Mahesh Veerina: 0:03

And then the idea struck us three years ago, why not leverage this to model a supply chain? Supply chain if you look at it, it's like, you have suppliers, thousands of suppliers. They're all like a node in a network. Think of it like a graph network you have your factories, your distribution centers, your trucks. All the moving parts can be modeled as little node in the network

Tom Raftery: 0:25

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. Welcome to the Digital Supply Chain podcast. My name is Tom Raftery and right, before we kick off, I just want to say. I've started a new way for you to support the podcast. You don't have to do it. Absolutely no obligation. It's just, if you think. This is a podcast that you get value from and you would like to thank me for that value in, in, a, financial way you can, now you can. I've created a support, the podcast page. Uh, you literally just go to the website of the podcast, www.digital supply chain, podcast.com. I'll leave a link in the show notes. Click on the support button and for the price of a cup of coffee, you can support the podcast. There's a monthly subscription there that you can sign up to. Absolutely no obligation. You do not have to do it. I would love it. If you did. It would be a nice gesture. And I would like to initially thank Lorcan Sheehan from PerformanSC who was the first person to step up and support this podcast. If you'd like to do it, as I say. Please do, please go to the website of the podcast. Click the support link. And. That will help me to create more podcasts like this, to help you and everyone else who listens to the podcast, keep on creating more podcasts. And with that. Back to the podcast. I have my special guest on the show today, Mahesh. Mahesh would you like to introduce yourself?

Mahesh Veerina: 2:17

Thank you, Tom. Yeah. Again, my name is Mahesh Veerina CEO of Parkour,SC. I'm based here in Silicon Valley for the last 30 plus years and um, engineer turned entrepreneur, a low transforming businesses using new technologies that are coming on the landscape. So we'll talk a bit more about that, how we can transform supply chains here.

Tom Raftery: 2:41

Okay. And for people who might be unaware, what's ParkourSC?

Mahesh Veerina: 2:46

We are ParkourSC Parkour is a French word means journey, moving something from a place to place. And so we are on the business of supply chain management and moving products. And how do you kinda help companies reduce variances to their plan and deliver better outcomes to the business.

Tom Raftery: 3:07

Okay. And how do you do that?

Mahesh Veerina: 3:09

What, what we built Tom is a cloud-based technology, a platform. Think of it like a four layer cake . At the bottom of the the bottom most layer is you have all these signals coming from across. Supply chains are usually a large network, and there are these signals coming from everywhere, right? That's your layer one, collecting those signals. Layer two is something we call a digital twin. How do you model your physical network as a digital model in the cloud? And layer three is intelligence orchestration. Right. Embedding some intelligence at every node in the network. As these events happen, what should you be doing and giving that decision intelligence. And layer four is like a, a single pane of glass to present to the management team to help in the decision making and reduce this variance from your plan to what's happening in the real world. So that's the stack we build.

Tom Raftery: 4:08

okay. And. You say you want to reduce the variance? Are we seeing a lot of variance?

Mahesh Veerina: 4:15

Yes, absolutely right and what the pandemic has started. Just to step back, Tom, if you look at. Before I comment on the variances and all the way, in a simplistic way in my head, I characterize supply chains like two faces of a coin. One face is you have all the planning for your business, your strategic plan, annual plan, quality plan. Once you have the plan, typical modus operandi, supply chains built over the last 20 years, you toss it over to the other side. The other face of the coin is all about execution. And execution is where things go wrong, because once you put it into play, either products are moving through trucks and planes and ships and so on, and factories, and so much of touch involved. All kinds of daily disruptions are happening, right? Whether port is in a strike or trucks are not showing up, or a product condition is going bad when these disruptions are happening, or your capacity in a plant is there's a lever problem, you can't meet the capacity plan. So how do you react to these, right? That's where the plan versus what's happening in the real world. There is a variance that's happening, and the whole challenge is how do you bridge reduce this gap or variance to the plan and then yet meet your goals and all that, right? So that's the challenge. So that's the variance problem. Now, you might ask, ok, why is this variance? We can't control all the external circumstances, but internally, the way, the second face of the coin, as I call it, the execution was built over the years in many, many, many silos. You have warehouse management system. You have a factory management like MES system. You have transportation management systems, third party logistics. So by nature it's like a multi-party enterprise and many, many people and organizations involved. And the data is all stratified in many silos and hence it's very hard to correlate all this and make decisions in real time. So that's that's the problem of variants, a gap and. And the what's happening on the supply chains today?

