Ganesh Gandhieswaran: 0:03

I remember you know, 10 years back we were worried about, Oh, the data warehouse is growing to five terabytes. Now, like five terabytes is nothing. Then it's very, very basic, right? Cloud data storage cost coming down. And so emergence of all these sensor data, a lot of startups out there to capture all the sensor data. That's a foundation for us to do anything.

Tom Raftery: 0:31

Good morning, good afternoon, or good evening, wherever you are in the world. This is the Sustainable Supply Chain Podcast, the number one podcast focusing on sustainability and supply chains, and I'm your host, Tom Raftery. Hi everyone. And welcome to episode 42 of the sustainable supply chain podcast. I'm Tom Raftery. And I'm excited to share the latest in sustainable supply chains with you. A big, thanks to our amazing supporters. You're the reason we're here each week. And I truly appreciate every single one of you. If you'd like to join this community and help keep the podcast going. It's easy. Support starts at just three euros or dollars each month. Less than the cost of a cup of coffee. And you can find the support link in the show notes. or at tinyurl.com slash S S C pod. Today, I'm thrilled to speak with Ganesh. And in the coming weeks, I'll be chatting with Thom Campbell from Capacity, LLC. Kenny McGee from Component Sense. Jon Goriup from vcg.ai and Aaron Lober from CADDi. But back to you today's show. And as I said with me on the show today, I have my special guest Ganesh. Ganesh welcome to the podcast. Would you like to introduce yourself?

Ganesh Gandhieswaran: 1:47

Yeah, thank you, Tom, for inviting me to the Sustainable Podcast. Looking forward to learn and share. Myself, Ganesh. I'm one of the co founder and CEO of ConversSight. ConversSight is a US based data analytics and generative AI company. We've been in this business for last seven years and delivering data analytics, primarily for supply chain companies like across, US and some Southeast and Asia countries. I have spent my 25 years in the supply chain manufacturing space looking forward to be part of this.

Tom Raftery: 2:25

Great. And tell me who are your typical customers and what kind of problems are you solving for them?

Ganesh Gandhieswaran: 2:34

Yeah. So, given, as I mentioned, I'll give you a little bit background and settle, give a context. So I spend my first 15, 16 years in delivering data analytics for large companies. Part of my prior job, we used to work only like 3 billion, and the above, so all the fortune 500 companies type. I was leading data analytics for manufacturing supply chain, like day in, day out, like automotives industry. What I have seen is that people are, it's so easy to collect data, capture data in multiple systems. You call it ERP, CRMs, and all the IOTs now sensor data, but unless you start using it when the business really use that to make a decision. It is not really meaningful. So, that's when I founded this company ConverSight. So how we can deliver that insights more easily. No, I like to have all my insights delivered to me. But as a startup, I can't have an army of people like a big company has. So I want to hire an AI. That AI can, collect the data and analyze and tell me Ganesh, this is what happening in your business. And that's what I'm really looking for. So that we are doing that, that delivering those insights for supply chain leaders, supply chain visibility, inventory optimization, like sustainability metrics supplier performance, for example, so end to end visibility and improvement towards cashflow as well as the delivery.

Tom Raftery: 4:14

Okay, great. And I'm glad you mentioned sustainability, given that this is the Sustainable Supply Chain podcast.

Ganesh Gandhieswaran: 4:21

Yeah.

Tom Raftery: 4:22

With sustainability becoming a key focus in supply chains, how do you see data driven approaches evolving? And could you share some examples maybe of how companies are using analytics to track and improve sustainability metrics like carbon emissions or resource usage or things like that?

Ganesh Gandhieswaran: 4:42

Oh yeah. I think I would say like the awareness on the sustainability metrics has tremendously increase in the last couple of years. Even though we all have, companies all have a great intention and all are aware of the global warming and all the challenges and obviously working with automotives, they not only have to worry about in their factory and when they are making, even after when the cars are on the road, trucks are on the road and they have to track that and figure it out. So the problem is very big. The awareness has come and people are realizing that capturing the right data, bringing the visibility is critical. Last couple of years, I have seen. It is becoming a, you know, top down initiative from a CEO on board is committing the CEO is committing and started. Now, I think I see in the last two years, at least two, three years, that there is a bottom up also like thinking at a, even from a water consumption in a, plant level, for example, like there is a somewhat put it a guilt feel like, you know, are we letting this water on as simple as like yesterday I was in my office. And all the lights are automatic, you go and on when I come out, you know, it takes maybe 20, 30 minutes to switch off immediately, like went back and press that button that 15 minutes of that 25 lights,

Tom Raftery: 6:20

Hmm.

