Nishith Rastogi: 0:03

I look at two costs which often go parallel. One is the financial cost and second is the environmental cost. You can't keep running trucks with more air than packages. That's not good for the bank balance. That's not good for our cities.

Tom Raftery: 0:18

Good morning, good afternoon, or good evening, wherever you are in the world. Welcome to episode 111 of the Resilient Supply Chain Podcast. I'm your host, Tom Raftery. You can't keep running trucks with more air than packages. It's bad for the bank balance, bad for our cities, and frankly, it's bad strategy. In this episode, I'm joined by Nishith Rastogi, founder and CEO of Locus dot sh to talk about why last mile logistics is becoming one of the most important decision layers in modern supply chains. For senior leaders in supply chain operations, and sustainability. This is where efficiency, resilience, and customer expectations now collide. The big takeaway here is simple. The companies that win won't be the ones with the most data, but the ones making better real time decisions from it. Nishith makes the point that if you miss a line haul slot, that's a financial loss for a day, miss a customer delivery slot, and you risk losing that customer entirely. That's the difference. So let's get into it. Nishith, welcome to the podcast. Would you like to introduce yourself?

Nishith Rastogi: 1:33

I'm Nishith, I'm founder and CEO at Locus.sh. Locus is a decision making platform in short haul and last mile logistics, and I'm looking forward to having a great discussion with you today.

Tom Raftery: 1:47

And Nishith, tell us a little bit about Locus. You're co-founder, so tell me, what was the thinking behind setting it up? What problem did you see out there in the market that you felt needed to be addressed and you had to set up Locus to do that?

Nishith Rastogi: 2:00

We started in 2015 in the midst of the on demand economy boom. A bunch of startups at that time was started by product specialist, not necessarily by logistics specialist, and the demands of last mile very interestingly, deferred from what existed yesterday while we still had answers to where my package is. Nobody was really going after the problem where my package should be. When you start doing decision making in logistics, it becomes a very interesting problem because logistics is very real world. It's made up of fuzzy decisions. Sometimes the address is not perfect. The traffic is unpredictable. The vehicles may not be available, and there's always a last minute problem, We'd literally live the Murphy's Law day by day in logistics and operation. So building an automation and a decision making platform that factors into all the edge cases was a pretty challenging problem that we saw and a gap in the market, and that's what we picked up at Locus as the core tenet. Can we build the most real world decision making platform in logistics?

Tom Raftery: 3:07

And let's say, at a high level, how would you explain what Locus does to someone who's outside of the logistics world?

Nishith Rastogi: 3:15

So let's take your favorite retailer from where you get your shoes or your favorite clothes. Today when you shop online and they deliver to you in 24 hours, there's a lot of magic that happens behind. They need to decide whether they're gonna deliver it themselves or they're gonna partner with a delivery company, whether they're gonna deliver it to you directly from their stores, or whether they're gonna deliver you from a distribution center, whether it'll come on a bike or it'll come on a truck. You may have given them a specific time preference. You may have given them a eco choice. Factoring in all of that and being the front face of the customer.'cause today, your delivery is actually when you're interacting with the customer, so it becomes a core responsibility for the shipper. We abstract all of this out into a magical experience where your customers experience, very, very predictable delivery and you get a very cost conscious delivery. And these, instead of becoming an orthogonal aim, actually become a parallel scenario for you.

Tom Raftery: 4:17

And how, is what you guys do different from a traditional TMS or routing tool?

Nishith Rastogi: 4:22

So we are fundamentally a team of PhDs who have spent the last decade optimising the maths to closely mimic the real life scenarios. Most existing TMSs exist to surface the correct information and use the users as the key decision making engine. Our tenet is that today because of affordable cellular networks, a lot more real time data is available and consumer expectations because of 24 to 48 hour deliveries have made decision making in these situations untenable for any human and very stressful. We actually use highly sophisticated computational algorithms. This factor in all of these objectives from the shipper, from the customer, combine it with a bunch of real time data and make these decisions, and these decisions are not theoretical, but very, very implementable on the ground. They also factor in whether they are easier to adapt via the change management methods. So that's where we differentiate on whether our solution is implementable in real world or not.

