Interview with Constantine Komodromos, Founder and CEO of VesselBot

• VesselBot started by offering digital solutions to the maritime industry and evolved into a full Supply Chain Sustainability Platform. What was the defining moment or insight that pushed the company to make this strategic pivot toward emissions intelligence and multimodal sustainability?

Basically, there were a couple of things. It’s not just business. Business change. It was a mission thing. We saw a lot of signs from the market interested in changing itself, because the shippers who were in contact wanted more information around the emissions being emitted from any transportation mode they were using.

On the other hand, a visit of mine to Parthenon, it was a ”wake up call’’. It made me think,
“Where are we today”? Will these monuments exist in 100 years? Will our children’s children be able to see these monuments in a way that we can appreciate them and visit them’’? Beyond by the fact that they they’ve survived for 2000 years.

So, it was a combination of how can we leave a positive impact ourselves, on the other hand side, how can we do that in a resilient way, and resilience comes from not just being able to offer digital services, but also at the same time enable our customers reduce their emissions, have a positive environmental impact. At the same time, not negatively impacting on their cost, their cost likes, their cost expenses. It was a kind of combination of things.

• Your platform claims unprecedented accuracy through Digital Twin models for vessels, aircraft, and trucks. Can you explain what differentiates VesselBot’s Digital Twin methodology from traditional emissions-calculation tools relying on industry averages?

We realised from the beginning that for any user to be able to take action, they needed to have real-time data that was accurate and granular enough so that it could enable their decisions.

From the first instance, we started building a lot of scientific models. I will give you an example. For every vessel of every container, we have their engine characteristics, their propeller, their auxiliary engine, main engine boilers, TEU capacity, all the lower breadth. So, what we do is use that data in combination, in conjunction with real-time data coming from AIS, geospatial data, weather, and satellite data.

So, all the information that we can collect from across the board, is simulating the power the engine needs to generate, so that it can propel the vessel at a given speed, taking into consideration the weather.

If we are cutting X amount of cargo and there is this speed and there is Y weather, how these come together, andncreate a need for the engine to power, that would satisfy the requirements of the taking into consideration the engine itself, the vessel, and the conditions that the vessel cannot.

So that’s how we go around estimating fuel consumption and respectively emissions.

• What do you see as the most technically challenging aspect of producing truly accurate multimodal emissions data, and how has VesselBot solved a problem that others in the market still struggle with?

There are quite a few things that create a bit of difficulty in this sense. One of the biggest problems is that usually in many organizations we see that they have fragmentation of their own data. Lots of their own data seen in different systems, have different formats, have different ways that they maintain it.

There is no consistency across regions, across subsidiaries, across systems. So, the uniformity of data, how they maintain the data, and the type of data they’re holding is inconsistent.

On the other hand, there is a lot of data that is being held by their carriers themselves. So, the shippers themselves do not have the data that carriers do have, in regard to the way that they are executing shipments.

“How these data of the carrier can be retrieved, and also come into one bigger database, data lake where you can use them in conjunction with their own data, and then other data like the AIS data, telematics data, weather data, ADSB, the satellite from aircraft, all these data, how can they complement it, and create a bigger picture for the shipper or even the calculation the service provider like us to be able to make the calculation“?

So, first of all is the data that the shipper holds, secondly, is the data that is being killed by the carrier and then other data that you can collect from other places. All these going into a model that can indeed utilize those data. We might be collecting a lot of data, but if our models are using just an average, then you cannot do too much with those data.

So how can you bring all these together, all these data from different sources together, uniform them, make them harmonized and utilize them to arrive at a proper calculation that is real time, accurate, has that characteristic of ground. That did not enable decisions.

• Integrating data from hundreds of carriers, each using different systems, formats, and technologies, is notoriously complex. What were the toughest technical and operational hurdles in building a platform that can unify all of that information automatically?

Although we’ve been making taking steps to improve the data throughout the market, not just the ocean transportation, over the road transportation, air transportation all across the board, what we are seeing is that apparently there’s no consistency in the data, and in many cases there’s low quality of the data themselves, so being able to create those systems that use the data, and make them usable in the sense. This is something that a system can utilize to produce meaningful information.

This has been one of the of the barriers that we face. However, we’ve built a lot of technology using artificial intelligence, machine learning, that does that and allows us to be able to have that quick turnaround of we integrate with another provider and that becomes part of our data pipelines.

• Scope 3 transportation emissions are often modeled using global averages, which you argue can hinder effective decision-making. How does your focus on primary and high-fidelity operational data change the quality of decisions companies can make around emissions reduction and supply chain design?

I’ll give you an example. Averages means that you get from one carrier or five carriers or you have one carrier over a trade lane.

So you aggregate. It means that the you lose information from having an average. That aggregation means that if you wish to see how two different schedules probably of the same carrier are performing, you wouldn’t be able to see that, because you have the carrier perspective. I’ll go a step deeper. Let’s assume that you have all these aggregates come from historical data. They don’t represent current operating patterns. So, for example, we all know some transportation vessels are moving faster or slower depending on freight market.

