Episode #67 Transcript | Marc Dragon of Reefknot Investments

Episode #67 Transcript | Marc Dragon of Reefknot Investments

[00:00] Radu Palamariu:

Hello and welcome to the leaders in supply chain podcast, I am your host Radu Palamariu and Managing Director of Alcott Global. Our mission is to
connect the supply chain ecosystem globally by bringing forward the most interesting leaders and topics in the industry, and I am happy to have with us today is Marc Dragon, Managing Director of Reefknot Investments. Reefknot Investments is a joint partnership between Temasek, Singapore’s state-owned fund, and the global logistics company Kuehne + Nagel. The firm is based in Singapore but will look for companies around the world that are raising their Series A or B rounds. With a focus on high growth technology companies pushing new frontiers within the supply chain and logistics space. Marc has more than 20 years in the ICT & Management Consulting industry in Asia, mostly focused in the areas of Analytics, IT and Supply Chain solutions. He started his career in Deloitte and IBM for many years and then transitioned in several roles in venture capital, entrepreneurial setups and he’s also very actively involved in the start-up community in ASEAN and Asia in general, being a mentor and advisor to different startups. He took over Reefknot about a year ago with a vision to build a global ecosystem of high-value partners who will bring added support to help accelerate startups in tech logistics and supply chain. Marc, thanks for making the time a pleasure to have you with us today.

[01:23] Marc Dragon:

Thanks, Radu and thank you for having me in this podcast.

[01:28] Radu Palamariu:

Maybe let us start by asking you to tell us a bit about yourself. What drove you in your career to switch from doing consulting to being very actively involved in VC and startup world?

[01:38] Marc Dragon:

Thanks for the introduction about the 20 past years’ experience in the startup industry.

[01:44] Radu Palamariu:

Sorry to make you sound old.

[01:47] Marc Dragon:

Best not, compared to the whole industry. Well, when I first started out in IBM in technology, these are the big MNC. So, start on a very big MNC perspective and even in the light as well, consulting for clients across the region as well. As I looked out into the space, I found that there was a lot of, even about 10 years ago, there were startups that were starting to challenge the norm, in the supply chain, the largest technology industry and this was way back 10 years ago, before the term “Big Data” actually came out. So, I sense that kind of change and I felt that I wanted to do something impactful other than being in a large company and running a business unit within a larger organization. That’s why I came out to do my startups, And it’s only when you do your own startup, then you realize the pain and the difficulty that a startup would face. Everything from, fundraising, cash flow, hiring for scaring projects, delivering projects and stuff like that. So, it was an interesting yet painful process for the first couple of years when I did my start up my first startup in the big data space. When I did my second one, we apply a lot of learnings, co-founded an analytics startup and we grew it quite large and it was quite a majority of women. After doing that whole big multinational thing and doing that startup from scratch to a certain size, there was a gap that I felt that I had and wanted to scratch, which was really bringing the company to IPO. So, therefore, I got the opportunity to lead an Asia Pacific supply chain technology firm as a CEO for a few years. Subsequently, when this opportunity came out, to really be much more involved in a global startup space, I jumped on it. Long story short, why the switch from a large company into this VC space is really, I saw the impact early on new business models and technologies and I really wanted to be a key player to help the transformation of the industry initially in the small of those space that was in the Asia Pacific and now at the opportunity to do it globally.

[04:46] Radu Palamariu:

Reefknot is a fairly unique set up because it’s a partnership between a state-owned fund we just which is Temasek and a private logistics company which is Kuehne + Nagel, one of the largest in the world. How did the partnership between Temasek and Kuehne + Nagel come about?

