Maritime IOT Analytics

VIDEO TRANSCRIPT

Kristofer Maanum: Maritime companies are trying to find what is the way forward?

Monikka Mann: The challenge is how do you take different platforms, different technologies, older technologies and really make them part of an interconnected information network.

Cody Falcon: The maritime industry is increasingly feeling the pressure from a regulatory compliance perspective.

Kristofer Maanum: How can we introduce technology and the digital future into our industry?

Cody Falcon: It’s really about visibility. How can I harvest that data to really inform my decision-making process all the way through my maintenance routine?

Karina Evers: We’re really passionate about what we’re building for the maritime industry, especially for an industry that, it’s so critical to have high, high uptime. You can really understand your fleet in a whole new way.

Cody Falcon: I think where the industry is heading is using analytics to augment that decision-making process, right, because vessels are physically constrained. You can’t add a few hundred more engineers onboard to help you. You have to use technology tor really scale capability.

Kristofer Maanum: So if you take that analytic model and deploy it to the edge and let it be available to provide actionable insights, even in a disconnected state. That is huge for the maritime industry.

Monikka Mann: How does this change my operations, or how does this improve the bottom line for my shareholders and for my employees?

Cody Falcon: Fundamentally we’re solving the connectivity and the visibility challenge. We were able to drop in a small piece of software and hardware, very small footprint package, unobtrusive, and have that up, running, installed, and streaming your operational data back in less than five minutes.

Mogens Mathiesen: Say you would have ten ships. You either have a main engine or you have diesel electric generators. You can then start to look across these and you can start to recognize whether these are operated in different ways. And you can make machine learning models that learn not from just the one ship, but learn from the data from all of the ships. Meaning that you get much better information and you can also improve performance.

Karina Evers: And what we are building will enable executives to better visualize their data to make great decisions in the future.

Kristofer Maanum: In the maritime industry, there’s a long history of best practices and, at Arundo, what we talk about is taking this industry knowledge that people have established over the years and actually be able to implement it.

Cody Falcon: And so we get back into things like “just in time vessel scheduling” as a terminal operator. Things like water optimization, route planning, preventive and data-driven maintenance programs. Fundamentally, that’s all driven from our edge agent, connecting to your existing equipment, your existing data stores, streaming that back into our platform where we’re doing these analytics and solving these challenging use cases.