EP42 | Navigating the Fourth Inflection Point in Supply Chain Management ft. LatentView Analytics

Speaker A: Are the core pillars of digital supply chain transformation relevant in all industries?

Speaker B: Yes, they are. Thumbs up.

Speaker A: Will AI and Genai completely replace human decision making in supply chain management within the next decade?

Speaker B: No, they won't.

Speaker A: Is balancing innovation with risk the biggest challenge for supply chain leaders today?

Speaker B: Hell yes.

Speaker A: If every company was to implement a mandatory dance break policy into all supply chain transformation meetings, do you feel this would boost team collaboration and creativity?

Speaker B: I'd say double, um, thumbs up for that.

Speaker A: I totally agree as well. I'm gonna make it, uh, a policy.

Speaker C: Welcome to the supply chain tech podcast with Roambe. Scott Meares here, senior marketing manager at Roambe and your host. We thank you for joining us today. In this episode, we speak with Sundar Balakrishnan, director of supply chain analytics at, uh, Latent View Analytics. We explore the challenges of adapting to the fourth inflection point in supply chain management, the era of data proliferation and rapid technological advancements. We then dive into how AI and Genai are, uh, revolutionizing supply chains from solving complex problems to and enabling autonomous decision making across the entire supply chain. Finally, we explore the essential pillars of digital transformation, the role of leadership in driving change, and how emerging technologies like AI agents and synthetic data are shaping the future of supply chains where human expertise and technology are seamlessly blending together.

Speaker A: Welcome, Sundar. It's great to have you on the podcast today.

Speaker B: Thank you so much for having me here. Really excited about the conversation.

Speaker A: Yes, me too. I really am. And it's interesting because when I was building, uh, this episode for today, I was struggling so much to take questions out. We tried to keep this episode to 30 minutes and I think I could keep you for six hours. I honestly do. It's just your expertise and your field right now. It's exploding. And, um, I think it's a combination of excitement, giddy and excitement and also fear. Uh, and I think this is a real episode that is going to, um, really resonate with a lot of people right now. And I'm hoping we can bring a lot of clarity, um, to both the excitement and fear today. But before we do jump into the thick of the topic today, I always like to start with a bit of a fun icebreaker. Uh, um, who for you would you say should be the leader? For leaders, who would you say they should be following to really stay up to date on the fast paced nature of digital, um, supply chain transformation today? Is it the Elon Musk's? The Sam Altmans? The Daniel Stantons? Who would you recommend?

Speaker B: I'll kind of go for this understated legend. Okay, there are a couple of them who I really like following but uh, I'll name both of them and I'll talk about both their journeys. Um, one of them is this gentleman called Sanjeev Siddhu and the other is this lady called Helen Davis. Sanjeev Sidhu is the, was uh, the founder of this company called i2 Technologies which then went on to become JDA Software, which then went on to become Blue Yonder. And then subsequently he founded this company 09. Pretty popular. He lives in, in the US and a lot of modern leaders and a lot of supply chain tech companies kind of oh their or their progress, their learning and where they have matured to supply Sanjeev Siddhu and his visionary leadership. So he's somebody I love following, following on LinkedIn, uh, look at what he's talking about and generally take that opinion uh, quite seriously. The other person, Helen Davis, SVP supply chain at uh, Kraft Heinz and again follow her career trajectory. Done a lot of amazing stuff including the most recent implementation of a generative AI type solution for supply chain insights within craft times. Pretty phenomenal the kind of outcomes they've achieved in their careers. Two people I really enjoy following.

Speaker A: This is great and I love um, you articulating the story to us as well. Um, these are two individuals that straight away at ah, the start of the episode, people can go to and subscribe to their LinkedIns, whatever their newsletters are to make sure they stay up to date in this very fast paced transformation of supply chain right now. So before we uh, even jump into the thick of it, I know we hit an uh, icebreaker question but I even want to hit some definitions right now because we're hearing all these words and they're getting quite overwhelming. AI, okay. It's been around uh, been shouted about for more years now, but now we start hearing gen AI, we now start hearing about AI agents. Um, and I think a lot of people have more of a generalized understanding of what these mean. So could you just, just a short three minutes on what these. Could you just define what these three are? Uh, ah, and maybe just give us a couple of supply chain examples just to really articulate in simple terms. Um, for me and the listeners today,