[05:03] Marc Dragon:

First Temasek, with more intolerant 2,000,000,000 worth of AUM. It’s an investment from submitting funds by itself. They’re driven by their own measurements and key and being squarely in the supply chain logistics- a key leader of this globally. The whole industry is basically looking at transformations in the industry. They’re looking at business models, it’s coming out. They are looking at new technologies and how they can be applied. I think I am talking about how they got together, but ultimately the find itself is really looking at early-stage business models and technologies that would transform the industry and I think as part of this road map, just bearing in mind we are not a CVC, we are a pure venture capital firm. You are driven primarily by returns but the other key metrics that we look at, it’s really the ability to support startups that transformed the industry, and that is very key. It needs to be transformative; it needs to be differentiated. If a startup that comes up, potentially it has great returns but it is similar to a lot of the others out there, we are likely going to pass that. Also, another thing I want to bring up is around how we’re looking at the industry. The supply chain and logistics industry are really facing multi-pronged pressures. There’s creating huge opportunities for new business models and technologies and it’s not only a case where startups come up, disrupted the industry and, cure the incumbents are traditional folks, it’s not that case. It is the case where, and now we’re starting to see where is there really values that’s created with these business models and technologies, is this value an incremental value, productivity improvement, operations improvement, or is it really changing fundamentally how the industry works in some certain areas and I think that is really what we’re focused on and to be very clear about investment in those specific areas. Is it purely for operational improvements in productivity gains? And if so, is it an incremental improvement or is it more case off really transformative and things are happening that hasn’t happened before, and the opportunities are there. So I think, with our focus on this place, I think the hope is that we can help our ecosystem partners and especially our piece to identify more these very new opportunities that are coming up.

[08:15] Radu Palamariu:

I was reading on your website that even more focused and more specifically, you have three main areas of interest which are artificial intelligence, digital logistics, and trade finance. I was actually curious why did you pick AI, Digital Logistics and Trade Finance as your focus areas?

[08:41] Marc Dragon:

A lot of buzz words are out there. There’s blockchain and even IoT, autonomous and drone. So it’s a whole bunch of buzz words being thrown out there. A lot of those buzz words they can be dinner, fundamental technologies or platforms to enable certain stuff with one technology be differentiated than the other and things like that’s one part of it. So, we are not that kind of a VC firm where we will go so deep into let’s say autonomous vehicles and LIDAS and stuff like that. We might look at it, but it requires a different type of deep tech, perhaps longer-term kind of VC. Now for us, we look at it more from a solution, clustering perspective and we think about what areas brought enough categories because industry changes but yet something stays the same. So brought enough categories for us to be able to capture some stuff that’s changing and yet be able to zoom down and focus on some areas that the industry understands these terminologies and that’s the first point. Secondly, how these areas need to be able to support new differentiated technologies and models. So, it is not so much of broad general statements, but more specific ones. So even if you say things like AI we will still go at least a few levels lower to see what kind of AI. Let’s talk more about that solution area later. The third reason for choosing these spaces is that it allows us to be able to focus and yet evolve our thinking on a daily basis. AI evolves, traditionally in the supply chain logistics AI was equivalent to operations research which is demand forecasting, they’re very traditional techniques indoors areas but why we actually put AI into this bucket was because we felt that this area was right for change with new technologies coming out. In the digital logistics space, it’s enough that there’s a lot more model coming out, and these might be purely business models or it might be technologies that enable the business models. Our interest is more in the technologies that enable the business models rather than the business models by itself. The reason is this, the core technologies or IP is actually the one that drives differentiation and sustainability of the business model into the future, it’s not a general business operating model. General remarks can be copied but if you have a core there that holds everything together that kind of sort, it can be copied. So that core IP is what we are looking for. Sometimes the core IP might be in the context of an enterprise software even that looks at specifically data either collection of data or creation of new data. So that in the combination with enterprise software is something that we’re looking for. Things like core analytics or predictive models around enterprise software and the kind of data that we collect are also another model in the digital industry.

[13:16] Radu Palamariu:

If I’m going to comprehend this in a very simplistic example. Let’s say I have 100 trucks and my competitors has 100 trucks and some 10 other competitors can buy 100 trucks, but what you’re after is that maybe as an example, that the software that optimizes the deliveries and optimizes the routes that will basically make that particular trucking company way better than all the rest because they wouldn’t have the same software.

