solution is fully developed on Microsoft Azure architecture and cloud services. Using a storytelling data visualization platform, data is immediately readable, allowing the retailer to make decisions in near-real time.

Retail Technology Partnerships Provide Scale

“Partnerships help create awareness and trust in the marketplace. Wonderstore chose Microsoft and Intel to align with their vision of the cloud and IoT services,” says Crudele. “Through these relationships, Wonderstore was able to quickly enter the market with a prototype, show the product to the customer, and build a business with the top luxury brands.”

Wonderstore also partners with Tech Data Corporation, an IoT solutions aggregator, which provides immediate scale to its business as well as awareness and trust in the marketplace.

“We are a startup and Tech Data is an international IT distributor,” says Crudele. “Tech Data transformed our team from two people selling our product to thousands of resellers across Europe. The company is helping us change our business model from delivering technology to a solution. Our customers are no longer the retail brand but the partner. This paradigm is allowing us to scale up our business more easily and faster.”

By leveraging data, Wonderstore is helping create smart stores that can adapt to the customer and understand their needs from the moment they enter the store—not just at checkout.

“The store of the future will be a place where customers will have personalized services with creative brand experiences that entice them to buy,” says Crudele. “And it’s important for retailers to move from a sales model to a service model. Recognizing and understanding their customers with the same precision as Google Analytics helps create an experience that’s more than just a mere transaction.”

About TD SYNNEX

TD SYNNEX (NYSE: SNX) is a leading global distributor and solutions aggregator for the IT ecosystem. We’re an innovative partner helping more than 150,000 customers in 100+ countries to maximize the value of technology investments, demonstrate business outcomes, and unlock growth opportunities. Headquartered in Clearwater, Florida, and Fremont, California, TD SYNNEX’s 22,000 co-workers are dedicated to uniting compelling IT products, services, and solutions from 1,500+ best-in-class technology vendors. Our edge-to-cloud portfolio is anchored in some of the highest-growth technology segments, including cloud, cybersecurity, big data/analytics, IoT, mobility, and everything as a service. TD SYNNEX is committed to serving customers and communities, and we believe we can have a positive impact on our people and our planet, intentionally acting as a respected corporate citizen. We aspire to be a diverse and inclusive employer of choice for talent across the IT ecosystem. For more information, visit www.TDSYNNEX.com or follow us on TwitterLinkedInFacebook, and Instagram.

The Full Scope of Deploying Industrial AI at the Edge

The smart-manufacturing space has been evolving rapidly over the past few years so as to keep up with the demands of the digital era. Edge computing is a big part of that digital-transformation journey. But edge isn’t a fixed destination; it’s part of the process.

But businesses may still need signposts on this journey. So who has the roadmaps? And how might those businesses know when they’ve actually arrived where they need to be? Blake Kerrigan, General Manager of the Global ThinkEDGE Business Group at Lenovo, a global leader in high-performance computing, and Jason Shepherd, Vice President of Ecosystem at ZEDEDA, a provider of IoT and edge-computing services, confirm that there’s no one-size-fits-all approach.

They discuss the orchestration of edge computing, bringing what’s best about the public cloud experience right to the edge, and the relationship between cloud and edge computing in the first place.

What does a digital transformation in the manufacturing space look like these days?

Blake Kerrigan: For the past 15 to 20 years most industrial customers have been focused on automation, but some of the biggest trends we’re seeing now are around computer vision and AI use cases. Other trends I’m seeing a lot in manufacturing and distribution are things like defect detection and safety applications.

The question is: How do you create efficiencies in the processes that already exist? We’re starting to see unique solutions, and they’re getting easier and easier for our customers to adopt.

How does this change the role of edge computing and the cloud?

Jason Shepherd: The only people who think that sending raw video directly to the cloud is a good idea are the ones who want to sell you internet connectivity. With computer vision, the whole point is to be able to look at live camera or video streams at the edge, where they can be continuously monitored, and intelligence can be built in to trigger human intervention if needed.

What’s the most successful way for manufacturers to navigate this edge journey?