Tom Raftery: 6:29

Okay. And we're seeing as well, obviously, that supply chains are consisting not just of standalone organizations with their supplies, but also you have partner organizations as well, and is working with partner organizations also contributing to some of the complexity.

Mahesh Veerina: 6:46

Absolutely. You look at both the upstream and downstream. If you don't exactly know what your supplier situation is, are you going to receive these parts or not? There is a huge implication to your production side of things. Same thing on the downstream when you're distributing product and there are issues, right? Can, you deliver on time in full to your customers, these are types of issues. And then you have a lot of third party you take a industry like pharma industry, they outsource a lot of contract research, contract drug development. They have third party logistics suppliers, trucks, and planes. How do you coordinate a common message across all these partners, right, at any given time when there is a disruption and how do you manage that? So yes, that's one of the big parts of the evolution or revolution we're seeing. And how do you kinda manage this communication across all the partners see one single source of truth, right? So you can all act in unison.

Tom Raftery: 7:45

Okay. And how do you do that? How do you enable that kind of visibility?

Mahesh Veerina: 7:51

Yeah, no, it's the, the way we are thinking about it first of all, to bridge the gap between this planning and what's happened in execution, it's no longer two different worlds. It's one continuous planning has to happen. Even though you have your big annual strategic plan, you need to continuously realign the plan to what's happening with the ground truth, and then feedback those signals into your annual plans. Right? That's number one. For that, it begins with this visibility. Visibility is only table stakes in my view. That's just the beginning, you know, visibility now, one of the predictions we are making is, number one, you need to go to this, what we call continuous planning, or there's number of different words industries using, is using integrated tactical planning. You have your strategic plan, but you need to now do a daily, hourly, weekly integrated tactical plan. Gartner calls it respond planning. When something happens, how do you respond? Second one is the topic you touched on my prediction that visibility is not enough. It's going to move to actionability, right? You need to be able to take a action and this collaboration that you touched on across all the parties so you can actually resolve that issue and a discussion happens in a transportation or something. Better yet, not just after it happened, can you predict something before it happens? That's the other thing. Right? So issues like that. These are the trends. Visibility is very clearly moving to more actionability and collaboration. Tom, right?

Tom Raftery: 9:33

Okay. And what about the likes of artificial intelligence? Because it's getting a huge amount of headlines in the tech press, at least in the last, in the last few days, just with the emergence of chat G P T. I'm dating this podcast now. It's early December. Probably won't get to publish this until January, but still chat G p t has upended what a lot of people's expectations for AI were. So I assume you haven't got the same level of AI built in yet, but it has to be playing a significant part in what you're doing as well, right?

Mahesh Veerina: 10:04

Absolutely. Absolutely. That's one of my third predictions on what's happening in the supply chain, where it'll go, what we call that is that decision intelligence are using AI ML. I mean AI has come a long ways and amazing stuff is happening but people are not ready, especially in supply chains, to hand the decision off to produce more product to AI. But AI will start assisting in that, what I call decision intelligence. So what happens is, so automating the workflows, for example, based on continuously changing demand signals out there, what kinda lot sizes should you manufacture in your factory? What kinda allocation strategy should you do? Today it's a lot of spreadsheets and running around and recomputing that scenarios, right? So where it'll help is these scenario plans and very quickly run some models through AI based on the past patterns, and then tell you, hey, here are likely outcomes you can see here is likely optimal outcome, but you, Mr. Planner make a decision or something. Right. So that's where we are seeing AI ML getting pushed to and many practical uses. Then there are other use cases I can talk about. For example, in pharma, when you ship a lifesaving medicine, let's say, right? And you have these thermal containers, They're all thermally, insulated. There's a certain charge, thermal charge or capacity they call, they'll put into this box and it has a 72 hour journey going from wherever, California to Frankfurt, let's say. Now along the way it's sitting at airport and you are continuously measuring the thermal charge. By using and predictively telling, Hey, based on several signals, this package might not make it to its destination intact means there is a patient outcome at risk. So these are tasks A I M L is quite suited to. We are call, we call that like a excursion management. Thermal capacity excursion management. So, tools like that are now getting lighted up. We'll see them in real world next year. Again, similar when pharma companies ship products across thousands of routes and lanes in the world, you can start predicting based on seasonality and timing and airport delays and so on, which routes and lanes are better suited for a particular type of package for a particular drug during a particular season. Again, you can use AI ML with a lot of accumulated data on these routes and lanes and be able to predict this, right? So these are kinds of applications. Definitely AI is making its way and that's one of my calls that this decision intelligence will be assisted and more automated workflows will happen using AI ML going forward.