Ganesh Gandhieswaran: 6:21

It has become very, very critical. And it now it's everywhere. I think everybody is understanding that it is our resources getting impacted. It's not like it's not just money. Earlier, people used to think more from a dollar perspective on the resource consumption, but now there is a social awareness. I think that's a starting point for everything I believe from here we need to see how we can really monitor and improve.

Tom Raftery: 6:50

Nice. And, obviously, The likes of predictive analytics can be a game changer in mitigating risks. How can companies leverage these tools, not just for operational efficiency, but to enhance the sustainability in their supply chains? What's the future here? Are we moving towards a world where AI can predict and solve sustainability issues before they even occur?

Ganesh Gandhieswaran: 7:16

Oh yeah, absolutely. I think there are the last 10 years, a lot of things happen in the data analytics. One fundamental is cloud because we have cloud, we are able to scale our infrastructure. That means we can capture a lot of data. Unlike our finance data or sales data, there is a limited amount of data you can capture because it's human input, but when it comes to sustainability, all the resources from carbon to water, electricity and all that. We need a lot more data than what we were traditionally capturing. It's not going to just come from transactions. The machines need to speak up and tell that what are they doing, how much are they emitting, right, how much are they consuming. So the machines needs to talk. So that is happening now because of cloud, because of the, you know, call it that ability to capture a lot of data. So all the IOT, the sensor data, telematics in the automotive, we call The ability to capture the data. From each of those machines, be it on the road or be it on the plant, all the machines are capturing the data in my view, maybe we could have captured it 20 years back also, but today we have the ability to scale up that ability to store Even a 45 days of data, every 15 seconds coming from a mission, like it's an unimaginable. It's a petabyte of data you're talking about, right? So that is my view. I think the revolution started from the cloud. Affording like we are able to afford we can't think of, I remember you know, 10 years back. we were worried about, Oh, the data warehouse is growing to five terabytes. Now, like five terabytes is nothing. Very, very basic, right? Cloud data storage cost coming down. And so emergence of all these sensor data, a lot of startups out there to capture all the sensor data. That's a foundation for us to do anything. We call it predictive analytics, preventive maintenance, what if analysis, all that kind. It's just a starting point is that base data and not necessarily just the company need to capture. It's also the suppliers need to capture the ecosystem need to capture. You know, we are working with a trucking manufacturing company. They're great. They have all the facilities, big data cloud and all those, but 80, 90 percent of their raw materials are coming from different, different countries. Those are small suppliers. How are they capturing it? How are they bringing the data? Do they have sensor data? Or do they are they manually entering? They are manually entering? Is it quality? We'll come to that. So once the data is available, now the technology is completely there from predictive maintenance perspective, applying machine learning, data science. There, two things. One is, Data science become more common skill now than 10, 20 years back, we need the really the scientists, like really the researchers, PhDs to do that. Now, I think there are a lot more, somewhat, it may be too many data scientists. So tools are there again, thanks to cloud, what we used to run for, you know, three days. Now it can run in three minutes, like kind of a performance in a predictive analytics. So I think technology is here right now and we can afford And the now and awareness is there. The technology is there. Now we need to just start executing it in a meaningful and in a consistent way.

Tom Raftery: 11:20

Sure. And I mean, the transition to a circular economy that we're starting to see very much on the starting blocks still, but it's, it's often touted as the holy grail of sustainability, particularly with things like the resource shortages that we're starting to see

Ganesh Gandhieswaran: 11:42

I think,

Tom Raftery: 11:43

How important is data integration in making this a reality? You know, can you talk about how bringing together data from different sources helps companies close the loop on their lifecycle management?

