Sustainable Supply Chain

Paving the Way for Autonomous Supply Chains with Real-time Data, AI, and Machine Learning

May 26, 2023 Tom Raftery / Sanjay Sharma Season 1 Episode 321
Sustainable Supply Chain
Paving the Way for Autonomous Supply Chains with Real-time Data, AI, and Machine Learning
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Show Notes Transcript

Welcome back to another compelling episode of the Digital Supply Chain podcast! I’m your host, Tom Raftery, and this time around we’re going deep into the world of real-time data, AI, and machine learning in the supply chain with Sanjay Sharma, the CEO of Roambee.

In this exciting episode, Sanjay and I explore the workings of Roambee, a company revolutionising supply chain management by utilizing real-time data, AI, and machine learning. This means keeping an eye on your goods in transit has never been easier or more precise - just one of the ways Roambee is moving us towards autonomous supply chains.

Sanjay shares some truly intriguing real-world examples showing how AI and machine learning are creating supply chains that can adjust and react dynamically. We’re talking about a future where supply chains self-adjust in response to hiccups and disruptions, even those as unexpected as a ship stuck in the Suez Canal.

If you're a business owner or involved in the supply chain management, you might be wondering where to start. Good news! Sanjay provides great insights on starting the journey towards a digitized supply chain, focusing on incremental improvements rather than being overwhelmed by the bigger picture. His advice is practical, applicable, and will put you on the path towards an efficient, technology-driven supply chain.

So, come join us as we dive into the future of supply chains in this dynamic episode with Sanjay Sharma. You'll discover the fascinating world of technology-driven supply chains and maybe even get a few ideas to implement in your own business.

I'm left wondering if I will need to re-brand this podcast to the Autonomous Supply Chain podcast!

Enjoy the listen and don't forget to check out the video version of this podcast at https://youtu.be/BjcNB9tIcmI

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Sanjay Sharma:

Customers who have long order to cash cycles, they're in the thin margin business. This is a perfect example where you can compress this order to cash cycles by eliminating a lot of receivable disputes because the data tells you, you know, where things were delivered and, and in what quality and condition.

Tom Raftery:

Good morning, good afternoon, or good evening, wherever you are in the world. This is the Digital Supply Chain podcast, the number one podcast focusing on the digitization of supply chain, and I'm your host, Tom Raftery. Hi everyone, and welcome to episode 321 of the Digital Supply Chain podcast. My name's Tom Raftery and I'm delighted to be here with you today sharing the latest insights and trends in supply chain. Before we kick off today's show, I want to take a moment to express my sincere gratitude to all of this podcast's, amazing supporters. Your support has been instrumental in keeping the podcast going, and I am really grateful for each and every one of you. If you're not already a supporter, I'd like to encourage you to consider joining our community of like-minded individuals who are passionate about supply chain. Supporting the podcast is easy and affordable. With options starting as low as just three euros or dollars a month, that's less than the cost of a cup of coffee, and your support will make a huge difference in helping me keep this show going strong. To become a supporter, simply click on the support link in the show notes of this or any episode, or visit tiny url.com/dsc pod. Now. Without further ado, I'd like to introduce my special guest today, Sanjay. Sanjay, welcome to the podcast. Would you like to introduce yourself?

Sanjay Sharma:

Hello. Thanks Tom for having me on the podcast. My name is Sanjay Sharma. I am the CEO for Roambee Corporation. We are a supply chain technology company based here in, Silicon Valley, California.

Tom Raftery:

Okay, great. And for people who might not be aware, Sanjay, what is Roambee? Who are you guys and what is it you do?

Sanjay Sharma:

Yeah, so Roambee is in the business of digitizing the supply chain. And the way we do it is basically bringing in a combination of sensors, a platform, and anaytics, wrap it up into a form of a SaaS service. That can allow us to empower our customers to ensure they can deliver their products on time, in good condition, in the right quantity at the right location.

Tom Raftery:

Okay, that's what everyone says. What is it you're doing? What is it doing a little bit differently, Sanjay?