Jason Shepherd: Edge is a continuum—from really constrained devices up through on-prem. Eventually you get to the cloud, and running workloads across that continuum is a balance of performance costs, security, and latency concerns.

For manufacturers, first and foremost, it’s important to understand that it is a continuum, then to understand the different trade-offs. If you’re in a secure data center, it’s not the same as being on the shop floor—the security needs are different, for example. Navigating the landscape is the first problem.

When you get into actual deployment, always start with a use case, then do a POC. At this stage we see a lot of experimentation. But taking the lab experiment into the real world can be really challenging—camera angles change, lighting changes, contexts switch, etc.

The main thing is to break down the problem, and separate out infrastructure investment from investment in the application plane. Work with vendors that are architecting for flexibility, and evolve from there. Eventually it comes down to domain expertise with consistent infrastructure—consistent infrastructure like we’re doing with Lenovo and ZEDEDA and Intel®.

Blake Kerrigan: You can build something in a lab, and typically the last thing an engineer’s going to think about is the cost of developing or deploying the solution. The biggest inhibitors to scale are deployment, management of life cycle, and transitioning from one silicon to another over time.

The first step is understanding what kind of business outcome you want to drive, and then being conscious of what costs are associated with that outcome. To select the right hardware, the customer has to understand what the iterations of the program are throughout the solution’s life cycle. At Lenovo, we work with people on solution architecture and thinking about what type of resources they need today—and then how does that scale tomorrow, next week, and next year, and the next five years?

Tell us more about how to approach edge computing.

Jason Shepherd: There are a lot of special, purpose-built vertical solutions. With any new market, I always say, it goes vertical before it goes horizontal. It’s about domain knowledge.

What’s new is that everything is becoming software defined—where you abstract the applications from the infrastructure. In the manufacturing world, control systems have historically been very closed, which is a play to create stickiness for that control supplier. And, of course, there are implications around not being tightly controlled in terms of safety and process uptime.

What’s happening with edge is that we’re able to take public cloud elements—platform independence, cloud-native development, continuous delivery of software that’s always updating and innovating—and we’re able to shift those tools back to the edge. Basically, we’re taking the public cloud experience, and extending it right to the box on the shop floor.

What we do at ZEDEDA is that—while we help expand those tools from a management standpoint, from a security standpoint—we also have to account for the fact that even though the same principles are in play, it’s not happening in a physically secure data center. When you’re in a data center, you have a defined network perimeter; if you’re not, we have to assume that you’re deployed on untrusted networks. Also, when you’re outside of the data center, you have to assume that you’re going to lose connectivity to the cloud at times, and you’ve got to be able to withstand that. One-size-fits-all doesn’t come into play here.

So when should you use the cloud versus the edge?

Blake Kerrigan: The cloud means different things to different people. At Lenovo we feel that, ultimately, edge will essentially become an extension of the cloud. Edge computing is all about getting meaningful data to either store, or to do more intensive AI on; what we’re trying to do is to comb down the amount of uneventful or un-insightful data.

There are really two main things to consider. The first one is orchestration: How can I remotely create and orchestrate an environment where I can manage applications off the site? And the second one is—to make these models better over time—doing the initial training. Training is a big part of AI and computer vision, and one that’s woefully underestimated in terms of the amount of resources and time it takes. One of the most effective ways to do it is in collaboration in the cloud.

Let’s use defect detection as an example. Let’s say you have 50 different plants around the United States, and every single one of them has a defect-detection computer vision application running on the factory floor. Ultimately, you’ll want to share the training and knowledge you’ve acquired from one factory to another. And the only real, practical way to do that is going to be in the cloud.

So I do think there’s a place for the cloud when it comes to edge computing and, more specifically, AI at the edge—in the form of crunching big data that’s derived from edge-computed or edge-analyzed data. And then, in addition, training AI workloads to be redistributed back to the edge to become more efficient and more impactful and insightful to users.