Ganesh Gandhieswaran: 11:58

yeah, I'll cover maybe both questions on the circular economy as well. The point is, now, as I mentioned in the prior point, now, companies, say, take a manufacturing company, the manufacturing company, so take an automotive or any industrial manufacturing, they have statutory requirement to report certain metrics to government. So they, they have the obligation, they are legally obligated to deliver that and also socially now. A lot more binding to do that. Now when we start tracking that, now we are in a shared economy. It's not like, you know, a car on this road, maybe assembled here, maybe 50 miles from here in Tennessee or in Ohio, but the parts are coming all the way from the other side of the world. So, like, there are lot, and then it's, then there's a motor and that engine, that engine has a lot of A lot more parts like emissions, fuel pumps to many things that's coming from other suppliers. So I think we are in a shared economy. That means any initiative related to sustainability will also go back all the way, right? So that is an opportunity as well as a challenge in my view. Opportunity in the sense that you are not alone in this. There are multiple people investing. For example, we're working with a tracking company. You're working with a logistics company, they're buying the trucks. we are working with a company which is making the diesel engines. Then we are working with a company. They are, providing the parts, raw material parts to the engine manufacturing company. Think about these four companies. All these four are equally responsible to track the data. Now comes the challenge. So that's an opportunity that you are not alone. Multiple people are putting money, energy, and their intelligence to capture the data. Now comes the challenging part. It's not just me. It's not just my system. I can just integrate it. Now, in the same way, a logistic company needs to really be responsible. They're buying, the trucks from four different leading trucking company. That means all the four need to provide the data in a consistent format. That's a problem. I don't think the technology or the industry is matured yet completely that it is what I'm, even though there are standards,

Tom Raftery: 15:10

hmm.

Ganesh Gandhieswaran: 15:11

it's not completely there like frequency. Some is monitoring 10 seconds. Someone is probably monitoring 20 seconds, like a lot of challenges. So, data integration now is becoming a big, big requirement. Mainly, it's not about do we have a technology to integrate? It's not that. It's availability of reliable and consistent data. Inputs from, especially related to the circular economy from your suppliers to supplier to supplier. By the time it hit the road or hit the factory here, it is basically maybe at least minimum for the touch point, but that's not even not given that, you know, we were working recently with the company. It's not about just the raw material. The raw material was shipped like it was sailing. Now you are talking about fright. Fright is not just cost. Earlier we think, okay, how much fright and do we need to optimize, do we need to near so sure this in Mexico and all that we used to think from a cost perspective. Now we need to think from a sustainability perspective, maybe the pot was made very, very good, not much carbon emission, water is consumed very well and all that, but how, how much diesel it's consuming while it's sailing,

Tom Raftery: 16:37

Mm hmm.

Ganesh Gandhieswaran: 16:39

right? So I think data integration without getting into technical flavor, data integration is the main challenge right now.

Tom Raftery: 16:50

Yeah.

Ganesh Gandhieswaran: 16:50

We need to figure out a way.

Tom Raftery: 16:52

Yeah, that makes sense. And I mean, circular economy is incredibly important because there is this concept, I don't know if you're familiar with it, of World Overshoot Day. It's the day of the year where we've gone through the entire year's worth of resources for that year, the budget, the global budget for resources for that year. And every year it's getting earlier and earlier. This year it was in August and it gets earlier and earlier every year. And it just means we're going the wrong direction. We need to be pushing that date back to the end of December and not consuming as much. Reusing the resources that we've already taken out of the ground.

Ganesh Gandhieswaran: 17:31

Absolutely. we, we spoke about data integration. I think you could collectively solve the data integration problem. Then we can really start testing that data and consistently measure, analyze that information. Then we can take action. Right. End of the day. Collectively, we need to think about, how our next year, if you need to push from August to September or October. Everyone needs to do their part and you know that means you need to collect all the data. In my view, companies have great intention 10 years to now. And people are ready to commit resources like meaning the capital to execute some of these projects. And in my view, I think It's not far away that we take that date into control, but at the same time, population is increasing and the number of cars are on the roads are increasing. So we need to balance it, right? Obviously, one side we are trying to control and, you know, one day, someday we think about, wow, everything from electric car problem solved.

