IoT and Predictive Analytics in the Supply Chain and Logistics Models

Tune in for a thought-provoking episode filled with insights on leveraging real-time data and AI to optimize inventory management, streamline logistics, and enhance supply chain resilience.

Join Frank Van Gelder and his guests
▪️ Mark van Os de Man, Global Air Freight Product Director Healthcare at Hellmann Worldwide Logistics
▪️ Richard Peck, Global Director of Compliance and Regulatory Affairs at OnAsset Intelligence 

Key highlights:

• The importance of understanding customer needs for tailored technology solutions
• Real-time asset tracking and temperature monitoring in the supply chain
• Opportunities and challenges in adopting predictive analytics and AI
• The crucial role of human factors in implementing next-gen technology

Transcript

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IoT and Predictive Analytics in Healthcare Supply Chain and Logistics

Insights, a podcast by Pharma .Aero 

Frank Van Gelder:  Good day everyone and welcome to the Pharma .Aero podcast series. The acceleration of different digital technologies seems now at light speed. By leveraging real-time data from IoT sensors and predictive analytics, companies can optimize inventory management, streamline logistics and enhance overall supply chain resilience. Today with us we have Mark van Os de Man, Global Air Freight Product Director Healthcare at Hellman Worldwide Logistics and Richard Peck, Global Director Compliance and Regulatory Affairs OnAsset Intelligence. Gentlemen, welcome. Thank you. Thank you. 

As a user, 3PL Hellman and a developer OnAsset, how do you look at the phenomenon as such, Richard? 

Richard Peck: So as you say, as a provider of technologies, real-time asset tracking devices, temperature monitoring devices, we have many customers who are looking at this technology and how they can better use it within their supply chains. And I guess the way we approach it, as a provider of solutions, is the first thing is to understand our customers' needs. So what is it the customer is doing? One solution doesn't fit all scenarios. In some cases, yes, they'll want real-time. In other cases, they won't need real-time. So again, it's making sure, well, where is it going to best fit your supply chain? Where is it going to add the most value? It's also, why they want that technology. It's not necessarily always the same reason. It may be they want the ability to monitor the temperature of their shipment live and may have the opportunity to intervene during the shipment. But it may be they have a high-value shipment and actually security is the priority and they need to know where their shipment is at all times to prevent it going astray. Then it's understanding how they can use the technology, how they can apply that technology, understanding the requirements within you know their operations and how the people that the warehouse teams are going to be handling to ensure that the technology is used correctly each time that it doesn't create problems. So that's how you know we would look at it it really is about understanding our customers requirements and making it work for them and I think that for us as provider allows us then to better create solutions that meet not only the needs of the industry today but the needs of the industry going forward. 

Mark van Os de Man: What Richard said is absolutely very valid right so the technical developments are going very very fast and I think it's even going faster than we can comprehend right from a 3PL point of view so everyone wants to have real-life visibility or at least we have a good cluster of customers that wants it. But you mentioned it well, Richard. Why do we want it? We want to have an intervention. No one wants to see in real life that the ship is not going well. Everyone wants to do something about it. But is the industry today ready? So to come back to your question, I think that it brings great opportunities to develop ourselves and also Hellman is in the middle of that change. But we also find that the industry itself is not fully ready yet. If I may, I want to give an example. We talk about real-life visibility here and intervention, so we have installed a control tower, but who do we call in the middle of the night in Australia, for example, when we see irregularity on a shipment? Who can we call or who can we reach out to that's actually going to do something with it? And it might not be technology -wise not that difficult, but in the end, it's also a human world and we need to take action these days. So, we're in the middle of that transition, and I think we as an industry are falling behind a little bit. 

RP: Just to add, I think that's great, and I've done a lot of work, and obviously, I've been on the customer side as well as the provider side. And the whole point with intervention is, sometimes with the real-time, it might be too late. You may not actually, and depending on where the shipment is and within whose custody the shipment is at that time, you may not be able to do anything with it. So other technologies, maybe predictive technologies, being able to look half a day in advance of if this goes somewhere, then maybe that's where we see improvements and be able to offer better services to our customers. 