Speaker B: Gargo, I'll also share a bit of an anecdote I'm doing with my team in my, within my own organization. But AI or artificial intelligence I think is a bit more prevalent, bit more known, but essentially using learning based algorithm algorithms that can automatically learn from data, from the patterns of data to generate outcomes. So classic examples could be something like uh, you know, I want to do a demand forecast or I want to do a demand disaggregation or I want to do a machine failure prediction. Problems like these which can learn from patterns of data and then generate those outcomes for the human to then go and consume out of or for them to integrate to further systems. Is sort of the traditional AI definition Where generative AI has started making it quite interesting is that you now start getting outcomes that are uh, generated out of the core AI engine. What I mean by that is the way you want to consume those insights. If you are able to start chatting with an engine, is able to make sense of the numbers and actually start spitting out contextual insights to you, contextual tests. Or if the outcome is something that you want to generate in the form of a picture that says hey you know what, look at this. And in terms of visibility, draw me the network. And in the context of your own organization, Rome, which does visibility based on that network, tell me where the red green ambers are, uh, in a nice little map of the US or the map of the UK built out. That is a form of generation that can be done with agent AI. It's starting to get quite fascinating, quite interesting if I ask my team to look at it as uh, there was business process management, then it became robotic process automation, then it became intelligent process automation and now it is agentic AI. I've actually asked my team, hey, why don't you map out some of the common supply chain use cases and tell me what's the evolution of those use cases. So one example of an agentic AI use case could be very classic demand forecasting planning situation of a demand planner was generated some forecasts and now is trying to simulate different scenarios. Now instead of simulating those scenarios with the user interface where uh, people are moving, dragging and dropping things or manually editing. What if I chatted with an engine that and said why don't you move price up by 2%? Why don't you add all these external constraints and use your own intelligence based on patterns to come up with the optimal plan and imagine the engine goes and does that in the background. That set of co workers of yours who are understanding the instruction, building out the logic, automatically going and executing the logic and then coming back with the responses. That is the idea of AI agents or agent AI, uh, that is giving you those responses.

Speaker A: Yeah, it makes absolute sense and you were able to break it down in a very clear way because I think so many people are talking about it and it can just get a bit overwhelming. And I mean even if you ask, uh, an AI interface like a chatgpt or a deep seq, um, it can give you a very long winded answer, uh, and then that can just make you more confused. So I thank you for not just giving us the answer, but applying it to the supply chain space as well.

Speaker B: Sure, absolutely.

Speaker A: Just one other definition I'll throw out there. As well as synthetic data we might touch on a little bit in this episode. In short, is fake data that looks and acts real. Um, it's used when real data is maybe missing or private. And it very much just acts as real for testing or for maybe a digital twin as an example. And we'll dive into that a little bit as well.

Speaker B: Mhm.

Speaker A: So I want to understand from your point, how would you define the fourth inflection point in supply chain management? And um, why do you feel this is a game changer for organizations today?

Speaker B: I usually tell the story in some of my presentations, etc. But the way I see the evolution of supply chain and supply chains effectively have been around for I guess about 5,000 years. If you think about the fact that the very first trade that ever happened in human history in the recorded trade is between two merchants who exchanged couple of goods somewhere in Mesopotamia. So it means that two pieces of material travel from somewhere with one human and somewhere else from another human and it exchanged, you know, uh, the barter happened. But if you start looking at a slightly more modern history, about two, 200 years or so of industrial supply chains being around, the very first uh, uh, uh, phase of supply chain was manual. As in humans were doing everything. Humans were picking up cards, boxes, humans were riding horse carts and transporting things around and humans were running different kinds of machines. And so humans brought skills to the table. That was kind of the first point in the, in the whole journey. Then switch to 1920s, Toyota came into the picture and processes become, became the way of running the supply chain. Right. So human skills was the first inflection. Toyota bringing processes in was the second inflection. Then in the, in the 60s, this lesser known company called IBM came into the picture and they revolutionized supply chain tech where tech became the way supply chains were run. Why I feel we are at this kind of fourth inflection point now is think about uh, the Internet era in the 2000s, where data proliferation in organizations means that data and the power of processing and algorithms coming together means that the fourth inflection point is the AI way of Running the supply chain and it is catching on. And that is what I mean by the fourth inflection point. We are right there. We're very much actually in that, in that phase where a lot of supply chain has already started running on AI. But I think there's a long journey still. Uh, and that's the opportunity a lot of us have.