Tom Raftery: 18:41

one thing there. I mean, you were talking about automotive manufacturers and getting engines and fuel pumps and all these kind of things. Of course, electric vehicles don't have engines, don't have fuel pumps. The drive train of an electric vehicle typically has around 20 moving parts compared to over 2000. for an internal combustion engine vehicle. So it does help. It doesn't solve all of the problems, but it certainly is a large help to shift to electrification and transportation. But we'll, that's a bit of a side issue. Let's, Let's, let's come back. This last couple of years, we've seen a huge uptick in people talking about and deploying AI solutions, for example, and you've mentioned that your company is a generative AI company. So tell me a little bit about what you're doing. AI and machine learning, you know, in the context of efficiency, how do these technologies specifically contribute to sustainability? Could you maybe walk us through some use cases where it's helped reduce emissions or minimize waste and supply chains?

Ganesh Gandhieswaran: 19:47

Oh, yeah, absolutely. So, I think overall, the capturing the data, there were technologies. Now it's getting better, improved. At the moment I talked about multiple suppliers, the data is not in an easy format. It's humanly impossible to code every mapping to get the data. So now we are all leveraging AI. Also called augmented analytics. We are augmenting that analytics based on the data input. Can the code automatically create a scenario for this data?

Tom Raftery: 20:27

Okay.

Ganesh Gandhieswaran: 20:27

This data has four elements, this data in a tree format, structure format, like there are many things, right? Somebody provide you in JSON format, somebody provide different format. So ability to deploy a data integration technology. Not manually human coding each and every line of that it is becoming huge so that AI, especially generative AI code generation tools are making that job easy. Today. Okay. The supplier's sending a format. I say, I have an ETL data integration developer who's re, you know, who couldn't maybe say one tool, like a data stage or Informatica or five trend type. But then tomorrow the data is coming in a different format. You can just ask the tools like Chat, CPT, or Hey, how do I read this data? it's making the data integration developers more smart. Step one.

Tom Raftery: 21:27

Right.

Ganesh Gandhieswaran: 21:28

It's augmenting, that is, so we got that. Now, so now because of that, we are a lot more capable of handling more data integration from variety of sources,

Tom Raftery: 21:40

Sure.

Ganesh Gandhieswaran: 21:41

Volume, we got the solution. Big data and cloud and scaling, all that, we got it. Now, after this, okay, now the data landed safe and all the sensors send me all the data, all the suppliers collected. I have humongous petapet of data, what should I do with this? Because unless we consume the data in a meaningful way and start converting that raw data into metrics and compare those metrics against goals, you can't really change that. So that's where the generative AI technology is coming is, for example, what Conversite is doing as the name says, it's conversational insight. To me, we cannot just create reports after reports. I was part of an automotive company before they had 40,000 reports, just one country, in my view, that itself is a sustainability problem because why do you have 40,000 report that's all stored and it's consuming compute power and many things like so technical solution from our side is that do you really need to create a report and store it? Rather, can you just ask a question,

Tom Raftery: 22:58

Right.

Ganesh Gandhieswaran: 22:59

How are we trending on the carbon emission in this region? I can ask a question and get an answer. And day two, the AI is taking over that, Ganesh, why are you asking this question? I know you will ask this question. I'll preempt and tell you that, okay, in the East region, your emission is going up. You need to watch this, so automated insights. That is where the AI becoming our good assistant, still not an advisor, good assistant to deliver that insight for us. So AI, generative AI is helping in across the chain, collecting the data, digesting the variety of data, and the ability to consolidate, convert into meaningful metrics. But The metrics are great, but we don't have time to open those 10,000 reports and analyze everything on our own. We need help. It's not possible to, hire 10 people, 20 people, 50 people to do this job. So we need to hire AI employees to analyze the data. That's the part I think it's going to change. Recently we were working with a manufacturing consortium. They are managing like a group of manufacturers in a country and they are responsible for serving global, like top eight automotive, for example. Now this organization has the responsibility to collect all the sustainability data from various different suppliers, as well as the group companies in that country, and then share that with the automotives outside. Then the automotives need to pick up the data, integrate, and use that for all other reporting. So now in this case, traditionally when the reporting analytics technologies create reports, dashboard, it's good because I can go ask a sales leader or a supply chain leader, Hey, what report do you like? What do you want? It's easy. They will give a requirement. You can create the report. But in this case, your providers and consumers Or not within your company, outside your company, so you cannot really put a boundary that I will provide a report like this. You need to consume it. So we need to give it in a lot more easy format. So generative AI comes perfect help. I can just ask a question. Hey, what's my month over month, you know, emission growth percentage or what, what is my month over month water consumption here? Give me top 10 cities where I'm consuming it. Give me top 10 plants where my electricity consumption is going up. For example, that's a concept of dark warehouse now. It's still in theory, how far we are moving on some of those key initiatives. So if we can make leverage generative AI and data analytics, In an efficient way, we can empower not only our business leaders, also our suppliers, and also our customers. That's where I think it's a very big change and it is happening. Technology is here and AI is everywhere.