Tom Raftery: 5:29

Okay. What kind of decisions are you talking about?

Nishith Rastogi: 5:32

This starts right from the point an order is created for a shipper, Or this could as well be a transporter. So now you need to first decide within the SLA of this order, should it be dispatched today. Often in modern last mile supply chains within a day, there are four or five different slots. You need to decide when it needs to be dispatched. Once you have decided when it needs to be dispatched, you need to decide which channel it should be dispatched, whether it's your captive channel, outsource channel, or maybe you know, it needs to be handled as an exception via the gig economy piece. What should be the correct vendor? If you're sourcing it externally, what should be the right price point? How should that be negotiated? If it's being handed over? What should be the documentation, if it's being delivered internally, what should be the allocation within the route? Then post-processing in case of, a failed delivery, what was the reason? How should it be rescheduled? Should there be a change in the order variables that should be done? Should you send a different type of vehicle in case the delivery reason was that, the customer address could only support a small vehicle, but you had sent like a 14 tonner. Troubleshooting through that and then post that, also analyzing, if on an aggregate basis, your distribution network is set up optimally. If a shift in the distribution network could lower your cost or increase your SLAs. So yeah, these are some of the operational decisions that we take for our clients on an everyday basis.

Tom Raftery: 6:59

And how do you see last mile delivery evolving now that it spans stores, warehouses, captive fleets, gig fleets, and more?

Nishith Rastogi: 7:10

Last mile delivery has crossed the chasm and it has moved past novelty. Customers expect it as a hygiene feature, which means brands and shippers and transporters must collectively look at cost as a key variable in continuing to do this sustainably. And when I say cost, I look at two costs and which often go parallel. One is the financial cost and second is the environmental cost. You can't keep running trucks with more air than packages. That's not good for the bank balance. That's not good for our cities. Which effectively leads us to cross fleet utilisation as a primary driver in ensuring we have effectively these partial truckloads, which run at full truckload capacities. That is where two things become very important. Collaboration between different players and a technology which can marry them together into a single, platform for a shipper to deliver from, for a transporter to collect demand from. So I believe all of these channels are really essential because each channel in different shipper use cases, addresses a certain density of customer curve. So when you have extremely high collective density, sending your own captive truck makes a lot of sense. But if your demand is in an area where your demand is paused, but per se, the area has high demographics, and it could be being filled by another shipper, then it makes sense to partner with them either via 3PL or if it's an, exceptional order to partner on a gig economy basis. Channel should not be limited by technology choice, and they should actually all collaborate together to give you a close to a full truckload experience across your delivery load.

Tom Raftery: 9:01

Sure. And where do traditional TMS tools hit their limit in that environment?

Nishith Rastogi: 9:07

Traditional TMS tools hit their limits first at identifying where the customer is. Often many of these tools work at a zip code level, where today the customer is expecting it at a latlong level, and they may have given you an address. Often, in, say white good deliveries, you really need to make sure the customer is at home. So you need to do validation of time slots into the address. You need to often allow the ability to either reschedule the time or sometimes within a certain radius, even reschedule the deliveries. So identifying truly where the customer is, both understanding where the address is, whether the customer will be present there at that time, is a very key part in last mile deliveries. The second is the, radius of accuracy. They were primarily designed to predict linehaul movement. And offer prediction in terms of with the same day or within the same hour. In last mile, often the entire duration of a delivery is an hour, So your zone of accuracy needs to be down to a minute and a five minutes and a minute, and five minutes is completely different level of tardiness. So, for example, when you're delivering groceries, if you're delivering to a customer who is only expecting, say, ambient deliveries. Versus someone where within the same truck, I also need to open the ambient section and maybe the frozen section. So my turnaround time is different, and this will completely, you know, on a cascading basis, change whether I'm doing 10 deliveries or 12 deliveries, and I would've already promised the customer I'm coming at that time. So factoring in such granular level of details becomes extremely critical. One very important thing we have to realize is that if a line haul truck misses a docket yard, it's a financial loss for that day. But if you miss a slot with a customer, it's a customer loss. All your money through your acquisition funnel is gone. And these days customers are very vocal on the social media platform. So we have often stitched together stories for our customer where, for example, let's say for whatever reason they had a trouble delivery, the next time we will ensure only a rider with say 500 positive ratings and who has been delivering for at least six months into the system will go with an extra 10 minutes of tardiness. Because today your deliveries are no more a cost center, but a revenue generator, and that outlook completely changes the kind of features that are present in the platforms.