So in many cases we have slow steaming, because the market is not great and the freight cost and freight rates are low, so the operators are slow steaming. That means that the fuel being consumed.

Under certain circumstances it might be lower. If you’re looking at last year over this year, the market might have changed, the current may have changed their strategy for some reason. Political geopolitical events may have occurred.

So, you have a Cave of Good Hope, Suez Canal closure, not closure, but you know the problem. Is having last year’s data and trying to make this year a decision, impairs your ability to use the current situation information and the real-time information, and you’re using last year’s data to make a decision for this year.

As you may understand, this cannot really work. So, this is type of information that not just aggregate prevention from, but in the data and how fast in each of these markets of the logistics is changing, prevents you from making any decisions. So, this is the problem that we’re solving. This is the way we provide our customers with that visibility to make decisions. Because exactly we have the granularity and the real time data.

• Your platform doesn’t stop at measurement, it aims to reveal hidden optimization patterns and opportunities for network redesign, consolidation, and alternative modes. Can you share a concrete example where VesselBot turned emissions data into a strategic operational change that reduced both environmental impact and cost?

There is this misconception in the market that, the available tools that we have is to go and buy biofuel or alternative fuels, and to a little extent the costly exercises that can be done (electrify fleets, all these kinds of things).

On the other hand, what majority of organizations are not doing at the beginning is to identify whether there are inefficiencies in the way that they are executing their transportation and their logistics. This is Vessel Boat’s way of approaching what this problem brings.

We can have a bigger picture of how you’re doing operations and then for example, you’re using truck carrier A over truck carrier B, but at the same time their cost is the same. So you could be shifting volumes between a carrier, ocean carrier or a truck carrier or an, airline carrier, without impacting your bottom line and without adding any extra cost.

For example, we’ve seen cases of customers of ours that they were dispatching truckloads every second day, and when they came in contact with their buyer, they said, OK, do you mind if we ship every three days? In that way they couldn’t, and they had low consolidation of the container size they were using. So, by doing this kind of collaboration with the customer or their supplier, they managed to reduce the volumes of their shipments.

We had customers that didn’t have visibility across their emissions at all, and in the end, they went back to their carrier and said how can we reduce our emissions together. From now on we are running an internal carbon price to the emissions, that we are emitting
on top of your freight cost, and we will be assessing you based on that internal carbon prices.

Comparing equal to equal emissions plus freight cost. So, a lot of things started changing in the way that our customers were collaborating with their carriers, collaborating with their suppliers, their customers in the way that the bigger ecosystem came into an alignment in many ways on the execution part of the shipments, which in the end results in emissions reductions, in cost reductions.

• With regulations like EU ETS and evolving ESG reporting frameworks, many companies see compliance as a burden. How does VesselBot help them flip that mindset, using emissions intelligence not just to “tick the box,” but to reduce carbon credit needs, lower compliance costs, and enhance stakeholder trust?

We’re looking at things from a holistic perspective. What we want to achieve is to bring that bigger picture to supply chain directors, to logistics directors and enable them to take action and devise different strategies from what they’ve been doing up to now. Our way of thinking is resilience in a broader sense.

We are not trying to tell everyone “you need to go and reduce your emissions and this is what needs to be done, and we you shouldn’t be caring about the cost”. We are trying to find those ways and give that information and data to our customers that would enable them to look at things from the bigger picture, and the grander scheme of things and incorporate sustainability in the environmental impact into any decision they make.

This becomes a more strategic initiative, not just a reporting mechanism, but a mechanism that can drive broader change within logistics within supply chain. This is the perspective that we have, and that’s how we work with chief supply chain officers and directors of logistics to enable that and bring those insights that would help them deliver that value to the broader organization. This can be really strategic, can provide resilient and a lot of strategic value to organizations.

• Looking five years ahead, how do you expect global shippers to manage and use emissions data, and what role do you see VesselBot playing in shaping that future?

It depends on a number of things. However, despite the fact that we’ve seen a lot of pushbacks on regulations and a lot of pushbacks on the green agenda in general, we see a lot of companies realizing that there is value out of this exercise, especially when you can prove a return on investment to some extent.

Some leading companies are taking all these steps, and we believe that in the next five years the market will have this rationale, where emissions data will be part of all tenders of all RFP. For example, a few years back many companies were not taking reliability of carriers of ocean carriers or over the road carriers, on their tenders.

Today, this is something that happens to a large extent. Everybody has reliability KPIs as part of their decision making at the spot or volume contracts.

That would be the trend going on from now to the near future. Also, sustainability and resilience in the sense of combining all this data together and making a different type of decision will be the status quo, rather than leading companies moving faster and moving into that. We believe that our path will be a leading one in this new setting.

We already seen a lot of companies changing suppliers from older suppliers that were doing something different from what we are doing and moving to us. I believe that we have a few more steps to become the dominant player in the market.

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