[13:45] Marc Dragon:

I would take it a few levels more than that. I think it’s not so much optimization because there’s a lot of capabilities out there to have capabilities to optimize routes. For example but, if there is some specific IP around where demand is really coming from, ability to predict prices along certain lanes; ability to predict capacity around certain lanes and then everything else just falls in order. If you know your demand, capacity then the optimization happens, the optimizing and the whole capacity utilization and you know what kind of a thing and that is the core piece that we’re looking at.

[14:43] Radu Palamariu:

Actually, you mentioned that you’re not looking into optimization. Yes, I chose the wrong example.

[14:50] Marc Dragon:

So, we’re looking for that secret sauce rather than a general capability. So, that’s on digital logistics space, of course, trade finance we feel that trade finance is really an area where there are some solutions and capabilities that I mentioned before incremental provide incremental changes. Trade finance is potentially an area that is right for revolutionary change, with its relation to supply chain and logistics and the reason for this is with greater visibility of data and the trust in the data that’s coming out there will be envisioned. There’ll be new business models and capabilities and financing opportunities around financing for various players in the chain. Not only the chain master and the first year suppliers but all through the second tier suppliers and retailers that were previously underserved by the traditional financing organizations. A new business model starts the emergence of trade finances. Well, as consumers, for example, we are faced with many options when it comes to e-commerce. If you went by-product, you order and sometimes that might be optional, a long term payment, one year you can have a thing with a certain interest rate. Why can’t that be the same for every other enterprise up there? Yes, I’ll accept this and for this particular transaction, I’m willing to get discounted really out of it and take the money now, that kind of thing, you don’t eat necessary mean into the DKYC with me previously but, the data is available outside everywhere else to ascertained that I’m actually good for that particular order. Trade finance is potentially an area that is right for revolutionary change with its relation to supply chain and logistics.

[17:16] Radu Palamariu:

Thank you for clarifying that and I wanted to drill in the first investment that you have done right, which is in an AI startup called Prowler.io which is an AI platform for decision making and it’s called VUKU and long story short, they help organizations make better decisions in financial markets in logistics and ride-sharing smart citizen robotics. So, I think the round was about 24 million Series B and you contributed but also your ecosystem partners contributed to which was the Singapore Government Innovation Platform, SG Innovate, I think you had Atlantic Bridge, RB Capital, Tencent Holdings and some other participated. Why did you choose this particular startup?

[18:15] Marc Dragon:

I think I’ll go back a little bit to the operations research and the whole traditional way of the industry looking at it and this was several years back. When I was leaving that regional technology, suppression technology firm, I was not convinced that the traditional ways of optimization were optimal. In the first place, the ability to do multi worth and multi-tier optimization were really not there and part of it was data and stuff. But also, part of it was the met and the technologies to be able to do that the moment you hit, X amount of notes the solution breaks out. So I was actively on the lookout for someone and generally, there were some new thinking around different models that sort of came up, but it wasn’t really applied industry. It was just met models, universities and stuff like that, so that’s several years back. When I was introduced to a Prowler.io early this year, I was quite excited because they showed, that using the Deep AI probabilistic model with reinforcement learning capabilities, with fewer data, have better results than the traditional methods. They’ve proven this impure cease with some large clients that they will give more between percent accuracy rate compared to a traditional optimization and forecasting solution would do. So because the core models are different, I was excited to be able to find them and let an opportunity to be able to co-invest in this because nowadays, for a very good startups it is not a case of them necessarily looking out for investors, but more cases of them, choosing which investors they will allow being part of their ecosystem in the company. So it’s the other way around, especially for the good ones. So we were actually very optimistic that with their new techniques, it can be quite revolutionary for the industry, and especially in a macroeconomic environment, there’s so much dynamic stuff happening, how do companies react to it and stuff like that. And that’s exactly where the models come in because you might not have data around the new stuff that’s happening but you still need a result, and you still need to be dynamic and that’s where they come in to help those planning and allocations and pricing decisions.