Sanjay Sharma:

Yeah, so I think when, when you look at Roambee. I want you to think about Roamebee delivering some very interesting business signals that enable the autonomous in the supply chain. So, every customer that we talk to have ambitions that their supply chain should be self healing. It should be contextual and dynamic. And it's easier said than done. And what we do is we basically bring in a lot of raw sensor data. We bring in lot of third party contextual data through the entire journey of your shipment, whether it's going from a distribution center to your warehouse, or it's a multimodal kind of transportation mechanism or your basically implementing transloading within your supply chain regardless of the format and the content, and the pattern of your movement. We basically deliver the signals business signals that can enable your supply chain to be autonomous. Examples would be proof of delivery. Okay, so for customers who are looking to automate the delivery confirmation, or sometimes it's also called, is an OTIF on time in full signals. A Roambee would be a sort of a stack of choice where, you know, our sensors can detect where your shipments are. Are they at the right place when it was delivered, and then derives some very interesting information from a variety of data sources, including sensors, and translate that into one high fidelity, high quality business signal called the OTIF, or called a proof of delivery.

Tom Raftery:

Oh, okay. And I mean, does that then kick off any further actions or is that just a data point that appears on a dashboard somewhere?

Sanjay Sharma:

Yeah, it does. So, The, the whole idea of autonomousness is basically bringing actionable information that can basically push or trigger other processes. So, here an example, right? Companies like Unilever, Proctor and Gamble they all are, you know, pushing the products to the Costcos or the Sam's Club of the world. And when that happens most often they land into receivable dispute. For example you know, I've shipped 300 pallets and the customer might say they have received only 290, or they might say they have received 300, and 10 were broken. Now, in a case of a receivable dispute the only way to resolve this is to have a single source of proof that actually can tell both parties that indeed 300 pallets were delivered. They were delivered in the right quality, they were delivered in the right condition, on the right time that would, and, and Roambee basically brings that signal out. Lot of our customers take advantage of that proof of delivery or that OTIF signal and freed it into their ERP. And when they do that, two things happen. Of course, you know, with the ship confirmation, the purchase order gets closed automatically. But even further, they are automating the invoicing process because the delivery has already been done, you can automate the invoicing to your customers, and by doing so, you can compress your order to cash cycles. So, you know, customers who have long order to cash cycles, they're in the thin margin business. This is a perfect example where you can compress this order to cash cycles by eliminating a lot of receivable disputes because the data tells you, you know, where things were delivered and, and in what quality and condition.

Tom Raftery:

And are there any particular industries, Sanjay, that you guys are working in or are you pretty across the board?

Sanjay Sharma:

Yeah, we have you know, we call it strong fit industries. So obviously pharmaceutical life sciences, chemical industries, food and beverages are part of our sweet spot. But we also extend our solution offering to industry like the automotive. A lot of reusable containers or assets that basically move in a circular economy moving products that's sort of the sweet spot for us. And then we also work with a lot, lot of retail companies and logistics providers worldwide.

Tom Raftery:

Okay. And you're, you're talking about an autonomous supply chain. Are you going beyond proof of delivery or is, is that it for now? Or, you know, where do you fall in that? Because it's, it's, it's a, it's a big claim. An autonomous supply chain.

Sanjay Sharma:

Yes. We think that there will be a new category created, you know, call it autonomous supply chain. And we think we are perfectly positioned to enable the autonomousness. I don't think we will be the ERP or the autonomous supply chain company, but companies will be using our platform to deliver those signals. So obviously, you know, proof of delivery is one example, but think about the 300, 400 signals coming out of the platform that could tell you things like demand shock. It can tell you skewed inventory. It can tell you transportor performance, it can tell you route quality. So, there are a variety of that signal that are needed to make the supply chain self-healing and to make decisions. And we think we can deliver those decision making data to our customers who will then implement the autonomous stack that is needed for their business.

Tom Raftery:

Okay, fair enough. So the signals that you're talking about coming into your platform, are they coming from sensors that you provide, sensors that your customers have? You mentioned third party as well, so I'm, I'm assuming you're, you're looking at other things like maybe weather signals or social media or something like that as well, you know, where, where do you fall in that category?