Tom Raftery: 11:39

Dig into that a little bit more for me, that they can become a revenue generator.

Nishith Rastogi: 12:17

Absolutely. And we often see that in our buying cycles, we have not just the CEO, but the head of sales involved as well. Because first, today, are deliveries, for growing companies, their fulfillment is limited by the throughput. It's not really limited just by the demand. Second. Now let's look at time slotted deliveries. Let's say you're a company which offers, 48 hours of delivery you can today, now start an option wherever it's feasible on a dynamic basis. If you're already going there to offer customers a choice of 24 hour delivery and get $5 or a $3 extra on that. Similarly, you could also nudge them. That, Hey, instead of taking today, why don't you take tomorrow and take a dollar of discount? Either this could be a dollar of discount, or this could also be green messaging that, hey, if instead of taking deliveries versus this, for example, we have really observed working well with brands operating in the premium price pad, where often you don't need, that soap or that, product on the same day. You're happy to wait, but you need like a certain brand aesthetics to go along. And that's where we are seeing that today. You know? When you're designing how your last mile deliveries work, you're talking definitely, you know, to operations and supply chain, but you, as we're talking to the marketing team, you're talking to the sales team, you're talking to the customer retention team that, hey, you know what happens if somebody had a bad experience? How can we fix it the next time? You can now offer different messaging to customers in case there is a delay happening, you know, if there's a five minute delay, you can proactively send them a sorry and an update. If there's a 15 minute delay, maybe you can also give them a 10% discount coupon. And that's why we are seeing that deliveries ecosystem is getting very, very closely stitched with both the demand as well as the retention ecosystem for a B2C shipper.

Tom Raftery: 14:07

And. How do you think then organisations should approach the shift from static automation to adaptive real-time decision making?

Nishith Rastogi: 14:16

If you have done static automation, you have won large part of the battle because the first step in this entire step is digitizing your data. And if you have already at least done the part of static automation, you have actually done the tough. And while it might not have, directly costed you financially, it really costed you in terms of time and change management. Now is actually the time to truly reap the rewards of that effort you put in change management where you need to add a layer of decision making over it, Lucas, for example, latches on to your existing automation setup as well. First trying to minimise the change. And then as your contracts expire over the year, it provides you that same functionality. But if you have already done the, key part of having digitization, having at least the first level of tracking, then you must must invest in this layer of intelligence, because today the modern consumer demand, and the business models just mathematically provide you practically an in finite number of choices where no human given no amount of time can take the best decisions, right? And that time is best used, interacting with the customers, providing them support, increasing your retention, increasing your sales.

Tom Raftery: 15:33

So then I guess this is where AI steps in. What's your view on how humans and AI complement each other in these kind of fast moving environments?