Tom Raftery: 26:14

Mm.

Ganesh Gandhieswaran: 26:15

It's all about how we are going to leverage them towards the sustainability initiative.

Tom Raftery: 26:21

Nice. And what about things like visualizing sustainability data? Because, you know, the old saying, a picture is worth a thousand words. How critical are data visualization tools in making complex sustainability data understandable and actionable? I mean, what should companies be looking for in these tools to make better decisions?

Ganesh Gandhieswaran: 26:43

Yeah, that's a great point. As I said, unlike operational data like finance, sales, purchasing, all that, there is somewhat you can put a boundary to that data and quality of the data is also better. Because it's generated and within the company most of the time, but when you come to the sustainability data, it's a huge volume, think about a one machine, like we are working with another industrial manufacturing, in an oil and gas space, every 10 seconds, and I think they have probably monitoring 20,000 oil wells. Monitoring that much data, it's humongous. It's not possible to visually even see that. So, and we also don't need to see it at this instance it happened, like it would be too much to see that. So you need to get, like the pattern, where it is trending towards. Millions of data points need to come to that one small chart, which we can see and digest that information. It should give more direction. Is it trending in the right direction or not? Is it increasing? Decreasing? How close are we to our goal? So visualization is critical, very critical in this initiative because it's easy to capture data. It's affordable today, but if you don't have the right visualization capabilities, it'll become very, very difficult. The moment we think about sustainability and all those, you know, immediately comes is geo chart. Okay. Across my, that's given that's taken within that, what happened? Why it is happening? Like many times we are focusing on what happened. No, we need to really focus on why it happened. Why this particular plant? Emissions started increasing. Is it because of what? What is the temperature? That's another metric. So metrics are related. And then from one metric to another metric, how do I drill? How do I visualize two different metrics side by side? It may be totally unrelated metrics. How do I bring it together? We call something called the data cardinality, the space like it is very difficult to bring together. A data from a supplier data and data from a production, for example, like sometimes it's, it's not in the same level, right? One is at a month level, one is at a day level, one at a minute level, like it's difficult to bring all those different data. So how are we visualizing that sometime in my view, we don't need perfect answer here. That's an another thing to. Also digest about the moment we think about analytics. We all think about we need perfect like because all the reporting analytics started from the finance world. So we all have that like it should match at a dollar level. No, not necessarily sustainability. You don't need to really worry about the perfect data as long as your data is 80 percent good 90 percent good. Good enough, good enough, not perfect, good enough, start visualizing, start putting together in a right chart, have a goal and measure it towards that. So we'll know, this is what we wanted to go, go achieve, we are already in August, and I think you also brought up a good point, predictive, we are in August, this is where we are, where will we be end of December? We don't want to know that in December, we want to know that today. So do we have the visualization and the predictive capability? To see that side by side, what happened in the last eight months, what potentially would happen in the next four months, and what's a target the leadership has said, are we going to be able to meet it? If we are going to slip, why? What can we do? Recommendations? Where can we implement? Where can we change? Visualization is very critical. Especially because of the data volume and the variety of data we are having.

Tom Raftery: 31:02

Sure, sure, sure. With AI becoming such a central part of supply chains, you know, you might wonder if we're headed towards a matrix like scenario where machines are calling the shots, how do you see the role of AI evolving? Will it, will it always remain a tool for humans or could we be looking at a future where AI takes a more autonomous role?