Sanjay Sharma:

Yes. So the quality of the signal is very important for you know, translating that into business decisions. So our thesis is on the sensor side, if there is a sensor in the market that basically is purposefully built targeting a specific use case we will OEM or we will you know, bring that sensor into our ecosystem and we will drop our own operating system on it. So that it can be consumed as an enterprise grade sensor. So things, think about it as an example of an Android. So Android works on Samsung, but Android also works on Google's own phone called Pixel. And our thesis is similar. So if there is a sensor, we will bring that into our ecosystem you know, by wrapping it into our operating system. But if there is no sensor in the market, We will design our own and, and again, using our own operating system, bring everything to our platform. And by, by doing that ingesting data and translating those raw signals, sensor signals into interesting events becomes much easier. But there are other signals that we also consume, like datas from, as you said, right? Weather route information third party telematics data. So there is a lot of telematics data crowdsourced in, in the marketplace that we can bring in. We also think there is a lot of data coming from the 3PLs. You know, they are bringing a lot of interesting information on a milestone basis. And we, we can consume that data, but there are also very supply chain centric data available. It could be around container movement, it could be around you know, customs data and things like basically bringing all of that data in and translate into you know, what we call it as curated signals or business signals to our customers. And, and the black magic happens in our platform. You know, we sort of derive and rely on a lot, lot of location related information. Mm-hmm. And then contextual information is around the location of where the shipment is.

Tom Raftery:

Okay. What about things like news sites maybe, or things like that for, I don't know strikes in ports that might delay shipments or that kind of stuff?

Sanjay Sharma:

We don't focus on the macro events. Our idea and our thesis is the macro events are available already through different sources, but what influences and makes the supply chain more dynamic is the micro events and the propagation of those micro events in not only your network but also in third party networks. And the network propagation is is not trivial, right? I mean, even we have not cracked this within our platform, but I think we are building those blocks that will lead us to take advantage of micro events, fuse these events into a meaningful business signal and translate that into how it impacts not only your supply chain network but also impacts other supply chain networks that are connected to you.

Tom Raftery:

Okay. What about things like, there's a big push on now globally in organizations for sustainability, for things like carbon footprint information, which is going to be heavily regulated in the coming years, for things like even child labor or slavery in supply chains. I'm not sure if that's something that you're tracking, but is it? Are you? Is, is that something that you're coming across or you're being asked for, anything like that?

Sanjay Sharma:

Yeah. Our customers are asking us to help them measure a) measure the sustainability within their current transportation network. But b) also bring out anomalies and opportunities to make it even more sustainable. And since we are collecting real-time data, you know, from end-to-end for the entire journey of the shipment, whether it's a container, whether it's as you know, air cargo, whether it's a pallet or even a car or the last mile. We are perfectly poised to, translate that data into emission ratings and translate that data into helping our customers measure the sustainability index within their organization. But even better have the ability to share that with the transporter, like lot of market vehicles or lot of 3PL companies or, many other parties that touch this product when it goes from point A to point B and sharing that information with some of these transporter consortiums really enables to up the game from a sustainability perspective. And we are not there yet. I mean, we are just getting warmed up in terms of delivering emission three type scope, three type emission data. But there is a lot that can be done. And obviously it's about engaging with not only our customers, but also the industry consortiums that not only define and develop these standards, but also sort of use the data to calibrate some of these standards in terms of good to have, must have or, you know, it should be done regardless of, of where the industry is going. So, we are very proud to be in the early stages of, delivering this value to our customers.

Tom Raftery:

Fantastic. Great and very prominent in the news these days is AI, of course. Ever since, like last November when ChatGPT was announced and kind of exploded on the scene, is AI does it feature in your solutions as well? I'm sure it does. And, you know what, what kind of uses are you making of it?