Speaker A: What a wild time to be alive,

Speaker B: truly. Yeah, yeah, absolutely.

Speaker A: Uh, to recognize this fourth inflection point and actually be right at the start of it and having to understand how to implement this into our old systems is just going to be such a ride for, for supply chain everyone around the world. Yeah, it's a blend of excitement and fear. Like I said at the start, uh, absolutely. For this. What, what do you feel straight away become those pressing challenges for companies? Uh, in this fourth inflection point?

Speaker B: I mean the obvious first one in a lot of customer conversations as recent as couple of days back, we were talking to a customer and data availability of the data itself and the quality of the information that is available becomes the, the very first thing that organizations do need to look at. Um, but data, just a whole dump of data by itself is not going to get you anywhere in the AI world. Um, while there are a lot of, what are a lot of these AI innovations that are happening, application of those innovations to real business use cases that will actually give you real value is still a bridge that needs to be crossed. So having that, being able to smell, those smell, where are the opportunity points in your organizations? Being able to ascertain that there are challenges, these are viable challenges, these are feasible, uh, challenges to actually even go solve. And it makes sense that I think becomes a of sense second element, building a good foundation for any AI work. I think there is a non negotiable. You can't go anywhere with AI. It's a lot of garbage in, garbage out if you don't build that foundation. And I think while typically with AI the whole focus is on, oh, what's the most complex algorithm that we can use? For anyone who's been seasoned enough and worked in the space long enough, you'll realize that the algorithm is actually the simplest part. The change management that comes with it, where you bring the human into the loop, you get the man in the machine or the woman and the machine to work together to generate better outcomes than any one of them could individually have done. That I think is, is where magic will happen. Uh, and my personal belief that AI needs to augment human intelligence to be able to generate better outcomes and getting those two groups, the AI outcomes and the humans to actually start working closer together. The change management that goes with it is going to be another pillar that organizations have to genuinely seriously think about in their transformations.

Speaker A: I uh, like that you bring that out because I think people, because people now have their hands on these AI systems so they can now really feel the, you know, the power of them. I think people can be quite enthralled by what's behind them and quite be like must be the most mind blowing algorithm ever. But it's quite interesting. They say don't focus on this. It's actually the focus of how are we going to implement this, how are we going to blend that human and tech together for a even more advanced uh, uh, knowledge or even more advanced process and operations which is interesting and uh, I feel very true in what you're saying, um, to dive into those ones before for companies to really transform their supply chain, what have you found to be the core pillars for successful supply chain transformation? In your experience.

Speaker B: The pillars here, and I'm just trying to clarify, when you think of pillars here, are you thinking of them from supply chain functional areas that you're focusing on or are you thinking of it or thinking of a data technology people process, Is there one or the other that you're looking at over.

Speaker A: Yes, a good question. So I, from a supply chain standpoint, so transforming supply chain and understanding what are those core things that you've found to always work? Whether it's leadership styles, whether it's um, upskilling your employees, um, bringing in new innovative people to run individual new roles though, what are those core things you've always found are uh, integral to transformation in supply chain as we go through this fourth.