Nishith Rastogi: 15:42

I have, spent the last 15 years in the domain of AI, starting from working for Amazon, building the Amazon web services systems itself, launching the first AI service, to now using a lot of, AI in, in the Locus platform. But where I first wanna call out is that in the first five years, we actually did not use any AI. And how that connects with the entire logistics ecosystem. So first thing we must understand is that anything that cannot not be AI, should not be AI. If you can have good SOPs, if the environment is controlled, if you can have rules that execution and a stable monitoring, that's always better than a dynamic response, right? But when AI can become really, helpful in logistics, isn't solving the interface barrier. All of us, every five years go to a warehouse manager and telling that person, Hey, there is a new user friendly way of knowing what orders got late in this last seven days. In logistics domain, often the users are actually the functional experts. They may not know how to interact with the software, but they really know their job. And AI can really become the interface which captures their intent and converts them into UI UX actions. So for example, you know, a warehouse manager. Every day on his drive back, getting like a voice clip, talking about how was the performance during the day. Create such a habit forming, around catching your analytics and metrics rather than, you know, enforcing a new tutorials or, any training methodology. So what we need to really understand is that often in logistics, the buyers and users of the system have been different, and that has often created an interface gap. I believe AI truly presents this huge opportunity via text, via voice, but basically via interacting in natural language to bridge that intent action gap, which has in some ways always hurt the adoption of technology in logistics domain. Where the users always have to adapt to the technology over technology taking the responsibility of understanding the user. And I think that's what's really possible now, and that's what excites me first.

Tom Raftery: 18:05

And a lot of the time around AI these days is around what they're calling agentic AI. Is that something that you are seeing any use case for in your platform?

Nishith Rastogi: 18:16

We have, long called ourselves a Digital Supply Chain Officer. Officer is basically an alternate lexicon for an agent. And what's the difference between an agent and a traditional, software? We differentiate it based on whether the output is information or if the output is an action. And that changes by your conviction in the decision your system is taking, right? So either you can have a system which only presents information, or you can have a system that presents you a suggestion. Or you can have a system that presents you a suggestion with enough confidence that it can be executed for action without intervention, And that's where the agentic comes in. And given the number of micro decision that needs to be made in logistics, say imagine, a typical, high traffic last mile warehouse doing 9,000 dispatches a day. Even if the exceptions are at 10%, or even a percent, you're looking at 90 exceptions, right? That's almost like nine exceptions an hour, 10 exceptions every minute. At that point, you really need to take a bunch of decisions, which is what these algorithms help you take. Again, customer interaction through all of this can be really simplified via AI agents, right? So then the interface to your field workers and resolutions to their issues that can be simplified at a much more faster pace. So that I definitely believe agents have a lot of role to play in the logistics domain. I don't think it's also, overnight change. It's been a gradual change where we have been going from information to suggestion to now accepting suggestions to actions without intervention. So that's really the journey.

Tom Raftery: 20:07

And do you have a, an example of the kind of decisions that are now automated but that used to rely on intuition?

Nishith Rastogi: 20:13

Dispatch is one of the biggest ones. So traditionally, always dispatch either has been along fixed rules or by fixed zip codes, or you really needed somebody who was very aware of the geography and they would look at an address and dispatch it between routes because dispatch just has so many variables. It is not about matching a hundred packages to 10 vehicles. It's more about, you know, matching a hundred packages to, you don't know the number of vehicles. Six of them needs to be large, three of them needs to be small, two of them needs to be sourced from the market. You need to decide which vendor what cost. Once you've decided that 40 of your customer wants it in the morning. 60 in the afternoon, but 20 of the ones who wanted the afternoon live next to the ones who want in the morning. And I can continue in a rap song, right? But I guess you get the picture.

Tom Raftery: 21:00

Hmm.

Nishith Rastogi: 21:00

The moment you keep shortening the time horizon of delivery, which is what happens in a last mile delivery at 24 hours, these decisions both increase more in number as well as more in complexity, completely making it untenable. So we have clearly seen a shift over the last 10 years where, they used to present all the information on a screen and somebody would allocate these orders via a dropdown giving you an approximate suggestion to, the humans making the last touch to now agent systems, which automatically dispatch the right route to the right driver.

Tom Raftery: 21:33

Where do organisations typically see the first measurable improvements? Is it in speed, cost, emissions, customer service, something else?

Nishith Rastogi: 21:44

That's the best part. All of them in, in this specific case. Right? this is a problem that is so fundamentally suited for computational decisions. And that's what I said, that reduction in cost and increase in customer experience instead of being at an intersection is actually becomes parallel aims. another advantage is that often the metrics are so, hard. Like for example, number of deliveries per vehicle per day. That right from a CEO to a delivery driver, everybody can align on a common metric.