Jason Shepherd: What we say at ZEDEDA is: The edge is the last cloud to build. It’s the fringes of what the cloud is. There are three buckets there. One is cloud centric, with lightweight edge computing and then a lot of heavy crunching in the cloud. A second one uses the power of the cloud to train models, and then deploys, say, inferencing models to the edge for local action. So it’s a cloud-supported, or cloud-assisted, model. And third, there’s an edge-centric model, where there might be training in the cloud, but all the heavy lifting on the data is happening on-prem. So, as Blake said, it’s not one-size-fits-all.

If manufacturers lack the proper IT expertise, what tools or technologies might help?

Jason Shepherd: Is a fair answer ZEDEDA?

It really is about finding the right tools, and then applying domain knowledge on top. There are a lot of people who have domain knowledge—the experts are the folks on the floor. But when you’re trying to deploy in the real world, you don’t usually have the staff that’s used to scripting and working in the data center space. Plus, the scale factor is a lot bigger. That’s why ZEDEDA exists: to just make that process easier and, again, to provide the public cloud experience all the way down into the field.

Where does Lenovo and its partnership with Intel® fit into this space?

Blake Kerrigan: The value of the relationship with Intel goes beyond just edge computing, and Intel is our biggest and strongest partner from a silicon perspective when it comes to edge computing. It holds a lot of legacy ground in the embedded space, the industrial PC space. But the other side of it is that Intel continues to be at the cutting edge. It continues to make investments in feature functions that are important at the edge—not just in data center, and not just in PC.

OpenVINO sits within the larger ecosystem of tools from Intel, but another one I really like—because it helps our customers get started quickly without having to send them four or five different machines—is Intel DevCloud. It lets those customers get started in a development environment that is essentially cloud based. They can control all sorts of different parameters, and then run applications and workloads in the environment. This creates efficiencies in terms of time to market or time to deployment.

At Lenovo we want to be able to create the most frictionless experience for a customer trying to deploy infrastructure at the edge, which is why Lenovo and ZEDEDA really complement each other in their alignment with Intel.

Jason Shepherd: ZEDEDA is basically a SaaS company—all software, but coming from the hardware space. And hardware is hard, so partnering with Lenovo makes things simpler. It’s important to work with people who are building reliable infrastructure.

Any final takeaways for the edge computing journey?

Blake Kerrigan: As Jason mentioned, hardware can be hard. I think a lot of people start there, but it’s not necessarily the best first step—though I say that coming from a hardware company. But at Lenovo we still do want to be a part of that first step on the journey. Reach out to our specialists and see how we can help you understand what the potential roadblocks are. And then we can also open you up to our ecosystem of partners—whether that’s Intel or ZEDEDA or others.

Bring us your problems, bring us your biggest and most difficult problems, and let us help you design, implement, deploy, and realize those insights and outcomes.

Jason Shepherd: It’s all about ecosystem. Invest in community so you can focus on more value.

This isn’t about free; it is about making money and all that. But it is also very much about partnership.

Related Content

To learn more about edge computing in manufacturing, listen to Manufacturers Unlock AI at the Edge: With Lenovo and ZEDEDA and read Cloud Native Brings Computer Vision to the Critical Edge. For the latest innovations from Lenovo and ZEDEDA, follow them on Twitter at @Lenovo and @ZededaEdge, and LinkedIn at Lenovo and Zededaedge.

 

This article was edited by Erin Noble, copy editor.

embedded world 2022 & COM-HPC: Exceeding Expectations

By almost every measure, the 2022 embedded world Conference & Exhibition was better than expected. Show attendance rebounded to more than 18,000 embedded technologists, a 50 percent increase over 2020. Another 3,900 participated in the show’s nascent digital and hybrid content. And on the vendor side, 720 exhibitors from 39 countries demonstrated their proficiency in IoT, edge AI, and functional safety electronics.

Events like embedded world are an opportunity to step away from engineering benches and take in new trends, techniques, and solutions shaping the next generation of intelligent, connected electronic systems. That was certainly the case this year, where a new young cohort of technologists made their intentions of moving the IoT from prototype to production known.

In concert, there was also considerable momentum around shifting from prototyping hardware to production-ready solutions at the show’s booth demos. And many of these were built around PICMG COM-HPC and 12th Gen Intel® Core processor-based devices (previously codenamed “Alder Lake”).