Speaker B: Got it, got it. I think the, the classic good old people process, tech, data and customer as kind of the broad framework or the areas to focus on always work the time tested. Um, when I think of people, you mentioned an interesting word there around leadership. Uh, when I think of my team and the kind of skills or the kind of thinking that we're trying to develop within them, um, we are not trying to develop more developers, we're trying to develop more business analysts who are able to look at business problems and then tie it to technology in that technology skills are fairly more pervasive, at least here in India. So I know that the kind of people I need, who need to be forward thinkers are people who are able to work with a client, able to understand where the uh, where the pressure points are and then be able to translate or convert it into data problems which can then be solved. So I, I definitely think there is an element of bringing that the Venn diagram m of bringing the supply chain understanding and the data and math skills and solving at the intersection as something that people skills need to develop on. Um, I think also of course some of the classic things around building empathy with customer problems is always going to be a big thing in terms of helping with uh, transformation roadmaps. I think I've already mentioned change management as an important area and people driving those change management. Uh, from a process and tech perspective. Uh, the one thing I see as an important lever today is uh, a lot of organizations are focusing on integrations and uh, in the sense that you have a bunch of retailers and you have a bunch of consumer product companies, the retailers are data rich, the consumer companies are usually always data hungry. So there is a possibility of an integration but there is also a possibility of an integrator who is able to harmonize and synchronize that information across consumer companies. So in, even in tech there are these integration plays which will go a long way in helping collaboration that I see as a, as a lever for transformation. Um, and of course a lot of that also then ends up driving the back end processes. Um, I think elements around data governance, data security will uh, be important in organizations transformation journeys. Um and finally vision towards a clarity of that vision towards okay, what are we even heading towards and having a little bit of that unwavering uh, uh you know, drive to get to that point that I think will be elements that I would think are key to the, to a transformation engagement

Speaker A: and I'd be interested A follow up question on this is because I know you've worked in a range of industries um, from manufacturing, automotive, cpg, E commerce sectors, many sectors. And the pillars that you've just mentioned there, would you say they transcend all, all industries or do you find that some industries are a bit more bespoke in how you would uh, approach supply chain transformation?

Speaker B: I think these pillars transcend all industries for sure. But what changes from industry to industry is if I had to take each of these pillars and start mapping out a maturity curve, different industries would map at different maturity points. So I worked in the tech industry and E commerce on their supply chain being m kind of digitally native already. Some of these elements are kind of covered for in some ways you almost assume them to be there because they were tech companies who started as digitally native companies. Whereas when you go to some of the more traditional automotive uh Industrial, uh, consumer companies, the, the people maturity, the chain management leadership maturity is high. But some of the tech integration maturity, some of the data maturity can be on the lower side and that's. Those are areas of opportunities for them. So I think as you go from industry to industry, the elements still apply. Where they need to, where their journeys are, is where in the maturity curve are they, what's the ground game?

Speaker A: Interesting. So I think that puts people's minds at rest, uh, you know, to address their custom industry, custom UK in understanding. There is a lot that transcends here and um, that's, that's really interesting.

Speaker B: One other observation I'll add is that in each of these industries, if you see kind of the average age groups that work in these industries, you will see that with some of these industries with people and the average ages of the people who are working in those industries, the kind of educational background that they've come from, some of them have been more embedded in technology right from their, you know, undergrad and graduation, uh, some of them have picked up those skills so kind of later in their career, so adoption, resistance. Those factors also of course eventually come into the picture. But having said that, I've seen leaders who uh, work as, I mean they're very innovative. Uh, uh, and I think it all comes down to the spirit with which you come into some of these things. If you're thinking of transformation, you have to have that spirit of innovation, a little bit of that risk factor in your beautiful maverick in you for you to attempt, as you said, the fear part. Right, that. Yes, that there has to be a bit of a maverick in you to overcome that fear and uh, you know, go into the transformation irrespective of which industry you are in. Uh, and that I see a good value to have.

Speaker A: M. I like that you're now touching on those leadership qualities, um, of uh, someone who can really drive this uh, new AI wave. And you know, on that fear part, how would you advise organizations to balance that need for innovation, um, that push for innovation, you know, from their competitors, with the risk of investing, you know, maybe even an in unproven technologies, unreally not fully understood, fully realized technologies. That's quite uh, an difficult thing to uh, to approach.

Speaker B: Right, right. I think this is, this is, this question itself could take the next six hours. Uh, right. This is.

Speaker A: See, I said six hours. We need six hours.