Tom Raftery: 12:58

And you mentioned digital twins as well earlier on, in, what's the big use you're finding for digital twins?

Mahesh Veerina: 13:05

Fantastic. Yeah, I, I, I get excited when I talk about digital twins. Again we saw this whole notion of how do you leverage, uh, Typically technologies get built in some other part of industry and then get grafted into other parts of the industry. So digital twins are not native to supply chain They're used heavily in manufacturing, modeling a car or something, right? Or airplanes and so on. And it's a very interesting structure taking something that's a physical network and nodes and connectivity and modeling that into the cloud, right? So that's how it began. And then the idea struck us three years ago, why not leverage this to model a supply chain? Supply chain if you look at it, it's like, you have suppliers, thousands of suppliers. They're all like a node in a network. Think of it like a graph network. You have your factories, your distribution centers, your trucks. All the moving parts can be modeled as little node in the network. And then all the relationships between them, right? And you can start any small part. So that's what is happening now. These digital models are being built in the cloud. And now you can embed, Hey, this factory has so much capacity, it has so much lever, it produces these four drugs, whatnot. Were all of the, what we call static data. And then there is all this dynamic data flowing from the factory floor or trucks or whatever into that, right? So I, I'm just giving you what a digital twin is. Once you model this twin, now you can start playing with it. It's like a little live model of your physical world. First of all, you can see what I call this network visualization right there in front of you. Before this was data buried in some tables, in a , in the computer, in a database. You can't really visualize and people are visual by nature and allowed to see it all. Hey, here is all my moving parts. And add to that overlay, all this dynamic here, truck is here, it's moving closer or it's not coming in. It's some roadblock happened and so you can bring in weather data, traffic data, all kinds data. So that's like a very simple out of the box visualization is step one, but it goes way beyond that. Now you can start adding what I call this intelligence orchestration at any node in the network, right? Which is as simple as if the ML's business rule you can write a complex model there AI model, right? Or some sort of simulation, and now you can start doing scenario plans. Saying, Hey, what if this link, this link are broken and this factory is not available? Tell me what will happen to my delivery schedule. Right? Can I still meet the demand? Or what if there is onslaught of demand? Run it down and say, what are my capacity issues? So without going anywhere, you are not doing all kinds of scenario plans dynamically, and then get ready for. this plays into this whole resiliency that lot of the executives are talking about. How do I make my, in spite disruptions make my network more resilient? So that's where digital twins will play in Tom. It'll start with visualization. It'll go into all this scenario plans. Then there is very interesting we didn't anticipate this, but we found this very interesting application. First we thought this is just for the companies to use it internally as a li single pane of glass, a dashboard for them, right? Then they turned around and started asking, Hey, I have these 800 customers and I have all these orders from them. I need to supply dates and transparency and e s g kinda issues. How do I give them a, a mini digital portal to my customer so they have a window into what they're doing with us?

Tom Raftery: 17:02

Okay. Mm.

Mahesh Veerina: 17:03

Suddenly digital twins became the answer again. Now we are lighting up this little mini digital twin for each of those 800 customers, which is a window into this company's operations and they share what they want to share. Here is your order. It's 50% done, it's scheduled for delivery, it's gonna get there. So all of that, they're calling it customer digital journey and leveraging these digital twins to deliver as a thread to their customers now. So many, many use cases, and I'm sure there'll be more that we didn't even imagine yet. And this technology, as you can see, even all the majors like Googles and Microsofts are touting now. And it'll go mainstream in the next couple of years.