Ganesh Gandhieswaran: 31:29

I think we are just talking about AI here. We are not even, we have not even scratched the surface, like, not even 10 of the capability what AI's could do we are leveraging. It has a lot of challenges because, data availability, quality, and many other things. Now when we are all thinking about AGI, the Artificial General Intelligence. In my view, the AI will make the humans even more intelligent and smart. And in my view, I don't see a anytime, even in the future, that AI will take over. AI will take over a lot of tasks. We are manually doing today, like if I am on my highway driving 45 minutes every day to my work, I'm actually not driving the car is driving the AI is driving, let's say, AI knows what speed to go, AI knows when to brake, like there's a truck moving the lane, it needs to slow down, it's slowing down, like technologies like adaptive cruise, these are all smaller, smaller, smaller AIs, and came out after a lot of learning, the deep learning they call, but it can just do that one job, that one job,

Tom Raftery: 32:50

hmm.

Ganesh Gandhieswaran: 32:51

and now it also has a limitation. Now, that's where, why I need to be there and holding steering, be aware, is there are other options. Like that, as human, we are capable of a lot more. I think these AIs will take some of the jobs, redundant. You know, I always look at, like, if someone else can do it. That means I should do something more better, like, so can, if the AI can do some job, which I'm doing today let the AI do it. So now I can think one level above the AI and do something. I think that's kind of how it's going to evolve. A lot of things which we are doing today, we may not do that. I don't know. There'll be a time where I, I just, I don't even need to go book a air ticket. It'll look at my calendar and book air ticket because that's a frequent flyer, airline mile. And it knows that, okay, every Thursday you are taking this flight, it can go do it automatically. For example, right right so booking a car and you know, booking, do I really need to call an oil change appointment? If the car is smart enough, it can call and make an appointment for itself.

Tom Raftery: 34:03

Not if it's an EV

Ganesh Gandhieswaran: 34:03

Ah, yeah. So I think. To me, the AI is like a kid, so it's like an infant, it's starting to grow, and obviously the kid used to depend on us for a few things, even simple tasks, even to walk. But now the kid grow and walk, can run, can do, can lift, and then still depend on you for maybe some, food. Now after some time, they can cook. So I think that's how I look at AI. There will be a bonding, always. We need to keep that bonding with AI that to me the AI is almost like a kid and it's a infant right now. So a long way to go

Tom Raftery: 34:51

we don't want it to become an angry teenager.

Ganesh Gandhieswaran: 34:54

Oh yes. Yeah. Again, there,

Tom Raftery: 34:58

Tell me.

Ganesh Gandhieswaran: 34:59

Then again, social responsibility, there are a lot of AI security, a lot of that we are talking about and countries are also bringing controls to it. That's equally important to avoid that to happen.

Tom Raftery: 35:09

Mm hmm.

Ganesh Gandhieswaran: 35:09

Yeah. AI, there are a lot of deep fake. As they call, like there are videos, there are images, which are not real voices, not real. So how do we take the, the jewel, the diamond out of the dust? We need to be responsible. We can't blame AI. It's human. We need to be responsible.

Tom Raftery: 35:31

In terms of measuring success in sustainability initiatives, what kind of metrics or KPIs should companies focus on to measure the success of their sustainability initiatives in supply chain and how can they ensure continuous improvement?

Ganesh Gandhieswaran: 35:49

Yeah, I have seen couple of companies do that already now. And especially when they have the more top down approach that they clearly put At, at a company level, these are all the different KPIs I want to measure, this is the goal, capturing that, and then so I've seen a, a sustainability, you know, initiative dashboard, every day morning it shows at the CEO level, four metrics, where you are on the four metrics today, as of today, against your goal, against industry. At the CEO level, then one level you go to, if they have, I think they used to have eight or nine plant, the plant managers have same four metrics, maybe four metrics are common, but the same four metrics. Now, what is your actual, what is your goal, month of date, how far to year, and how are you compared against other plants? So not only give you the visibility on where you are. It also give you the, the visibility on where you are compared to other peers. It would either motivate or give them a fear. They need to do it better. So I think that's that's the best way I have seen that. It's a clear KPA scorecard at all the levels. All the levels. I mean it, right? So every office, electricity, simple. All of you're responsible. Do we really need to run that meeting room light on. You really need to turn, you know, to keep that TV on that meeting room, be responsible. Maybe it's not coming from your pocket. You are not paying the electricity bill. It is our energy. It's our world, our resources. So that awareness will go all the way, but it should, it should start at the top. The board, the CEO level, they need to have the four or whatever metrics, measurable metrics. If they have 25 metrics, I'll tell you, no, you're not going anywhere. Just keep four, five. That's it. That's what you can measure.