Speaker B: I think at the core of this question it's less about technology, more about culture and DNA, organizational DNA. Um, I've seen organizations which, which have that in them to want to experiment. They've set up centers of excellence within their organizations where when you're trying some of these technologies, it's going to be a case of you make something, you kind of break it, then you make it again, then you break it again, you make it again and break it again till the time, till you hit gold somewhere and you have to be at it, you have to kind of, you know, keep trying it. A lot of organizations set up R D centers, center of excellences as we call it. Uh, I'm also seeing organizations that are setting up capability centers, uh, you know, in different offshore centers if you will, where part of a significant part of the offshore center is to take up some of the more regular uh, work. So, you know, keep the lights on work. But a segment of that is see if we can push the envelope on um, experimenting with a new technology. I don't think any of these organizations, and I'm, I'm, when I say organizations here, I'm referring to client organizations from our perspective, which is consumer product organizations, retail organizations, industrial automotive technology organizations as uh, well as financial services. Let's say they're not necessarily interested in creating the next large language model. That's not really the core of their business. They're never going to even think of trying that. But what they're always interested in experimenting is, okay, given that this new revolutionary technology is coming out, where in our business is their application and where can I go and experiment? Um, the, the kind of the safer bet organizations are taking is hey, let's go with a partner who is doing this. Uh, I know it's new technology. The number of instances of having done this may not be as many. It's still experimental. But we'll onboard a partner who'll come work with us for a six month year period, experiment with some of this. They do the make and the break and the make and the break and the make and the break till we hit a few use cases which will be those value use cases. Once we do, then we can see if we can onboard that technology, integrate it into our world and then run it ongoing. There is still funding that is required for this. There is still effort that is required. A couple of my clients I know who have set up lab environments, uh, and one of this is public so I can still talk about it. So Unilever has set up this data labs ecosystem and Latent View is a part of the data labs ecosystem. But there are a whole bunch of data experimentations that they look to do. This is Public information, anybody here wants to can go and check it out. It's quite interesting that they've, they've looked at it that way and they are ready to put those dollars in to try and see if things break and make again. And uh, with the idea that you can hit goal. I'll leave the last thing I'll say in this is some, sometimes I said DNA at the start of it. Right. Sometimes it's interesting to wonder if are you a consumer product company, an automotive company, a retailer, or are you a data company who happens to do this, these businesses? So that's the sort of the DNA question a lot of organizations have to ask themselves being so data rich. You can be that innovative company who happens to sell consumer products or uh, the innovative data company who happens to be a retailer rather than a retailer who happens to have a lot of data. So it's a DNA question for a lot of organizations to think about.

Speaker A: I'm very impressed. You've condensed a six hour answer into six minutes. Very impressive. Yes. And a lot of actionable things as well. You know, having uh, an innovation hub in the company that drives and tests these uh, technologies that maybe they don't want to apply to their full, uh, their full operations, having a partners on the front of that to test and run this as well and just driving the overall culture of innovation. I think this is uh, some really practical things that companies can be doing um, in this space so they can stay ahead and also do it, stay within that level of risk where they're comfortable uh, within the space. I feel we're diving into a lot of interesting pieces here and I might just touch on one more final point for the transformation piece because I know this is the area where companies are really trying to figure it out. And you know you mentioned culture uh, quite a few times and you mentioned you know, really bringing the company together. You even mentioned about the age demographic have an impact on, on the innovation, which is an interesting one. Uh, how do you feel leaders can really foster that collaboration between the departments to really ensure a successful digital transformation in supply chain management.

Speaker B: There are again so many layers to this but uh, if I, if I could kind of try and simplify this, right. I think uh, by, by nature we as humans we, we want to communicate, we want to collaborate but when it comes to the professional world we kind of do that, there is incentive to do so. So if a work outcome, um, I need to generate and a work from, you need to generate are going to contribute a, to a greater good for an organization, but also for our own development and greater good, whatever those motivations might be, that's when we will sit down to collaborate. And this is true in any organizations across all levels. This is very when collaboration happens. Um, but one layer I'll add on top of that, which I find quite fascinating in most organizations is when it comes to evolving our knowledge and sort of improving self, improving ourselves in terms of our skills, our ah knowledge adept. I think humans are generally more open to collaboration. So if leaders want to think of say two pillars or two levers one I find knowledge based collaboration generally works in that if you want two departments to collaborate is there common knowledge that you can tap into is there. If you're giving them an exercise to do, let's say it's a collaborative planning exercise and out of that exercise both those groups can walk away more enriched in their understanding of the business and understanding of their own roles in how that collaboration can give them a dollar more than what they're generating right now and therefore take a percent of it back home as their own incentive. There is definite uh, there is more of an intent from people to then want to collaborate. Then of course I said knowledge as the first bit incentive for collaboration as a second and then ease of collaboration. So you, you try and build technology that helps you collaborate. Uh, and that's where I think generative AI as a plugin over here can actually help in that we're seeing solutions and some of which we ourselves ah have also built where instead of building a tool that sits somewhere as a chat engine and you go and log in and then ask ah questions can you build those tools into a Microsoft Teams into a Google chat where I can open a chat engine, I can build it as part of my team, I can ask questions and I can start deriving insights straight out of there. The whole team can look at it, comment on it, ask more questions out of it. So your AI is truly becoming your copilot, your co runner is working with you and fostering that collaboration. That's the best way in which tech can foster collaboration. I think if these three pillars are kind of getting taken care of my, my sense is that organizations will start seeing a lot more interdepartmental collaboration, interpersonal collaborations way more harder than you know, it's a lot easier said than it is done of course but as leaders you don't have a choice. You have to keep trying things. And these are three things I think you can try fairly easily.