COM-HPC is a next-generation #computer-on-module standard that defines a series of higher-speed, higher-performance, and higher-power client- and server-size modules designed for next-generation #edge workloads. @embedded_world via @insightdottech

COM-HPC is for Real, and It’s Edge-Ready

As an open industry standard being released live on a big stage for the first time, it should come as no surprise that the new COM-HPC family of computer-on-module specifications was well represented at embedded world 2022. But “well represented” might be an understatement, as companies like ADLINK Technology, Advantech, Avnet Embedded, congatec, Kontron, SECO, and more made it a centerpiece of their show activities.

COM-HPC is a next-generation computer-on-module standard that defines a series of higher-speed, higher-performance, and higher-power client- and server-size modules designed for next-generation edge workloads.

Christian Eder, Chairman of PICMG’s COM-HPC working group and Director of Product Marketing at congatec AG, a leading supplier of embedded computer modules, was at the event to launch the new standard. He explains how the upgraded COM-HPC connector almost doubles the pins of previous-generation standards and supports interfaces like PCIe Gen 4, 5, and 6 and 25 GbE to deliver unprecedented bandwidth for edge systems (Video 1).

Video 1. congatec’s Christian Eder discusses the benefits of COM-HPC compared to COM Express. (Source: insight.tech)

This allows end users to fully utilize the unique performance of new Intel® Xeon® D and 12th Gen Core processors in a multi-vendor, off-the-shelf solution that safeguards technology investments.

Another benefit is that larger COM-HPC form factors dissipate more heat, which opens a path to higher-end processors like those just mentioned at the far edge (Video 2). This also happens to positively impact connectivity, as some of these processors—like select 12Gen Core devices—support emerging technologies like Ethernet Time-Sensitive Networking (TSN), according to Kontron, a leader in embedded computing technology.

Video 2. Kontron’s Martin Unverdorben discusses how COM-HPC will accelerate the deployment of IT/OT infrastructure. (Source: insight.tech)

The Wide World of COMs

Avnet Embedded has also seen increased interest in COMs across the board since the pandemic pushed supply chain issues over the tipping point, as a COM-based approach can reduce development time, complexity, and deliver most of the electronic components required by a system design from a single source. At the show, the company discussed how its in-house design expertise and partnership with Intel helps drive intelligence into end markets ranging from smart agriculture to electric vehicle charging (Video 3).

Video 3. Alex Wood, Marketing Director for Avnet Embedded, highlights the versatility and efficiency of the latest Intel® architecture-based COMs. (Source: insight.tech)

On the data logging front, ADLINK Technology enables the IoT edge with COM-HPC by taking advantage of the expanded pinout in systems like rugged industrial servers. At the show, the company explained how this flexibility—along with the longevity afforded by replacing COM-HPC modules but retaining application-specific carrier boards—combines with the company’s Edge IoT software stack to help companies quickly connect applications with cloud providers like AWS, Microsoft, or other server infrastructure (Video 4).

Video 4. ADLINK Global Account Director Marco Krause explains when businesses should make the move to COM-HPC. (Source: insight.tech)

SECO is another company working to push data from the edge into intelligent cloud-based applications using a flexible software platform, Clea, which SECO’s CPO Maurizio Caporali described at embedded world (Video 5).

Video 5. SECO discusses the importance of data to a business’ success. (Source: insight.tech)

To serve the diverse embedded market, the Clea Edge SDK interfaces data from a range of targets, including power-efficient Atom®, Celeron®, and Pentium® modules and high-performance computing 12th Gen Core and Xeon D processors devices with integrated functional safety capabilities. In addition to COM-HPC, these solutions are also available in industry standards like SGeT’s SMARC and PICMG’s COM Express.

It’s important to note that the introduction of COM-HPC doesn’t mean the end is near for other standards like COM Express at all. Claus Giebert, Business Development Manager for embedded and automation solutions provider Advantech, responsible for CPU-based COMs, revealed why “COM Express, for many existing application areas, will remain the dominating form factor for many years to come.” (Video 6). For those needing faster data transfers, more I/O, and higher performance, COM-HPC offers a path forward.