[21:55] Radu Palamariu:

I guess a key question that would be on the minds of a lot of our listeners would be how do you decide which startup to invest? In this case, we speak specifically about this one and you know there’s tens, hundreds, thousands of startups that could pass on. So, what’s the crux, the gist of that? How do you make that decision?

[22:18] Marc Dragon:

We haven’t reached that thousands of startups that passed on, but I can see it’s hundreds. I think the number one criteria is really the investment returns that are relatively straightforward to the other two things. The second is, it needs to potentially have a capability, be it an IP or technology to potentially be transformative for the industry. That is a big word and we spend it in we’ve not. We spent a lot of time thinking true about business models and how you know those solution areas. How is the industry-changing within those solution areas that we are focused on and how certain startups can potentially with their capabilities transformed that small little solution here they were looking at. So transformative is very important for us. The third area shows our propensity to be a bit more active in our investments, which is the ability to actively support them to grow. We are the kind of investors that believes in ‘for better or for worse.’ We are not one of those investors that will just invest, we become part of the company and we’ll sit back and relax and let them run, we are not. Going back to the vision of us supporting companies and helping them grow and helping the industry to transform. We would like to actively support these startups to transform the industry and to grow. So, what you see is that other initiative that might sound ancillary to this, but, bringing ecosystem partners together, thinking about certain spaces within supply chain logistics, all that actually comes together during an investment. So, when we invest in companies generally, the Prowler, for example, we would expose them to our industry ecosystem partners if it makes sense- it has valued contribution. These ecosystem partners can be shippers, consumer goods companies, or it can be other investors or even individuals that might have a strong capability to support those startups. In some cases, it might be a case where the startup would like to expand into a region and it needs very strong individuals. Then we would bring our ecosystem partners to help to support them indoors.

[25:23] Radu Palamariu:

In terms of the startups that you see and common mistakes that you see especially, hundreds of people have pitched to you. Do you see certain mistakes that startups make? It can be the business models or the way they structured it or the way they pitch. Both for startups and corporates that listen to this.

[25:47] Marc Dragon:

I wouldn’t really call it mistakes. I’ll answer it in a different way. There are uncertain terms of what we’re looking out for and whether or not we invest, or whether or not we like it, this is what we typically are looking out for. Number one to be clear about your proposition and differentiation. So some startups, generally they might be very tech-heavy, but really, what is it that your customers are looking for? What kind of problems are you solving? How is your technology different from the next guy out there, or is it the same? So that’s number one, be very clear about the proposition differentiation. Then, number two, I think there’s a need for startups and maybe even extend the corporates as well, to be very self-critical about the defense ability and the strength of your solution and your proposition versus other incumbents and the new competition is coming up. This area is very key, it’s one of those things where it is to know yourself kind of thing and really know yourself. Who your competitors? Do you need to change your proposition, technology or business model and pivot? Does it need for something like a sense and respond, which is my third point. So, which is always sensible and respond and enhance on a daily basis from your discussions. If you start that discussion with clients, there’s always new learnings coming in. How can you enhance your business model, your technology, your go-to-market based on those individual learnings? That’s the sense and respond piece and always enhances. Those three pieces, I think if the startups are very clear about their proposition differentiation, self-critical and being able to sense and respond and enhance, that is something that is part of what we are looking for because we might like a startup at that point in time but we must also be comfortable that one year down the road, even with new competition coming, they can still be competitive. So those are the three elements that we look at.

[28:32] Radu Palamariu:

What would you say is your vision for Reefknot five years down the line in terms of its an ecosystem?