Speaker A: No, you're right, we're going into Some uncertain times. But you know, those leaders will step up and they will drive that um, even with the uncertainty. And, and I think it's um, you know, an exciting change for, for a lot, a lot of opportunities out there for the companies out there. And, and I feel we've, we've dived now into, you know, how to make that transformation and some core pillars and mindset and skills that will drive this as an organization and as a leader, uh, so listeners can really grasp the, the real power of, of AI. I just want to hit you with this one question to um, really. And again we could go into a whole different episode on this so maybe we have a round two. Um, but let me just touch on it so people can really grasp why we're spending so much time on understanding that transformation pieces. When you look at advanced analytics combined with AI and you know, even maybe throw synthetic data in there, uh, do you, when we look at that, how is that going to impact, uh, actionable insights for supply chain decisions and will it, you know, it feels like it's going to get to the point where it'll be driving all the supply chain decisions for companies around the real world and overcoming these problems, these disruptions that maybe we're facing today that we feel are uh, continuous and impossible. But AI is going to come along uh, and understand that a much faster pace than us. What's your feelings on this vision? Do you feel that's a realistic vision, uh, of the future?

Speaker B: When I think of. I'm just, I'm just trying to recall a lot of implementations with customers where should typically look at a roadmap that they follow. They want to test the waters, they want to run a. I don't like to call it a proof of concept, I like to call it a proof of value in that you take a small bit of an experiment, you, you run your data plus algorithm plus uh, insights for a chunk of a business, a really small measurable chunk of a business and then see if you're actually deriving anything out of it directionally, at least uh, the key value out of that is one to see that whatever algorithm you're thinking of, it even makes sense. It can apply the feasibility of it. But also visually when you're trying to map out your network or you're trying to map out your process or whatever, you at least get a visual view of where are your strengths and weaknesses. You then try to move into an mvp, a minimum viable product for your business which has all the integrations, all slightly more sophisticated stuff. It's refreshing more often so that it looks, feels runs like a product your organization is actually using. And then you look to baseline it and scale. That's kind of the roadmap. I see the role of synthetic data along with the algorithms. I see in the very first phase if you're trying to do a proof of concept, proof of value, where you want to just test the waters to see if I put in some dummy data. But it, the structure of the data is very similar to the business I actually run. Uh, to see what would the algorithm even spit out. What kind of a visual would I even get? Would it make sense if I showed this to five business users? What's the feedback I might get to answer these questions? You'd still be able to use it, but then once you're moving into the MVP phase, uh, the minimum viable product, I would think that data has to be real at that time. The algorithms will have to be rethought, retrained, recalibrated for the real data as against synthetic data. Uh, I'll share this example. We were trying to do a predictive maintenance use case, uh, machine failure, breakdown prediction. And then when do you need to run maintenances? We picked up some data set which was synthetic. It was so clean that our accuracy of our models was 99 point something percent. We said impossible. Models cannot be that accurate. The moment a model is more than 90% accurate, I start getting very nervous. I'm like there's something off with this particular one. It's too good to be true. So realized is that with that we could figure out the framework of the problem. We could figure out what kind of visuals we could develop with it so that users can consume something out of it. But the core of the data integration and the AI model itself, I wouldn't trust that if it was built on a synthetic, purely synthetic data. That's my take.

Speaker A: Interesting, interesting. And when you broke that down, that 99%, was it 99% accurate when you broke it down or did you find some faults in there?