Video 6. While COM-HPC comes with many new benefits and features, Advantech says COM Express will still be relevant for a long time. (Source: insight.tech)

But there’s also the option of merging next-generation processors with current-generation standards. For example, during the exhibition, Prodrive Technologies introduced its 95 mm x 95 mm Atlas 12th Gen Series COM Express Compact Module based on i3, i5, or i7 P-series Core processors (Figure 1).

The Prodrive Technologies Atlas 12th Gen Series COM Express Modules blend the latest Intel® Core™ processor technology with the existing COM Express standard.
Figure 1. The Prodrive Technologies Atlas 12th Gen Series COM Express Modules blend the latest Intel® Core processor technology with the existing COM Express standard. (Source: Prodrive Technologies)

Alder Lake Everywhere

Of course, Intel technology on display at embedded world 2022 was not limited to just COMs. Elsewhere, companies showcased their latest solutions based on 12th Gen Core processors in all manners of form factors and systems such as the Supermicro SYS-111AD-HN2 1U Embedded Systems and SYS-E300-13AD Mini-1U Super Servers.

The key takeaway is that more so than in normal years, the return of embedded world injected a lot of newness into the embedded-technology sector. New 12th Generation Core processors could be found almost everywhere, and new industry-standard form factors capable of supporting them like COM-HPC were widely available. A sizeable number of new-to-the-industry developers were also in the crowd, preparing to usher in a new phase for connected embedded and IoT systems: mass commercial deployment.

Together, all this suggests big things are in store for the next iteration of edge computing.

 

This article was edited by Christina Cardoza, Associate Editorial Director for insight.tech.

AI Self-Checkout Eases Retail Experience

It’s a Saturday afternoon and the grocery store is full of shoppers. Like any other busy store on the weekend, the checkout queues are long. The best option is to head to the self-checkout lane.

Why? Self-service registers make the checkout process easier, and the lines move faster. You can review the price of your items as you scan them. You can bag your groceries how you like. It feels like a mini victory during an otherwise routine errand.

But when it comes to non-packaged items, self-checkout can be a painstaking process. You must search for the item name on the display, or find the sticker on a piece of produce, and then manually enter it into the system. And what happens if the system doesn’t recognize the item, or you make a mistake in entering the code? You will have to wait for assistance.

That’s why retailers leverage innovative smart retail solutions, like making self-checkout even better. AI, computer vision, and cloud technologies work together to help solve the small nuisances that shoppers encounter to make big improvements to the in-store shopping experience.

Thanks to companies like Wintec, a provider of smart retail solutions, this possibility can be a reality.

“The self-service checkout option is great. So many shoppers prefer it because it saves them time—they can handle everything themselves. But there are still flaws in the existing systems that deter customers from using that option,” says Lu Xuefeng, General Manager of the AI Division at Wintec.

AI-Powered Solutions Account for Human Error

The manual weighing process relies on staff to accurately identify many types of goods—from peaches to watermelons—and find the corresponding product code. Unskilled staff may encounter problems like inputting the wrong code or taking too long to find it. This leads to significant delays in the weighing and labeling process. During peak retail hours, customers may have to wait in especially long queues.

On top of that, using manual scales means spending time training staff initially and when new products are added to the store. As produce varies seasonally or by market, training requirements can be high. And as labor costs continue to rise, operational costs increase as well.

This aspect of the checkout process is hard for staff, but even harder for customers. Today, CV and AI enhance image recognition capabilities at the register—offering a better customer experience and lower costs for retailers.

Created to help #retail stores realize #ImageRecognition and automated weighing of prepackaged foods, the Wintec Smart Weighing solution identifies, weighs, and prints price tags for items. @Wintec_China via @insightdottech

Cloud-Edge-AI Architecture Enhances Checkout Systems

Created to help retail stores realize image recognition and automated weighing of prepackaged foods, the Wintec Smart Weighing solution identifies, weighs, and prints price tags for items. And through continued CV-enabled model training, it also provides high levels of detection accuracy.