FVG: Can I grab this word predictive technology because that brings me to another question I have for you gentlemen. Is AI technology today already not overestimated as the solution to everything? I give an example, the Red sea crisis, or the hurricane that destroyed the warehouse of Pfizer, or recently a container ship that hit the bridge in Baltimore. These are things you can't predict, even not with AI. With other words, what can technology do and can't do, Mark? 

MM: I think we're not overestimating it. I think the technology itself is great, but it's depending on the way that we embed it. I think the examples that you give is like the extreme examples, right? So we talk about disasters, we talk about irregulations, and yes, they are more and more common these days, so they're very, very valid. If you take away the disaster scenarios, then what are we confronting today? We see temperature fluctuation, the normal temperature fluctuation, and a lot of customers that are not always choosing the best packaging for their shipments. I think if we look at the first step, how can we make the journey of the shipment more secure? How can we deliver the shipments without temperature excursions or with a limited amount of temperature excursions? And how can we make sure that we limit the amount of irregulations from an industry perspective? And I also think from the industry focus should also be to bring the cost down. The cost these days for transporting are tremendous and increasing actually. So how do we do that? If we have the right technology in place and if we can predict temperature excursions from happening, If we can make a module and give it to the customer to say, hey, actually, your shipment with this sensitivity level in combination with that packaging on this routing, for now, is going to repredict the temperature excursion. And this is actually technology that is already there. It's not new. But how do we connect the dots and make sure that we use it properly? In the future, I actually agree with you. How can we use, right, also for more disasters, irregularities? We saw a heavy rain now in Dubai as well. We saw the Israeli strike to Israel, which is causing closure of the airspace, delays on both sides, and delays means more risk often. How can we predict that better? And how can AI steer us as human beings to choose the right decisions from that perspective? 

RP: Yeah, you hit the nail on the head there. And I think if we go back, for me, if we think about those natural disasters you mentioned, And we can't necessarily predict those. You mentioned the Baltimore Bridge, I mean, when I watched the video of that, it crumbled like a piece of Lego. You couldn't have predicted that. So I think it really starts with risk assessment, and risk assessment does not go away. You know, we need, as an industry, we do, and we need to continue having detailed risk assessments, trying to capture as much as we can, working with all the stakeholders within the supply chain so myself as you know if I was the shipper our partners the freight forwarders the technology to provide us to try and predict that. 
Risk assessment obviously is is a continual process you don't just do it once and stop. The risk assessment and talking as you were talking there Mark that forms part of the AI solution so AI it is a buzzword right now you know but it isn't that magic wand that's going to fix everything for us and when I look at AI and the learning I gained with AI recently, the tool itself, the complicated, detailed mathematical calculations is only really 20%. The 80 % is the data. So the volume of data, the quality of data that we have is key, and that is where it's going to have. So the sharing of that data between each of us and having that to allow us to start to look at situations, to start to try to make those predictions to try and maybe see a natural disaster Dubai that I saw the pictures you mentioned Dubai that was incredible but and I think to that point I mean I love and I've had this conversation and you know and I've thought about this a lot over recent years I'd love it if in my warehouse I had multiple shipping systems and then through the predictive analytics AI based technologies that would say right Like today Richard, you're shipping this product, you need to use solution A. Or tomorrow you're shipping this product, you need to lose solution B. Or the next day, don't ship. Because if you ship, you've got an 80 % chance to wait a day. Well it's not practical. You can't do that. You know, you're going to have to ship your product. You can't have 100 different types of solutions. You can have a selection and use the right ones. But I think having that better visibility, that better prediction of a percentage and maybe just putting a little bit extra controls in place will, to your point, reduce temperature excursion. And in my mind we're not losing product but it means that the time to which that product is available is increased, the manpower, the people to review the data to say yes the shipment is okay, you know that creates problems, it creates delays and we want to get rid of that and it creates cost. 

MM: Yeah, absolutely. But do you really believe that AI at one stage in the near future, I would say, can predict disasters? 