Sanjay Sharma:

Absolutely. AI plays a role to perfect the data. An example I would give you is not always you know, we, we can say with a high level of confidence that you know, where the device says the shipment is, is supposed to be, is actually there. There is a lot of physics limitations around connectivity and accuracy of, of GPS and, uh, GSM signals. And if you have a wrong location, it'll translate into you know, wrong business events getting triggered. For example you would have a sensor, let's say on, on your air cargo. And it's sitting in Luft hansa hub at Frankfurt airport, but very next to the Lufthansa hub is a Delta hub. Now the signal is not very accurate enough for a variety of reasons, and now the signal will start bouncing between the Lufthansa hub and Delta hub. How do you really figure out with a high level of confidence where actually your shipments are? This is a perfect example of where AI and ML can play a big role to sort of eliminate this noise and really enrich and, look at the data and say, it's actually in the Delta hub, or it's actually in the Lufthansa hub and not in the Delta. So that's one example, which is under the hood is a lot of AI in ML is at play within the Roambee platform. The second aspect is we think like ChatGPT it's a mechanism to learn from the from the, all the data sources out there. In the supply chain space, luckily the volume of data and the type of data is very indexed and very tailored. So what I like to call is Roambee or any company's ability to build knowledge models on top of a, a typical supply chain, kind of a spec. And an example would be something like, if let's say a platform generates an alert saying, Hey, your shipment is out of this temperature band and is temperature excursion and occurred, can you feed that event into a LLM type model? And that model can then give recommendations to the customer, Hey, if the temperature has gone up by four degrees, sitting at Dubai Airport, It, it would be a perfect way to feed it into a knowledge model that can give some very interesting recommendations to our customers. And that's sort of, I feel you know, stacks like ChatGPT, and, and the likes could be used for delivering you know, what we call it as prescriptive analytics to our customers. And it's very interesting times. I mean, we can we can take this a few notches up very quickly. And, and, and customers had this ambition of having their supply chain being autonomous maybe in the next five to seven years and some of these new developments in AI, ML can actually accelerate the ability to make their supply chain automated.

Tom Raftery:

Fantastic. And where to next? I mean, what, what are your plans for the next three, five years? What do you see changing?

Sanjay Sharma:

I think the big problem that we, we think we should solve is our ability to see the impact of a micro event in an interconnected supply chain. So, for example, let's go back to this very famous Suez Canal example. Okay. I have a shipment. Its in a container. It was on a ship, and the ship is stuck in Suez Canal. Can I take that event and start modeling the impact of that event, not only on my inventory, not only in my ability or inability to service my customers, but can I now take that micro event and start looking at macro models such as would there be an impact on pricing? Because the choke point is, you know, for 15, 20 days. Who else get impacted? You know, within the interconnected supply chain. So looking at technologies like graphical neural networks and bringing those technologies combining with AI, ML combining with RPA kind of technologies and translating that into network propagation detections is something that we would love to embark on.

Tom Raftery:

Interesting. Interesting. Cool. We're coming towards the end of the podcast now, Sanjay. Is there any question I haven't asked you that you wish I had or any aspect of this we haven't touched on that you think it's important for people to be aware of?

Sanjay Sharma:

I think the, the, the you know, a lot of customers and prospects basically ask us how do we get started? And I think You know, while the ambitions is to really become automated, dynamic, and contextual, I think you should start with the basics of simply lighting up your supply chain and identifying the glitches in your supply chain and translating that into improvements. That itself will get you 50% of your, towards your ambition of digitizing the supply chain. And there are many offerings out there, not just Roambee, but there are very many, many ways to bring that to fruition. And I think rather than looking at a bigger roadmap, if customers or companies can basically, you know, take this into small bites and translate those learnings into, you know, the second building block and the third building block, I think it'll just be very impactful you know, across all industry. So, thanks Tom for having me on, on your podcast.

Tom Raftery:

Sure, no problem. And if people would like to know more, Sanjay about yourself or any of the things we talked about on the podcast today, where would you have me direct them?

Sanjay Sharma:

www.Roambi.com or you can reach me@sanjayroambi.com.

Tom Raftery:

Ah, fantastic. Great. Sanjay, that's been fascinating. Thanks a million for coming on the podcast today.

Sanjay Sharma:

Thank you, Tom. Have a good day.

Tom Raftery:

Okay, we've come to the end of the show. Thanks everyone for listening. If you'd like to know more about digital supply chains, simply drop me an email to TomRaftery@outlook.com If you like the show, please don't forget to click Follow on it in your podcast application of choice to be sure to get new episodes as soon as they're published Also, please don't forget to rate and review the podcast. It really does help new people to find a show. Thanks, catch you all next time.

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