[28:45] Marc Dragon:

Good point, they brought that ecosystem as well. In this space on a global basis, when I say space means supply chain largest technology, VC firms there’s basically a handful on a global basis, so basically few, but we’re very focused. For a Reefknot in five years, I would like us to be a very clear global leader. In number one, identifying and number two catalyzing, the next generation’s supply chain technology leaders. We will not spread as in, in the sense that way, we don’t intend to invest in many companies, but we intend to invest in a few, but the few that we invest in, we’re looking for them to be really that sector leader, the solution leaders. So that’s very clear. So for us to be globally recognized as a leader in growing and helping and identifying these companies. And the ecosystem that you mentioned, the vision is that with the ecosystem partners, this whole Reefknot and ecosystem will come out as almost like a global leading think tank type of thing. We think about new areas and solutions and opportunities within the supply chain logistics business model technology. We spot them early, and we support these areas of growth. So, I think that’s the kind of vision that I’m looking for Reefknot and our partners over the next five years.

[30:42] Radu Palamariu:

Moving it to the talent and Human Resources side which is such an important topic in general for companies, but maybe even more so for startups and you’ve had your own startup, you have led companies and now you have a man investing perspective. Obviously, you have to look at companies and how they attract talent. What would you say are some of the key factors when it comes to finding, attracting, and retaining top talent in a startup environment?

[31:11] Marc Dragon:

I think attracting talent for startups now is probably slightly easier than it worse, maybe 10 years ago. Ten years ago, it is still very much of needing stability and that kind of thing big corporations provide. Now startups tend to have a bit more on this love excitement and opportunity to grow both personally and potentially from a wealth perspective when you’ve become a unicorn. So, there’s a certain attractiveness inherently into startups for certain individuals that’s the first point. The second point is around problems, so challenging part. There’s generally scarce talent everywhere, from sales to leaders but there’s two areas that I feel that there’s a particular shortage in. Data scientist is the obvious one, specifically around data scientists that are familiar with the supply chain and logistics program. Then, number two it’s actually good product leaders, product development folks of leaders that can think around what is the product or the solution that they are building that’s relevant for the market. The differentiation part of it and the pricing part of it all goes into it but the product piece, I think, is very key because, and that differentiates, a services firm from a products firm and that also differentiates a successful startup with a good ARR and bad luck versus one that is always chasing next year. So, I think these are the two key ones that a lot of startups actually face challenges hiring. And the third area is actually location because where the talent recides might not necessarily be where the markets are. So the startups, we need to be very careful to manage the resources, especially since it’s very scarce resource. Across, getting the right talents from the right places and addressing the markets that they have the best products. So, I think those are a couple of challenges that the startup would tend to face that needs to be addressed.

[34:21] Radu Palamariu:

I’ll pick up on the point with the data scientists and the product leaders because we are currently in the process of figuring out a few searches exactly for that, and I can attest and confirm is the most in demand skill sets across. Actually, it would be across anything to do with, I would say, data scientists are probably in demand in any industry because pretty much data will drive any industry and it’s driving at the moment and for the future, where is the product? Definitely anything to do with tech they need a good product people and that product people are so scared because it’s a combination, actually a lot of common sense. Fundamentally, it’s common sense and ability to put yourself in the shoes of the client and understand “Okay, yes, I have this product” but then, you know I need to constantly refine, adjust to the realities of my customer, and that customer will be different depending on the market. So, the more market, the more customization of the product and everybody needs and wants them. Its like that kind of rare commodity of human being up to roll but it’s incredible.

[35:36] Marc Dragon:

This is an era that is not well understood by folks that haven’t experienced it before. So, I came from IBM environment which is a product centric environment. Yes, it’s services but it is also global product leaders and there’s a clear demarcation and line in terms of what their roles and responsibilities are, so that is one extreme. So, the other extreme is startups just building stuff for what their clients are asking for. As startup grow, they need to be able to sense, so sense and respond again, going back to sensing of what the customers are asking for to actually where their vision or what they want to build. I think you mentioned common sense, but I think it is way more in common sense. There needs to be a very good thinking around, how do you put up ties? What’s the road map for? What’s the strategy for it besides building this stuff, and not a lot of people understand that.