Tom Raftery: 17:48

Right, right, right. And what about sustainability because that seems to have been a big trend the last couple of years that seems to be rising in terms of demand. Is that something that you're seeing as well?

Mahesh Veerina: 18:02

It's top of mind, Tom. I mean, certainly in the last year and a half we see that as one of the big topics being discussed. Especially e every industry and especially in the pharma industry cuz they consume a lot of packaging. And there's this thermal insulation, which is not biodegradable. So there's a lot of push towards. Uh, today these packages, you have a cardboard box, some sort of thermal material ships, and then goes to that dump. And so there's a big push from the top in the brands, the big brands saying how do we do reusability? Take this box and do 10 trips or 20 trips before you say, so the recondition this, and then use it to ship another medicine or whatever. Right? So there is a big push on overall ESG from varieties of actors. And they want, they're asking their upstream suppliers and so on, giving them metrics on how they're doing and so on. Right. So, and then the other part is the wastage in the pharma industry. It's a well known fact like 35. If you look at pharma industry, it's a 1.2 trillion industry, roughly. 500 billion of that is cold chain and growing very rapidly because of all this personalized medicine, cell therapies, gene therapies, right. Means they're all shipped with more and more cold chain means more packaging and more thermal. It's a, it's a very costly endeavor too. And when something gets thrown away, huge waste. So 35 billion of waste in vaccines and so on. So they're trying to reduce all that waste and this packaging is one big low hanging fruit, I would say. So these are kinds of things. The other third factor I've seen is, Lot of medicines get shipped, but they call it IQE risk the quality of this and expiration date, and if it's not within a window, in a certain market, it's not sold. Even though it's maybe technically good to use, it gets thrown away. Right. And again, huge amount of waste there. So a lot of scrutiny and the focus on that. How do you optimize conditionss in these cycles, yeah?

Tom Raftery: 20:49

Okay. And control towers, I mean, they're the, the big new fancy thing at the moment. But are they, are they gonna stay there or, or are we, are we heading for something even better?

Mahesh Veerina: 21:00

No, I think that's one of our calls to control towers have been around and control towers are more designed. For a functional silo, you take your transportation and build a control tower for that using the data, and you're able to manage stuff that they work beautifully there. But as I said, once you move into execution, there are many silos. There is a warehouse management, there is transportation management. You have CRM, you have MES, so companies went around building control towers for each function. That doesn't work or scale very well in management station making. So I think the new method and where they're trying to go is yes. When you're a large organization, control towers are fine, but there's a overarching spanning across all these silos, something called the command center is kind of the new version that the market is thinking about. How do you build these command centers where you're taking signals from all these silos, be able to kinda make decisions quickly, use AI ML and other algorithms optimization simulation. So on. So you start again. The, the, the whole story there is how do you better allocate your resources? How do you meet your customer demand? How do you how do you not either react or predict and better react to any disruptions that are happening, so on. Right? So that's where these command centers are the new way industry is looking at Tom, moving to, and these command centers are nothing. But what I said that integrated tactical planning right means you are you. It's not like you did one grand plan and sit there and keep reacting to stuff. You are continuously looking at the plan and realigning with your master plan. How much of a gap or variance, how do I bridge to, because that determines their profitability and so on, right? So that's the other direction industry is clearly headed in the next 18, 24 months.

Tom Raftery: 23:03

Okay, and we're coming towards the end of 2022. Now we're heading into the start of 2023. Obviously, this episode will likely be published in early 2023. What do you think is gonna happen 2023, 2024, 2025 to the likes of supply chains? What do you see coming down the line?