The solution enhances image recognition through powerful edge-to-cloud computing.  The software is trained for inference and real-time data processing, coordinated with cloud-based image model training using the YOLOv3 algorithm. This algorithm uses machine learning to achieve target recognition of fresh-food images by converting the task into a regression problem.

During the subsequent filtering process, the appropriate bounding box will be selected. By integrating object detection and object localization into a single one-stage network, YOLOv3 significantly increases detection speed.

“We created the solution to fix the hassle of manually inputting information for non-packaged items. But we weren’t focused on just that aspect,” says Xuefeng. “We wanted to create something that would continue to get smarter as it received more data. We also wanted to be able to take that data and make it available to retailers so they could gain useful insights from it for their overall operations.”

For instance, the solution integrates weighing functionality into the payment terminal, allowing customers to check out without visiting a weighing station. And eliminating the need for barcode readers helps reduce labor costs and expenses incurred from the maintenance and replacement of equipment.

Powerful Hardware and Software for Smart Retail Solutions

The Wintec Smart Weighing solution is built on Intel® processors, which provide powerful computing performance, safety, and reliability at low power consumption. These capabilities are essential for running edge AI workloads. The system also uses the Intel® OpenVINO toolkit to help with optimizing image recognition applications.

This is particularly useful in providing retailers with the ability to automate their entire business from the selection of goods to the weighing and checkout. Beyond that, real-time data processing provides actionable insights that help inform and facilitate business decisions in areas of operations beyond checkout.

The Future of Retail Automation

With transformative technologies and solutions available to the retail industry, the possibilities are almost endless.

As AI and automation technology continues to evolve, they can be implemented for the retail market segment beyond supermarkets and grocery stores. While requirements may differ based on format and application scenarios, tailored AI solutions have huge potential.

Grocery stores and other retail businesses are undergoing digital transformation. AI self-checkout is just one way retailers can improve customer satisfaction, increase competitiveness, and grow profits.

 

This article was edited by Christina Cardoza, Associate Editorial Director for insight.tech.

This article was originally published on July 14, 2022.

Predictive Maintenance Edge AI Gets Railways Back on Track

If I had to pick one system to use predictive maintenance technology on until the end of time, it would be a train. Modern, AC-powered engines are complex systems that can cost upward of $2.3 million. By using predictive maintenance AI algorithms to monitor sensors, actuators, and control subsystems for anomalous behavior, rail operators can reduce costs, drive revenue growth, and maximize their return on investment.

But to do predictive maintenance work effectively, data must be transportable across smart railway communications networks. For instance, operational data must be easily accessible and available to be used for detecting anomalous train behavior, training machine learning models, and informing edge algorithms what to perform inferences against. That’s why the EU-funded SCOTT (Secure Connected Trustable Things) project was launched with a focus on building trust in wireless solutions, like autonomous wireless networks (AWNs).

While the project addresses use of IoT devices, 5G, and cloud computing across 15 industrial use cases—including cross-domain applications and heterogeneous environments—it uses a standardized, ISO 29182-compliant multi-domain reference architecture that can be tailored to the requirements of smart rail transport use cases. The building blocks defined in the SCOTT reference architecture help map out the wireless technology and service architecture in these applications.

Software-Defined Wide-Area Networks Streamline Smart Railway Systems

One thing that is not evident from the reference architecture is how much connectivity already exists in railway environments today that AWNs could leverage—especially on passenger trains.

Consider that these vehicles already support information systems, train control, and passenger productivity and entertainment networks via access to a variety of wired and wireless communications mediums. In fact, one could argue that the primary challenge facing the SCOTT program is too much connectivity. For instance, the sensitive operational data required for predictive maintenance algorithms to function must be isolated from other, less critical traffic.

Securing and isolating this traffic could be achieved by installing additional, separate networks. Of course, this adds cost, complexity, and additional equipment for train engineers to maintain. And that seems like the wrong direction for a project focused on predictive maintenance.

Another option for segmenting operational data communications in this environment would be implementing a secure virtual private network (VPNs). But those too can get complex very quickly and become difficult to manage.