[36:42] Radu Palamariu:

It’s an art. I remember when we have this role as a Chief Product Officer and always in terms of finding the right feed, we tend to ask the client, “In your past when you hired somebody like this, how did it go and what was successful?” And then the answer that we got was “Well, actually, if I’m to think in my history” and it was somebody that had been around for a while an Executive or CEO level and they are like “In my history, the best that I hired was actually not a product person, they had come from operations, had done a bunch of things that studied psychology, understood people really well ,had done P&L roles, had done a bit of product, but then you know all these things together made her an incredibly good and talented product person.” So, it’s really an art on the products. In the data science it is in some ways much more, how to say, surgical because you’re kind of can track people and what the product is a bit more of an art. Final question from me, what would you say wereIt’s an art. I remember when we have this role as a Chief Product Officer and always in terms of finding the right feed, we tend to ask the client, “In your past when you hired somebody like this, how did it go and what was successful?” And then the answer that we got was “Well, actually, if I’m to think in my history” and it was somebody that had been around for a while an Executive or CEO level and they are like “In my history, the best that I hired was actually not a product person, they had come from operations, had done a bunch of things that studied psychology, understood people really well ,had done P&L roles, had done a bit of product, but then you know all these things together made her an incredibly good and talented product person.” So, it’s really an art on the products. In the data science it is in some ways much more, how to say, surgical because you’re kind of can track people and what the product is a bit more of an art. Final question from me, what would you say wereIt’s an art. I remember when we have this role as a Chief Product Officer and always in terms of finding the right feed, we tend to ask the client, “In your past when you hired somebody like this, how did it go and what was successful?” And then the answer that we got was “Well, actually, if I’m to think in my history” and it was somebody that had been around for a while an Executive or CEO level and they are like “In my history, the best that I hired was actually not a product person, they had come from operations, had done a bunch of things that studied psychology, understood people really well ,had done P&L roles, had done a bit of product, but then you know all these things together made her an incredibly good and talented product person.” So, it’s really an art on the products. In the data science it is in some ways much more, how to say, surgical because you’re kind of can track people and what the product is a bit more of an art. Final question from me, what would you say were some of the most successful or useful pieces of advice that you’ve gotten that have helped you the most?

[38:04] Marc Dragon:

I think it’s a combination of a few things but first and foremost and central to it all is actually following your passion. So following your passion it might sound like a generic statement but I think you need to wrap around a few things around here. So, number one, be real about it, can your passion make money? Because you need to survive. How good are you versus that next other guy? That kind of thing. And going back to sense and respond, you have to apply back to your individual self as well, really knowing yourself and most part of it is to be aware of the individual role in the grand scheme of things and that might change from time to time. So, for example, I was fortunate enough and I’m passionate in the supply chain and technology spaces and I have been in a very long time. Because of that passion, I mean folks, they are passionate about this industry and we know it and you can see it. And these folks constantly read stuff, what’s happening on the day to day basis and to think about it on a day to day, hour to hour basis and that is not possible without passion. So with passion, that becomes a central part of it, but really around it, you need to think about “Okay, where’s my place in the world? Can I influence my small little part of it and if given other opportunities, maybe I can influence a bigger part of it”. But again, going back, I think the central point that, follow your passion and build your capabilities and ecosystem around your passion. It would just be a very positive kind of cycle and just get better and better.

[40:20] Radu Palamariu:

Marc, thank you so much, appreciate the time. Good luck in the journey with Reefknot and I would encourage anybody listening that is interested and curious to look up Reefknot, to pitch Marc on Linkedin.

[40:42] Marc Dragon:

Thank you very much Radu and thank you for the opportunity.

[40:45] Radu Palamariu:

Thank you for listening to our podcast. If you liked what you heard, be sure to go to www.alcottglobal.com and click the podcast button for all the show notes of the interview. Also subscribe to our mailing list to get our latest updates first. If you’re listening through a streaming platform like iTunes, Spotify or Stitcher, we would appreciate a kind review. Five stars work best to keep us going and our production team happy and of course share it with your friends. I’m most active on LinkedIn, so feel free to follow me, and if you have any suggestions on what to do and who to invite next, don’t hesitate to drop me a note. If you’re looking to hire top executives in the supply chain or transform your business, of course, contact us to find out how we can help.