Tom Raftery: 22:16

And how then can smarter allocation or load planning genuinely improve sustainability outcomes, not just operational KPIs.

Nishith Rastogi: 22:27

As we have done these changes, we have often seen fill rates go up from 50 to 60% to all the way up to 80% on a sustained basis, and sometimes on some days 90%. Which effectively means your average miles per package that your vehicle is moving reduces by anywhere between 15 to 20% in mature organisations to all the way up to 40, 45% in young operations. So for every, delivery you are doing, you're literally, polluting the environment less. If you can also further via intelligent dispatch factor in the right packages, in the right areas to deliver via an electric vehicle. As often, you know, shippers and transporters are making transition to electric vehicles, it's not a full transition always, you're operating a mixed fleet and selecting the right fleet makes for the right orders can further lower your emissions. I'm proud to say that just over the last year, Locus has helped sequester as much emissions as would've taken about 20,000 acres of forest.

Tom Raftery: 23:31

Very good. And what about things like then, like tribal knowledge and reducing tribal knowledge? Is that something you can do and is it something that helps teams stay resilient when disruptions hit?

Nishith Rastogi: 23:43

That's a great question. December is our festive season. Which is effectively the logistics peak across the world for different festivals. But at the same time, during the festivals is where people often go home. And if you look at a dispatch center, which has traditionally been operating on a more intuitive paper, pen based approach, all the knowledge that made that distribution center mature is now sacrificed. Now you're faced with a tough problem of, you know, finding a substitute person, in a crunched environment who also needs to get onboarded quickly. So festive seasons used to become a double whammy. And in many of our implementations, often the first note of appreciation we got from the leadership was during the festival season because here you are clearly reducing both the dependence on, a lot of experienced workers. where they can actually be more free for customer interactions and leadership roles, effectively scaling their teams rather than being occupied in the operational details. This is one of the outsized advantages of a learning system that more you use, instead of a regular wear and tear which deteriorates, a learning system, actually increases in value. And one of the ways it does it is by absorbing tribal knowledge and making it available as structured enterprise knowledge for the organisation.

Tom Raftery: 25:11

And do you see companies getting better at measuring resilience, not just efficiency?

Nishith Rastogi: 25:18

Two ways in which automation impacts your resilience. First, it allows you to have more options. Imagine if you know a traditional operation, where you're outsourcing it to a carrier. If you overnight needs to onboard three more carriers, how would you do it? You need to contact them, you need to negotiate with them. You need to do a paperwork with them. Versus let's say if you're connected to a platform where you go search for three more carriers in an area, you click a checkbox, say yes, and now you have three more. And this is not just in the number of carriers over there, it could be in the number of channels you operate in, it's number of, you know, the kind of vehicles you operate with. So using technology allows you to increase your option because now you can have more complexity, but with the same simple interface, simply because you have more options, it allows you a significantly greater resilience. Then comes the second part that is selection between these options. As you increase that options, which correct option to switch over also becomes a complex decision. This is where I also believe the future of TMSs lie, that today most of the TMSs, including ours, which on the leading edge process, the decisions, not just the information, still concentrate on a single orders journey. Many of us are leading the way by also now building capabilities, which can reconfigure the organisation itself in a dynamic way. So every day that you wake up, your load may be going through a D different dominant distribution network based on macro variables and your business objectives.

Tom Raftery: 27:00

How do you see last mile decision making evolving over the next five years or so as, for example, volatility becomes more the norm?