Tom Raftery: 38:11

Keep it simple.

Ganesh Gandhieswaran: 38:12

Keep it simple.

Tom Raftery: 38:13

Cool. Ganesh, a left field question for you. If you could nominate any celebrity or fictional character alive or dead from any time as a spokesperson for supply chain sustainability, who would it be and why?

Ganesh Gandhieswaran: 38:35

See, even though I'm not too close, you know, I'm in supply chain but not too close. I am most of the celebrities are most of the ones I, I have gone through is through your show, your podcast. So I will say probably Tom, you

Tom Raftery: 38:57

Oh, dear, we're in trouble.

Ganesh Gandhieswaran: 39:02

No, I, I think, I think as I said it's, it's a social responsibility. All of us have it. There are a lot of great influencers, not just to name one or two, but a lot of great influencers,. Right now we need more of those more influencers. And unfortunately, it's also becoming political, right? So, like if someone voice, you know, they become a real great, leader and talk about and care about, but then, They don't voice over a few issues and that become an issue. I think there is social media, for example, it's really helping to make someone a hero, but at the same time, it's also showing the other side. So it's like, we need to be very careful what to follow, what not to follow. But there are great influencers. We just need to take the right thing from them.

Tom Raftery: 39:50

Okay. Great. We're coming towards the end of the podcast now, Ganesh. Is there any question I didn't ask that you wish I did? Or any aspect of this we haven't discussed that you think it's important for people to be aware of?

Ganesh Gandhieswaran: 40:05

No, I think we have covered a lot and purposefully we did not go anything technical and in simple, we no longer can blame that we don't have data. We no longer can blame that oh, suppliers not giving, they are not giving. It's our responsibility. So start measuring it. If you start measuring, have a clear goal, you can improve because you are measuring, you'll end up collecting all the data, whether it is your own plan, whether it is from suppliers, all that. I think I, you know, if there is a will, there's a way

Tom Raftery: 40:44

Hmm. Yeah.

Ganesh Gandhieswaran: 40:45

Let's get on, measure it

Tom Raftery: 40:47

Lovely. Great. Ganesh, if people would like to know more about yourself or any of the things we discussed in the podcast today, where would you have me direct them?

Ganesh Gandhieswaran: 40:57

Absolutely. So, I have my LinkedIn you can Google it, Ganesh Gandhieswaran or you say Ganesh CEO of ConverSight

Tom Raftery: 41:07

Okay, I'll put the links in the show notes as well Ganesh, so people will find it there straight away. Cool. Great. Super.

Ganesh Gandhieswaran: 41:12

And yeah, anytime I'm active on LinkedIn, feel free to contact me. Looking forward

Tom Raftery: 41:18

Fantastic. Great. Ganesh, that's been fascinating. Thanks a million for coming on the podcast today.

Ganesh Gandhieswaran: 41:23

Thank you for inviting me, Tom. Looking forward. Thank you.

Tom Raftery: 41:26

Okay. Thank you all for tuning into this episode of the Sustainable Supply Chain Podcast with me, Tom Raftery. Each week, thousands of supply chain professionals listen to this show. If you or your organization want to connect with this dedicated audience, consider becoming a sponsor. You can opt for exclusive episode branding where you choose the guests or a personalized 30 second ad roll. It's a unique opportunity to reach industry experts and influencers. For more details, hit me up on Twitter or LinkedIn, or drop me an email to tomraftery at outlook. com. Together, let's shape the future of sustainable supply chains. Thanks. Catch you all next time.