Speaker B: It was because the variability in the data, uh, that synthetic data was so low that naturally the model was able to read all the patterns and predict everything correctly. Real data is this, it's never really this. So it is impossible. The thing is deployed it, that model would have failed very badly in production because m, it's never understood the patterns in the real data.

Speaker A: Interesting. So I feel that uh, is a great end questioner to really, you know, really gives us the insight into what this future could look like and you know, things are evolving every single day. And no doubt if we had this conversation in a month's time, you'd have even more inputs and more things you would. Um, but I want to finish off with a fun thumbs up or thumbs down segment that we like to finish the episode. All I need you to do is give me a big thumbs up or a thumbs down and if you could vocally say thumbs up or thumbs down for our listeners as well, that'd be wonderful. Wonderful. And by the way, again, like I mentioned, we go on for six hours. We usually only ask six questions here. I've got eight because again, I just couldn't take out these questions. They were just too, too many I wanted to ask. So let me see, uh, your views on these. So, number one, are the core pillars of digital supply chain transformation relevant in all industries?

Speaker B: Yes, they are. Thumbs up.

Speaker A: Will AI and Geni Gen AI completely replace human decision making in supply chain management within the next decade?

Speaker B: No, they won't. Thumbs down.

Speaker A: Does organizational inertia pose the greatest barrier to supply chain transformation?

Speaker B: Absolutely, yes. Thumbs up.

Speaker A: Is balancing innovation with risk the biggest challenge for supply chain leaders today?

Speaker B: Hell yes. Thumbs up.

Speaker A: Yeah, that's a big yes. There uh, are uh, advanced analytics and AI agents enough to be to future proof supply chains against disruptions?

Speaker B: Not a chance. No.

Speaker A: Interesting.

Speaker B: Not many more things required than that.

Speaker A: So. And I know there'll be people out there that would say the opposite. So this is why it's always interesting to throw these questions out there.

Speaker B: Only supply chain could be solved with some crystal balls. We would have solved for supply chain the whole, whole, you know, uh, while ago. Uh, never works like that.

Speaker A: Interesting. Okay, and do you feel it's possible for a company to fully realize the benefits of digital supply chain transformation without investing in employee training and upskilling programs?

Speaker B: Not a chance. No thumbs up.

Speaker A: Not a, ah, chance. And a last one is if every company, this is important. If every company was to implement a mandatory dance break policy into all supply chain transformation meetings, do you feel this would boost team collaboration and creativity?

Speaker B: I'd say double thumbs up for that.

Speaker A: Double thumbs. Um, up. I totally agree as well. I'm gonna make it a policy and so should you. And um, wonderful. I thank you Cinder, so much for, uh, coming onto the podcast. Uh, please let note, let the Watchers listeners know where they can find you. If there's any projects you want to let them uh, know about, please just let them know.

Speaker B: Absolutely. Thank you so much again for this opportunity Spark. Because of uh, the fun conversation. Uh, you can find me on LinkedIn. You can look up Sundar Balakrishnan on LinkedIn. I represent this organization, Latent View Analytics. It's a public listed data analytics company. We eat, breathe, sleep data and that's all we do. Based in India and serving clients globally. Uh, always interested in good conversations in supply chain that can help solve important problems with the use of data, tech, AI and just good logical thinking. So always happy to hear from uh, people. You can check out uh, www.latentview.com and if you see a word connected, view as one of the solutions. Uh, I'm one of them along with my team who builds that. Uh, do come check what we have there and uh, always happy for a chat.

Speaker A: Wonderful. And I'll make sure I tag you in all the posts and everything so everyone can get in contact with you, no problem. So thank you again. Sunday, uh, if we just together give the listeners a little wave goodbye and say thank you very much.

Speaker B: Thank you so much. Have a good one. Bye. Bye.

Speaker A: Uh, thanks for joining us this time.

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Speaker A: Supply Chain Tech podcast with Roamy. If you'd like to support us and

Speaker C: invest in yourself while you're at it, visit roambe.com you'll find blogs, ebooks, case

Speaker A: studies, webinar discussions, digital solutions and a bunch of other helpful resources about supply

Speaker C: chain visibility and the related technologies.

Speaker A: Thanks again for listening.

Speaker C: I'll see you next time.