Nishith Rastogi: 27:10

Given the increase in the volume of last mile, we'll also see more and more business models evolve. We have already seen lockers come in. We have seen kirana shops in the eastern world. We have seen grocery store pickups in the western markets. And we will see more interesting business models evolve, especially as autonomous vehicles enter into the freight. And when I say autonomous vehicle, I mean both rather, all three, one unmanned drones. Second ground robots operating in small proximity on curbs, and third, autonomous delivery vehicles within the city limits, because that will also completely change the cost equation, which will effectively make quicker replenishment, much more easier thing. So we'll see a complete explosion in the kind of business models that will evolve. When you look at the cost of a delivery, the human in that is a very big cost. So as you take that away, it's truly, a disruptive change that will happen in how people experience, especially in urban, residential areas where home shelf space is a very key constraint in how you shop and how frequently you shop and what you shop in. So that's on the infrastructure side of what I believe will happen on the last mile. I already touched a bit on the demand side, but, zooming in on that last mile delivery will no more remain, is already no more, but will definitely become the hygiene expectation. What I mean by that is when you switch on a bulb. It switches on, there is no unpredictability in it. So similarly, last mile delivery will reach people who are not at all practitioners of it, who are not involved in the technology landscape. And for them, when you say a, it's gonna be delivered at 5:30 PM it means 5:30 PM. Why should it mean 15 minutes before or after? So consumer expectations will climb through the roof simply because you know, it'll get to most market. That's on the demand out front and then on the technology out front, right? From all the decision making, one of the areas I believe I'm very excited, to see the change is in operations with both augmented reality as well as humanoids. So now we have designed a lot of last mile ecosystem for humans. And because they're last mile, they're closer to urban centers, so they're often smaller in scale. So doing traditional level of warehouse automation is impossible in there, but both helping a human loader understand where to load a truck using augmented reality, then conveying that three dimensional information becomes so much easy. Screen is great for two dimensional information, so I can tell a driver in which order you should deliver the packages, but how do I tell a loader on the ground where to load it in the truck for opt? There's no. Convenient way to transfer that instruction. So that's one of the use cases. Similarly, you know, measuring boxes without huge dimension scanner in small urban locations will undergo a change. So between augmented reality and robotics automation operations are also undergoing to go a big change. You know, four wall operations are going to undergo a big change within the next five years. So those are the three dimensions between, you know, consumer demand, infrastructure, and, the, four wall operations that I see the last mile evolving.

Tom Raftery: 30:42

Okay. And interesting that you, me mentioned things like, virtual reality. Are there any, gaps you see in the logistics tech stack that no one is adequately solving yet?

Nishith Rastogi: 30:55

So more augmented reality than virtual reality. because logistics is something which forces you to be in the real world. But, imagine the life of today a warehouse worker who's often scanning barcodes, right? And or if you're a pick and light person, you go in your wide field, your glasses scan everything, and it highlights the one box you have to pick up. So it really reduce fatigue. And when you have to do that hundreds of times a day, it really increases efficiency. When you are picking a package, a return package as a transporter or a package from someone's home, you could measure the dimensions in real time, generate them a receipt, right? So the whole reconciliation process goes away. Measuring, again, dimensions at the warehouse itself get simplified. And then as I mentioned about the loading, so yeah. But given, you know, we, we operate with often unlabelled data in the real world. If there is a vision-based system, which can often add identification as well as measurability to that, I think it can dramatically change, the four wall operations.

Tom Raftery: 32:06

For supply chain leaders listening who feel that their delivery complexity has outrun their tools. What's the first practical step you'd advise?

Nishith Rastogi: 32:18

Advise to start with a, a simple audit or a simple, consulting engagement where you can look at your last six months of deliveries and engage with someone to see if you could have done fundamentally better there. And that gives you great questions to ask in the organisation that are feasible changes possible? If those changes happen, what will be the benefit? And often when you quantify those benefits, it makes, championing the change easy. and it's a very simple virtual and mathematical exercise. It. can be done quickly without extensive, operational changes, and it really quantifies the impact.

Tom Raftery: 33:02

Okay. Fair enough. A left field question for you, Nishith. If you could have any person or character, alive or dead real or fictional as a champion for delivery automation, who would it be and why?

Nishith Rastogi: 33:20

Sir Arthur C. Clark, uh, because he gave this beautiful quote that any sufficiently advanced technology is indistinguishable from magic,

Tom Raftery: 33:34

Yeah.