A third solution resides in the Goldilocks zone between cost and complexity in rail environments, while also adding capabilities you’d expect of a core network. Software-defined wide-area networks (SD-WANs) are intelligent network architecture, deployment, and management topologies designed to bring flexibility to edge AI technology and environments. They can run on top of off-the-shelf hardware, which allows users to extract the value of software intelligence while minimizing the cost and complexity of specialized networking hardware. 

SD-WAN Meets the SCOTT Program

The SCOTT program required the flexibility and openness of an SD-WAN given all the communications and amount of data types flowing across their multiple networks. Of course, they also needed a platform to run their SD-WAN on that could withstand the rigors of rolling-stock environments and provide security robust enough to keep the multi-ton projectiles that are trains out of hackers’ hands.

This led program stakeholders to Klas, an international design engineering company that focuses on communications solutions for the network edge. They eventually selected the company’s onboard compute gateway TRX R6 for their wayside communications and control needs (Figure 1).

The TRX R6 streamlines predictive maintenance by supporting SD-WAN-like capabilities, facilitating the ability to bond multiple channels into a secure tunnel for secure onboard connectivity with the Network Operations Center over public internet networks.
Figure 1. The TRX R6 streamlines predictive maintenance by supporting SD-WAN-like capabilities, facilitating the ability to bond multiple channels into a secure tunnel for secure onboard connectivity with the Network Operations Center over public internet networks. (Source: Klas)

The TRX R6 is an open, modular compute network mobile gateway platform designed for specific use on trains, light rail, and buses. It is a uniquely designed piece of equipment combining hardware and software solutions running on a range of multicore x86 Intel® Core processors. In addition, it hosts the advanced operating system Klas OS Keel, which is specifically designed to optimize the power of the Intel x86 processors and Klas hardware.

The operating system comes with a lightweight hypervisor, allowing applications to be supported on a single platform within virtual containers—which allows the operator to add features over time. KlasOS Keel also meets federal government security compliance and hosts a variety of advanced features like SD-WAN.

Because the Intel processor devices come with built-in hardware virtualization, Klas engineers could isolate the SCOTT program network stacks and applications by use case within securely partitioned virtual machines running on different cores.

KlasOS Keel was also critical to managing each VM to ensure the right resources were delivered at the right time for critical, latency-sensitive communications. As Mark Lambe, Senior Product Marketing Manager at Klas, explains, essentially the TRX R6 integrated hypervisor allows for multiple systems to be run as virtualized machines, delivering cost and space savings onboard for train operators.

With all this in place, Klas engineers went on to implement an SD-WAN that not only supported the new AWN requirements alongside existing networks but also offered a path for reducing the amount of networking hardware onboard trains in general.

In other words, after plugging in the appropriate connectivity modules to the primary TRX R6 host compute platform, the SD-WAN could route and prioritize the traffic from multiple heterogeneous networks as if it were traversing separate pieces of hardware. Therefore, operational data AWNs, passenger networking and information systems, and control networks that all require different levels of security and reliability could be managed according to their needs, thanks to the intelligence of the software and the openness of the hardware.

Real ROI on Railways Thanks to Predictive Maintenance

In addition to enabling a new network type while being cost-effective, what the SCOTT Project gained as a result of their partnership with Klas was the ability to host third-party applications regardless of the connectivity required—because, of course, it’s already supported. These applications can exist within the framework that the SCOTT project has defined, allowing train engineers and operators of other industrial equipment a straightforward path to deploying predictive maintenance at the network edge.

“When you standardize on a platform like the TRX R6, there is no need to forklift hardware when technology changes; you’re not having to retrain personnel on new hardware, operating systems, or management software,” says Arnold Allen III, Principal, IoT Industry Solution and Partner Development at Klas. “From a logistics perspective, you’re running spares and components are streamlined across all your vehicle platforms, which helps to reduce the cost of maintenance and ownership.”

If that’s not on the track to ROI, I don’t know what is.

 

This article was edited by Leila Escandar, Editorial Strategist for insight.tech.

This article was originally published on July 15, 2022.