RP: Exactly. I don't know if it's... No, I don't believe it. And that's why I go back to the risk assessment, actually. I think the human, looking at the risk assessment, continual updating the risk assessment with the aid of the AI. But yeah, I mean, as I said, nobody you would have predicted the Baltimore Bridge collapse. 

MM: So AI more in steering us, steering us and giving us the better solutions. 
RP: Very much so. And we talk about the predictive analytics and I think letting us look at the right data rather than all the data, focusing, honing in on what the critical elements are so that we've got the bandwidth to maybe then start to say, well, let's step back and let, what What if, as you say, what's happening in Israel? So how does that impact and how can I protect against that? 
MM: Indeed, I agree.

FVG: Let's now move away from the real operations. We sit at the table at the strategic level in a company and we want to write a strategy for the future. How important is technology going to help you for customer service and writing your future strategy?

MM: Massively important. I always look at, and it helps me a lot, to look at technology as dumb. You need to teach them everything, but you can also, because of that, you can teach exactly what it should do. So if we look at customer service, for example, how can we speed up pricing? How can we speed up, quotation, but also how can we offer the customers the more safer routings and solutions and do that faster than we're doing today? I think everything is about speed, right? Customers would like to see exactly how fast their shipping will be, how much it will cost, what the risk impact is, but also CO2 emission, they want to see everything in one bundle. And if we need to do that all manually, that is a pretty big task. So from that perspective, and also we as Hellman, we have actually already developed an AI department, we have our own GPT system, because it helps as well our staff members if they have a question, that we have one central place where they can be, where they can ask it, and they will see automatic answers from it. And that is what we can feed based on our processes and based on our risk analytics. 

FVG: Okay, very good. And my ultimate question, gentlemen. Technology, technology, but the human factor, human or machine. So, what are your both visions on the human factor involvement in the optimal implementation of what I would call the next -gen technology? 

RP: So, I guess from my perspective and OnAsset's perspective, I think that the technology and the AI element can deal with, let's call them, the tedious tasks and the processes. I can't even imagine the hours I've spent reviewing temperature data for shipments, for testing of systems and things like this, but that has had to be done throughout my 20 -something year career. The AI could do that for us. It could give us the useful insights, the real issues to ourselves, our peers, the decision makers within our organizations to be able to make, to your point, quick decisions around things. I spoke to a colleague recently within their organization and we were talking about the analytics and I was asking exactly that question around AI and how it could be used. And they said, the problem is, Richard, we don't have a team of analysts within our organisations who are looking at temperature performance or, you know, our partner performance, lead time variation, things like this. It's just kind of the end users, they receive the shipment, they pull off the devices, they check the temperature and can it be used, can it not be used. So I think that's where, and again, that's an overhead, which is a challenge. You know, everybody's challenged with cost and trying to be as streamlined as possible with personnel within the team. So using the technology that can create those dashboards, those reports that give you that information you need for the people there who are looking at that to allow them to then make decisions, speak to their partners such as yourself Mark and say right this we're seeing an issue here how can you help us fix that you know honing in again and focusing on that is key. 

MM: I agree and to be very fair as I said just earlier I look at technology as dumb someone needs to feed it and someone needs to organize it and especially with all the irregularities on the market someone with brains needs to be able to correct what is happening but we can work more efficiently and I think also it can help us a lot to make better decisions with the end goal so that we can deliver the cargo in a better status than we are doing today. 

RP: Yeah and I think you know we certainly in my previous roles right first time was always used a lot and there is a huge drive on sustainability obviously and what the impacts we're having in the environment. The biggest impact we have is when a shipment goes wrong because we have to do it all again. We've just doubled our CO2 emissions for that shipment. So by having that data... 

MM: And cost and pricing and everything, workload, so everyone is costing for that. 
RP: They are. So by having that information, by being able to mitigate and stop that happening, that's huge. 

FVG: Gentlemen, Mark and Richard, thank you very much for your insights in this very interesting topic and thank you for being here in our podcast. And also thank you to our listeners who are listening to our podcast Pharma .Aero. My name is Frank van Gelder, I'm Secretary General and I see you next time. 

Insights, a podcast by Pharma .Aero