Nishith Rastogi: 33:35

and that's exactly the job of technology in last mile. Make it invisible to the shipper and customer. Why should a customer care how the package is coming to me? Whether it's your delivery, whether it's someone else, right? Today we are close to a point where we are able to deliver a physical package with almost a, at least in happy cases, with almost the same reliability and same ease as a virtual notification in your phone. Over the last 10 years, there has been a very fundamental shift in how we buy things. We no more go to a store and pull from there. Our packages are pushed down from it. Right. It's a very key flip in retail yeah, I think it's a very hard operational change and a lot of players in the world have come together over the last 10 years to make it happen, and it's quite magical that it happens with a seamlessness, that we don't really think about it on a day on day basis. Accept the time in COVID when it suddenly stopped and it became a dining table conversation.

Tom Raftery: 34:43

Cool. We're coming towards the end of the podcast now, Nishith. Is there any question that I did not ask that you wish I had or any aspect of this we haven't covered that you think it's important for people to be aware of?

Nishith Rastogi: 34:55

I would love to talk a bit more about the evolving role of operators in the logistics chain and how, this technology is really providing growth opportunities, right? Over really replacement opportunities.

Tom Raftery: 35:08

Okay, go for.

Nishith Rastogi: 35:10

So continuing from where I mentioned, what we are seeing with some of the top end retailers is that they're actually maturing, the delivery team into a customer interaction team. They're evolving them often into a co-sell and an upsell team. We work for this, leading Japanese retailer and we were doing, a leadership review. One of the metrics is turnaround time, at the customer. And traditionally, every organisation has asked us to reduce it. Over here, we saw that it was increasing and the leadership, commented on how this is a great thing. And that's definitely got us curious. And we were like, why? And because, you know, what had happened was that they had actually spent, like the last quarter training their delivery guys, to ask questions to the customer that how did they like the product, what changes, would they like, and those, conversation logs don't remain in operations, but actually go to the product team for evolving, you know, their next line. Similarly, we are seeing know a lot of, team whose primary job earlier was, you know, control tower monitoring actually involved into a customer facing team where their job is shifting to responding to customers solving their problems. Because now when they need to make operational changes, it's like, operating like a sim city environment on their screen. And their focus can be a lot more human, a lot more empathetic rather than being frustrated with operational changes. And similarly on the, management and leadership side, we are very clearly seeing a strong shift of looking at your last mile as a revenue generator, as a true differentiator, as a customer interaction point. So we are seeing often in the buying process also a lot more teams and a lot more department gets involved. There are a lot more use cases and people are looking for a programmable platform to stitch together stories. Often this is also becoming, a core layer of data across various teams in the, e-commerce marketplace, which is between the payments, the logistics, and the demand. We often work with our enterprise buyers for 5, 7, 10 years and, you know, it's really in interesting to see their roles over the last decade evolve,

Tom Raftery: 37:24

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

Nishith Rastogi: 37:33

Our website is WW dot Locus dot sh and you can personally reach me at my first name Nishith at locus dot sh. you.

Tom Raftery: 37:43

Fantastic. That's been great. Thanks a million for coming on the podcast today.

Nishith Rastogi: 37:48

Thank you, Tom. Appreciate your questions.

Tom Raftery: 37:51

Okay. Thanks everyone for listening to this episode of the Resilient Supply Chain Podcast with me, Tom Raftery. Every week, thousands of senior supply chain and sustainability leaders tune in to learn what's next in resilience, innovation, and transformation. If your organisation wants to reach this influential global audience, the people shaping the future of supply chains, consider partnering with the show. Sponsorship isn't just brand visibility, it's thought leadership, credibility, and direct engagement with the decision makers driving change. To explore how we can spotlight your story or your solutions, connect with me on LinkedIn or drop me an email at Tom at tom Raftery dot com. Let's collaborate to build smarter, more resilient, more sustainable supply chains together. Thanks for tuning in, and I'll catch you all in the next episode.