Mahesh Veerina: 23:23

Well it's a summary of all these things we are talking, Tom, so clearly there's no question supply chains are in. If you look at from a technology perspective they've built over a couple of decades ago, and they've become systems of record, systems are transaction for companies. You look at ERPs, they're more like a financial tool and the CFOs drive addition a company to do that. Then you turn around there are all these functional silo systems that got built over the years. And Day in a Life, McKinsey published a study for supply chain professional is spreadsheets, phone calls and text messages, and we are trying to put fires away. More than 60% of right as a McKinsey study. And it's still that way unfortunately. So what I see is we are seeing this trend towards all the different technologies we talked about. We're on very interesting times. As one of my good advisors, John Chambers calls this market transitions and supply chain is in one of those clear market transitions. Pandemic just accelerated, very much became a C-suite board level topic for most companies. How do we become agile? How do we become resilient? Make supply chain part of our overall strategy? So towards that I always like to think of I'm a technologist at heart, what are those three or four vectors that are driving this towards a very interesting future? I think the cloud was the first one that came along. All this edge data, which supply chains really now leveraged very well. And the third is all the AI ML and technologies like digital twin. With all of that, I think we're gonna see some very interesting tools and agility. And some of them are brave enough to start calling a SNOPs, as we know. Is that end of SNOP are we moving into a new era? Right? When you use a Google Maps, think of the world before Google Maps. You had a atlas. You open it up, chart, your course. I'm going to Columbus, Ohio, and boom, you keep going. Today, you jump in a car. And Google Maps is telling you, turn left, turn right. Hey, there is a traffic problem or an accident, and take this route. Right? Same thing. I think there is that convergence. We'll see between planning and tech will be leveraged and we are on a irreversible path now, Tom. Many, many companies, even big companies are pushing tools into this direction But supply chains are hard to change. It's a slow, long process because there, it's a , it's a mid-air problem. There is a plane flying. You can't just go take engines out. So it's a, it's a interesting times we're in

Tom Raftery: 26:08

In, in terms of the sustainability journey, you're talking about Mahesh, do you think that this is a flash in the pan, or do you think that this interest in sustainability is going to be sustainable?

Mahesh Veerina: 26:22

Great question. I think people genuinely, companies genuinely believe it. But it's not an easy one to implement, and it's it's, it's going to be uh, how do you connect all the dots to really arrive at, and there'll be a lot of piecemeal efforts, I think, which will themselves yield a lot of results. Like we talked, just the packaging. I, we work with some of the. World's largest packaging companies right now. There is a genuine effort from the top on how do I reduce number of trips made by this box or number of boxes going to the dump that itself will start making a dent in the equation, I think. But that being said, oh, are you going to be all this carbon credits? And there's a. Geopolitical stuff in world two, will that all come together? I think it's a long journey. Slow and long journey, but, but people in all the executives, all the big brands are very conscious because the consumers are asking for it and driving that. Tom, it's on the right path, but it'll take just like supply chain transformation. It'll take a decade or longer, but it's slowly moving in the right path.

Tom Raftery: 27:26

indeed. Indeed. We're coming towards the end of the podcast now, Mahesh, is there any question that I haven't asked 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?

Mahesh Veerina: 27:38

No, I think we covered good ground here, Tom. And uh, again, one thing I'll add is this is not particular to a pharma industry or any one industry. Some of these industries are first movers by need, and they're doing it because a large big stakes there. But it's pervading all industries, whether it's CPG or retail or food and beverage, which is also heavily regulated and all kinds of FISMA standards. And you have to be complaint for that, right? So it's going to be a revolution that will sweep across supply chains in many industries in the next coming and then even automotive. Automotive has a very rigid structure inside that's all set up. But because of this revolution in ev, electric vehicles and all that, the entire supply chain is different now. Right? How do you recycle these lithium batteries after they're used up And again, e s g concerns. So this will pervade, this kinda revolution in technology that's changing the landscape will go everywhere.

Tom Raftery: 28:37

Cool. Cool. Okay, great. Mahesh, if people would like to know more about yourself or ParkourSC or any of the things we discussed in the podcast today, where would you have me direct them?

Mahesh Veerina: 28:49

Just go to our website, www.parkoursc.com or look us up on LinkedIn. We're quite active there and yeah. Thank you. Okay. Superp. That's been great. Thanks a million Mahesh for coming on the podcast today. Thank you, Tom. Thanks for the opportunity.

Tom Raftery: 29:05

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.